Honors Research: Summer Institute

HONORS RESEARCH

Honors College Summer Program Advances Undergraduate Research

The Honors Summer Research Institute (HSRI), an eight-week program

launched in June 2018, promotes interdisciplinary collaboration between Albert Dorman Honors College (ADHC) scholars who are engaged in on-campus research during the summer. Through regular meetings and discussions moderated by ADHC faculty and staff, it also introduces them to peer review, fosters presentation and communication skills, and provides instruction in perfecting presentations and writing concisely. This past summer, the HSRI was fully remote, culminating in an online mini-conference.

To take part in the institute, scholars undergo a competitive proposal and review process. Participants receive awards from the Dean’s Fund for Student Development, while others are funded by the Provost Summer Research Fellowship.

The Honors Summer Research Institute is just one component of an ADHC research pathway that includes granting students course credit for reaching Honors College research milestones, such as filing a patent with a faculty member, crafting a peer-reviewed manuscript that gets accepted for publication, working on an Undergraduate Research and Innovation Phase II team, and, of course, engaging in the institute. Involvement in the Research Experience for Undergraduates program and the McNair Summer Research Institute at NJIT also qualify. FOLLOWING ARE SELECTED ABSTRACTS FROM SUMMER 2020 PARTICIPANTS.

Analysis of the Accessibility of Health Care Facilities in the Tri-State Area Vrushali Koli, Class of 2020 This research investigated the location of health care facilities relative to mobility access points (e.g., train stations, parking lots, etc.) in the tri-state area. Our work suggests that health care facilities have limited mobility access (mostly parking).

An Exploratory Study into the Effects of Total Sleep Deprivation Using fNIRS Katherine Ji, Class of 2021 (NJMS Accelerated Program) We explored the application of functionalnear infrared spectroscopy (fNIRS) to measure changes in the brain caused by sleep deprivation. We demonstrated that sleep deprivation reduced functional connectivity across different regions of the brain and lowered accuracy under a task condition, but not at rest.

Effects of Age and Surface on Muscle Co-Contractions During Walking Matthew DaSilva, Class of 2021 We investigated the neuromuscular adaptations associated with aging and how they are affected by walking over uneven and even surfaces. Our results suggest that older adults have greater muscle cocontractions at the ankle joint on uneven surfaces, resulting in increased joint stiffness and motor control deficits.

Improving Collaboration, Motivation, and Engagement in a Participatory Learning System Zoraiz Naeem, Class of 2021 We implemented a commenting and flagging system to improve coherence in different workflows of an online course management system. This system is built to conduct assignments based on participatory learning principles.

fMRI Study of Functional Brain Connectivity in Parkinson’s Disease (PD) Shruti Varshney, Class of 2022 We investigated the effects of PD on functional brain connectivity using fMRI. Our work suggests that PD alters connections in the brain. Further work may lead to new avenues of R&D on related treatments.

Analysis and Visualization of LongTerm Thermal Comfort Performance of a Net-Zero Energy House Anuradha Kadam, Class of 2022 Understanding high-resolution thermal comfort data is critical for communication of net-zero energy house performance to decision makers, building operators and occupants. This research project measures and evaluates the thermal comfort performance of net-zero energy houses to develop a whole-house thermal comfort rating system and explore innovative visualization and modeling methods.

Dynamics of Generalized Half-Center Oscillator (HCO) Neuronal Networks Shiva Senthilkumar, Class of 2022 We used mathematical modeling to study the biophysical parameters of 2- and 3-cell HCO neural networks. We found that our model effectively characterized the electrical activity that underlies HCO functionality.

An Algorithm for Restructuring of Coated Soot Aggregates Divjyot Singh, Class of 2022 We developed a novel algorithm to model the restructuring of coated soot nanoparticles. We showed that a Morse potential can simulate partial restructuring of soot and that there is a mathematical correlation between the Morse parameters and coating volume.

Revisiting the 80-Year-Old Moody Chart: A Novel Graphical Representation Manisha Kannan, Class of 2022 This research sought to simplify pipe design calculations by enabling explicit approximation of parameters through a novel graphical representation of Moody’s chart. Our novel method simplifies the calculation process while maintaining a low mean calculation error of 3.71%.

HONORS RESEARCH

Analyzing Tardigrade Locomotion Steven Munoz, Class of 2021 (NJMS Accelerated Program) This research investigated machine learning with DeepLabCut to characterize tardigrade locomotive patterns. Our results suggest that machine learning effectively captures tardigrade gait patterns and future work in this area is justified.

Apolipoprotein E4 and Cholesterol Metabolism in Alzheimer’s Disease Lindsey Riggs, Class of 2021 We used molecular dynamics simulations to study the apolipoprotein E4 (apoE4) gene and cholesterol metabolism in Alzheimer’s. We show that cholesterol effectively binds to apoE4; further study is warranted to understand lipid binding capacity and lipoprotein formation.

Morphological Evolution Between Social Parasites and Their Hosts Nitya Shah, Class of 2021 We studied the influence of parasitic specialization on morphological evolution in social insects. Interestingly, we found that specialist parasites were not necessarily morphological matches to their hosts, nor were generalists a mosaic of their combined hosts. Neural Mechanisms for the Discrimination of Moving Sensory Images Rita Vought, Class of 2021 (NJMS Accelerated Program) Our research explored the processing of moving sensory stimuli in animals’ brains. We deployed a nonlinear feedback control model, which incorporates a Reichardt detector and relies on short-term synaptic depression, to interpret data.

Examining Parameter Estimation Unidentifiability in Dynamic Models Dylan Lederman, Class of 2023 We evaluated the efficacy of parameter estimation algorithms in the presence of model degeneracy. We found that the parameters were nonidentifiable and the algorithms’ performance was not improved by perturbing the ground truth data with noise.

Role of Neuromodulation in Circadian Signaling Victoria Vought, Class of 2022 (NJMS Accelerated Program) This research investigated the role of neuromodulation on mammalian circadian signaling. We found that neuromodulators play an important, yet complicated, role in mediating circadian rhythms, yet much is still unknown. Geoacoustic Inversion in Ocean Environments via Neural Networks Akaash Patel, Class of 2023 We evaluated the use of machine-learning models with acoustic data to characterize seabed conditions to improve submarine performance. Our results showed that these models successfully classified various seabed conditions based solely on the input of transmitted acoustical signals.

Retrieving Population Density From Entomological Optical Sensors Joseph Torsiello, Class of 2022 We studied the correlation of insect transit data from entomological optical sensors and population density. We found an analytical relationship for population density and used numerical simulations to elucidate the observation time necessary to retrieve population density.

Using Long-Read Sequencing to Assemble Genomes With Repetitive DNA Andre Pugliese, Class of 2021 We investigated the effect of repetitive transposable elements (TEs) on genome assembly using long-read sequencing technologies. We concluded that removal of TEs from a human genome leads to greater assembly quality when using long-read sequencing techniques like Nanopore simulations.

Ultrasonic Testing to Determine Porosity in Shale Michael Tuma, Class of 2023 We utilized ultrasonic testing to elucidate the relationship between shale softening and changes in shale porosity. Our results suggest that there is a correlation between porosity and ultrasonic signal attenuation, i.e., heat generation, in shale.