SWR Volume 9 2025

RESEARCH & DISCOVERY

How Age, Sex, and Use of Glasses Affect Color Vision

Jack Bogue

Abstract

Our color vision allows us to distinguish various colors and take in information about our environment, but not everyone has the same level of ability when it comes to discerning colors. The purpose of this study is to determine if factors such as age, sex, and use of vision corrective wear like glasses impact the strength of one’s color vision. Knowing how these factors may affect color vision would give insight into how to better design visuals online to be most accessible to the greatest number of people. The methods used include two tests. One test determined if the participant has a form of color blindness The other test measured the ability of the participants to match colors of various shades and color schemes together. The results show that age and wearing glasses do not impact the strength of color vision, but sex does. Neither males nor females seem to have higher rates of color blindness, but the results show that females are better at shade matching colors than males, which may point to females having stronger color vision. The time it took for males and females to complete the tests was the same, which suggests neither sex is faster at processing visual information than the other. In future research, it would be informative to study people specifically with Age-related Macular Degeneration (AMD), to see how this disease progresses with age and affects color vision and overall visual acuity.

Introduction

When thinking about color perception abilities in people, the thought of color blindness may be the first thing that comes to mind. In the human eye, the cells that give us the ability to see are called rods and cones, and they are filled with pigments that absorb light. Cones are the main structures that give us color vision, while rods mainly detect value, or how light or dark an object is. However, in low light conditions rods do have a role in detecting color (Lamb, 2016). There are three types of cones in the eye, which are the red, green, and blue cones. However, rarely some people are born with four types of cones, making them tetrachrome, instead of the usual trichome of people with three types of cones.Those with tetrachrome vision can perceive colors beyond the usual visual light spectrum for trichrome vision (Lee et al., 2024). Because these cones don’t just detect a singular wavelength of color, but rather a specific spectrum of colors, the three usual cones are commonly referred to as the long, medium, and short wavelength cones. The names are also often shortened and referred to as the L, M, and S cones, and the presence and formation of the cones are coded by L, M, and S genes. Mutations to these genes cause color vision deficiencies. When one or more types of these cones, or the pigment inside of the cones, are not present then it causes color blindness to occur (Carrol et al., 2004).

It is commonly known that color blindness is found more often in males than in females (Wong, 2011). Around 8% of males have some form of color vision deficiency while only 0.5% of females experience the same condition. The reason behind this is that the L and M genes are X-linked recessive. Males only have one X chromosome, so if the mutated L and M genes are present on the X chromosome passed down to a male child, the child will possess the color vision deficiency. Females have two X chromosomes, so they need both X chromosomes to possess the recessive mutated genes for them to have a color vision deficiency, making it less likely for females to have color vision deficiencies than males (Bowmaker, 1998). The L and M cones are the cones that perceive the red and green spectrums of light, so mutations to the L and M genes can cause forms of red-green color blindness. Because these genes are sex-linked, it makes red-green color blindness the most common type of color blindness. (Neitz et al., 2000).

Color vision deficiencies and color blindness have also

been found to be connected to eye diseases such as Age-related Macular Degeneration (AMD). This disease progresses with age, and negatively impacts overall eye function. It decreases visual acuity, can cause blindspots in vision, can diminish color vision and the ability to see contrasts. AMD is caused by cellular loss and dysfunction in the macula, which is a region of the central retina that gives humans and other primates high visual acuity and color vision.Vision loss occurs primarily from the loss of retinal pigment epithelium cells, which in turn negatively impacts the function of rods and cones in the eye (Kaarniranta et al., 2013)

Color blindness is a major topic when discussing color perception, but it is not the only significant topic. The ability to distinguish very similar colors from one another, to match these colors together, and the speed at which one can do these are also important to consider when discussing color perception. Color is something we use often, especially in a world increasingly dominated by technology and computer screens. We use color

to symbolize many different meanings and as a tool to convey information. Studying color perception abilities allows us to design websites or other forms of presentations in a way that is the most accessible to the most people. The objective of this experiment is to study how factors like sex, age, and use of corrective vision wear impact color perception abilities. Specifically, I will see how these factors may impact the speed at which one can detect color differences and match colors to one another. I hypothesize that females, people from a younger age group, and people who do not need corrective vision wear will outperform males, people from an older age group, and people who need visual corrective wear when it comes to color perception abilities.

Methods

To perform this experiment, volunteers who have given informed consent will be given tests to determine their color vision proficiency. The volunteer’s age group, sex, and use of corrective vision wear will be noted and they will be sorted into groups from this information. Each volunteer will receive the same tests, Test 1 will be an online color blindness test from Enchroma called the “Enchroma Color Blindness Test,” (GómezRobledo et al., 2018). Their results they received and the time it took for them to complete the test will be recorded. Test 2 will involve the participants playing the “Color Game” from Method of Action. Volunteers will play the game on a laptop, where they will have to match colors to each other in stages that measure hue, saturation, complementary colors, analogous colors, triadic colors, and tetradic colors. Points are collected in each stage for each color category, and the overall result for Test 2 is calculated by taking the average score of the color categories combined. The accuracy of their results, and the time it took them to complete the test will be recorded.

I will use Claude Ai with model Claude 3.7 Sonnet to analyze the data with t-tests to determine any potential effects that the factors of age, sex, and use of corrective vision wear has on the speed at which participants can perceive color differences and match the same colors together, and the strength of their color vision.

Results

The data was compared by sorting into the variable groups: male vs female, student vs adult, and people who wear

Fig. 1. Comparison of average time taken by males and females to complete Test 1. Error bars represent standard deviation

vision corrective wear like glasses and contacts vs people who do not. This sorting made many trends in the data clear, and revealed some statistically significant results. All statistical tests and analysis were conducted by Claude Ai with the model Claude 3.7 Sonnet. Test 1 is a color blindness test to determine if participants have color vision deficiencies. Test 2 is a color matching test to determine how well participants can match colors together. In Test 1 all participants were determined to have normal color vision, besides one participant who was determined to have tritan color blindness. Because only one participant had a color vision deficiency, no statistical significance was found from the Test 1 results. However, the Test 1 time can still be analyzed.

In the male vs female grouping females performed on average 3.01 seconds faster than males in Test 1 (Figure 1), but with a p-value of 0.092 determined by a t-test, this result is not statistically significant (Table 1). For the Test 2 results females, on average, scored 1.89 points higher than males (Figure 2). With a p-value of 0.007, this result is considered statistically significant. For the time of Test 2, males and females performed with nearly identical times, which shows no significant difference (Figure 3).

Students and adults averaged similar times in Test 1, with students only being faster by an average of 1.95 seconds (Figure 4). This difference was not statistically significant (Table 1). For Test 2 students and adults performed similarly in both scores and times,

Fig. 2. Average points scored in Test 2 achieved by males and females. Error bars represent standard deviation

Fig. 3. Comparison of average time taken by males and females to complete Test 2. Error bars represent standard deviation

Fig. 4. Comparison of average time taken by students and adults to complete Test 1. Error bars represent standard deviation.

Fig. 5. Average points scored in Test 2 achieved by students and adults. Error bars represent standard deviation.

Fig. 6. Comparison of average time taken by students and adults to complete Test 2. Error bars represent standard deviation

Fig. 7. Comparison of average time taken by participants who wear glasses or contacts and participants who do not to complete Test 1. Error bars represent standard deviation.

with students achieving only slightly higher scores and completing the test only slightly faster (Figures 5 and 6). Still, no statistical significance was found from Test 2 or even from the student vs adult comparison at all.

In Test 1, participants who wear glasses or contacts performed at nearly the exact same speed as participants who do not (Figure 7). Participants who do wear glasses or contacts performed slightly faster, but this was not statistically significant (Table 1). Participants with glasses or contacts on average achieved higher scores in Test 2 (Figure 8), but these results were not significant either. In Test 2, participants without glasses or contacts performed on average only slightly faster than participants with them (Figure 9), but like all results in the glasses vs no glasses comparison, the results were not statistically significant.

Fig. 8. Average points scored in Test 2 achieved by participants who wear glasses or contacts and participants who do not. Error bars represent standard deviation.

In the Test 2 categories (hue, saturation, complementary, analogous, triadic, and tetradic) there appears to be a downward trend where participants performed better in the hue category and got progressively worse with each new category. The analogous category breaks the trend somewhat because the average score was higher than the category before it, complementary, but it was still lower than the category before complementary, saturation (Figure 10). Linear regression analysis confirms a negative slope of -0.56, and a t-test confirms a p-value of 0.01, meaning that the downward trend from the hue category to the tetradic category is statistically significant.

Discussion

The objective of this experiment was to measure the color vision abilities of the participants and determine if there is any correlation between factors such as sex, age, and use of visual corrective wear like glasses and the strength of one’s color vision. I hypothesized that female participants would perform better than males, students would perform better than adults, and participants who don’t use visual corrective wear would perform better than those who do. I concluded that females have stronger color vision than males, while age and use of glasses or contacts do not have an impact on color vision.

The comparison between age for students and adults showed no significant difference (Figures 4, 5, and 6), and neither did the comparison for people who wear glasses or contacts and those who do not (Figures 7, 8, and 9). The data showed no significant difference for these factors, showing that age and use of glasses or contacts have no effect on the strength of one’s color vision. However, in the male vs. female comparison, females scored significantly higher on test 2 than males (Figure 2). Test 2 looked at how accurately the participants could match colors in multiple categories. However, the speed at which the participants took the tests was not significantly different between males and females (Figures 1 and 3). These results mean that females can match colors together with more accuracy than males, but don’t necessarily do it faster than males. This advantage of matching colors demonstrates how females have, overall, stronger color vision than males.

Fig. 9. Comparison of average time taken by participants who wear glasses or contacts and participants who do not complete Test 2. Error bars represent standard deviation.

Fig. 10. Average points scored in the color categories of Test 2

The results of test 2 show a downward trend of points scored from the first category, hue, down to the last, tetradic (Figure 10). With each category, the color schemes became more complex with more colors to match. Although discovering a pattern in the points scored in each category was not a goal in this study, a trend still exists. The trend points to how, in general, people are better at matching colors in simpler color schemes than more complex ones with multiple colors.

An alternative interpretation of the data is that females don’t actually have stronger color vision than males, they just put more effort into the tests and therefore achieved higher scores. One may think this is the case because they assume that males, especially student aged males, would not care much about the study and therefore put less effort into it.While this is a possibility, the data shows that both males and females completed the tests at similar times. If female participants really did put more effort into the tests, I would expect that their times would be longer than the male participants’, as they would have taken their time to get higher scores. However, the fact that male and female participants took the same amount of time, yet females still scored higher, points to females just having stronger color vision than males.

Some sources of error in this experiment were the brightness of the computer screen used to take the tests, the strategy each participant used when taking the test, and the sample size. Each participant was allowed to adjust the brightness of the computer screen to their liking, which may have been different for every participant. The difference in brightness may have caused the colors on screen to display slightly differently for each participant, causing discrepancies in the data. However, every participant chose to take the test on a relatively high brightness level, so the color difference should not be too extreme. The fact that participants selected a brightness level they found best might even lead to them achieving higher scores.To minimize this issue, the computer brightness could be standardized so that each participant uses the same brightness level. For the strategy aspect, some participants may have gone for speed over accuracy, while others may have taken their time to score higher points. If I were to repeat this experiment, I would give clearer instructions and tell participants to focus on accuracy rather than speed. The sample size was also problematic.With only 22 participants, a small sample size like this could bias the results and lead to a conclusion that isn’t accurate for the general population. Small sample sizes introduce the risk of bias, because it would be applying a result from a small number of people onto the entire population. In future experiments, it would be important to make sure the sample size is large enough, with about 30 or more participants.

In future research, I would like to study people who have been diagnosed with Age-related Macular Degeneration (AMD). In this experiment, I included age as a factor, but none of the participants were tested for AMD. Researching people specifically with AMD may show more accurate results of how age impacts color vision, and will show more about how AMD affects overall vision progresses with time. Many studies have examined the presence of color blindness and the causes of it (Carrol et al., 2004), but this paper studies the strength of one’s color vision even without a diagnosed color vision deficiency, which gives more insight on the minor differences between people rather than just the obvious cases of color blindness.

Works Cited

Bowmaker, J. K. (1998). Visual Pigments and Molecular Genetics of Color Blindness. American Physiological Society, 13(2), 63-69

Carrol, J., Neitz, M., Hofer, H., Neitz, J., Williams, D, R. (2004). Functional photoreceptor loss revealed with adaptive optics: An alternate cause of color blindness. PNAS, 101(22), 84618466

Gómez-Robledo, L., Valero, E. M., Huertas, R., Martínez-Domingo, M. A., Hernández-Andrés, J. (2018). Do EnChroma glasses improve color vision for colorblind subjects? Optica Publishing Group, 26(22), 28693-28703

Jain, N., Verma, P., Mittal, S., Mittal, S., Singh, A. K., Munjal, S. (2010). Gender Based Alteration in Color Perception. Indian J Physiol Pharmacol, 54(4), 366-370

Kaarniranta, K., Sinha, D., Blasiak, J., Kauppinen, A., Veréb, Z., Salminen, A., et al. (2013). Autophagy and heterophagy dysregulation leads to retinal pigment epithelium dysfunction and development of age-related macular degeneration. Landes Bioscience, 9(7), 973-984

Lamb, T. D. (2016). Why rods and cones? Cambridge Ophthalmological Symposium, Eye(30) 179-185

Lee, J., Jennings, N., Srivastava, V., NG, Ren. (2024). Theory of Human Tetrachromatic Color Experience and Printing. ACM Trans Graph, 43(128).

Neitz, M., Neitz, J. (2000). Molecular Genetics of Color Vision and Color Vision Defects. JAMA Ophthalmology, 118(5), 691-700

Wong, B (2011). Color blindness. Nature Publishing Group 8(6)

RESEARCH & DISCOVERY

Soil Chemical and Physical Properties Influence Microbial Susceptibility to Drought and Rewetting

Ben Feeley

Abstract

Extreme weather events can have significant impacts on soil microbial communities. With climate change predicted to worsen over the next several decades, increased drought severity and frequency pose a threat to the stability and current structure of soil communities and, by extension, the ecosystems to which they belong. The aim of this study was to investigate the influence of soil properties on microorganisms’ susceptibility to drought and rewetting stress. Soil samples were collected from ten sites and measured for initial microbial biomass carbon and a variety of soil properties—pH, conductivity, water-holding capacity, texture, and active carbon. Samples were then subjected to drought conditions of 32 °C for four weeks, after which the soil was rewetted and microbial biomass carbon redetermined. There was a significant difference between relative biomass carbon prior to and after drought conditions, with a mean difference of -0.426 mg/g (p < 0.05, paired t-test). The relative change in biomass was also correlated with several soil properties. Conductivity and the soil sand fraction were the most strongly associated with susceptibility to drought and rewetting stress, with conductivity being positively correlated and the sand fraction being negatively correlated. pH and the clay and silt fractions were also moderately correlated. Overall, our findings indicate that soil chemical and physical properties influence microorganisms’ susceptibility to drought and rewetting stress, and we suggest that soil properties be considered when evaluating the impact of climate stressors on soil communities.

Introduction

Soil structure and properties are critical components of overall forest health. Likewise, soil microorganisms are an important aspect of soil health, playing diverse, often critical roles across a variety of ecological processes, particularly nutrient cycling (Arias et al., 2005). Soil bacteria metabolize organic matter, improve the bioavailability of insoluble nutrients through enzymatic activity, and fix gaseous nitrogen, facilitating nutrient cycling and plant growth. Bacteria can also metabolize harmful pollutants and improve soil structure and aggregation. Furthermore, microbial communities form the foundation of numerous food webs, supporting a variety of other organisms and trophic levels (Aislabie & Deslippe, 2013).

Throughout the next several decades, climate change is predicted to worsen, with an increasing number of severe weather events and droughts also predicted (IPCC et al., 2022). While occasional drought is ubiquitous, the predicted increase in severity and frequency as a result of climate change poses a risk to many ecosystems’ stability (Gillespie et al., 2020; Schimel, 2018). Because of microorganisms’ critical role in soil health, understanding microbial communities’ responses to drought and subsequent rewetting stress is crucial to understanding climate change’s overall impact on ecosystem functioning.

Drought impacts a variety of soil processes and aspects, particularly soil organisms. To retain water, microorganisms must rapidly accumulate solutes to reduce their internal solute potential. Soil rewetting also stresses organisms, as they must then quickly discard these excess solutes so as to limit the flow of water into the cell. Drought stress can reduce access to soluble resources and resource use efficiency (Schimel, 2018). Many organisms resort to dormancy during drought. An evolutionarily tested strategy, dormancy is an effective technique used by many microorganisms to survive unfavorable environmental conditions and stressors (Jones & Lennon, 2010). Different microorganisms

have varying resistances and responses to drought. Certain types of bacteria, such as Actinobacteria and Firmicutes, have been found to be far less vulnerable to drought and rewettingrelated stress than other phyla, like Chloroflexi (Chodak et al., 2014). Additionally, communities dominated by fungi have been found to be more drought-tolerant than bacterially dominated communities (Gillespie et al., 2020).

The effects of drought on soil microbial communities have been fairly well studied. Drought and rewetting stress tend to decrease microbial biomass and can significantly alter the structure of bacterial communities, although this response is highly variable. (Schimel, 2018; Chodak, 2014). Hueso et al. (2012) found that drought conditions negatively affected microbial biomass and activity. In a study by Siebielec et al. (2020), researchers found that water deficit impacted enzymatic activity, inhibiting the activity of dehydrogenases and phosphatases, and that drought significantly impacted microbial diversity, resulting in changes in the relative abundances of several phyla. Despite the significant research surrounding drought-related stress on microbial communities, few studies have incorporated soil chemical and physical properties into their analysis. Soil properties play an important

Feeley Soil Chemical and Physical Properties Influence Microbial Susceptibility to Drought and Rewetting

role in determining microbial structure and composition, as well as in regulating the solutes organisms may be able to produce to protect themselves from drought-related osmotic changes. As a result of this, we propose that soil chemical and physical properties impact the susceptibility of microbial communities to drought and rewetting stress.

Methods

Sampling and soil preparation:

To investigate a correlation between microbial susceptibility to drought and soil properties, we collected soil samples from 10 sites. Sampling locations were selected based on differences in observable environmental characteristics and geography to promote a sample pool with diverse physical and chemical properties. Sample locations ranged from grass covered fields to deciduous and coniferous forests, had a variety of land use regimes, and varied in human disturbance level. All samples were collected during late February from sites within New York State’s northern Finger Lakes region. We used plots of 1 m by 1 m for sampling, with soil cores taken from five positions within each plot—two from each corner and two from the center—and pooled together. All samples were collected from the top 15 cm of soil with a 1.5 cm diameter soil core sampler. After collection, samples were stored at approximately 4 °C in the dark until being transported for analysis. Before being analyzed, samples were briefly air-dried overnight to remove excess moisture, gently ground, and sieved through a 2 mm mesh. Initial moisture was then determined gravimetrically (Johnson, 1962), and each sample was divided into two parts, one of which was dried at roughly 50 °C for 24 hours and used for chemical and physical analysis, while the other was left undried and used for microbial analysis.

Drought Stress Experiment:

We placed roughly 50 g of soil dry weight (dw.) from each sample into respective beakers and added sufficient water to reach approximately 50% of the soil’s water-holding capacity; water amendment was calculated based on the difference between the soil’s current moisture and desired moisture content. The samples were then covered with Parafilm to prevent evaporation and left to stabilize for a week at room temperature. After this acclimation period, soil subsamples were taken to measure initial microbial biomass carbon using substrate-induced respiration (Anderson

and Domsch, 1978). The soil samples were then left uncovered under drought conditions of 32 °C for 4 weeks. After the 4 weeks, the soil samples were rewetted to achieve approximately 50% of their water-holding capacity and covered with Parafilm for 24 hours before being tested again to determine the amount of microbial biomass carbon.

Microbial biomass carbon (Cmic) was measured using the substrate-induced respiration method. First, 0.2 g of glucose was ground together with 0.8 g of talc. For each sample, 10 g of soil (dw.) and 1 g of glucose-talc mixture were then added to an airtight glass jar and incubated at 24 °C for two hours. Produced CO2 was captured by a small beaker of 10 ml of 0.05 M NaOH solution within the jar. After the incubation period, 2 ml of 0.5 BaCl2were added to the NaOH solution and titrated with 0.05 M HCl using phenolphthalein as an indicator.The CO2 evolution was calculated according to the equation

where R is the amount of CO2 produced in ml CO2 g-1 h-1 and Vsample and Vblank are the number of ml of HCl used for the sample and for a blank test incubated without soil, respectively. R was then used to calculate Cmic in mg g-1 using the regression equation Cmic = 40.04R+ 0.37.

Soil Chemical and Physical Analysis:

Soil pH and electrical conductivity (EC) were measured in a 1:2.5 soil-to-water suspension (dw:v) using a Gidigi digital pH/ EC meter calibrated with a 7 pH buffer solution. Soil texture was estimated using the sedimentation method (“Sedimentation Test of Soil Texture,” n.d.). Water-holding capacity was calculated as the amount of water that remained in the soil after saturation and drainage overnight (approximately 18 h), based on a method by Nelson et al. (2024). Additionally, soil active carbon (known as permanganate-oxidizable carbon or POXC) was determined by permanganate oxidation of organic matter and photometric measurement, following a procedure by Schindelbeck et al. (2016).

Statistical Analysis:

A paired t-test was used to check for significance between the mean biomass C prior to and after drought conditions using a significance level of p < 0.05. To assess potential correlations

between biomass and soil physical and chemical properties, we used Pearson’s correlation analysis. Analysis was performed in Microsoft Excel and in Google Sheets using the TableTorch add-on.

Results

Soil pH was fairly moderate for all samples collected, with a mean of 7, a low of 5.7, and a high of 8.1 (Table 1). Electrical conductivity ranged from 75 to 580 µS/cm, with a mean of 196.2 µS/cm. The mean amount of active carbon between soil samples was 578.4 mg/kg (Table 1). Soil texture was fairly similar between samples 1-5 and 10, while samples 6, 7, 8, and 9 had higher sand fractions, peaking at over 90% for sample 8. Overall, soil samples had moderately high sand fractions, with many also having substantial silt fractions. There was a significant difference (p < 0.05, paired t-test) between mean biomass C prior to and after

Fig. 1. Microbial biomass C prior to and after drought conditions and rewetting for 10 soil samples.

drought and rewetting stress (Table 2). The mean change in Cmic was 0.426 mg/g. Of the 10 samples, 8 decreased in biomass, while 2 increased slightly (Figure 1).

Using Pearson’s correlation analysis, a variety of soil characteristics were found to be associated with microbial susceptibility to drought. Conductivity, pH, and the clay, silt, and sand fractions were all moderately correlated with the relative change in Cmic; conductivity and the silt and clay fractions

Fig. 2. Pearson correlation matrix between a variety of soil physical and chemical characteristics and the relative change in biomass of soil samples incubated under drought conditions and then rewetted.

were positively correlated, while pH and the sand fraction were negatively correlated (Figure 2). Active carbon and water-holding capacity were weakly related, although the correlation coefficients for both were borderline for this classification. Conductivity and the sand fraction had the strongest relation to the change in Cmic (r = 0.47 and r = -0.46, respectively). Interestingly, while conductivity was only moderately correlated with water-holding capacity using Pearson’s correlation analysis, using Spearman’s rank correlation analysis, EC was very strongly correlated with water-holding capacity (rs = 0.70; Figure 2).

Discussion

While the impact of drought on microorganisms has been extensively studied, few have explored how soil properties influence microbial response. In this study, we aimed to investigate how soil chemical and physical properties influence microorganisms’ susceptibility to drought and rewetting stress. We found that drought and rewetting significantly reduced mean relative biomass carbon and that this change was associated with a variety of soil characteristics. Of the properties tested, several were moderately correlated with the relative change in biomass, the strongest of which were conductivity and the soil sand fraction. These results support the idea that soil properties influence susceptibility to drought.

Microbial biomass decreased in the majority of the studied soils, with a mean relative change of -25.7%. As drought is known to lower microbial biomass, this decrease is consistent with previous studies (Hueso et al., 2012). Interestingly, of the ten samples, two increased in biomass C after drought and rewetting. While drought tends to decrease biomass, microbial response to drought stress is highly variable. For instance, many studies have observed that biomass often remains stable or even increases during prolonged drought, with a reduction only occurring upon rewetting; dry periods can also shift microbial activity and alter the relationships between specific groups, with certain organisms becoming more active or dominant while others decline (Schimel, 2018). Different organisms have greatly different tolerances to osmotic stress, and as no genetic analysis was conducted on the soils, differences between the microbial composition of samples were not known. Variation in microbial composition could thus

Feeley Soil Chemical and Physical Properties Influence Microbial Susceptibility to Drought and Rewetting

also explain the increase in biomass for the select samples. Similarly, the association between pH and the relative change in biomass C is somewhat consistent with the findings of Chodak et al. (2014), who reported that pH had a strong influence on the reaction of bacteria to drought and rewetting stress and that this varied between bacterial groups. While we found only a moderate association with pH, this could be due to differences in the samples’ microbial composition. For example, Chodak et al. observed that in more acidic soils, the relative share of Betaproteobacteria decreased after osmotic stress, while for Alphaproteobacteria and Acidobacteria, it increased. As our results do not differentiate between microorganisms, the weaker association may be the result of the average correlation across different microorganism groups.

Methodological issues with the substrate-induced respiration procedure may also have impacted our biomass measurements. It is recommended that the optimal glucose concentration for substrate-induced respiration be determined for each studied soil (Anderson and Domsch, 1978). As this was not practically feasible in our study, we used an estimate based on previous research (Chodak et al., 2014). If the glucose concentration for our samples was too low, this may have limited microbial respiration, and if it was too high, the glucose may have led to osmotic stress and the Crabtree effect—a phenomenon where organisms favor fermentation over cellular respiration in environments with an excess of glucose (De Deken, 1966). Additionally, because SIR is an indirect measurement technique based on a regression model, it is also inherently less accurate. Some of our samples, such as sample five, were also slightly alkaline in nature. Alkaline soils are known to trap gaseous CO2, which could have also led to underestimates for the biomass of these samples (Swain et al., 2014). If this research were to be expanded upon, many of these issues could be mitigated by using a more direct measurement technique, like chloroform fumigation-extraction (Vance et al., 1987).

A primary limitation in this study was the lack of replication in testing. To minimize sampling error and improve test accuracy, measurements, such as those for microbial biomass C, POXC, and texture, should be run with multiple duplicates. Due to time and resource constraints, we were unable to perform replicate tests. This lack of replication in testing, as well as the relatively small sample size, significantly increased the potential sampling error. Additionally, no genetic analysis of the microbial communities was conducted. As microorganisms vary greatly in their tolerances to drought and various soil properties, testing to distinguish between the influence of soil properties on susceptibility to drought of specific groups of organisms could provide more thorough analysis. We suggest that future research combine genetic analysis with a larger sample size and replicate testing to gain a more comprehensive understanding of the impact of drought and rewetting on soil microbial communities. As climate change worsens, it becomes increasingly important to identify the ecosystems and regions most at risk. Given the substantial influence of soil characteristics, we recommend that soil chemical and physical properties be considered when assessing the impact of climate stressors on soil communities.

Acknowledgments

This study was funded by and conducted using resources from Allendale Columbia School. We would also like to thank Travis Godkin for his advice in both planning and conducting this project.

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Nelson, J. T., Adjuik, T. A., Moore, E. B., VanLoocke, A. D., Ramirez Reyes, A., & McDaniel, M. D. (2023). A Simple, Affordable, Do-It-Yourself Method for Measuring Soil Maximum Water Holding Capacity. Communications in Soil Science and Plant Analysis, 55(8), 1190–1204. https://doi.org/10.1080/00103624 .2023.2296988

Feeley Soil Chemical and Physical Properties Influence Microbial Susceptibility to Drought and Rewetting

Sáez-Plaza, P., Michałowski, T., Navas, M. J., Asuero, A. G., & Wybraniec, S. (2013). An Overview of the Kjeldahl Method of Nitrogen Determination. Part I. Early History, Chemistry of the Procedure, and Titrimetric Finish. Critical Reviews in Analytical Chemistry, 43(4), 178–223. https://doi.org/10.1080/ 10408347.2012.751786

Schimel, J. P. (2018). Life in dry soils: Effects of drought on soil microbial communities and processes. Annual Review of Ecology Evolution and Systematics, 49(1), 409–432. https:// doi.org/10.1146/annurev-ecolsys-110617-062614

Schindelbeck, R.R., B.N. Moebius-Clune, D.J. Moebius-Clune, K.S. Kurtz and H.M. van Es. (2016). Cornell University Comprehensive Assessment of Soil Health Laboratory Standard Operating Procedures. https://cpb-us-e1.wpmucdn. com/blogs.cornell.edu/dist/f/5772/files/2015/03/CASHStandard-Operating-Procedures-030217final-u8hmwf.pdf

Sedimentation Test of Soil Texture. (n.d.). University of California Agriculture and Natural Resources. https://ucanr.edu/sites/ UrbanAg/files/263165.pdf

Siebielec, S., Siebielec, G., Klimkowicz-Pawlas, A., Gałązka, A., Grządziel, J., & Stuczyński, T. (2020). Impact of water stress on microbial community and activity in sandy and loamy soils. Agronomy, 10(9), 1429. https://doi.org/10.3390/ agronomy10091429

Swain, A., Bastiray, A., Jitendra, K., & Haibru, R. (2014). Estimation of Soil Microbial biomass using Substrate Induced Respiration: An Experimental Review Study with Loamy Soil of North Bangalore. https://doi.org/10.13140/2.1.2139.9049

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RESEARCH & DISCOVERY

Cutting to the Chase: External Examination in Determining Cause of Sharp Force Trauma Deaths Over a 30 Year Period

Evie Jackson

Abstract

Forensic science is a significantly under-funded industry and within this field an often neglected area of study is that of external examination.This meta study was conducted in an attempt to find the accuracy and importance of external examination post-autopsy in the determination of sharp force death. Data from sharp force trauma deaths across a 30-year period (19902020) was collected and assessed and individual variables (manner of death, wound classification, weapon/inflicting object) were isolated. Individual variable likelihood could then be calculated and used in an overall product likelihood in order to determine if these factors found in external examination were effective enough to find significant difference in likelihood of different manner of sharp force death for undetermined cases. This data study can then inform if external examination should affect the subsequent process of investigation into death and streamline determination criteria for sharp force autopsy.

Introduction

Forensic science is a significantly underfunded industry, and yet the pressure to deliver in jobs surrounding this topic is extremely high. (Innocence Project, 2017) Due to the development of toxicology and forensic technology, which allows us to examine DNA (blood, hair, fingerprints etc), forensic analysis is quickly moving away from analysing causes of death through external indicators and examination. (Vanatta, 1987) This transition is developing into more of a problem than many people are willing to accept, as external indicators are the key to determining cause of death in instances where toxicology proves inefficient. A leading example of this can be seen in cases surrounding sharp force injury. At a scene of a death caused by sharp force trauma with no fingerprints, no note and no traces of a perpetrator, investigators have very little toxicology and DNA evidence for testing. Over 25% of homicide deaths are due to stab, incision or chop wounds, alongside a small portion of suicides each year (700,000 cases/1.8-3%). (Marrone, 2024) While 0.29% of accidental deaths are a result of sharp force injury. (Vanatta, 22.1987) These cases are increasingly difficult to solve with only DNA analysis and toxicology, as they often leave little DNA, other than the victim’s, for forensic analysis. As well as this, sharp force trauma often has little to do with drug intake, leaving chemical toxicology redundant. For this reason, solving cases in this respect is helpless. Yet in many of these instances external examination is still overlooked. It is considered in a sense primitive and unreliable due to its operator-dependent nature. However, the detailed examination of sharp force injuries in this way, can provide vital information in reference to the type of object that inflicted wounds and/or caused death, and eliminate other objects found at the scene (Prahlow, J. 2022).

The purpose of this analysis is to find a common way to differentiate between accidental, homicide and suicide cases of sharp force trauma through external factors. If external examination can be shown to find differentiation between these causes of death it could drastically aid the solving of sharp force trauma cases in which toxicology is inconclusive or unable to be utilized. Due to the advancement of modern technology, new causes of sharp force trauma have presented themselves and there is a need to find discrepancies between these (Prahlow, J, 2022). A study from the urban medical trauma center looked at the trauma deaths over an 11-year period in an emergency room. This showed that sharp force wounds accounted for 11% of the cases (Prahlow, J, 2022). In cases such those found in this study, it is the differences between certain types of sharp force trauma that is truly beneficial in determining cause of death. These differences however are little known and poorly documented (Burton, J. L. 2007). A need for research in the discrepancies between sharp

force fatalities pushed me to look further into this research as it is a neglected and poorly understood set of information (Madea, B., & Rothschild, M. 2010).

Methods:

Examination of the significant injury, as well as any accounts of previous such trauma can indicate the probability of cause of death, as homicide, suicide and accidental death can have drastic differences in these areas (El-Sharnagawy, 2022). Previous accounts of the victim such as attempted suicide, self harm and mental health difficultly, as well as examination of previous or current wounds caused by other factors, such as abuse, can also aid in the indentification of cause of death due to sharp force trauma. (Office for People with Developmental Disabilities, n.d.)

Data was taken from 6 different sources on post-mortem examination, with cause of death having been securely predetermined, analysis of the factors of these autopsies was

Jackson External Examination in Determining Cause of Sharp Force Trauma Deaths Over a 30 Year Period

carried out (Sources are indicated by *). Documentation of trends and elements of examination found to have significant differences between causes of death, was then used to find if differentiation and likelihood of suicide, accident or homicide could be found from just these indicators (without toxicology). Each subject was seen to fit certain criteria prior to their inclusion in this study, autopsy must have been performed between 1990 and 2020 with death having been securely predetermined. Differences in these cases were compared including differences in type and variety of incision (chop, stab, etc), biological sex, inflicting weapon/object type, as well as other physical and psychological factors relating to each subject. Once the data was condensed it was formatted to figures and graphs, in order to more easily view differences within the data sets. After data collection a Maximum Likelihood Estimation (MLE) using a basic product likelihood function was used. This way percentage likelihood of suicide, homicide and accidental sharp force death in individual cases could be found, based of the data from over the 30 year period (1990-2020). Depending on the outcome of this data collection and likelihood model, this analysis will be able to find discrepancy between different causes of sharp force trauma, using factors other than toxicology, helping with this identification in future cases. In an instance where the data showed little to no difference, this information would be used to show how the cause of sharp force trauma is unable to be determined through only external indicators.

Results

Data from over a period of 30 years (1990-2020) was used and analyzed. Over this 30 year period it was found that 1.17% of autopsies were determined to have sharp force trauma death as their cause. Figure 1 demonstrates the average percentage of each manner of death within the 1.17% of sharp force deaths over the 30 year period.

The smallest percentage of this being accidental (23.7%), followed by homicide (33.6%) and suicide (42.7%). Within each of these subcategories the number of male versus female cases differed slightly. Whilst homicide and suicide death had little variation (Suicide = 62.5% male, 37.5 female Homicide = 61% male, 39% female) there was a slight statistically significant difference with accidental death (77.27% male, 22.73% female; ANOVA, p≥0.05).

Suicide, homicide and accidental sharp force deaths had notably varying wound types. Sharp force fatality deaths over the 30 year time period were split into three categories of fatal wounds. Stab, being a wound deeper than it was long. Incised being wider than it was deep (or equally wide as it was deep). Chop was the least common wound classification across all three categories, and was defined as a stab or incised wound with significant signs of blunt force impact in and around the fatal wound. Homicide had the highest percentage of stab wounds at 89% with significantly lower numbers of incised and chop wounds (9% and 2% respectively). In contrast, suicide had the lowest percentage of stab wounds (18.75%) with incised being the most common in this category (81.25%). <1% of suicides were found to be chop wounds. Accidental death however had a more even split of wound classifications, likely due to the wider variation in the inflicting weapon/object. Like homicide, accidental death had a higher percentage of stab wounds (50%) followed by incised and chop wounds (22.72% and 18.18% respectively).

2. Classification of fatal sharp force wounds % (19902020). Error bars represent standard error.

Wihin each manner of sharp force death, there was a significant difference between classification in regards to the inflicting weapon. Figure 3 shows these differences. Over the 30 year period 93% of inflicted fatal sharp force wounds were found to be knife crime. Razors and unconventional blades were the only other sharp force wounds inflicted within homicide (6%) with <1% of cases being caused by other means of sharp force inflicting weaponry. 79% of all homicide sharp force deaths also had greater than three wounds total from the inflicting weapon. Suicide also had notably high knife statistics with 67.75% of fatal wounds being knife inflicted with significantly higher usage of razor/unconventional blades (25%), and <1% of suicide victims

Fig. 1. Manner of Sharp Force Death (1990-2020)

Fig.

Fig. 3. % weapon usage in suicide, homicide and accidental sharp force death. Error bars represent standard error.

with greater than three fatal wounds total. Accidental deaths however had comparatively low knife wounds (4.3%), the leading cause being 50% fatal wounds from machinery, followed by 31.51% caused by sharp force trauma as a result of motor vehicle accidents. Accidental death due to penetrative glass wounds were relatively low but significantly higher than homicide and suicide. 13.63% of accidental deaths were caused by fatal glass wounds. Accidental deaths had only 36.36% of deaths with greater than three fatal wounds.

Although intoxication (defined by a BAC of higher than .08%) had little variation, this factor could contribute to the cause of death and is therefore important to observe. Whilst intoxication levels in suicide and homicide sharp force death victims were very similar (60% and 60.3% respectively) accidental sharp force death had a slightly higher percentage of 68.18%. This difference may not be noticeably high it is statistically significantly different from the rates in homicide and suicide.

Overall it was seen that homicide had the highest rates of stabbing and knife wounds (89% stab, 93% knife), with few other objects causing sharp force fatal injury. Suicides had a slightly lower rate of knife usage (67.75%) (although knives were still it’s leading cause), with incised wounds being the most common form of fatal wound (81.25%). Accidental sharp force death however, had a varying number of causes behind fatal wounds with a more even split of chop, stab and incised wounds (18.18%, 50%, 22.72%).

It can also be noted that intoxication rates were slightly higher in accidental sharp force trauma victims which may have also affected the cause of death.

With noticeable significance between data in all collected variables of sharp force trauma death, a product likelihood function was used to further the effects of this data collection. This product likelihood function allowed for the ability to see the overall likelihood of each manner of death in any single case of sharp force death. Often denoted as L(θ; x1,x2…xn), this function is able to use the likelihood of multiple individual variables (likelihood of wound, likelihood of weapon, sex and number of wounds) and multiply them together to find the overall likelihood of those variables occurring simultaneously.

Discussion:

The aim of this study was to understand if the manner of sharp force death could be determined through external examination alone. Data collected from over a thirty year period and compiled into a product likelihood function, showed that the likelihood of each type of sharp force death could be found using only factors that would be examined externally. The significant difference in the likelihood of individual variables for each manner of sharp force death, resulted in significant shifts between likelihoods of homicide, suicide and accidental death for each case. For example a shift from 71.8% likelihood of Homicide to 93.3% likelihood of homicide can be seen with only a change from a wound being classified as incised in one case to stabbed in the other, respectively.

However, the product likelihood function does not create a definite answer for the manner of each case of sharp force trauma death, it creates an outline from which much of the process of investigation can be based. The likelihood function helps to rule

out certain causes of death, such as accidental, based on external examination only. Seeing the most significant shift in likelihood based on wound type and inflicting weapon helped to further support the idea that external examination is extremely beneficial when working with sharp force trauma autopsy and investigation. Using individual variables in the function was beneficial as this meant each case could use the variables of that specific case to find the likelihood of each manner of sharp force death for that particular situation. The subsequent investigation can then take a more precise course of action based upon the results of the function. For example, prioritizing investigation into possible suspects if there is a high likelihood of homicide as opposed the course of action that would be take with suspected suicide.

Whilst factors from only external examination were able to show significant likelihoods between manners of sharp force death, there is no doubt that a greater number of variables would increase the accuracy of the function in determining cause of death. Future investigation using both external (wound classification, inflicting weaponry etc) and toxicological (drug presence in blood, previous psychological evaluation etc) factors within this function would increase accuracy and provide even stabler ground on which to base the course of investigation.

This product likelihood function was able to show, that using individual data likelihood from only external variables of sharp force trauma death, can determine the likelihood of each manner of death (homicide, suicide, accidental) for individual cases of sharpforce trauma. The results of this likelihood function could be utilized to inform investigation prior to sharp force death and stream line the investigation processes.Whilst this function shows significant likelihoods, its accuracy would likely be increased with an increased number of variables from fields of toxicology. Whilst this function can not determine the exact manner of death, it does show how the likelihood of death should be significantly based upon external indicators and that external examination should continue to inform the results of sharp force death cases’ determined causes.

Works Cited

*Assunção, L. A., Santos, A., & Magalhães, T. (2009). Suicide by sharp force injuries – A study in Oporto. Legal Medicine, 11, S216–S219. https://doi.org/10.1016/j.legalmed.2009.02.018

Burton, J. L. (2007). The external examination: An often-neglected autopsy component. Current Diagnostic Pathology, 13(5), 357–365. https://doi.org/10.1016/j.cdip.2007.08.001

*Handlos, P., Tereza Švecová, Adéla Vrtková, Klára Handlosová, Marek Dokoupil, Ondřej Klabal, Juraj Timkovič, & Matěj Uvíra. (2023). Review of patterns in homicides by sharp force: one institution’s experience. Forensic Science Medicine and Pathology. https://doi.org/10.1007/s12024-023-00576-8 Innocence Project. (2017, September 18). Strengthening Forensic Science Includes Supporting Forensic Laboratory Funding. Retrieved from: https://innocenceproject.org/strengtheningforensic-science-includes-supporting-forensic-laboratoryfunding/ Madea, B., & Rothschild, M. (2010). The Post Mortem External Examination. Deutsches Aerzteblatt Online. https://doi. org/10.3238/arztebl.2010.0575

Marrone, M., De, P., Papalino, M. et al. (2024). The Noble Suicide:

The Case of a Self-Contained Dagger in the Heart and a Literal Raw. Case Reports in Psychiatry, 2024, 1–7. https://doi. org/10.1155/2024/3017903

*Neblett, A., & Williams, N. (2014). Sharp Force Injuries at the University Hospital of the West Indies, Kingston, Jamaica: A Seventeen-year Autopsy Review. West Indian Medical Journal. https://doi.org/10.7727/wimj.2013.252

*Ong, B. (1999). The pattern of homicidal slash/chop injuries: A 10 year retrospective study in university hospital kuala lumpur . Journal of Clinical Forensic Medicine, 6, 24–29.

*Prahlow, J. (2022). Forensic Autopsy of Sharp Force Injuries: Overview, Definitions, Scene Findings. EMedicine. https://emedicine.medscape.com/article/1680082overview?form=fpf

*Rizk, M. M., Herzog, S., Dugad, S., & Stanley, B. (2021). Suicide Risk and Addiction: The Impact of Alcohol and Opioid Use Disorders. Current Addiction Reports, 8(2), 194–207. https:// doi.org/10.1007/s40429-021-00361-z

Vanatta, P. R. (1987). Limitations of the forensic external examination in determining the cause and manner of death. Human Pathology, 18(2), 170–174.

RESEARCH & DISCOVERY

Perception of AC Athletes of Sports Nutrition

Ana Henrique

Abstract

A balanced diet is fundamental in athletic performance, supporting energy demands, enhancing recovery, and reducing the risk of illness and injury (Rodrigues,V. B., Ravagnani, C. d. F. C., Nabuco, H. C. G., Ravagnani, F. C. d. P., Fernandes,V. L. S., & Espinosa, M. M., 2017). This study investigates the nutrition knowledge of students at Allendale Columbia School, with a specific focus on protein and carbohydrate intake, which are two components often misunderstood or improperly balanced in athletic diets. The research aims to determine whether these athletes have the knowledge to meet their dietary needs to optimize performance and maintain long term health. I developed a survey, divided into three sections covering pre-, during, and post-competition nutrition, and sent it to Upper School students. Responses were collected using a three point scale - Yes, No, or Maybe - and the data were analyzed using percentage of questions answered correctly, averages, and ANOVA via Jamovi Software (version 2.6.44). Results indicate a moderate level of nutrition knowledge among participants, with an average of 9.19 correct answers across all questions. While 63% reported eating before training and 100% after, only 37% consumed food during training. There was a statistically significant difference in dietary behaviors across pre-, during, and post-training periods (p<0.05), although no significant differences were found across different workout types. Participants performed better on “Pick a Meal” questions (mean = 8.6) compared to “Meal Choice” (mean = 5.3), highlighting a disparity in applied nutrition knowledge. These findings suggest a need for improved education on strategic timing and macronutrient balance, particularly regarding carbohydrate intake and realistic protein needs. Enhancing nutrition literacy among student athletes could better support performance and long-term health.

Introduction

Proper nutrition is essential for athletic performance, offering a variety of benefits that support both the energy demand of sports and the overall health of the athlete. Sports diet is required before, during and after the activity in order to sustain energy, optimize recovery, and build resistance to illness and injury (Lambert, E. V., & Goedecke, J. H., 2003). Inadequate nutrition not only adversely compromises performance but also significantly affects the functional capacity of the body of the athlete. By meeting individual energy and nutritional needs, athletes can optimize their training and enhance their performance.

Appropriate adjustment in nutrition by both short- and long-term diet interventions have the potential to alter substrate utilization, especially during endurance exercises. Athletes should aim to associate their training with nutrition goals, which include maintenance of energy supply to the working muscles and other tissues; promotion of tissue adaptation, growth and repair; optimize immune function and resistance to illness and infection. In addition, with appropriate nutrition and training, athletes can rehearse and refine their competition strategies by effectively manipulating their body composition for the chosen sport (Maughan, R., 2002). Nutritional supplements are used for a wide range of purposes in sports and are primarily consumed by athletes to achieve both their higher daily needs of energy and nutrients. However, the need for additional vitamins and minerals is not necessary if the athlete consumes a wide variety of foods that cover their energy expenditure (Devlin, B. L., Leveritt, M. D., Kingsley, M., & Belski, R. (2016). Furthermore, protein intake is commonly overestimated while underestimating the importance of fluid balance. For instance, a power athlete’s protein requirement is 1.7-2.0g/

kg body weight . In contrast, endurance athletes require a daily protein intake of only 1.2-1.5 g/kg BW (Williams, C., 1998).

Another major determinant of substrate selection is exercise intensity. An energy-restricted diet can lead to several negative consequences, including an increased rate of protein catabolism and an increased release of cortisol and catecholamines which can lead to compromised immune function, particularly when engaging in hard physical exercise (Lemon,1991; Pedersen et al., 2000). Young female athletes who reduce their food intake are at particular risk of receiving less than they need of iron (Fe); calcium (Ca) and folic acid. Restrictive diets can also lead to a range of eating disorders (Vogt et al., 2003). Additionally, nutritional requirements can differ significantly between young and adult athletes due to physiological and metabolic changes associated with growth and maturation. It is important to emphasize the subsequent influence of age-specific dietary interventions to meet the nutritional requirements of developing athletes. Furthermore, recent research has clearly shown that there are gender differences in metabolism, particularly during endurance exercise.

Important factors such as training status, gender matching criteria, menstrual cycle phase, amenorrhea and nutritional status suggest that gender-specific nutritional strategies may be necessary to optimize athletic performance across sexes (Hannon et al., 2020).

In conclusion, adequate nutrition is vital for the health and performance of athletes at all levels. Understanding the nuances of dietary needs, ranging from macronutrient intake to fluid balance, and the risks of low energy availability, enable athletes to not only optimize both their training and long-term health, but also prevent fatigue and the risk of injury and diseases. The purpose of this study is to analyze if AC students know how to properly balance their diet in order to support the energy demand of their sport and their long-term health. This experiment is specifically focused on protein intake, which is commonly overestimated, and carbohydrate intake, which is our primary source of energy.

Methods

For my experiment, I created a survey on Google Forms regarding basic knowledge of sports nutrition and sent it to all Upper School students at Allendale Columbia School during the time of the study. For the collection of the data required, there were a total of 36 questions, divided into three sections – diet provided before competition, diet provided during competition, and diet provided after competition. All of them had only one correct answer. There were 25 questions with three options –Yes, No, or Maybe; 8 decision making questions; 3 critical thinking questions; and 3 general questions (Table 1). The data collected from the respondents was analyzed by using ANOVA with Jamovi version 2.6 44., percentage and mean average as statistical tools (Khan et al., 2017).

Results

A total of 19 AC students responded to the survey, in which 18 (94.7%) play sports (Figure 1).

The mean of all correct answers for all participants (n= 19) is 9.19. There is no significant difference between the average number of correct answers between each workout type.

The graph below shows the mean of correct responses for pre-training diet (average.= 8.71), during the training diet (avr.= 8.00), and post training diet (avr.= 10.80).

The average score of correct answers in which the participants had to choose the best option for each workout type was 8.6.The average score of correct answers in which the participants were asked if a certain meal was adequate for a certain type of workout was relatively low, average= 5.3. There is no significant evidence between the groups (ANOVA; p>0.05). Table 2 shows the average and standard deviation of correct answers of “Pick a meal” and “meal choice” questions for all participants. On the “Pick a meal” questions, I asked the participants to choose the best meal option for a certain workout type; for the “Meal choice” questions, I asked the participants to say whether they agree or not with a meal option for a specific workout type.

There is a significant difference between food intake pre, during, and after training (ANOVA; p<0.05). 63% of the participants

Fig. 1. Percentage of participants who play sports

Fig. 2. Average correct responses. The error bars represent the 95% confidence level.

Henrique Perception of AC Athletes of Sports Nutrition

claimed to eat before training, 37% during training, and 100% after training. Table 3 shows the food intake for different types of training for all participants.

There was no significant difference between the average correct answers for different workout types (ANOVA; p<0.05). However, there was a significant difference between food intake during pre, during, and after training. While only 37% of participants eat during training, 100% of them eat after training. When it comes to before training, more than half of participants (63%) fuel themselves. Although the “Pick a meal” and “Meal choice” questions were similar and asked the same question in different ways, there was a disparity between the average correct answers. For “Pick a meal”, an average of 8.6 of the participants answered correctly, while for “Meal choice”, the average was 5.3.

Discussion

My study indicates that AC athletes have a superficial knowledge of sports nutrition, with an overall accuracy rate of 38.2%. This suggests that many participants may not know how to properly balance their diet to support athletic performance. The lack of a significant difference in correct answers across workout types implies that their knowledge does not vary based on training style (ANOVA; p > 0.05).

However, a pattern emerged when analyzing participants’ knowledge of meal timing.The average number of correct answers for post-training nutrition (10.80) was higher than that for pretraining (8.71) and during training nutrition (8.00). This trend may reflect more commonly known for emphasized post-exercise fueling strategies, potentially due to broader public messaging around recovery nutrition. However, the significant difference in correct responses across these three time periods (ANOVA; p < 0.05) underscores the inconsistent understanding of how to strategically fuel the body at different stages of exercise.

Behavioral data on actual food intake supported this gap in knowledge. While all participants (100%) reported eating after training, only 63% fueled before training, and just 37% consumed food during training. These findings further illustrate a possible disconnect between best practices in sports nutrition and current habits among AC athletes. The low incidence of intra-training fueling, in particular, may reflect either lack of perceived need or misunderstanding of its importance, especially in prolonged or intense exercise scenarios.

The disparity in performance between the “Pick a Meal” (average = 8.6) and “Meal Choice” ( average = 5.3) questions also raises concerns. Although both sets of questions were designed to assess the same concepts – optimal meal selection for specific workouts – the substantial difference in correct responses suggests confusion or inconsistency in understanding. One interpretation is that participants found it easier to select from provided options than to independently evaluate the adequacy of a meal. This may indicate a reliance on recognition-based knowledge rather than critical decision-making ability.

In conclusion, these results suggest that AC athletes would benefit from targeted nutritional education that focuses not only on what to consume, but also the proper time and the reason. Instruction should aim to develop both conceptual understanding and practical skills in meal planning and nutrient timing. Given the variability in nutritional needs across different sports and training

intensities, future studies should consider segmenting participants by sport or competitive level. Additionally, supplementing surveys with interviews or focus groups could provide deeper insight into knowledge gaps, beliefs, and behavioral drivers, while reducing potential responses bias.

Works Cited

Devlin, B. L., Leveritt, M. D., Kingsley, M., & Belski, R. (2016). Dietary Intake, Body Composition, and Nutrition Knowledge of Australian Football and Soccer Players: Implications for Sports Nutrition Professionals in Practice. International Journal of Sport Nutrition and Exercise Metabolism, 27, 130-138. https://www.researchgate.net/profile/MichaelKingsley-2/publication/308927154_Dietary_Intake_Body_ Composition_and_Nutrition_Knowledge_of_Australian_ Football_and_Soccer_Players_Implications_for_Sports_ Nutrition_Professionals_in_Practice/links/5d5d12cea6fdcc Hannon, M. P., Close, G. L., & Morton, J. P. (2020). Energy and macronutrient considerations for young athletes. Strength and Conditioning Journal, 42(6), 109-119. https:// researchonline.ljmu.ac.uk/id/eprint/18152/8/Energy%20 and%20macronutrient%20considerations%20for%20 young%20athletes.pdf

Khan, S. U., Khan, A., Khan, S., Khan, M. K., & Khan, S. U. (2017). Perception of Athletes about Diet and Its Role in Maintenance of Sports Performance. J Nutr Food Sci, 7, 592. https://d1wqtxts1xzle7.cloudfront.net/81688606/ perception-of-athletes-about-diet-and-its-role-inmaintenance-of-sportsperformance-2155-9600-1000592libre.pdf?1646470380=&response-content-disposition=inline %3B+filename%3DPerception_of_Athletes_about_Diet_an Lambert, E. V., & Goedecke, J. H. (2003). The Role of Dietary Macronutrients in Optimizing Endurance Performance. Current Sports Medicine Reports, 2, 194-201. https:// www.researchgate.net/profile/Julia-Goedecke/ publication/10683124_The_Role_of_Dietary_ Macronutrients_in_Optimizing_Endurance_Performance/ links/5f8128fba6fdccfd7b554e9c/The-Role-of-DietaryMacronutrients-in-Optimizing-Endurance-Performance.pdf

Maughan, R. (2002). The athlete’s diet: nutritional goals and dietary strategies.Proceedings of the Nutrition Society,61,87-96.https:// www.cambridge.org/core/services/aop-cambridge-core/ content/view/E7C61FAB06177AB6E1A299B91879E902/ S0029665102000149a.pdf/div-class-title-the-athlete-s-dietnutritional-goals-and-dietary-strategies-div.pdf

OpenAI. (2024). ChatGPT [Large language model]. https://chatgpt. com/share/672ccc57-1bb4-8002-91d6-89f84b95ab54 Rodrigues,V.B.,Ravagnani,C.d.F.C.,Nabuco,H.C.G.,Ravagnani,F.C.d. P., Fernandes,V. L. S., & Espinosa, M. M. (2017).Adequacy of energy and macronutrient intake of food supplements for athletes. Rev. Nutri., Campinas, 30(5), 596-503. https://www.scielo.br/j/ rn/a/JQhQJ85C46ZJGrR7pCRNS9G/?format=pdf&lang=en Vogt, M., Puntschart, A., Howald, H., Mueller, B., Mannhart, C., Gfeller-Tuescher, L., Mullis, P., & Hoppeler, H. (2003). Effects of Dietary Fat on Muscle Substrates, Metabolism, and Performance in Athletes. Med. Sci. Sports Exerc., 35, 952960. https://eclass.hmu.gr/modules/document/file.php/ YD238/%CE%86%CF%81%CE%B8%CF%81%CE%B1%20

RESEARCH & DISCOVERY

Sleep Deprivation and Its Effects on Cognitive Function

Alex Ketz

Abstract

This study focuses on finding a correlation between sleep deprivation and its effects on cognitive function. My hypothesis for this study was that there is an effect on the cognitive function of the brain based on how much sleep you get in a night. I created a survey and administered it to the high school students of Allendale Columbia. The survey was scored with an in depth point system revolving around which math problems you got correct and incorrect. From the data I collected I averaged the scores of each sleep group and graphed it. I found significant data on the amount of sleep you get and the cognitive function of your brain (p=0.0020) in which I reject the null hypothesis and accept my alternative hypothesis. In conclusion my findings show that there are effects on the cognitive function of the brain based on how much sleep one gets.

Introduction

In today’s world, life and society have been evolving to be more fast paced. And while the world moves faster and faster, sleep is often thrown aside to pursue the increase in social engagement, productivity, and consumption of technology. As a result, sleep deprivation increases heavily around the world for men and women of all ages. A study conducted by Saad Mohammed AlShareef showed that of the 9.7% of people he surveyed said they got fair-very bad sleep the following night, 95.1% of those respondents who answered fair-very bad sleep had a smartphone in their room. The consequences of sleep deprivation are usually long-term effects. This includes symptoms such as declines in: attractiveness, cognitive performance, overall health, muscle function, and most importantly mental and physical health. Recent studies have also shown that depriving the body of sleep can leave it susceptible to higher risks of obesity, diabetes, weaker immune function, and even cardiovascular disease. Furthermore depriving your body of sleep can greatly affect your muscle growth and productivity. For example John Axelsson found that perceptions of health and attractiveness in the sleep deprived condition decreased on average by 4% to 6% (John Axelsson, 2010) A big problem when it comes to the world wide issue of sleep deprivation is that some deprivation is caused by time consuming schedules that cannot be fixed. This only makes the problem of sleep deprivation around the world increase with its effects only getting worse in terms of cognitive thinking and physical performance. That is why I’m conducting this research. My research strives to find an answer for the effect sleep deprivation has on cognitive function. My hypothesis for this study is that sleep deprivation significantly decreases cognitive function. By conducting this research I aim to significantly expand the current knowledge on sleep deprivation as a whole and to show how critical of a role sleep plays in our everyday life and how important it is to make sure you get a healthy amount of sleep for your body to function properly.

Methods

The basis of my study relied on a survey to collect data on sleep deprivation and its effects on cognitive function. The people who took the survey came from a variety of places. The people I planned on taking data from were either people at school or random people online through submitting my form on social media platforms. There was no requirements to take this survey because I wanted my data to be more random and less controlled creating a better environment for significant data/findings. The layout of my survey is as follows: ask a couple background questions such as; how old are you?, what is your sex?, how many hours of sleep did you get the night prior?, and how many hours were you active yesterday? After asking a couple background questions I then moved the survey taker onto a set of math problems that got harder/easier depending on whether or not they got it right. This way I could graph the data by age group and analyze not only

which age group was more affected by lack of sleep but also how many hours of sleep/activity affected cognitive function. Before the participants in this study continued with the survey they were asked a question regarding consent and using their answers as my own personal data, and of course the answers did stay anonymous as mentioned in the consent form. The way I interpreted the data I received from this experiment relied on the questions I decided to ask. For example I would take the average score of the people who said they got 3 or less hours of sleep last night, and so on. From this survey I planned on inferring the data by taking the average of each section. From the averages I would be able to find the age group and amount of sleep that affects the body’s cognitive function the most. This experiment took no more than just a laptop and a well written survey. I required no funding for this experiment.

The are a lot of limitations to this experiment. One of

the biggest limitations to this experiment is the possibility of bias since I am conducting this experiment by survey. Which can greatly increase the bias of data because the perception of different people on their productivity and endurance levels can be significantly different. Another big limitation to this experiment is the sample size as it can either be large or small depending on how many people I get to take my survey. Considering my survey includes people actually getting up to test their physical abilities, cognitive thinking, etc… I don’t think it will be easy to come by a lot of people who are willing to do that.

Results

Fig. 1. This graph shows the trend in which the average points scored on the survey increases as the amount of sleep in each group increases

I compared the hours of sleep to the average points scored on the survey (Figure 1). The comparison between the hours of sleep and the average points scored on the survey has a positive correlation. As the hours of sleep increase so does the average points scored on the test. With a P-value of 0.0020 my data proves to be significant in which I reject my null hypothesis and embrace the alternative hypothesis. My alternative hypothesis is that there is a significant correlation between hours of sleep and the cognitive function of the human brain.

Discussion

This study was conducted to find evidence that there is a correlation between sleep and the cognitive function of the brain. My hypothesis was that there would be a correlation between sleep and cognitive function. In the comparison between the hours of sleep and the average points scored on the survey I found that the more hours of sleep one receives the better their score is, implying increased cognitive function. In my findings my data was significant with a P-Value of 0.0020. My findings being significant shows that there is a correlation between sleep and the ability to solve problems.

However despite my findings I have some sources of error in this study. One of the biggest sources of error in this study is not having the ability to view the responses real time so cheating is a possibility. Another source of error I ran into during this study is the fact that some people will just give up on the math problems and will just guess for the rest of the questions. This factor could

result in the data being skewed because guessing the answer to a question takes significantly less cognitive function than actually trying to solve the equations which means my data won’t be accurate with every response. The last source of error I ran into during this study was early on which meant it only affected 2 of my responses, this mistake being one of the questions having the incorrect answer which led to 2 of my responses to be wrong on that question no matter what they picked.

Works Cited

John Axelsson (researcher), Tina Sundelin (research assistant and MSc student2), Michael Ingre (statistician and PhD student), Eus J W Van Someren (researcher), Andreas Olsson (researcher), Mats Lekander, (researcher). 22 October 2010, Beauty sleep: experimental study on the perceived health and attractiveness of sleep deprived people

Zachary A. Caddick, Kevin Gregory, Lucia Arsintescu, Erin E. FlynnEvans. A review of the environmental parameters necessary for an optimal sleep environment, Building and Environment, Volume 132, 11-20. 15 Mar 2018.

Polo-Kantola, P. (2007). Sleep deprivation: Impact on cognitive performance. Neuropsychiatric Disease and Treatment, 3(5), 553–567. 25 Nov 2022.

Marie E. Gaine, Snehajyoti Chatterjee, Ted Abel. Sleep Deprivation and the Epigenome, Neural Circuits, Volume 12. 26 Feb 2018.

Bishir, Muhammed, Bhat, Abid, Essa, Musthafa Mohamed, Ekpo, Okobi, et al. Sleep Deprivation and Neurological Disorders, BioMed Research International, 2020, 5764017, 19 pages, 2020, 23 November https://doi.org/10.1155/2020/5764017

Frederick Grady & Laura Weiss Roberts. Sleep Behaviors of Medical Students in the USA, Sleep Deprived and Overwhelmed, Volume 41, pages 661–663, 14 September 2017.

Joseph A. Hanson; Martin R. Huecker. Sleep Deprivation, June 12, 2023. www.cedars-sinai.org, ND, Sleep Deprivation

Ołpińska-Lischka, Marta, Karolina Kujawa, and Janusz Maciaszek, Differences in the Effect of Sleep Deprivation on the Postural Stability among Men and Women, 5 April 2021. https://www. mdpi.com/1660-4601/18/7/3796

Jani P.Vaara, Hermanni Oksanen, Heikki Kyrlinen, Mikko Virmavirta, Harri Koski, Taija Finni. 60-Hour Sleep Deprivation Affects Submaximal but Not Maximal Physical Performance, 15 October 2018.

Karolina Kujawa, Marta Ołpińska-Lischka, and Janusz Maciaszek. The Influence of 24-Hour Sleep Deprivation on the Strength of Lower Limb Muscles in Young and Physically Fit Women and Men, 1 April 2020.

Mathew Walker. Why we sleep, September 28, 2017. https:// icrrd.com/public/media/16-05-2021-080425Why-We-SleepUnlocking-the-Power-of-Sleep.pdf

Paula Alhola, Päivi Polo-Kantola. Sleep deprivation: Impact on cognitive performance, Oct. 3 2007. https://pmc.ncbi.nlm.nih. gov/articles/PMC2656292/#sec16

AlShareef SM. The impact of bedtime technology use on sleep quality and excessive daytime sleepiness in adults. Sleep Sci. 2022 Apr-Jun 15

RESEARCH & DISCOVERY

Effects of Media on the Fear of Sharks

Mollie Klinkbeil

Abstract

In this study, I looked into why people are afraid of sharks and whether that fear comes more from the media portrayals than actual danger. Since I live in New York and could not study sharks directly, I created a guided survey to understand people’s perceptions and experiences. Ninety-one participants were split into groups based on whether they already feared sharks or not, and each group was asked different sets of questions involving movies like Jaws, scary videos, headlines, and shark images.The results showed that the media definitely has an impact; most people even admitted their fear was influenced by what they’ve seen in the media. However, some responses were inconsistent, like saying the media affects their fear in one question, then saying it would not change their opinion in another. That inconsistency might be due to the way the questions were worded. Overall, the results suggest that the media plays a big role in shaping fear of sharks, but also highlights how complex and unpredictable fear can be. I would be interested in doing a follow-up study to dig deeper into why those inconsistencies happen and whether the media could create similar fears for other animals or topics.

Introduction

Sharks are one of the world’s apex predators, potentially only second to Orca whales (Towner et al., 2022). An apex predator is an animal that is at the very top of the food chain and has no natural predators. Humans, lions, tigers, orcas, sharks, etc. are all apex predators. Sharks have been one of the top marine predators for millions of years, but not only do they instill fear within any fish, they also instill fear in humans. One of the main reasons is because of their teeth. Most sharks have teeth that are evolved to cut and tear through flesh, others have flat molar teeth that allow them to crush prey (Thurman, 2023). Besides their teeth though, sharks just do not look like inviting creatures. Throughout history sharks have been categorized as deadly creatures, and even when unprovoked will attack humans. Contrary to popular opinion, this is just not true. Most unprovoked shark attacks do not result in death and instead result in relatively minor injuries (Midway et al., 2019). For example, studies show that between 1960-and 2015 only about 15% of global shark attacks were fatal (Midway et al., 2019). So, most if not all of the terror towards sharks comes from the media perception of them (Price, 2022).

Fear is defined as an unpleasant feeling caused by the belief that something or someone is dangerous or a potential threat. Often fear leads people to have major anxiety or develop a phobia. Unfortunately, fear is determined by some scientists to be a psychological construct, and cannot or is very difficult to be discovered through a scientific investigation (Adolphs, 2014). A psychological construct is a way to describe patterns in behavior so that they can be investigated, explored, or discussed. However, fear has neurological components too.When a person experiences fear, their brain can release a series of responses, often triggering a fight or flight response (Newman, 2021). So, according to studies, it seems that fear can be both psychological and neurological depending on the response.

However, humans seem to have a predisposed fear of some animals, like spiders and snakes, (Bennett-Levy & Marteau, 1984). According to one study, people are more likely to be scared of animals that have characteristics such as slimy, ugly, and moving suddenly, with other characteristics showing little statistical significance (Bennett-Levy & Marteau, 1984). However, there is almost no evidence demonstrating that humans have a predisposed fear of sharks. The only evidence is that a shark is bigger and stronger than humans, so it seems they have a natural fear of predators that are large (Foy, 2023).

The objective of this study is to determine what causes the fear of sharks and if it is based on media perception or actual fear. I feel as though this study is important because it gives us an understanding of how fear works, but also gives us an understanding of the fear of the ocean. Does it come from fear of what’s in the ocean, or is it the fear of the unknown? A study will be conducted through a survey where we establish why people are scared of sharks, and find out their opinion on sharks with the media. Although this study may be difficult due to the lack of ocean, it can be accomplished through analyzing the responses people give to the questionnaire.

Methods

Due to the fact that this experiment was conducted in New York, there was no ocean or sharks that could be used to help understand the fear of sharks. Instead, a guided survey/ questionnaire was created using Google Forms to gather information on this topic. Depending on the response the participant gave, they were taken to a different section of the questionnaire. The guided questionnaire gives an understanding of the participants’ fears. Before the questionnaire was sent out, I obtained consent from each participant. In order to separate the data, I started by asking for names, age ranges, and genders. I was

able to create more data pools with this information. In this study, the word “media” is used a lot. This term, in the case of this study, includes social media, news outlets, broadcasting, cinema, etc..

Participants then started on the questionnaire, where the first question asked them about their feelings about the movie Jaws. I chose different prompts to direct participants to the next section of the survey based on their responses. My audience was non-scientists so I made sure to use language that was accessible. Those who appeared to already show signs of fear were directed to a section of the survey aimed at understanding the source of their fear, whether it stemmed from personal experience, the physical features of sharks, or horror stories. The people who did not seem to indicate prior signs of fear were directed to a section that allowed me to see if their feelings changed. There were questions about video titles, photos, article headlines, and even jumpscares, to understand if the media was able to change their perspective. Both groups were also asked about animal appearances, most of which were sharks, and if they think their opinion of sharks could be changed based on what the media says.

To analyze the data, the data was put into the form of graphs. From there the data was looked at and by looking at the percentages to the answers how the participants responded to each question. From there the analysis of each individual set of data was compared with other sets of data that were either related or in the same groupings of questions. A lot of the comparisons were to find if there were any discrepancies in reponses.

Results

The survey had 93 responses, but one person did not give consent for their responses to be included in the study. The data represented below reflects responses of the 90 people that gave consent. The results section is separated based on what route the survey participants took; when analysing the data it is stated which section of the survey the data belongs to.

As stated in the methods, participants were asked for their opinion of the movie Jaws, and based on their responses, they were taken to a specific section of the survey. Some people could have been taken to a section they should not have taken if they chose specific answers to this question. For example, if people responded that they had not seen the movie Jaws, they

1.

Fig. 2. Graph showing participants’ opinion of the movie Jaws using premade prompts. “Liked, hesitant” refers to an answer that was “I liked the movie but I’m definitely checking twice before going into the ocean”

were taken down the non pre-existing fear of sharks route. However, it’s possible they could have been scared of sharks.

The question shown in Figure 1 was used as a basis to establish if participants had seen a movie that depicts sharks in a discomforting manner. We know that not everyone could have seen the movie, so there were more options other than yes or no.

Based on the response given in this question, the participants were directed to a different section of the survey, either the section for people that show signs of pre-existing fear or does not show signs. So responses of “sounds cool”, “ I loved the movie!”, “Not seen”, “Good movie, not accurate” and “other” led the respondents down the path of non pre-existing fear.The ones that chose “like, hesitant” were taken down the path of pre-existing fear. Note, there were more options for responses than what is represented in the chart, but the chart represents the answers that were chosen by the participants because the other choices were not selected. Therefore, the majority of the participants that took the survey chose an option that gave indications that they did not have a pre-existing fear of sharks or the movie Jaws

When taking the survey there were thirteen people that took the path that considered them to have an already existing fear

Fig. 3. The physical features of sharks that participants with pre-existing fears thought were unsettling.

Fig.

The percentage of people who have seen or heard of the movie Jaws.

of sharks. When asked this question, the participants were able to choose more than one answer, which is why there is not a 1:1 correspondence between the reason and number of people. Figure 3 shows that everyone that took that path thought sharks teeth were a physical feature that scared them, with close second and third being that their speed and size were also big contributing factors.

Fig. 4. Percentage of people with pre-existing fear that think the media affects their opinions on sharks.

For the most part, the people who took the pre-existing fear track of the survey believe that media stories affect their opinions on sharks. I thought this was a good question to ask because it shows whether or not people understand some aspects of where people get their fears from. It also gives an insight into any potential bias in questions related to the media that could have been asked later on in the survey.

Fig. 5. Percentage of people that are scared of sharks in the self-identified not fearful track.

The people that answered this question were in the non preexisting fear track. The majority of the people said that they were not scared of sharks. Realistically, all of the people that answered this question should have said no since this group is supposed to not have a fear of sharks prior to taking this survey. However, this goes back to the error of the “other” category when figuring out which path the participants took, since some people could have mentioned a fear when writing their response.

6. Participants’ opinion on what the role of sharks is in the ocean (in percent).

The majority, 86%, of participants know that sharks are a necessary part of an ecosystem based on the media. However, there could be some error on this question, since a lot of people that took this survey understand that every animal has purpose in every ecosystem.Therefore, some participants may have answered based on what they know, and not necessarily what the media has caused them to understand. This response also shows that, despite any fear the participants have, they still understand that sharks serve a purpose. Additionally, a little over 5% of participants believe that sharks do not serve any purpose, a little less than 5% did not know there was a purpose, and about 1% know they have a purpose but are unsure what it is.

Fig. 7. Percentage of participants that think their opinion on sharks is based on the media.

Everyone that took this survey was required to answer this question. Similar to the question asked for Figure 4, it was meant to establish bias and see if people knew a potential source of where their opinion of sharks came from. The difference between this question and the one in Figure 4 is that everyone was asked this question, regardless of the track they took, and there was only yes or no for a response. Having the answers being only yes or no forced the participants to think and choose whether the media does or does not influence their opinion. In figure 7, the majority of people that took this survey believe that the opinion they have on sharks was influenced by media platforms.

Fig.

Fig. 8. Percentage of participants that their opinion on sharks would change based on if the media was more positive towards sharks.

Based on the information given in Figure 8, the majority of the responses contradict the response prior, that the respondents opinion on sharks was based on media influence. Since the majority, 40%, of respondents believed that their opinion would stay the same, this question could potentially mean that no matter what they would not have a positive opinion of sharks. Otherwise, they would have chosen the option I’m already not scared of sharks. Everyone that took the survey answered this question.

Fig. 9. Percentage of participants that’s opinion on sharks would change based on if the media was more negative towards

These responses are shown in figure 9, showing an even bigger contradiction. The majority of the responses, 47.8%, say that even if sharks were portrayed in a negative way in the media, their opinion would not be different. Even that number can be higher, because of the response of “it depends”, there can be some people that might say it would stay the same had that option not been there. Everyone that took the survey answered this question These discrepancies could be considered sources of error because the percentage of people that said the media does influence their opinion of sharks does not match the percentage that says their opinion would be the same depending on the way sharks are portrayed. There is also the possibility that if a person developed a fear of sharks it won’t matter how the media portrays sharks; their opinion will always be negative. These graphs were

definitely interesting to encounter, and it may not be a source of error and the response just actually reflects what the participants thought. However, it is just strange that the answers do not directly relate to each other.

Additionally, the people that took the non pre-existing fear track were shown a shark jump scare and asked their thoughts. Along with being asked if they would watch shark related videos, varying between educational and click bait titles. These questions were to see if a potential fear could be brought out, and if participants would watch scarier shark videos. For the most part it seemed that the participants would watch the videos, or at least would be willing to watch the videos. However, there was one that the participants were not interested in, but that video was less about sharks and more about ghosts and the ocean. Lastly, all participants were asked what six different animals looked like, two of which were not sharks. The main idea behind this was to show participants that not all sharks necessarily look scary or have the same look. An interesting thing that came out of the shark identification questions was that a lot of people did not know that some sharks look very similar to other animals that typically seem less threatening.

Discussion

The objective of this experiment was to determine whether the fear of sharks is related to media influence.The short answer is yes—there is a correlation between the fear of sharks and the way the media portrays them. Based on Figure 7, 62% of participants said that their opinion on sharks stems from how they are shown in the media, compared to the 37% that said it was not. However, there were some inconsistencies in the responses. When participants were directly asked if the media influenced their opinion of sharks, they answered yes. But when given specific scenarios, the majority of responses indicated that their opinion would not change. This inconsistency could be considered a source of error—perhaps participants misread or misunderstood the questions. I believe that the main way I could have reduced this error would have been to make the wording differences between the questions more noticeable or to avoid placing the two questions right next to each other. The question asking how participants’ opinions would change if the media portrayed sharks in a positive or negative way was worded too similarly to the previous question.

The major source of error in this study arose when determining whether participants would follow the pre-existing fear track or the no pre-existing fear track. The question that determined which route participants would take asked how the movie Jaws made them feel. However, just because the movie evoked a certain emotion didn’t necessarily mean they felt that way about the animal featured in the film. Additionally, this question included an “other” category, which allowed participants to express their opinions outside of the prompts I had provided. The issue with the “other” category was that someone might describe the movie as fear-inducing, yet still be directed to the no pre-existing fear track. To minimize error, I could either remove the “other” category or revise the question that determines which track participants follow. Lastly, one of the question answers was “I have not seen the movie,” which brought the participants down the path for people that are not scared of sharks. The error with

this is that the person could have definitely been scared of sharks, just because they did not see the movie did not mean that they were or were not scared of sharks.

Researching and learning about where different fears come from is a very important study. Through this research, we are able to gain a deeper understanding of the true nature of the fear in question—specifically, whether it stems from a legitimate psychological phobia or if it is a fabricated fear that has been shaped and amplified by misinformation, social influence, or mass media narratives. This study also shows the effect that the media can have on people, and just how much the media influences people’s opinions. As shown in survey questions that ask about media portrayal of sharks affecting fear, the majority of people said that if the media portrayed sharks in a specific way their opinion would change. In a follow up study I would be interested to find out more about the inconsistencies shown in my research, and why they occured. I also think that conducting another study to see if the media can induce fear, whether it is of sharks or of something else, would also be beneficial as an add on to this study.

Works Cited

Adolphs, R. (2013, January 21). The Biology of Fear - PMC. HomePMC. Retrieved October 18, 2024, from https://pmc.ncbi.nlm. nih.gov/articles/PMC3595162/

Bennett-Levy, J., & Marteau, T. (n.d.). Fear of Animals: What is Prepared? 6. https://www.researchgate.net/profile/JamesBennett-Levy/publication/229979695_Fear_of_animals_ What_is_prepared/links/5a29d684aca2728e05daefcf/Fear-ofanimals-What-is-prepared.pdf

Foy, G. M. (2023, August 18). Sharks! What Lies Behind Our Fear of Them (K. Perina, Ed.). Psychology Today. https://www. psychologytoday.com/us/blog/shut-up-and-listen/202308/ sharks-what-lies-behind-our-fear-of-them

Midway, S. R.,Wagner,T., & Burgess, G. H. (2019, February 27).Trends in global shark attacks. PLOS. Retrieved October 6, 2024, from https://journals.plos.org/plosone/article?id=10.1371/ journal.pone.0211049

Newman,T. (2021, October 30). Fear:What happens in the brain and body? https://www.medicalnewstoday.com/articles/323492

Price, C. S. (2022, October 21). Influence of social media on fear of sharks, perceptions of intentionality associated with shark bites, and shark management preferences. Frontiers. Retrieved October 6, 2024, from https://www.frontiersin.org/journals/ communication/articles/10.3389/fcomm.2022.1033347/full

Thurman, M. L. (2023, October 3). Anatomy of a Shark - Shark Body Parts. https://www.animalwised.com/anatomy-of-ashark-shark-body-parts-4873.html#anchor_1

Towner, A. V., Kock, A. A., Stopforth, C., Hurwitz, D., & Elwen, S. H. (2022, November 27). Direct Observation of Killer Whales Preying on White Sharks and Evidence of a Flight Response. https://pmc.ncbi.nlm.nih.gov/articles/PMC10078210/

RESEARCH & DISCOVERY

Comparative Analysis of Judson Pond’s Health: Using Data Collected in 2022 and 2024

Oliver Riveros

Abstract

The Judson Pond, located in Victor, New York, was constructed in the 1980s and has served as a recreational area for the family, as well as a research site for the Allendale Columbia School Science department for the past six years. In this observational study, we sought to assess the pond health using comparative analysis of data collected in 2022 and the data we collected in 2024 (Riveros, 2023). We measured different health indicators such as Secchi Disk depth, nutrient levels, dissolved oxygen, pH, pond temperature, and zooplankton/phytoplankton population densities. We utilized our pond health criteria to determine whether the pond was within the healthy range for each of these factors. We found that the pond met 6/6 health criteria goals, but some interesting trends were observed with Secchi Disk depths and the phytoplankton/zooplankton population relationships.

Introduction

Ponds are essential in our global ecosystem as biodiversity hotspots and vital carbon sinks. Across the world, there are upwards of 3.19 billion bodies of water identified as ponds (Hill et al., 2021). Although there are many ponds worldwide, their actual surface area is extremely small. Freshwater bodies account for roughly 3% of the total world surface area, but ponds only make up a fraction of that percentage. For example, ponds cover about 0.0012% of the total surface area of the United Kingdom. Despite this minimal surface area, ponds account for a disproportionately high carbon dioxide (CO2) burial rate. The CO2 burial rate of ponds in the United Kingdom was found to be 34,200 metric tons annually. In comparison, coniferous woodlands in the United Kingdom make up 5.7% of the total surface area and account for 64,700 metric tons annually of CO2 burial (Taylor et al., 2019). This difference in burial rates is a 2511 to 1 ratio (meaning that if ponds took up 1% of surface area, they would account for 28,500,000 metric tons per year; if woodlands only took up 1% of surface area, they would account for 11,351 metric tons of CO2 burial). Ponds are also essential conduits for biodiversity. A study that accumulated 15 years of pond conservation data and tracking measured the gamma diversity of ponds compared to other small water bodies in the United Kingdom. In this study, it was found that ponds have a significantly higher gamma diversity than rivers, streams, and ditches (Biggs et al., 2005). Pond carbon sinking ability, combined with its biodiversity, makes them an essential part of the global ecosystem.

Ponds are complex ecosystems that rely on a vast number of factors to maintain ecological homeostasis. These factors can be grouped into two larger categories, abiotic and biotic. Both abiotic and biotic factors can be measured and observed to understand the overall health of ponds. There are a large number of abiotic factors that affect pond health though the factors that this experiment measured include dissolved oxygen, temperature, turbidity, nitrogen, and phosphorus–– excessive nitrogen and phosphorus levels can lead to the eutrophication of ponds which can ultimately result in die-offs (Wang, et al 2012). There are many other factors, naturally occurring or human-induced that can affect a pond’s abiotic health. One of the other factors is the copper levels. Pond copper levels can vary depending on location and surroundings, natural and manmade. Copper sulfate is a pond treatment used to kill bacteria, algae, roots, plants, snails, and fungi (NPIC, n.d.). This treatment is used to improve the aesthetic quality of ponds by clearing up excess weeds and algae. Usage of this treatment can result in higher levels of copper in ponds–– the Judson pond was treated with copper sulfate up until roughly four

years ago, though no data was collected on copper levels in this examination. Higher copper levels have been found to inhibit pond primary consumer growth as well as increase shredders’ feeding activity (increased feeding rates can result in lower survival because of a lack of food; Silva, et al 2017). Each of these factors has specific ranges, standards, or criteria that healthy ponds should fall within. For example, turbidity is the measurement of water clarity and can be affected by the levels of sedimentation, phytoplankton and zooplankton populations, and other natural or unnatural water clarity factors. Turbidity depends on the pond’s location, size, and the types of organisms that inhabit the pond. Higher turbidity is oftentimes associated with lower dissolved oxygen levels, specifically in early morning tests of D.O. and turbidity (Global Seafood Alliance, 2020).

Biotic factors of ponds consist of all living organisms, from the top of the food chain to the bottom. Microorganisms such as algae, phytoplankton, and zooplankton each play essential roles in ponds. Phytoplankton can be categorized into some groups, like cyanobacteria, silica-encased diatoms, dinoflagellates, and

Riveros Comparative Analysis of Judson Pond’s Health: Using Data Collected in 2022 and 2024 green algae. Phytoplankton are primary producers, meaning that they create their energy through photosynthesis, and are the foundation of the food chain (NASA Observatory, 2010). Zooplankton, such as daphnia and copepods (the two types of zooplankton found in the Judson pond), are primary consumers in ponds. Using population measures of zooplankton can be an effective measure for pond health because zooplankton are an essential food source for larger organisms in ponds.

Pond owners have several means of managing their pond ecosystems for aesthetic, health, and personal preferences for the pond. Some of these management methods include the use of aerators, chemicals (like copper sulfate), and the introduction of different species to manage different factors. Aerators, which are one of the management tools used in the Judson Pond (there are two aerators on each side of the pond, and were installed 3 years ago), are used to prevent pond eutrophication and to better disperse D.O. the water. In a study observing several different urban ponds facing issues of eutrophication, one of the pond’s D.O. went from 1.8 (pre-aerator) to 8.1 (after a year of aerator introduction) mg L−1 (Hao et al., 2021). Copper sulfate is used to manage fungus, algae, and other microorganisms in ponds but there are negative effects that can be seen earlier in the paper. A specific species of fish that has been used to manage pond aesthetics and health are grass carp. Grass carp are used to control levels of aquatic plants to prevent overgrowth. Studies have found that small grass carp can consume more than 100% of their body weight in food per day (Leslie et al 1987). This ferocious appetite makes them an effective tool for controlling aquatic plant levels in ponds.

This study will examine the water health of Judson Pond, located in Victor, New York. The Judson Pond is a man-made pond that was constructed in the 1980s as a private outdoor recreational water body. The Judson Pond came into the focus of the Allendale Columbia scientific community in 2019 when the first examination outing occurred. During this outing, the pond health measures such as Secchi disk depth, D.O. levels, Chlorophyll, Zooplankton, and Phosphorus and Nitrogen levels were taken. This was a single outing in which pond health indicators were measured from the Judson pond and 6 other ponds located near each other in the Victor, New York, area. This comparative examination was undertaken again in the spring of 2020 and 2022. In the summer of 2022 and now (for this study) in the summer of 2024, water health samples were measured every week for the duration of the summer. Using the historical data from 2022 and 2024 will allow interested researchers, such as the Allendale Columbia Scientific Community, to analyze whether the pond is still healthy or not.

Pond health reflects the broader ecological strength of a region. Ponds are essential parts of the world’s greater ecological success, from being havens for biodiversity to being powerhouses in carbon sequestration. Understanding the health of the Judson pond not only allows for the Judson family and friends to enjoy their pond but also provides an indicator of the greater ecological health in the area.

Methods

Field Methods:

Data was collected throughout the summer of 2024, nearly

every week, with June 13th being the first collection date and October 26th being the final. This study measured the pond’s temperature, dissolved oxygen, turbidity, nitrate, phosphate, pH, phytoplankton, and zooplankton levels. Each of the measurements were drawn from the deepest location in the pond (that being approximately 3 to 3.5 meters.)

On Water Procedures:

For the retrieval of water samples, the zooplankton tow, and Secchi disk measurements, a kayak was used to navigate to the approximate deepest point of the pond. The measurements were then drawn.

Water temperature was collected using the EcoSense DO200M Dissolved Oxygen and temperature measurement device. Air and surface temperatures were collected then temperature was measured in increments of 0.5 meters (Surface temperature being 0 meters and the bottom measurement being 3.5.)

D.O. was collected using the EcoSense DO200M Dissolved Oxygen and temperature measurement device. D.O. measurements were taken from the surface and then at increments of 0.5 meters until the device reached the bottom layer of the pond where D.O. levels are expected to be very low (This was typically around 3.5 meters, though there was variation depending on weather, exact positioning of the kayak and other non-controllable factors).

Turbidity was measured using the Secchi Disc method. The disc is placed into the water and is lowered until it is barely visible, the depth is measured, then lowered just below visibility, and the depth is measured.The average of the two measurements is taken, and that is the Secchi Disc depth. The measurement is typically meant to be 40% of the pond’s photic depth; photic depth being the level at which there is insufficient light for plants and algae to grow (Moss, 2017)

Zooplankton were collected using a 30 cm diameter, 100 micrometer mesh zooplankton tow. The tow was lowered between 2- 3 meters, and then slowly retrieved. The zooplankton crop from the tow was then brought back to land in the tow’s zooplankton container, and placed into a 95% ethanol solution in SPL tubes to be preserved–– the actual transfer of the zooplankton into the 95% ethanol solution was done on land (E.P.A., 2016). All zooplankton methods followed the EPA standard zooplankton procedures (EPA, 2016).

Water samples were collected using a water sampler trap from the surface and then in intervals of 0.5-meter depths until the water sampler trap brought up sedimented water. Each of the depth samples were placed in a bucket to mix the different water layers.This integrated water sample was then used in the On Land Procedures.

On Land Procedures:

All on-land procedures were done directly after water procedures were complete.

The LaMotte Nitrate-Nitrogen Tablet Test Kit was used to measure the nitrate level of the pond. The LaMotte NitrateNitrogen Tablet Test Kit instructions were used to find the measurement.

The LaMotte Low-Range Phosphate Table Kit was used to measure the phosphate levels of the pond. The LaMotte Low Range Phosphate Table Kit instructions were used to find the measurement.

Riveros Comparative Analysis of Judson Pond’s Health: Using Data Collected in 2022 and 2024

pH was measured using the sample water and a Vivosun Digital pH Meter with 0.05 pH High Accuracy Pen Type.

Chlorophyll samples were collected using a suction system and Ahlstrom Munksjö 0740-0700 filters. The integrated water sample was strained through the filter, in increments of 100 mL, until the filter became visibly green. The filters were then placed in tin foil and (typically 20-30 minutes later) placed in a freezer to preserve the chlorophyll pigment.

Lab Methods:

All lab methods were based on the procedures described in “The Judson Pond Project” methods (Riveros 2023).

Chlorophyll levels were found using absorbance measured with a spectrophotometer. The sample Ahlstrom Munksjö filter paper was cut into pieces and placed in a mortar and pestle with 2- 3 mL of 95% acetone. The filter paper was then ground for 5 minutes. The ground filter paper was then placed into a closed container with 10 mL of acetone and left refrigerated overnight to extract the chlorophyll (Shengqi Su et al., 2010). The mortar and pestle were cleaned with approximately ½ mL of acetone. The same procedure was done with a blank filter paper to use for spectrophotometer calibration. The following day, the solution’s absorbance was analyzed using a spectrophotometer. Following calibration, the solution was placed into a 3.5 mL quartz Cuvette produced by Lifestyle Visions and placed in a Go Direct® SpectroVis® Plus spectrophotometer produced by Vernier Software & Technology. Using Bluetooth, the spectrophotometer was connected to an Apple MacBook, and absorbance data was collected within the Vernier Spectral Analysis platform and transferred to a CSV file to be analyzed. Chlorophyll was calculated using equations from Limnology (Lind, O.T., 1985).

Formula to Find Chlorophyll a, b, and c Concentrations

A - 11.6(abs. at 665 nm)

B - 20.7(abs. at 645 nm)

C - 55.0(abs. At 630 nm)

Abs. = Absolute value

Mg Pigment per m3 of lake water:

(C × Extract Volume(mL))/(Liters of Lake water Filtered × Path Length(cm))

For extract volume we used the total solvent used to extract the pigment; 10 mL was used to extract the pigment in this experiment (Lind, O.T., 1985)

Zooplankton were preserved in 95% alcohol in a SPL tube. A stereo microscope was used to identify species and the relative density of zooplankton. To analyze zooplankton density, a single 1 mL sample was drawn from each SPL tube. The total number of zooplankton, differentiating daphnia and copepods, was tallied. To keep track of zooplankton count, a clear plastic petri dish with grids drawn on the bottom was used as the container in which the sample was counted. The number of zooplankton in each mL was multiplied by the volume of the SPL tubes (used to preserve the plankton) to get the number of plankton in the tube (Each tube had between 45- 50 mL of 95% ethanol). Using measurements of the plankton net, we found the aperture of the net and multiplied

that by the distance of the tow: vt = pi r2 (dtow)

Where:

vt = volume of tow

r is the radius of the net opening

dtow is the distance of the tow

We then found the volume of the tow by converting cubic meters to liters (m3 x 1000 = L).

We then multiplied the number of zooplankton we observed in the one ml sample by the volume of our tubes used

p x vs = number of plankton in sample

Where:

p = Number of plankton observed in one ml vs = the volume of the sample tube

We then divided the number of zooplankton in the sample by the volume of the tow, giving us the number of zooplankton per liter

n/vt = number of plankton per liter

n number of plankton in the sample

vt = volume of tow

Methods Analysis:

Using the ‘pond health criteria’ used in the 2022-2023 SWR Research- Judson Pond study, the overall health of the pond will be compared between the 2022 summer and 2024 summer (Riveros, T. 2023). The criteria were then evaluated to determine if it is an accurate and acceptable standard after two years of new research. Results from the different measurements were compared side by side, looking at how the two summers’ data compare in a parallel manner. The measurements were then used to do similar comparisons that were made from the 2022 data; correlation between turbidity and Chlorophyll, Chlorophyll vs. number of zooplankton per liter, D.O. and average temperature, etc (Riveros, T. 2023). The Judson Pond’s condition was also compared to other similar freshwater bodies.

Results

Fig. 1. The Secchi Disc depth readings for each of the pond outings and a dotted line indicating the minimum criteria depth.

Water Clarity

The pond water clarity met the criteria for acceptable average Secchi depth (Tables 1 and 2). The maximum Secchi disc reading of 2.37 m occurred on June 22nd, and the minimum of 0.63 m reading occurred on August 28th (Figure 1). From June 22nd to August 28th, there was a declining trend in water clarity (Figure 1). The peak water clarity was found on June 22, which is typically caused by lower levels of phytoplankton activity. The average Secchi disk depth during the summer of 2024 was lower than the average depth during 2022 (Tables 2 and 3).

Dissolved Oxygen

The pond’s Average (8.16 mg/L) and median (7.12 mg/L) dissolved oxygen levels met the criteria for pond health. The minimum measured D.O. was 5.69 mg/L on June 22nd, and the

Fig. 2. The average dissolved oxygen for each of the pond outings and a dotted line indicating the criteria mg/L minimum. The average was taken from the D.O. measurements of depths 0.5m, 1m, 1.5m, and 2m

Fig. 3. Linear regression of Secchi disk depth (m) vs chlorophyll c (Chl/m³). R2=0.374 and R = 0.612

maximum was 8.16 mg/L on August 24th (Table 2). The Lower D.O. values coincide with higher temperatures, specifically on June 22nd, when both the lowest D.O. and highest temperatures were recorded. Though we did not find a statistically significant correlation between water temperature and D.O., it is known that higher water temperatures can affect oxygen solubility (USGS,

Fig. 4. Average temperature vs. chlorophyll scatter plot.

R2=0.371

Fig. 5. Number of copepods and Daphnia per liter based on the zooplankton sample estimations for each outing. R2=0.371

Fig. 6. The nitrate (ppm) readings for each of the pond outings and a dotted line indicating the maximum concentration acceptable for the criteria.

2019). The majority of average D.O. readings were above 6 mg/L, indicating that the pond maintains sufficient oxygen levels for aquatic life (Figure 2).The Average D.O. level during the summer of 2024 was the same as the average D.O. level found in the summer of 2022 (Tables 3 and 4).

Nutrient Levels

The pond’s average nutrient levels both met the pond health criteria, with the average nitrate being 0.41 ppm and the average phosphate level being 0.02 ppm. Nitrate levels fluctuated throughout the summer between 0 and 1 ppm, with the highest readings of 1 ppm on June 13, July 13, and August 24th (Figure 6). Phosphate levels only saw one spike of 0.2 ppm on June 13th. On all other dates, the phosphate levels were 0 ppm.

Water Temperature

The average pond temperature (23.75 °C) met the healthy pond criteria (Table 2 for criteria). The peak temperature of 28.54 °C was recorded on June 22nd, and the low of 13.18 °C was recorded on October 26th (Table 2). Higher temperatures correspond to lower dissolved oxygen levels (Figure 2)

pH Levels

The average pH level (7.53) for the summer stayed within the criteria range for pond health (Tables 2 & 4). The maximum pH (8.03) was recorded on July 27th, and the minimum (7.08) was recorded on July 13th. Throughout the summer, pH levels stayed steady and did not fall outside of the pond health range (Table 1).

Chlorophyll/Phytoplankton Trends

Chlorophyll concentration varied throughout the summer. It increased during the warmer months and decreased in the fall. Figure 3 illustrates the relationship between chlorophyll and Secchi depth; higher phytoplankton levels reduce water clarity.

Zooplankton Trends

Zooplankton levels throughout the summer fluctuate in a cyclical pattern of breeding and die-off. In Figure 5, the concentration of daphnia and copepods per liter were higher in the beginning months of the summer and then decreased during July and into August. In the second half of August through September, there was a high zooplankton concentration per liter. These late summer/ early fall increases in Zooplankton density did not coincide with an increase in phytoplankton. Zooplankton populations are typically higher when there is more phytoplankton, so the trends found in the later months are unusual (Figure 7). Other factors can result in zooplankton population increases, though no clear examples were found in our data.

Discussion

This study aimed to evaluate the health of the Judson Pond using samples and data collected during the 2024 summer. Using the pond health criteria created for the Riveros (2023) SWR Judson Pond research paper with some modification, such as the inclusion of pH and a higher threshold for acceptable phosphate according to NY State regulation, we found that the Judson pond met six of the six health criteria, including turbidity, nutrient levels, dissolved oxygen levels, pond temperature and pH levels (Table 2).

Fig. 7. Number of zooplankton per liter versus chlorophyll concentration Ch/m3 during the summer of 2024. The blue line indicates the zooplankton per liter, the primary y-axis shows the scale, and the red line indicates the chlorophyll concentration Ch/m3, and the secondary y-axis shows the scale. Chlorophyll concentration used an average of chlorophyll a, b and c’s absorbance.

In addition to the factors measured on the criteria, zooplankton and chlorophyll concentration were observed.

Determining the health of the Judson Pond is difficult because of how complex and interconnected pond ecosystems are, but by using the different health indicators we measured, we can gain a stronger understanding of how the pond functioned through the summer months. This stronger understanding allows us to look at the relative health of the pond based on our criteria. It is important to understand that the criteria we created are generalized to small ponds, and it is not an absolute way of measuring pond health. Using the criteria, we found that the pond maintained healthy conditions throughout the summer, though several measurements raised questions.

Water Clarity Inconsistencies

Throughout the 2024 summer, the average pond secchi disk depth was 1.18 meters, which met the one-meter minimum criteria, but during the middle of the summer, there were five consecutive outings ranging from July 27th to August 28th where the depth did not meet the criteria. The 2022 average Secchi Disk was 1.84 meters, which means that the average 2024 depth was over half (0.66 meter) a meter shallower than the depth measured in 2022. This reading indicates that during the middle of the summer of 2024, there was lower water clarity. This lower clarity could be the result of a number of different factors. On two of the five low clarity outings, we recorded rainy conditions on the day or in the days prior to the measurement. Rainy conditions lead to runoff, which in turn creates higher water turbidity. Thus, the high turbidity readings from August 10th and August 19th could be the result of runoff from rainy conditions. For the other three outings, it is less clear what caused higher turbidity. Higher phytoplankton levels could have contributed to turbidity. In both 2022 and 2024, we observed a moderate negative correlation between phytoplankton levels and water clarity (Figure 3). Higher phytoplankton levels on these outings would have decreased water clarity and thus provided an explanation for the lower water clarity (Global Seafood Alliance 2004). This relationship was

seen when comparing the average chlorophyll concentration for the entire summer, excluding an outlier measurement from June 13th, to the average chlorophyll concentration for the five sub-1meter outings. We found that the average concentration for the five sub-1-meter Secchi Disk outings was higher than the entire summer’s average chlorophyll concentration (0.00577 for the five outings and 0.00461 for the entire summer)

Although we did not record wind levels, windy conditions can stir up bottom sediment, thus increasing turbidity (Moss, 2017). It is also important to take into account that the one-meter minimum Secchi disk depth criterion is somewhat arbitrary. The study we used to create our Secchi Disk criteria provided a list of ideal water quality parameters based on the primary use of the pond. The one-meter minimum Secchi Disk depth reading is under the condition that the primary use of the pond is for fishing (Swistock, 2015). While the Judson pond is used for fishing, that does not necessarily mean that the one-meter depth criterion is the most appropriate measure. Our criteria are meant to give an approximation of health rather than a rigid and exact standard. Each pond has unique needs that are ever changing, so the criteria are more of a generalized range of health than a specific standard that the pond must attain. Ultimately, the average and median Secchi Disk depths for the Judson pond during the 2024 summer exceeded the one-meter minimum, so overall clarity seemed healthy.

Abnormal Chlorophyll/Phytoplankton Trends

During the beginning of the summer, zooplankton and chlorophyll concentration followed typical cyclical trends; that being that an increase in phytoplankton population caused an increase in zooplankton population because of the predator-prey relationship and vice versa. From August 19th to September 28th, the zooplankton-phytoplankton population relationship was not observed. This disconnect of the food source, the phytoplankton, from the predator, the zooplankton, is quite unusual, so further investigation was needed to analyze this abnormality.

There are four possible explanations that we extrapolated for this unusual plankton population interaction. First, there could have been a methodological error in the data collection or data analysis process. Second, there could have been an overrepresentation in zooplankton concentrations as a result of deeper zooplankton tows accompanied by diel vertical migration. Third, it is possible that zooplankton shifted from phytoplankton to a different food source, not detected by our chlorophyll tests. Finally, there may have been a relaxation in predation, which allowed zooplankton populations to grow larger.

Inaccurate methodology or poor data analysis could have resulted in inaccurate phytoplankton and zooplankton measurements and/or interpretations. Zooplankton concentration relied on a single sample to estimate the total zooplankton capture in the tow. Although we thoroughly mixed the tow contents before taking the sample, it is possible that the sample could have over or underestimated the plankton concentrations. When a one-variable statistical test was conducted with the zooplankton populations, there were no outliers found. The ‘high’ zooplankton concentrations measured in 2024 were much lower on average than the 2022 zooplankton density; the 2022 average was 77.703 zooplankton per liter, while the 2024 density was 26.003

zooplankton per liter. The 2022 sample population standard deviation (43.299) was also much higher than the 2024 sample population standard deviation (15.256). This lower variability and consistency in higher readings may suggest that the data analysis methodology was accurate to the true zooplankton populations.

Inconsistent or faulty methodology for phytoplankton concentration measurements could have caused this discrepancy between zooplankton and phytoplankton concentrations. The analysis of phytoplankton density through the spectrophotometric measurement of chlorophyll absorbance is meant to use chlorophyll pigments a, b, and c to indicate the amount of phytoplankton present in each sample. Phytoplankton use photosynthesis to create energy, which means they have chlorophyll pigment present in their chloroplasts. We can measure the amount of chlorophyll pigment absorbance in each water sample to approximate the phytoplankton density of the pond. This method is meant to give an estimate of the amount of food available for the zooplankton. There is a possibility that the absorbance measurement could have inaccurately captured the true amount of chlorophyll on the dates on which the chlorophyll concentration was low. While this is possible, there were three readings in a row where this discrepancy of plankton densities was present, so it is unlikely that testing error caused this issue.

The second hypothesis behind why the zooplankton populations were much larger than the phytoplankton densities was that deeper zooplankton tows could have resulted in populations being overrepresented. The two highest zooplankton per liter concentrations were found on August 28th and September 28th, both days when the tow depth we drew a 2.5-meter tow instead of the standard 2-meter tow that was done throughout the earlier parts of the summer. This higher concentration could be a result of more zooplankton staying at lower depths. Diel Vertical migration could explain this higher density in deeper waters. Diel Vertical migration is “the process by which aquatic organisms migrate through the water column on a daily basis” (Godkin 2001).This migration can be a response to several factors, but is often connected to predation avoidance. It could stand to reason that on the dates on which we took a deeper tow depth, we tapped into population pockets of zooplankton. Assuming that there is a consistent number of zooplankton per liter throughout the entire pond is not necessarily accurate because one part of the pond could have very high zooplankton densities while the other part has very low; this same variance in population is possible for different depths as well. On those days, zooplankton may have migrated, in mass aggregation, to deeper depths to avoid predation. By tapping into these deeper pockets of zooplankton, we could have overrepresented the population densities on a given date. In our population density calculation, we used depth as part of the volume calculation, so deeper tows would have greater volumes. Despite this depth adjustment in the calculation, there still could be overrepresentation. For example, if we find there are 5,000 zooplankton in a 2-meter tow, there would be approximately 35 zooplankton per liter. Say that the same tow was taken at 3 meters, and in that additional third meter, there was a pocket of an additional 10,000 zooplankton. In this second hypothetical scenario, there would be roughly 70 zooplankton per liter. As illustrated by this example, tapping into a deeper pocket of zooplankton could cause zooplankton populations to be much

higher. This hypothesis is certainly feasible, though there are some findings that could indicate it is inaccurate. While the two highest zooplankton densities were on dates where the depth tow was 2.5 meters, the third highest density was measured on a date when a 2-meter tow was drawn. There were also two dates in which a 3-meter tow was taken, and the zooplankton was not significantly higher than the other dates; on August 24th, a 3-meter tow date, the measured zooplankton density was lower than the average density for the summer. While diel vertical migration and deeper tows could explain the unusually high zooplankton densities observed at the end of the summer, our data suggest that deeper tows do not necessarily result in higher concentrations per liter. It is also important to recognize that diel vertical migration varies from day to day, so it is possible that on one day, more zooplankton were at deeper depths than on another day.

The third hypothesis is that the zooplankton shifted their food or became less reliant on phytoplankton. If zooplankton were solely relying on phytoplankton as their food source, we would expect there to be much lower zooplankton densities, but from mid-August to October, we measured zooplankton increases that were not accompanied by significant phytoplankton increases. This hypothesis argues that it is not metabolically possible for these zooplankton increases to occur without an alternate food source. Zooplankton eat other foods besides phytoplankton such as some bacteria, diatoms, and microalgae not measured with our chlorophyll tests. To point out the discrepancy in the predator-prey relationship seen in the Judson pond, we compared our results to another body of water. We compared our results with data collected in 2003 from Christine Lake, a freshwater lake located in Northern New Hampshire, which contains small fish and phytoplankton (Godkin, 2004). Christine Lake is an oligotrophic (a body of water that has very low nutrient levels) water body. Christine Lake had an average chlorophyll of 1.6 µg/L during its testing period, while Judson Pond had 0.213 µg/L. Christine Lake’s higher chlorophyll level was accompanied by an average zooplankton concentration of 5.13 zooplankton/Liter, while Judson Pond had an average zooplankton concentration of 26.003 zooplankton/Liter. Christine Lake’s higher chlorophyll was accompanied by low zooplankton levels, while the inverse was seen in the Judson Pond. Looking at the Judson pond’s 2022 data, we can observe that the average zooplankton concentration was 77.703 zooplankton/liter while the chlorophyll concentration was 0.797 µg/L. Based on this data, it is clear that the chlorophyll threshold for maintaining zooplankton populations is much lower at Judson Pond than at Christine Lake. This lower threshold could be the result of the zooplankton being more resilient and adaptive when their food sources are depressed (Klüttgen, 2000). As zooplankton have less access to phytoplankton, they shift their feeding habits to other food sources not measured by chlorophyll tests. Though we did not conduct any quantitative procedures to find whether this hypothesis is supported by data, we can look at our results in a logical sequence. Zooplankton densities increased while their food source did not. If phytoplankton were the only food source for zooplankton, then a decrease in phytoplankton would necessarily cause a decrease in zooplankton populations as they no longer have a food source.

The fourth and final hypothesis is that zooplankton predation decreased in the second half of the summer, which allowed

populations to become much larger. This hypothesis would line up the growth cycles of the fish in the pond. For example, bass spawn from April to May. Bass rely on zooplankton such as copepods and daphnia as their primary food until they reach 25 mm in size. Once they have exceeded this size, which typically takes between two to four weeks, they shift from solely depending on zooplankton as their food source to eating other larger organisms (GarcíaBerthou, 2002). Thus, it could stand to reason that the fish in the Judson pond suppressed the zooplankton population during the spring to early summer months, as they still relied on them as their primary food source, but as the summer progressed and the fish got larger, they shifted to other, larger prey. This absence of predation could have allowed the zooplankton populations to increase significantly. Like the third hypothesis, this hypothesis does not have quantitative data supporting it, but it would logically stand to reason. To definitively state whether or not this hypothesis is supported, we could have taken samples to find the pond’s fish and other lower trophic-level organisms’ population densities.

While each of these hypotheses have valid scientific reasoning and logical support, our team found that the alternative food source and lack of predation hypothesis had the most scientific and logical standing. The most likely reality is that there were factors from each of these hypotheses that played a role in this abnormality. What we can say with certainty is that ponds are ecosystems with such complexity that it would be essentially impossible to trace down the exact cause of this unexpected trend.

Final Thoughts

While there were unusual trends between phytoplankton and zooplankton, as well as five consecutive sub-criteria secchi disk readings, we found that the Judson pond was in healthy conditions. Though one could point to the different dates where the pond exhibited sub-criteria measurements, that would fail to acknowledge the holistic condition of the pond throughout the entire summer.

Our research did not include any sort of examination of larger predators such as fish, freshwater mammals, or birds, so examining these larger organisms’ health and activity could provide deeper insights into the health of the pond and the surrounding area. An example of this research could be through analyzing DNA from larger predators. Using DNA sampling, we could monitor genetic diversity as well as population density of fish and larger predators. Through the process of this multi-year observation of the Judson pond, beginning in 2019, the Allendale Columbia School science department has gained invaluable knowledge and experience about the pond and how a large-scale observational study can be conducted. Throughout this time, we observed a pond die-off in 2020. This die-off created significant concern with the owners and the A.C. science community alike. As the pond has been recovering, we have been monitoring health, and our team has found that as of the summer of 2024 the Judson Pond is in good health. When a pond is healthy, that not only helps the flora and fauna of the pond but also often leads to the surrounding ecosystem to thrive.

Acknowledgments

On behalf of the Allendale Columbia science department and myself, we would like to thank the Judson Family for their openness and support in giving AC students the opportunity to gain hands-on learning and research opportunities. I would like to thank the Allendale Columbia school science department for providing all of the equipment and resources needed to conduct this study. Finally, I would like to personally thank Travis Godkin for his support during this project, both in guiding me through the research process and bringing such enthusiasm and passion into this field of research.

“Many men go fishing all of their lives without knowing that it is not fish they are after.”

-Henry David Thoreau

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Silva, V., Marques, C. R., Campos, I., Vidal, T., Keizer, J. J., Gonçalves, F., & Abrantes, N. (2017). The combined effect of copper sulfate and water temperature on key freshwater trophic levels. Ecotoxicology and Environmental Safety, 148, 384–392. https://doi.org/10.1016/j.ecoenv.2017.10.035

Standard Operating Procedure for Zooplankton Analysis. (2016, July). U.S. Environmental Protection Agency. https://www. epa.gov/sites/default/files/2017-01/documents/sop-forzooplankton-analysis-201607-22pp.pdf

Swistock, B. (2015).Water quality concerns for ponds. Pennsylvania State University. https://extension.psu.edu/water-qualityconcerns-for-ponds

Taylor, S., Gilbert, P. J., Cooke, D. A., Deary, M. E., & Jeffries, M. J. (2019). High carbon burial rates by small ponds in the landscape. Frontiers in Ecology and the Environment, 17(1), 25–31. https://doi.org/10.1002/fee.1988

U.S. Geological Survey. (2019, October 22). Dissolved oxygen and water. https://www.usgs.gov/special-topics/water-scienceschool/science/dissolved-oxygen-and-water

Wang, Q. S., Sun, D., Hao, W., Li, Y., Mei, X., & Zhang, Y. (2012). Human activities and nitrogen in waters. Acta Ecologica Sinica, 32(4), 174–179. https://doi.org/10.1016/j.chnaes.2012.04.010

RESEARCH & DISCOVERY

The Effect of Caffeine on Reaction Time in Adolescents

Elie Swan

Abstract

Caffeine is the most widely consumed psychoactive drug, known for its stimulating effects, specifically with alertness and cognitive performance. This study focused on the impact of caffeine consumption on reaction time among adolescents. Participants, who ranged from ages 12 to 18, completed a survey about their recent caffeine intake and habits, then performed a visual reaction time test using Human Benchmark Test. Reaction times were measured across five caffeine consumption groups. ANOVA analysis identified a significant difference between caffeine groups (p=0.0311). Interestingly, the moderate caffeine consumption group (50-100 mg), was associated with slower reaction times when compared to both higher and lower caffeine consumption groups. These results suggest that caffeine has a nonlinear effect on reaction time, with higher doses of caffeine potentially impacting reaction time and stimulatory effects. Small sample size, self-reported surveys, and uncontrollable variables all could have affected the results. Despite these sources of error, the study gave valuable information about how caffeine consumption influences cognitive function in adolescents. These findings could help adolescents understand their individual reactions to caffeine and how this can impact their driving, athletic performance, and education.

Introduction

Caffeine is the most ingested psychoactive drug worldwide (Aepli et al., 2015). In fact, on average it is estimated that 2.25 billion cups of coffee are consumed each day worldwide (Henly, 2023). Caffeine is a popular stimulant consumed worldwide due to its effects on alertness, fatigue reduction, overall cognitive function, and mood (Kamimori et al., 2014). Caffeine is a good stimulant when attempting to restore function, most commonly due to prolonged work (Smith, 2021). Classified as a stimulant, caffeine works to activate the central nervous system, blocking adenosine receptors to counteract fatigue and ensure focus (Kamimori et al., 2014). Adenosine receptors are essential in the body since they help to regulate many physiological processes. Adenosine is a molecule that builds up throughout the day due to being a byproduct of cellular energy use, particularly from ATP (adenosine triphosphate). As adenosine binds to its receptors, it enables increased sleepiness and drowsiness, signaling the body needs rest. Since caffeine blocks adenosine receptors, it causes your body to feel more awake and alert, especially when the body is not well rested (Smith, 2021). While caffeine is a stimulant and can help with focus and energy levels, there are negative effects associated with consuming it. It is important that people do not abuse the amount of caffeine intake, because drinking too much can cause serious health issues. Often caffeine becomes a staple of an individual’s morning, which can create a tolerance for caffeine (O’Callaghan et al., 2018). Depending on how much caffeine you eat or drink, side effects may include restlessness, shakiness, fast heart rate, and anxiety. The effects, however, are complex and depend on numerous factors such as age, weight, and dosage (Medline Plus, 2021).

Numerous studies have been conducted and show that caffeine can greatly impact reaction time, and create measurable effects in both visual and audial responses. A study by Balko et al. (2020) found that as caffeine consumption increased, visual and audial reaction times decreased. However, another study conducted by Kamimori et al. (2014) found that caffeine was able to increase reaction time. The methods for each experiment varied, which could have altered the results. In Balko’s experiment, participants were given caffeine in increments and then tested by visual and audial tests for reaction time. In Kamimori’s experiment, each participant received numerous amounts of caffeine in increments, then tested cognitive function through psychomotor (PVT) and field (FVT) vigilance, logical reasoning (LRT) tests, and a vigilance monitor. Although these are just a couple of examples, there have been many studies conducted to learn more about the subject

of caffeine and its effects (see. Jones (2018) and Smith (2021) for additional examples).

As a caffeine consumer myself, I found its effects to be intriguing and want to further understand the effects it has. I will further investigate the effects of caffeine and look into its influence on reaction time. Caffeine is widely consumed, so it is incredibly important to look into the effects it has on cognitive performance as well as your health. Reaction time and cognitive performance are of particular interest, due to the known fact that caffeine can increase alertness and reduce fatigue in individuals. Since there is a lack of experimental work examining the impact of caffeine particularly among adolescents, that age group will be my primary focus (Cooper et al., 2020). For this study, I will give a survey to a variety of people to determine their average caffeine intake levels then proceed with a reaction time test. I will

test their ability of quick response, and hope to gather data to better understand how caffeine affects reaction time in teenagers. I think that as caffeine intake increases, the quicker the reaction time. I will determine the experiment’s significance by looking at a regression analysis of my data. My goal for this experiment is to understand how caffeine can impact reaction time, and how this can cause greater effects, such as cognitive performance.

Methods

I surveyed and tested the reaction time of a sampled group of individuals using the Human Benchmark test to determine if caffeine intake interferes with reaction time (Human Benchmark, 2007). The Human Benchmark test is a brain speed test that measures cognitive processing speed and attention, with a focus on working memory capacity. Each participant completed a survey prior to the test. I obtained informed consent and methods to keep data private. Each participant was prompted with questions about age, caffeine intake in the last 24 hours, and how often they usually drink caffeinated beverages. I attempted to gather data from all grade levels to ensure I had data from all possible groups. Once the survey was complete, I used my computer to perform the Human Benchmark test. Studies have shown that caffeine consumption improves wakefulness and visual-motor performance (including reaction time), so a visual reaction time test is of particular interest (Balko et al., 2020). In order to maintain a low source of error, it was crucial I used the same computer, since some computers have a slower latency. I had the participant repeat the test three times then recorded the results. To analyze the relationship between caffeine intake and reaction time, I used statistical tests rather than just visually inspecting the data. I performed a one-way ANOVA test to determine whether there were significant differences in reaction times across the different caffeine consumption groups. Since the ANOVA test indicated significance (p < 0.05) after removing outliers, I conducted independent samples t-tests to compare each caffeine group to the control group. Additionally, because each participant completed three reaction time trials, I calculated the mean reaction time for each individual before running statistical analyses. These methods ensured that my findings were based on objective data rather than just observations.

Results

Analysis of reaction time across different caffeine consumption groups revealed notable variations in cognitive performance. The outliers (values exceeding more than three standard deviations from the mean) were removed to provide the most representative results. After removing these three data points, the data consisted of 55 measurements across five caffeine levels. The largest group was the 0 mg caffeine group (11 participants), while the smallest groups were the <50 mg and >200 mg groups, each with only one participant. The age of the participants ranged from 12 to 18, and were students from 7th grade up until 12th grade.

A one-way ANOVA test was performed to determine whether there was a significant difference between groups (caffeine consumption in this case). There was no significant difference between groups, in terms of caffeine consumption (ANOVA, p>0.05). However, when three outliers were removed from the data set, the p-value dropped to 0.0311, and the difference

became significant (p<0.05).

Once the ANOVA test showed that there was a significant relationship between caffeine levels and reaction time, a post-hoc Tukey test was performed to look at each level separately, and determine which specific groups are significantly different from the others. The post-hoc analysis using the Tukey test revealed that there are significant differences between the caffeine groups, specifically the 50-100 mg caffeine group. The findings show that moderate doses of caffeine (50-100 mg) significantly slow reaction time, in comparison to the lower and higher doses of caffeine (Figure 1).

Fig. 1. Mean reaction time (ms) across different caffeine dosage groups. Error bars indicate standard error. Asterisks indicate groups significantly different from the 50-100 mg group.

Discussion

The objective of this experiment was to examine the effects of caffeine on reaction time. I hypothesized that caffeine intake would result in faster reaction times than lower to no caffeine consumption. Overall, the results indicate that caffeine does impact reaction time, but the relationship depends on the dose. A small dose of caffeine (50-100 mg) appears to slow reaction time compared to no caffeine, while high caffeine levels (>200 mg) significantly increases reaction time (Figure 1). This suggests that low doses may not be able to provide as much of a stimulatory response as a high dose of caffeine would.

However, some may argue that reaction times can be impacted by stress, sleep, and a variety of other factors. I agree with this, and believe it is possible that these variables could have affected the results. Although I tried to limit bias among my peer group, it is possible that my participants may not have been truthful, since there was no “proof” of caffeine consumed. Participants may have also logged their caffeine incorrectly, and could’ve been unaware of the intake of caffeine they actually had. Additionally, my participants likely did not consume caffeine immediately before taking the Reaction Time Test, so their level of alertness may have decreased by the time the test was conducted, since the effects of caffeine wear off with time.

The sample size is another limitation. It was difficult for me to find participants, and I was limited to those free during my free

Swan The Effect of Caffeine on Reaction Time in Adolescents

time. There was a small number of participants, particularly in the <50 mg and >200 mg groups. Although I can not influence caffeine doses, it is likely a larger sample size would increase diversity of consumption levels in each population. In addition to impacting caffeine consumption, increasing the sample size would reduce outliers, and likely average out the confounding variables like stress, timing, and amount of sleep. The small sample size could have impacted my findings, so if this experiment is repeated in the future, it would be smart to incorporate more participants. This could be done by sending a survey to the entire upper school, or expanding my research during other blocks. I would also like to verify and supervise the caffeine consumption, and have participants take caffeine within a certain window of time to improve reliability.

The findings have important implications for real-world applications, especially with driving, sports, or in the workplace. Effects of caffeine can vary from person to person, so people should learn and understand how caffeine affects themselves, and make informed choices based on that. In fact, studies have shown that age could be a factor into how caffeine affects individuals. In comparison to adults, teens may have less consistent results than adults since teenagers brains are developing at different rates. In a study done by Souissi et al. (2012), caffeine consistently improved reaction time, however the adolescents in my study had varied results. Specifically, for the adults, as caffeine doses increased, reaction time did as well. This was not the case in my study, but I think further investigation should be taken to determine whether it is due to age or a discrepancy in my data.

Although it was found that high doses of caffeine enhance reaction time, relying on it for performance should be used with caution. Caffeine has become increasingly prevalent in sports as well, and is extremely common in products marketed towards athletes. For example, Celsius, an energy drink commonly found in gyms, has 200 mg of caffeine per serving. People may drink this in addition to taking pre-workout or an energy gel, which can raise alarm for excessive consumption. It is recommended that teenagers and adults don’t exceed more than 400 mg daily (Mayo Clinic). However, athletes may exceed this limit unknowingly, and may overuse these products which could diminish their performance, rather than enhance it, as it is advertised.

Works Cited

Aepli, A., Kurth, S., Tesler, N., Jenni, O., & Huber, R. (2015). Caffeine Consuming Children and Adolescents Show Altered Sleep Behavior and Deep Sleep. Brain Sciences, 5(4), 441–455. https://doi.org/10.3390/brainsci5040441

Balko, S., Simonek, J., Balko, I., Heller, J., Chytry, V., Balogova, K., & Gronek, P. (2020). The influence of different caffeine doses on visual and audial reaction time with different delay from its consumption. Science & Sports, 35(6). https://doi. org/10.1016/j.scispo.2019.11.004

Cooper, R. K., Lawson, S. C., Tonkin, S. S., Ziegler, A. M., Temple, J. L., & Hawk, L. W. (2020). Caffeine enhances sustained attention among adolescents. Experimental and Clinical Psychopharmacology, 29(1). https://doi.org/10.1037/ pha0000364

Human Benchmark. (2007). Reaction Time Test. Humanbenchmark. com; Human Benchmark. https://humanbenchmark.com/

tests/reactiontime

Kamimori, G. H., McLellan, T. M., Tate, C. M., Voss, D. M., Niro, P., & Lieberman, H. R. (2014). Caffeine improves reaction time, vigilance and logical reasoning during extended periods with restricted opportunities for sleep. Psychopharmacology, 232(12), 2031–2042. https://doi.org/10.1007/s00213-0143834-5

O’Callaghan, F., Muurlink, O., & Reid, N. (2018). Effects of caffeine on sleep quality and daytime functioning. Risk Management and Healthcare Policy, Volume 11(1), 263–271. https://doi. org/10.2147/rmhp.s156404

Smith, A. (2021). Caffeine and long hours of work: effects on alertness and simple reaction time. World Journal of Pharmaceutical and Medical Research, 10(2), 79–89. https:// doi.org/10.20959/wjpr20212-19694

RESEARCH & DISCOVERY

Development of an Experimental Ablative Pulsed Plasma Thruster

Nathan Thornburg

Abstract

This study focuses upon the development of an experimental ablative pulsed plasma thruster (APPT) for use in future research. The purpose of this research was to improve the viability of electric propulsion systems in the microsatellite industry. The design was conceptualized and subsequently iterated upon using a computer-aided 3D design software. Additionally, a preliminary electrical diagram of the required circuitry was developed. The finalized experimental thruster design is a parallel electrode manually fed model and currently awaits fabrication for use in future research aimed at evaluating fuel efficient operating conditions.

Introduction

As the use of satellites continues to dominate the space industry as a safe and cheap alternative to human spaceflight, microsatellites have grown in popularity among researchers and businesses interested in lower mission costs. The general appeal of microsatellites originates in their simplicity, their small size has reduced both production and launch costs significantly increasing the accessibility of space flight. While the majority of satellites that reside in low earth orbit lack proper attitude control or station keeping abilities, electric propulsion systems potentially provide a practical solution to these issues. For example, the SC Zond satellite and the satellite constellation which it is a part of, plan to utilize APPT in order to maintain phase positions with satellites in the constellation (Antropov et al., 2015). Additionally, combinations of propulsions systems have been proposed in which engines with higher thrust values provide the craft with orbital maneuvering abilities while smaller electric propulsion systems such as ablative pulsed plasma thrusters provide station keeping and attitude control. (Kaseev et al., 2019). Combinations of different propulsion systems and smaller satellites could potentially even provide mission planners with the opportunity to explore the deep space environment. While a multitude of electric propulsion systems have been developed and implemented with a myriad more of designs theorized, the concentration of this research will remain focused upon electromagnetic thrusters, specifically ablative pulsed plasma thrusters (APPT).

The ablative pulsed plasma thruster is a member in the family of electric propulsion systems which utilize electrostatic forces for the production of thrust. Unlike the majority of electrostatic thrusters, APPT’s utilize solid fuel rather than gaseous propellant such as xenon, krypton, or other noble gasses in combination with stored electrical charge. The electromagnetic field of the thruster is created using two copper plates acting as an anode and a cathode between which is situated the solid propellant. To fire, a spark plug initiates an electrical arc between the two plates in close proximity to the fuel ablating and ionizing the propellant. The ionized propellant will then interact with the electromagnetic field produced between the anode and cathode plates propelling it down the nozzle and producing thrust in accordance with Lorentz Law (Barquero et al., 2022). The process is repeated several or hundreds of times in order to achieve the desired orbit due to the low thrust of the system.

Despite advantageous characteristics such as the small simple structure and surplus of available �v (change in velocity), APPT’s are currently considered disadvantageous compared to other electric propulsion systems for their high inefficiency in propellant usage. Commonly found issues include late-time ablation (LTA), which produces slow moving gas along the electrodes (Yang et.

al, 2019), and carbon deposition which leads to uneven ablation of the solid fuel supply (Ling et. al, 2020). Current studies into fuel systems have found no alternative fuel type comparable to the simplicity and effectiveness of plastic polymers, especially Polytetrafluoroethylene (PTFE). PTFE remains the most commonly used fuel in APPTs due to its resistance to carbon deposition. Effective alternative solid fuels must achieve lower values of carbon deposition to be considerable which requires alternatives to be comparable in surface structure (Ling et al., 2020). Attempts to minimize effects of LTA have led to experiments in both electrical discharge systems and nozzle geometry. Changes to the nozzle geometry have often revolved around the angle at which the electrode plates are mounted. While it has been concluded that angles up to 20 degrees show increased performance (Arrington et al., 1997), it should also be noted that as angle increases the reliability of the thruster decreases (Li et al., 2021), potentially undermining the practicality of angled electrodes. Inquiries in electrical discharge systems have found that initial capacitance directly affects performance of discharge. Increases in stored electricity have been observed to increase plasma plumes when used in systems with low capacitance, plasma plumes are cited as factors which decrease overall performance (Schönherr

Thornburg Development of an Experimental Ablative Pulsed Plasma Thruster et al., 2010). The effects of capacitance were also confirmed by an experimental thruster study that determined higher starting voltage can induce increases in distributed current and effectiveness (Yang et. al, 2019; Nawaz et. al, 2010). APPT systems will require additional research into maximizing thrust while balancing fuel efficiency in order to become a viable competitor to other electromagnetic and electrostatic propulsion systems. As the space industry continues to move into the private sector with independent researchers and rocket launch companies such as SpaceX shifting away from dependence upon government agencies, the need for independent organizations and researchers to push the development of innovative technology increases. Thus it is our goal in this experiment to develop the previously described ablative pulsed plasma thruster with an efficiency and performance on par with industry standards. Excluding experimentation with several fuel types and unique geometries of thrusters, the experiment will aim to assess the thrusters performance in thrust, specific impulse both at sea level & in a vacuum, total �v, and other characteristics in unique working conditions.

Methods

Apparatus

The experimental thruster design models a rectangular geometry placing the PTFE between two parallel copper electrode plates. The electrodes are commercially available oxygen-free copper ensuring maximum conductivity capabilities. Mounted inside the cathode electrode in close proximity to the PTFE block, the spark plug is installed and is responsible for initiating ablation of the PTFE via creation of electrical arc between electrodes. Specifically, the spark plug is a surface discharge model which carbon power is added onto to reduce activation energy. A housing assembly for insulating and supporting the electrodes and electrical pathways is assembled using plexiglass, aluminum, and rubber. All electrical components including the spark plug, main power supply, capacitor, and engine nozzle are mounted using the plexiglass frame ensuring proper insulation of every component from the electrodes.

Modeling a rectangular parallel geometry, the electrodes, which are constructed from chromium free copper to ensure maximum conductivity, are one centimeter wide, one centimeter

apart vertically, 7.62 centimeters long, and 0.95 centimeters thick situated parallel length wise from each other (Barquero et al., 2022). The electrodes are fully insulated from the structural aluminum walls by plexiglass and rubber on the rear half of the thruster. The PTFE is manually loaded from the front of the nozzle and is manually fed by a plexiglass screw through the rear of the thruster housing. An external power bank connected to the capacitor electrically charges the electrodes and the capacitor in between firings. Schottky diodes are positioned between electrical lines between the capacitor and power bank protecting the power bank during discharge.

Electrical Apparatus

Electricity for the thruster was supplied by two independent power sources which charged the electrodes, spark plug, and the capacitor in between firing sequences. Protection from reverse flow was ensured through the use of Schottky diodes mounted upon all electrical pathways which fed into the electrodes, spark plugs, and capacitor. The capacitor was directly connected to a power bank which simultaneously served as the conduit through which power flowed to the electrodes prior to the capacitor’s discharge. The ten uF primary capacitor possessed a maximum capacity of 1500 which was completely depleted between firings. The initial voltage of the capacitor was stepped in 100 volt increments in order to determine the thrusters optimal conditions for production of plasma. The spark plug was supplied by an independent power supply in order to meet voltage requirements of the plug.

Data Collection

In this experiment we attempted to determine the optimal condition under which thrust production and fuel efficiency is maximized. In order to determine said conditions initial voltage levels were modulated and the resulting impact upon total ablated mass per fire and surface degradation were measured. In order to measure fuel consumption per firing, the PTFE was weighed both before and after firing to determine the difference in mass. Surface degradation patterns were measured by photographing the PTFE in order to analyze carbon deposition and overall degradation of the fuel block as a result of vaporization over continual use. Using this data, initial voltage values with the most ablated mass per fire and least carbon deposition on the fuel’s surface were considered the most fuel efficient conditions.

Results & Discussion

Fig. 1. Finalized experimental APPT 3D design modeled using FreeCAD.

Fig. 2. Simplified preliminary electrical diagram for the experimental APPT. The design was created in Lucidchart.

Thornburg Development of an Experimental Ablative Pulsed Plasma Thruster

The finalized APPT model required certain sacrifices to be made in order to reduce future fabrication complexity, cost, and time of the thruster. Although current research regarding APPT’s supports outwardly angled electrodes as providing greater performance than parallel electrodes, angled geometries significantly increase the difficulty of thruster fabrication. This is due to the unorthodox structure they require in order to mount the electrodes, secure the fuel, and integrate the ignition circuit. Thus, a parallel electrode geometry was opted for in the final thruster design. Similarly, an automatic spring powered fuel feeding mechanism was replaced with a manual screw operated feeding mechanism in order to simplify the rear end of the thruster for ease of electrical circuit integration. Although the thruster can no longer fire completely independent of human input making endurance and long duration firing tests nearly impossible, the rear end of both electrodes is now unobstructed allowing for ease of access for electrical wiring. Despite these sacrifices, future research using this experimental APPT model will still be of significance to forwarding the electrical propulsion and micro satellite industry in addition to aiding overall exploration of space.

Utilizing the finalized experimental APPT model, future research will center around improving fuel efficiency with regards to electrical discharge and ablation. Specifically, initial voltage of the capacitors in the thruster will be modulated to compare lower and higher starting voltage conditions. Comparing these conditions will allow for optimal capacitance to be determined for certain parallel electrode APPT geometries. Future research will also take into consideration the degradation of the solid PTFE fuel block in an attempt to diminish carbon deposition and other processes which inhibit proper ablation. Fabrication of experimental series will begin shortly following publication of this paper with E.V.A. (Experimental Vacuum Accelerating) Vehicle Unit 01.

The finalized experimental APPT model and the planned future research are critical to expanding the electrical propulsion industry and in turn, the microsatellite industry. Since fuel efficiency is one the largest issues plaguing APPT’s, improvements in this area will greatly improve the applicability of APPT in microsatellite missions that wish to extend mission length and/or change their orbit (inclination, apoapsis, deorbiting maneuvers, transfers, etc.). This study is the beginning of an attempt to expand the scientific understanding of this field and solve the issues currently plaguing APPT’s. Expansion of the microsatellite and electric propulsion system industry will also improve the accessibility of space and the range of individuals who can participate in scientific research in space overall benefiting human civilization.

Acknowledgements

This study and equipment for future research were funded by Allendale Columbia School through the Science Writing and Research class fund. Additional resources for future construction of E.V.A. Vehicle Unit 01 were provided by Samuel Lacina.

Special thanks to Science Writing and Research teacher, Mr Godkin for acting as an advisor in this research. Additionally, special thanks Samuel L. for assistance in initial planning of the thruster design and advice on the creative design process.

Works Cited

Antropov, N. A., Kazeev, M. K. & Khodnenko, V. K. (2015). SSC ZOND with APPT-95 Based EPS [conference presentation]. International Electric Propulsion Conference, Hyogo-Kobe, Japan.

Arrington, L. A., Haag, T. H., Pencil, E. P. & Meckel, N. M. (1997). A performance comparison of pulsed plasma thruster electrode configurations [conference paper]. International Electric Propulsion Conference, Cleveland, Ohio.

Barquero, S. B., Navarro-Cavallé, J. N., & Merino, M. M. (2022). Experimental plume characterization of a low-power Ablative Pulsed Plasma Thruster (APPT) [Conference presentation]. International Electric Propulsion Conference, Cambridge, Massachusetts.

Kaseez, M. K., Khondenko, V. K. (2019). Hybrid electric propulsion system on the basis of SPT and PPT [Conference presentation]. International Electric Propulsion Conference, Vienna, Austria.

Li,Y. L., Dorn, K. D., Hseih, H. H., Kuo, T. K. & Hsu,Y. H. (2021). Effect of electrode angle on pulsed plasma thruster performance. Journal of Aeronautics, Astronautics, and Aviation, 53 (3), 353 - 368. 10.6125/JoAAA.202109_53(3).02.

Ling, W. L., Zhang, S. Z., Fu, H. F., Huang, M. H., Quansah, J. Q., Liu, X. L. & Wang, N. W. (2020). A brief review of alternative propellants and requirements for pulsed plasma thrusters in micropropulsion applications. Chinese Journal of Aeronautics, 33(12), 2999 - 3010. https://doi.org/10.1016/j.cja.2020.03.024

Nawaz, A. N., Albertoni, R. A. & Auweter-Kurtz, M. A. (2010). Thrust efficiency optimization of the pulsed plasma thruster SIMP-LEX. Acta Astronautica, 67, 440 - 448. 10.1016/j. actaastro.2010.03.006

Schönherr, T. S., Komurasaki, K. K., Kawashima, R. K., Arawaka, Y. A. & Hedrich, G. H. (2010). Effect of capacitance on discharge behavior of pulsed plasma thruster. Journal of IAPS, 18(1).

Yang, L. Y., Liu, Q. L., Zhao, X. Z., & Huang, Y. P. (2019). Analysis of Distributed Energy Release Characteristics in an Ablative Pulsed Plasma Thruster [Conference presentation]. International Electric Propulsion Conference, Vienna, Austria.

RESEARCH & DISCOVERY

The Sounds of Science: An Analysis of Optimal Microphone Placement in the Curtis Performance Center

Victoria Timpani

Abstract

Clear, high-quality audio is essential when documenting live performances for educational, archival, or promotional use. This project aimed to evaluate whether the choice of recording location, based on its physical position in the Curtis Performance Center (CPC), impacts perceived sound clarity.

Multiple recording locations were tested during live events, with recordings made simultaneously using identical microphones and equipment. Each recording was analyzed for root mean square (RMS) amplitude and frequency content to assess clarity and balance. These characteristics were compared across both recording locations and audio types. Statistical analyses, including one-way ANOVAs, were conducted to evaluate whether observed differences were significant.

While the ANOVAs did not reveal statistically significant variation in RMS or frequency response among recording locations, visual and descriptive analysis showed consistent trends. Piano, mic stand, and exit door placements had the best frequency and RMS results, especially in amplified voice and music recordings. These patterns suggest some locations may have advantages even if statistical thresholds are unmet.

These findings can help guide future recording setups in the CPC. Future studies could expand the dataset and include formal listening tests to understand better how microphone placement relates to room acoustics and different types of performances.

Introduction

Spatial audio, a technique that captures sound in three-dimensional space, has become increasingly recognized as the ideal method for recording high-quality audio. Unlike traditional stereo recording, which limits sound perception to two channels, spatial audio creates an immersive experience that allows listeners to perceive sounds as they would naturally occur, with direction and depth. Zhang et al. (2017) emphasize that spatial audio recording techniques offer a more lifelike auditory experience by capturing nuanced sound reflections and directional cues. However, achieving this effect requires sophisticated technology, such as multi-microphone arrays and 3D audio processing, to capture sound accurately from various angles. This complexity poses challenges, especially in live performance settings, where quick setup and adaptability are crucial.

Current research into spatial audio suggests that its effectiveness depends heavily on precise placement and calibration of recording equipment to accurately capture a sound field that matches listeners’ expectations (Zhang et al., 2017). Zhang et al. discuss how multi-microphone arrays and ambisonic techniques capture nuanced spatial soundscapes by recording audio from multiple directions and integrating environmental reflections and reverberations. For example, their findings indicate that optimizing microphone positioning can significantly reduce phase distortions and improve the perceived spatial accuracy of sound reproduction in enclosed spaces.

Hong et al. (2017) expand on these findings by highlighting the role of room acoustics, microphone configuration, and sound source positioning in achieving effective spatial audio. In their study of soundscape design, they demonstrate that improper microphone placement near reflective surfaces can amplify unwanted frequencies and introduce distortions. In contrast, strategic placement can enhance the clarity and depth of the recorded sound. Their research also mentions how the type of

microphone, such as omnidirectional versus cardioid, can influence the balance between capturing direct sound and ambient noise, making these factors critical in live performance settings.

Studies have also highlighted the limitations of conventional recording methods, such as stereo and mono recording, for live environments, where they fail to capture the spatial dimensions needed for an authentic experience (Hong et al., 2017). Grigoras (2005) explored how traditional recording setups struggle to accurately reproduce the spatial characteristics of sound in dynamic environments like theaters, often leading to flat or unbalanced audio outputs. These limitations can diminish the immersive quality of recordings, reducing their ability to convey the regional performance’s emotional and auditory impact.

Scholars like Grigoras (2005) and Zhang et al. (2017) advocate for further research to refine spatial audio techniques, particularly for settings like theaters, where accurate audio reproduction is essential to maintaining the integrity of live performances. For instance, Zhang et al. highlight how advancements in spatial audio reproduction, such as binaural rendering and wavefield synthesis,

hold promise for overcoming these challenges, but emphasize the need for context-specific studies to optimize these methods for different acoustic environments.

In our current recording setup at the school’s auditorium, the Curtis Performance Center (CPC), we capture audio from on-camera microphones and through direct feeds from the soundboard. This method, while convenient, often degrades the quality of sound reproduction we aim for, especially for theatrical productions where ambient and directional sounds enhance the viewing experience. Inadequate sound capture can result in recordings that lack clarity, depth, or balance, making critical dialogue or musical elements unclear. This reduces the value of these recordings, which are shared with cast members as keepsakes and educational tools. By contrast, an optimized spatial audio setup could provide a richer auditory experience, preserving the nuances of live performance and enhancing recordings for educational and archival purposes.

This research is essential for improving the CPC’s recording quality. The production team records all performances and shares the recordings with cast members as keepsakes and learning tools. To maximize the value of these recordings, they must capture sound as authentically as possible. Given the current limitations of our recording methods, investing in an optimized spatial audio setup would significantly improve the production quality of these recordings.

This study aims to identify the optimal methods and equipment placements for recording spatial audio in the CPC. This research seeks to establish a configuration that ensures high-quality, reliable sound reproduction specifically for theater productions. Findings from this study could also offer a replicable model that may benefit similar educational and performance spaces, providing practical guidance for future audio recordings.

Methods

I conducted this study to figure out where microphones should be placed in the CPC to get the best possible audio recordings. Four Audigo Pocket-Size Recording Studios were used to record audio from various locations in the CPC, including the stage, audience seating, and technical booth. These locations were chosen based on research indicating that microphone placement in enclosed spaces is affected by sound absorption, reflections, and spatial aliasing (Zhang et al., 2017; Hong et al., 2017). Five types of audio were recorded at each location to simulate the range of sounds encountered during performances: pre-recorded music, unamplified music, amplified voices, unamplified voices, and pre-recorded sound effects (Hong et al, 2017). These recordings account for different sound sources and acoustic interactions to ensure a thorough analysis of microphone placement.

My first test was during the middle and upper school play, Charlotte’s Web. This play consisted of live voices (no microphones), one voice through a microphone, and playback audio. I spread out the four microphones around the CPC: one on each side of the stage next to the stairs, one in the right middle of the audience on the camera tripod, and the last on the wall of our tech booth.

Next, I recorded our senior forum but used only three microphones and chose different locations from the previous show. One was placed on the table on stage, where the panel was

seated, one on the projector stand in the pit, and the third on the wall of the tech booth. These recordings included both live voices and voices through the speakers.

I continued recording school productions and forums throughout the year, trying different recording locations each time. After each recording, I used the Audigo app to transfer the recordings from the recorders to my phone. Then I uploaded the files to my computer to analyze with Audacity, a computer software that can evaluate audio quality. Each recording was reviewed by ear to identify distortion, reverb, or balance issues (Zhang et al., 2017; Hong et al., 2017). Then, I used Audacity to analyze elements of the recordings like clipping, frequency imbalances, or noise levels that may not be as noticeable through listening alone (FutureLearn, n.d.; AltexSoft, 2022). Results were compared across all tested microphone placements to determine the best location for consistent, high-quality audio reproduction. The locations that resulted in the least clipping, frequency imbalances, or noise level issues were considered to be the better locations.

In my statistical analysis, I used Jamovi to see whether there was a significant difference based on location and audio type. I analyzed RMS levels, frequency, event type, audio type, and recording location to assess their impact on overall audio quality.

After recording various types of audio in different locations and analyzing which spots produced the best sound quality, I selected the most effective positions. I developed optimized setups tailored to each school event. Since many events feature multiple audio sources, a single microphone placement would not always be sufficient. To address this, I designed flexible, mixed-location setups that could adapt to different needs. To evaluate their effectiveness, I then tested these configurations during the middle school musical, Seussical Jr., and the Sophomore Forum.

Results

After analyzing all of the audio recordings, I separated the results into live voices, voices through speakers, and playback audio. I compared average frequency, average root mean square (RMS)—noting that the suggested RMS level for audio tracks is between -7 dB and -18 dB (eMastered, n.d.)—and whether or not there was any clipping.The suggested frequency range for vocals is 60 to 250 Hz (Same Sky Devices, n.d.). All clipping was during applause and not considered problematic, as it was not part of the actual event audio.

The data collected from various school events were categorized by location, event, and audio type, comparing peak frequency and average RMS levels. Piano placement consistently produced the strongest recordings, with speaker audio RMS values reaching as high as -26.86 dB, indicating higher loudness and clarity. Mic stand and exit door locations also produced strong RMS values, often falling within the optimal range for mastered audio tracks (-7 dB to -18 dB) or slightly below, suggesting they are viable alternatives for capturing high-quality sound (eMastered, n.d.). Speaker audio typically produced higher peak frequencies (ranging from 900 Hz to 1259 Hz), especially at the piano and mic stand positions, while live voice recordings had more variation, with some placements, such as the booth and audience panel, recording frequencies below 300 Hz, indicating muffled or unclear vocal capture. Panel and podium placements during forums

provided balanced frequency responses and moderate RMS levels, making them suitable for primarily live-speech events.

These findings indicate that optimal recording setups vary by audio type, and a mixed-location strategy is most effective for events with multiple audio sources. Locations like the piano, mic stand, and exit door provided the most consistent quality across speaker and live voice types.

After picking out the best locations for each type of audio, I used the tables above (Tables 2, 3, and 4) to create better microphone setups for each school event. In the tables, I included the best microphone placements based on frequency levels, RMS levels, clipping, and what I think sounded best. I included the last row because there were cases where the quantitative variables of a recording were at reasonable levels, but there were moments where there was unnecessary or unwanted sound. For example, during the junior forum, the microphone on the podium had the best RMS, and the one on the booth didn’t have any clipping, but the podium microphone picked up sound from page turning, and the one on the booth picked up the booth audience’s voices. In most cases, the booth was a good location to record from, but unfortunately, it picked up too much chatter from the booth and clicking or other noises from the equipment.

The quantitative variable of audio recordings can be adjusted with programs like Audacity to reach optimal levels. That being said, I want quality sound, even if that means the suggested levels are not reached. The best way to achieve quality results is to combine the best locations for each type of audio that will be recorded. For example, it is best to record audio for forums from the panel facing the panel members, the panel facing the audience, and the projector stand facing the stage.

Figures 1 and 2 display maps of the CPC indicating the

recommended microphone placements for each type of event. These locations were selected based on the “Best Sound” rows from Tables 2, 3, and 4. This category was prioritized because the primary goal of this research was to determine where the overall sound quality was best, rather than focusing solely on which setup produced the strongest quantitative metrics.

Discussion

This study aimed to identify the best microphone placement in the CPC for capturing high-quality audio recordings during school events. The results show that no single microphone location was universally ideal. Instead, the most effective placements depended on the event type and the primary audio source. This supports the conclusion that using multiple microphones in strategic locations, a blended microphone setup, provides the best overall recording quality.

The data showed that the piano, mic stand, and exit door placements consistently performed best across both live and amplified audio types. These positions offered strong loudness (RMS values often above -30 dB, with the piano reaching -26.86 dB) and good clarity, especially when capturing playback and amplified speech. Frequency analysis also supported these conclusions: speaker audio showed consistent peaks between 900-1759 Hz at these locations, indicating clear sound. On the other hand, live voice recordings at locations like the booth and audience panel had lower peaks (as low as 277 Hz), resulting in a more muffled sound. The booth would have been a strong location, but it picks up too much chatter and background noise.

These findings emphasize that blended setups tend to have better results than relying on a single fixed location because

Fig. 1. A map of the CPC showing where Audigo microphones should be placed and facing for Plays/Musicals (Voices Through Speakers, Live Voices, and Playback Audio).

Fig. 2. A map of the CPC showing where Audigo microphones should be placed and facing for Forums (Voices Through Speakers and Live Voices).

multiple microphones can capture different aspects of the sound more clearly, with each placed in the ideal spot for its specific audio source. This is especially true for events that include a mix of live voices, amplified speech, and playback audio. For example, tailored setup templates can now be suggested for future events:

➣Forums, which feature both live and amplified voices, benefited from placements such as the panel facing panel members, the panel facing the audience, and the projector stand facing the stage.

➣The mic stand, piano, camera tripod, and exit door locations provided the best results for plays and musicals, which include a broader mix of audio sources.

Using multiple microphones allows for capturing different aspects of sound simultaneously, with each mic optimized for its specific location. This approach provides greater flexibility in both live mixing and post-production, thereby enhancing overall audio quality.

However, alternative interpretations of the results exist. For example, lower RMS levels in the booth could have been influenced by obstructions, distance from the source, or inconsistent speaker volume, rather than being called a poor location. Additionally, while higher peak frequency often indicates better clarity, it is not the only determinant–event context and content must also be considered.

For example, a scene might involve quiet dialogue or a whispered monologue during a theatrical performance. A microphone placed too close to the stage might result in higher RMS or frequency due to unwanted noise (such as set movement or footsteps), but fail to capture the vocals. In contrast, a more distant microphone might produce a lower RMS but still result in a more natural recording for that specific moment. This suggests that higher levels do not always equal better quality, especially when the dynamics or tone are more important than loudness or frequency strength.

This study had several limitations. Background noise, varying speaker volume, and the limited number of recordings per location may have affected the accuracy of the results.The analysis assumes that ideal RMS and frequency ranges apply equally across all event types, which is not always true. To address these issues in future research, it would be helpful to perform more standardized testing. For example, a constant volume reference track can be used to test at each location with identical conditions, ensuring uniformity, and trials can be conducted in a controlled environment without an audience. Also, performing more trials may help achieve more accurate results.

Future studies could explore additional locations, such as placements near the booth but away from distracting background noise, or compare different types of microphones (such as those already owned by the school). This is important because the current study was limited to a few common placements and did not account for how different microphones might handle complex sound environments. Additionally, while this study focused on RMS and peak frequency, other acoustic factors, such as clarity or reverb, could also be valuable in determining audio quality.

High-quality recordings are essential for preserving the work of performers, sharing events with families who cannot attend in person, and keeping an archive of school productions. Clearer recordings also support students and staff in reviewing their work,

improving future performances, and sharing accomplishments with a larger audience.

Works Cited

Audio Analysis With Machine Learning: Building AI-Fueled Sound Detection App. (2022, May 12). AltexSoft. https://www. altexsoft.com/blog/audio-analysis/ eMastered. (n.d.). RMS level for mastering. https://emastered.com/ blog/rms-level-for-mastering#:~:text=With%20a%20loud%20 track%20that,%2D16dBFS%20to%20%2D18%20dBFS. FutureLearn. (n.d.). Quantitative sound analysis and the visual representations of sound. https://www.futurelearn.com/info/ courses/music-moves/0/steps/12681

Grigoras, C. (2005). Digital audio recording analysis: The Electric Network Frequency (ENF) Criterion. International Journal of Speech, Language and the Law, 12(1), 63–76. https://doi. org/10.1558/sll.2005.12.1.63

Hong, J.Y., He, J., Lam, B., Gupta, R., & Gan,W.-S. (2017). Spatial audio for soundscape design: Recording and reproduction. Applied Sciences, 7(6), 627. https://doi.org/10.3390/app7060627

Same Sky Devices. (n.d.). Understanding the audio frequency range in audio design. https://www.sameskydevices.com/blog/ understanding-audio-frequency-range-in-audio-design?srsltid =AfmBOoouiCsyFvtx6ga3ooNWC8UleDcsc92GAuV4zw31 FB3dToAuPqG9

Zhang, W., Samarasinghe, P., Chen, H., & Abhayapala, T. (2017). Surround by sound: A review of spatial audio recording and reproduction. Applied Sciences, 7(5), 532. https://doi. org/10.3390/app7050532

Different Modalities of Learning Tested Through Spelling and Foreign Language

Sarah Zurell

Abstract

In many schools, one method of learning is applied to multiple students, and this is solely dependent on the abilities of the teacher. One classroom may use only textbooks and slides while another may use auditory lectures as the only form of teaching. In this experiment, we tried to determine if people are more successful with a mode of learning that works for them specifically.

To look for a correlation between learning styles and relative success, we used an experiment tool called Psychopy to create a 16-word language learning test. There were two modules: a visual module, where the participant would see a Romanian word on screen and then its English translation, and an auditory module, where the participant would hear the Romanian word and its English translation. After each module, the participant was asked to recall the meaning of each word.

These results were then compared to a pre-experiment survey given to each participant that asked which mode of learning they typically preferred, their estimated IQ, and if they had ever been diagnosed with a language learning disorder, among other similar questions.

The most important test was comparing the method the participant typically prefers with their relative success in each module, but the other data was important for contextual information and to answer another question: could a preference in learning methods impact success in the experiment more than a person’s IQ or the presence of a learning disorder?

Introduction

There is a large debate in the educational community about learning preferences and styles among students. A popular theory, first developed in the mid twentieth century and expanded on later by numerous researchers, is the VAK theory (Forehand, 2010; Bloom, 1956; Kolb, 1983). This hypothesis suggests students have a preference for visual (V), auditory (A), or kinesthetic (K) tools and cues. Some researchers add a fourth category, reading/writing (R), which splits up the visual category into picture vs. word related tools (Navaneedhan, 2015). For this experiment, reading and writing tools can be grouped into the same category as visual learning because of its purpose as the visual representation of language.

Origins of the VAK theory can be traced to Bloom’s taxonomy, a theory that defines learning as processes that vary in complexity from simply remembering to understanding, applying, analyzing, evaluating, and eventually creating (Forehand, 2010; Bloom 1956). This early version of the VAK theory was expanded upon by many different researchers throughout the twentieth century. David Kolb’s 1983 LSI test, for example, separated learners into accommodators, convergers, divergers, and assimilators, which classified different types of creativity and abstract thinking. He believed all four processes had to be completed for successful learning (Tritsch, 2020; Willis, 2017). Peter Honey and Alan Mumford’s refined Kolb model categorized learners into activists, reflectors, theorists, and pragmatists (Honey et al, 1986). Though their model contained similar topical categorizations, Honey and Mumford believed that these were actually different types of learners, not learning methods, and that not all four categories had to exist in the learning process (Lea 2019). Ned Fleming, a prominent education scientist, author, and teacher, built on all of this research and hypothesized that learners could, instead of being categorized by more vague learning processes, be separated

by their preference in sensory learning modalities, thus developing a modern version of the VAK theory (VARK Learn Limited, 2024). This was a revolutionary concept that changed the common ideas from a difference in learning processes to a difference in learning styles and sensory information processing.

This experiment will utilize multiple theories of learning to develop a test to understand how different people learn. Building on earlier theories, it can be concluded that learning can be classified into multiple separate processes in the brain instead of one, at the very least on a foundational level. First the material must be ingested, then reacted with by some elements of the brain, and finally remembered. The study will test engagement with the material and the ability to remember a word from a foreign language once learned through either visual or auditory tools. My study aims to answer the following research questions:

1. Do learners have a preference between visual or auditory learning methods?

2. Can the ability to spell or learn words come not from intelligence but from a difference in preferred learning style?

3. Do learners have differences in their ability to learn and process words in foreign languages?

Further research on learning modalities is vital to the educational well-being of students at every age group. Many teachers choose a single tool to teach their students, which can be frustrating to those whose learning preferences differ from their teacher’s. I hypothesize that in many circumstances, the ability of a student to learn in a classroom has little to do with native intelligence or perseverance, and much more to do with the way the student’s brain processes material. It is frustrating for both teachers and students to enforce a singular learning style; further research would hopefully promote understanding of different learning styles and awareness that oftentimes educational struggles are not the inherent fault of the student or the teacher, but rather a difference in the neurologic preferences of the learner.

Methods

This study used words from the Romanian language in a multipart process to determine a person’s preference in learning styles, and it included both visual and auditory learning tools. The results were measured through a combination of self-reported survey answers and test results. There is not a lot of research existing on the categorization of visual vs auditory learning through foreign language, and as such, we performed a thorough examination of this topic using both a largely subjective method, a survey, and a more objective method of collecting data, a word test.

First, we created a short survey using Google Forms for each participant to collect background information. To protect anonymity, each participant was asked to provide a unique identification number between 99 and 999 on the survey so we could pair their survey with their word test results. For context and additional data, the participants were asked about their age, approximate IQ, language learning aptitude, fluency in different languages, and if they generally prefer auditory or visual learning methods. There was also an optional question in the survey that asked if they had any diagnosed language learning disorders, which was helpful to know when considering their results in the test. Finally, they were asked if they consented to all of their recorded information being used anonymously in data collection and analysis.

The experiment then involved a 16-word test in Romanian using Psychopy. Instructions were written on the initial screen before the test began, and participants could press any key to continue once they were finished reading. In the visual section of the test, half of the words were shown to the participant, in Romanian first, followed by their English translation, each for three seconds. In the auditory section, the other half of the words were read and translated auditorily to the participant via the computer’s sound system. At the end of each section, the participants were asked to recall the English translation of each word, and their accuracy was recorded and analyzed. Participants and their data remained anonymous. To avoid errors in the timing of the different learning styles, the order of the sections of the test was randomized, and the order of the words was randomized for each participant.

To create the word test, we used AI programs, including Claude, ChatGPT, and DeepSeek, to write a Python script for the experiment, and then we inserted the code into an application

called Psychopy. The experiment was performed on the same computer for each participant.

The participants in this study were from the Allendale Columbia School. An email was sent to all high school students asking them to fill out the survey and, if possible, to come to the library between 1:00 pm and 3:00 pm on either May 13th or May 14th, depending on their availability, to take the word test. Donuts were offered as a form of payment to entice a larger sample size.

Creation of the visual and auditory modules

Romanian was chosen because of its lack of local recognition and because of its use of the Latin alphabet, which is also used by English speakers. Sixteen Romanian words of a similar difficulty were obtained, avoiding cognates with other romance languages and unclear meanings (i.e, the word ‘back’ was not included because of its multiple distinct definitions). This process mitigated potential bias in a participant’s recollection of certain words. In total, there were 32 recordings that could be played and 32 words that could be printed on screen: 16 Romanian words and 16 English translations. Each pair consisted of a Romanian word and its English translation, for a total of 16 pairs used in the experiment.

For the visual module, the participant was shown eight random pairs of words. During the testing phase of this module, each word was shown on screen for two seconds, and a blank screen was shown for one second in between each pair. The font, placement, and size of the words shown in the visual section were all identical.

The audio files for the auditory module were recorded in the same room, at the same time, with the same person’s voice and intonation. The pronunciations by the researcher were not completely accurate to the native pronunciations of the Romanian words, but they were syllabically consistent with each other, regardless of their native accuracy. All of the aforementioned steps were to ensure as much standardization as possible between words, further mitigating error in word recollection. We used the Resonate Recordings website and saved the recordings as .wav files on the researcher’s personal computer. For the testing phase of the auditory module, the participant heard a random Romanian word first, followed by its English translation, with a one second pause between each pair.

After each module was completed, the Romanian words were either shown or read again (depending on the module just completed) in a random order, and the participant was asked to type the English meaning of each word.

Results

A total of 42 people filled out the survey sent out via email. Of those, 20 reported their estimated IQ, 39 provided their estimated ability to learn languages, and 39 answered if they had been diagnosed with a language learning disorder or not. We also asked them if they self-identify as a visual, auditory, or hands-on learner, or if they prefer both visual and auditory learning styles (Figure 1). (The word test did not include kinesthetic learning methods because of the difficulty required to collect this information and the time restraints of this project.)

Data was also collected on a learner’s preference in speaking, hearing, and reading words to learn them (Figure 2). In accordance with the VAK method, speaking and hearing are considered

Different modalities of learning tested through spelling and foreign language

Fig. 1. The proportions of each type of learning style participants identify with, collected from a survey that gathered contextual information about learning preferences and abilities.

Fig. 2. The proportion of learning methods preferred by participants in a survey that collected contextual information about learning preferences and abilities.

Fig. 3. The proportion of answers correct in each testing module, where the participant is tested on their recollection of the translation of Romanian words after learning through both auditory and visual stimuli. The participants are sorted into groups who identify as visual learners (visual), those who identify as auditory learners (audio), and those who find they don’t have a preference (both). Orange represents the proportion correct out of total correct answers in the auditory module and blue represents the proportion correct in the visual module out of total correct answers. The error bars represent the standard error automatically calculated by Excel.

auditory methods of learning, and reading is considered a visual method. This question allowed for a broader interpretation when categorizing learners into either visual, auditory, both, or neither, so we used this data when comparing learning styles to a participant’s word test data.

Of the 42 people who filled out the survey, we were able to have 15 people take the word test. We analyzed three different comparisons with their results: self-identified preference in learning style vs performance on each module; total number of correct answers vs estimated IQ; and total number of correct answers vs presence of a learning disability.

Because the error bars for the proportions for each module seem to not overlap for the group who identifies as visual learners vs the group who identifies as able to learn through both auditory and visual methods (Figure 3), we calculated the p-value for the occurrence where the null hypothesis is pV = pB and the alternate hypothesis is pV > pB, where pV is the proportion correct in the visual module out of total answers correct for each participant who identifies as a visual learner and pB represents the proportion correct in the visual module for learners who are able to learn through both visual and auditory methods. We used the proportion correct in the visual module, but we could have used the proportion correct in the auditory module and gotten the same result in our test because their proportions add to one.

In sum, we calculated the probability of our result or more extreme outcomes happening in a world where the proportion of answers correct in each module is the same for visual learners and people who learn through both methods. We calculated the standard deviation and mean of each group, performed a onetailed two-sample z test, and found a p-value of 0.103 for our hypothesis of pV > pB. This calculation means that if our null hypothesis was true, our result or more extreme would happen with a frequency of 0.103, which is not more significant than the typical significance threshold of 0.05.

In addition to testing the correlation between visual and multimodal learners and their relative success in each module, we also tested the correlations between IQ, learning disorders, and the total number of correct answers in the test. These tests were performed to provide contextual data and to be able to compare these correlations to our main data.

Fig. 4. The mean number of correct answers from eight total participants who took a 16-word-pairs experiment in which they recalled the English translation of as many Romanian words as they could. The participants are sorted in this figure by IQ ranges. The IQ estimate of each participant comes from a selfestimation in a survey we sent out.

Fig. 5. The mean number of total correct answers from 14 participants who took a 16-word-pairs experiment in which they recalled the English translation of as many Romanian words as they could. The participants are sorted in this figure by the presence of a learning disorder. In a survey we sent out, each participant was asked, only if they felt comfortable releasing this information, if they had been diagnosed with a learning disorder (yes or no).

There is a downward trend (Figure 4) for the higher three of the four IQ groups, but this trend does not extend to the first group (IQ of 85-100). The sample size for the test was very small because many people chose not to disclose their IQ. Out of the 15 participants who took the word test, we only had IQ data for eight of them.We did not perform any statistical tests because the sample size was too small to yield any meaningful analysis.

Discussion

For this project, we wanted to determine if there was a possible correlation between a person’s preferred style of learning and their actual performance when learning through different modalities. If participants self-reported that they typically prefer one modality, and then performed better when learning through that modality, that would indicate that in many circumstances, the ability of a student to learn in a classroom has little to do with native intelligence or perseverance, and much more to do with the way the student’s brain processes material.We also theorized that this correlation between learning style and performance in the test could be equal or even more significant than the correlation between learning disorder or IQ and performance.

From our collected data, we conclude that our hypothesis was not statistically supported. However, there was some evidence showing that there could be a slight correlation between an individual’s preferred learning style and their performance in the word test. Our p-value for the difference in correct answers in the visual module for visual learners vs multimodal* learners was 0.103, meaning in a world where there is no true correlation in the population, the probability of our results or more extreme happening by chance is 0.103. The typical significance threshold in the scientific community is accepted to be 0.05, so our result was not statistically significant. However, 0.103 is still fairly low; the value is close to a significance threshold of 0.10, which is less

*multimodal in this case means either bimodal or multimodal: the learner does not prefer only auditory or only visual learning, but prefers some combination of auditory, visual, and/or kinaesthetic learning.

commonly used but is still accepted in some contexts. Therefore, while there was no statistical significance in our experiment, a similar experiment with a larger sample size could theoretically produce more significant results.

For the test to measure the correlation between a preference in learning style and a person’s performance in each module, the reason we compared the data between visual learners and multimodal learners, and did not compare data from auditory learners at all, is because of sample size. Our sample in this case was the number of correct answers in each module, and was categorized by learning styles. Our sample size for auditory learners was eight, which was too small for a meaningful analysis. For visual learners, we had 30 correct answers in the visual module and 14 correct answers in the auditory module, which means the correct answers in the visual module made up 68% of the total correct answers for visual learners. For multimodal learners, there were 11 correct answers in the visual module and 10 correct answers in the auditory module, which means the number of correct answers in the visual module only made up 52% of the total correct answers. These results make sense according to our hypothesis, as visual learners did better in the visual module, and multimodal learners showed little preference for either module. However, the results were not statistically significant, with a p-value of 0.103, as mentioned previously.

The results for the correlation between IQ and performance in the test had an odd presentation. In a vacuum, one might expect the number of correct answers to increase as a person’s IQ increases. The difference in correct answers between the IQ groups 85-100 and 100-115 followed this expectation, but there was actually a negative correlation among the higher IQ groups. This was most likely due to the very small sample sizes, but could have also been because of our sources of error or even because of a true slightly negative correlation between IQ and this type of language learning, though the latter is highly unlikely. We did not perform a statistical test due to our very small sample sizes: we had three people with an IQ between 85-100, one between 100115, three between 115-130, and one above 130.

There also appeared to be a correlation between the presence of a learning disorder and a person’s overall performance. Using a one-tailed two-sample t-test, we calculated a p-value of 0.047.This is more significant than the correlation between learning styles and performance, so our secondary hypothesis of learning styles mattering more in a learning environment was not supported in this experiment.

Overall, we found some evidence to support a correlation between learning styles and performance, and we found a statistically significant correlation between the presence of a learning disorder and performance. Still, our results came from an extremely small sample size and may be misleading of the true values of a larger population. Just because our results showed evidence for a correlation or were statistically significant does not mean that it was impossible for them to happen by chance: a 0.103 and a 0.047 probability are not incredibly small numbers; a 1 in 10 or a 1 in 20 event occurs frequently.

There were many sources of error in this experiment, as is common with psychological studies. As such, we will only discuss the major sources of error. In the creation of the test, we made the instructions too unclear, so some participants later had trouble

understanding the specifics of their task. We also accidentally included a homonym, ‘water’, even though we had purposefully tried to stay away from ambiguous words. Much more egregiously, we included a homophone, ‘where’, and many participants did not know what to type when this word appeared in the auditory module. We tried to regulate the difficulty of the words, but this process was not exact. ‘Employee’, for example, may be seen as a more advanced word than ‘love’. This type of difference may have led to a bias in which words participants were able to remember.

There were also issues during testing.We conducted the word test in the school library, and our original plan was to use the quiet room. However, since this room was unavailable, we performed the test in an open space. This issue led to distractions and unnecessary noise that most likely affected our participants’ focus and abilities. Participants also noted that the maximum volume on the computer was too low, leading to occasional missed words in the auditory module when the noise in the library was high.

The last couple of major errors had to do with the intrinsic design of the test. First, we needed participants to have randomly assigned numbers to be able to match their survey data with their results anonymously. Because Google Forms does not have a randomization tool, we asked participants to pick a number between 99 and 999, hoping that the lower bound would prevent repeats of common small numbers like one or five. Unfortunately, humans are extraordinarily bad at randomization, and we could also have been more specific and asked them to avoid other common numbers. Out of 42 responses, we had four people pick 100, two people pick 113, and four people pick 777. This error ultimately disqualified two of the people who took the word test from data analysis because we couldn’t match their survey data to their word test data.

Finally, due to our school’s small size and the inability to send out the survey to a wider population due to time and technology restrictions, we had the possibility of both convenience and nonresponse bias. People were selected based on the ease of sampling them, which may have affected our test’s representation of the true population, and others may not have responded to the survey because they thought they would be bad at the test, which may have shifted the mean of total correct answers.

Our results in this experiment have some effect in the wider body of scientific knowledge of learning methods. While not statistically significant, our results indicate that a preference in learning style could affect a person’s ability to learn and perform in tests. With further research, we could explore more of the possible impacts of learning styles and perhaps influence the way learning and teaching are perceived. In the future, there are a couple steps we would like to take to perform a similar study: first, we would like to increase the sample size and get the testing tools accessible to an online population through Psychopy or a similar application. It would also be interesting to include kinaesthetic learning in a future experiment to capture the entirety of the VAK learning model. Ultimately, students may have more success in academia if given the correct tools and support for their personal learning preferences, and this theory, if supported through future research, should be reflected in schools and curricula.

Works Cited

Biographies of the founders ofVark®.VARK. (2024,April 26). https:// vark-learn.com/introduction-to-vark/biography/#google_ vignette

Bloom, B. S. (1956). Taxonomy of Educational Objectives : The classification of Educational goals. Handbook 1 Cognitive Domain, 10–207. https://eclass.uoa.gr/modules/document/ file.php/PPP242/Benjamin%20S.%20Bloom%20-%20 Taxonomy%20of%20Educational%20Objectives%2C%20 Handbook%201_%20Cognitive%20Domain-Addison%20 Wesley%20Publishing%20Company%20%281956%29.pdf

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