FYR 2023 Panther RISE Impact Report- Research & Innovation- Prairie View A&M University

PA N T H E R

RISE Research & Innovation for Scholarly Excellence Grant Program

2023

Message from the Vice President

Magesh T. Rajan, Ph.D., P.E., M.B.A. VICE PRESIDENT, RESEARCH & INNOVATION

The PRISE grant program, a joint faculty research development initiative between PVAMU and Texas A&M University in its second year, continues to enhance collaborations between the two universities.

The second cohort of ten teams of investigators, selected from a highly competitive pool of 37 teams, opted to address challenging problems, utilizing emerging technologies. Each of the selected teams was awarded a grant of $40,000.

The projects included Smart Agriculture: Reducing Greenhouse Gas Emissions through Plant Root Secretion, Serverless Computing Risks Analysis and Mitigation, Leveraging Geospatial Big Data and Artificial Intelligence (AI) to Improve Disaster Resilience in Vulnerable Communities, Developing an Adaptive Toolkit for the Prevention of IPV and IPV-Related Mental Health Sequelae among Black College Students, and Examining Post-traumatic Stress Disorder Symptoms, Resilience, and Posttraumatic Growth in College Students with Disabilities During the COVID-19 Pandemic, to name a few examples.

The PRISE program provides a competitive edge to the investigators for obtaining external funds by enabling the researchers to collect strong preliminary data, thus establishing the feasibility of proposed studies.

PVAMU, a Carnegie R2 institution, is particularly excited about the PRISE program, as it serves as an effective model for establishing collaborations with other R1 institutions.

Message from Dr. Henry Fadamiro

Dr. Henry Fadamiro ASSOCIATE VICE PRESIDENT FOR RESEARCH & STRATEGIC INITIATIVES, TEXAS A&M UNIVERSITY

Working together, researchers at Texas A&M University and Prairie View A&M University (PVAMU) are making our world a better and safer place. As you will discover in this report, our combined interdisciplinary teams are: •

Using a combination of big data and artificial intelligence to make vulnerable communities more resilient to natural disasters.

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Helping college students with disabilities cope with the aftermath of the COVID-19 pandemic.

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Reducing the risks of foodborne disease through more intelligent management of soil.

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Blending spatial analytics with crime pattern theory to improve our understanding among our youth.

These are among the 10 projects funded in 2022 through our Panther Research and Innovation for Scholarly Excellence (PRISE) program. In all, PRISE invested $400,000 for 2022. These projects run the gamut from psychology to engineering, from information management to construction science, from nutrition to nursing, from education to social work.

Each of these projects is the result of research collaborations between the outstanding faculties of our two universities.

Given our shared history as our state’s only land-grant universities, it’s fitting for Texas A&M and PVAMU to unite through our common research mission: to generate discoveries and innovations that produce useful, tangible results for the people of Texas and the rest of the world.

It’s also appropriate for our outstanding faculties to pursue this mission through the emerging methods and technologies of our century—such as machine learning, smart agriculture and geospatial tools—as well as time-tested approaches.

The projects presented in this annual report provide a glimpse into how cutting-edge research can take on society’s greatest challenges.

PA N T H E R

RISE

Table of Contents Smart Agriculture: Reducing Greenhouse Gas Emission Through Plant Root Secretion.......................................................................................................2 Peter Ampim, Ph.D. Mitigation of Food Safety Risks from Agriculture Fields Through Soil Microbiome Management............................................................................5 Javad Barouei, Ph.D. Serverless Computing Risks Analysis and Mitigation................................................................8 Suxia Cui, Ph.D. Investigating the Effectiveness of Prediction Markets in Teaching Project Risks...................12 Vahid Faghihi, Ph.D. Leveraging Geospatial Big Data and Artificial Intelligence (AI) to Improve Disaster Resilience in Vulnerable Communities.........................................................................15 Thiagarajan Ramakrishnan (Ram), Ph.D. Developing an Adaptive Toolkit for the Prevention of IPV and IPV-Related Mental Health Sequelae Among Black College Students...........................................................20 Temiola Salami, Ph.D. Assessing Teachers’ and Leaders’ Perception and Application of Culturally Responsive Teaching and Culturally Sustaining Pedagogy......................................................23 Beverly Sande, Ph.D. Leveraging Machine Learning for Fundamental Investigation of Anomalous Transport Phenomena of Nano-Fluids under the Effect of External Fields for Energy Storage Applications....................................................................26 Shahin Shafiee, PhD Examining Posttraumatic Stress Disorder Symptoms, Resilience, And Posttraumatic Growth in College Students with Disabilities During COVID-19 Pandemic..........................................................................................................32 Beverly A Spears, Ph.D., MSW, BSW Integrating Crime Pattern Theory & Spatial Analytic Techniques to Examine Youth Crime................................................................................................................35 Ling Wu, Ph.D.

Smart Agriculture: Reducing Greenhouse Gas Emission Through Plant Root Secretion

Peter Ampim, Ph.D. ASSISTANT PROFESSOR Department of Agriculture, Nutrition and Human Ecology College of Agriculture and Human Sciences Prairie View A&M University

Introduction Modern crop production relies heavily on nitrogen (N) fertilizer to increase yield. Fertilizer-derived NH4+ is converted to NO2- and NO3- in the process termed nitrification. Nitrification and subsequent denitrification (reduction of NO3- and NO2- to gaseous N2, NO, and N2O) promote massive loss of N from the soil, accounting for more than half of N added. Some plant species such as sorghum secrete compounds that inhibit nitrification in the root zone, (termed Biological Nitrification Inhibition, BNI), which prevents N loss and increases the N availability. However, agriculture since the 20th century largely ignored the BNI trait because it has been easier to simply increase N fertilization to compensate for the loss, leading to environmental problems such as eutrophication and greenhouse gas emission. Therefore, the goal of this project was to: (1) to screen through 200 sorghum genotypes (elite hybrids and hybrid parents) for sorgoleone secretion and (2) to build a genomic model to predict the secretion capacity. Outcomes for these objectives are discussed below: Methods 1. Sorghum Genotype Screening The screening has been completed. The results showed a moderate to high variation within the populations tested (> 5-fold difference between the lowest to highest genotypes), indicating that there is room for genetic gain through breeding. The entire screen has been repeated twice to estimate the repeatability. The results showed that in general, the results between the two experiments correlate well (R2>0.6). These results served as the basis of the second objective.

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Figure 1. Results of the sorgoleone secretion screens. “A” and “R” represent female and male parents, respectively. All 42 parental lines, as well as selected hybrids among them have been screened. The values have been normalized to the control genotype, Tx430. The values are expressed as mg sorgoleone / mg root dry weight.

2. Genomic Model Building The above genotypes were all genotyped through whole genome sequencing prior to this project, making it possible to directly feed the results into model building. This has been completed as well. The key results were: 1. This trait had moderate to high heritability, which justifies the improvement through genomic gains.

2. No strong heterosis was observed.

3. Lines with high general combining abilities have been identified in both male and female parents.

4. The GBLUP model has been constructed, using 70% of data as the training set and 30% as the validation set. This model will be used to predict the potential high sorgoleone hybrids in the future.

Student Training Two graduate students have been engaged in this project, one at the Ph.D. level (TAMU) and the other a master’s student (PVAMU). Work at PVAMU is still ongoing owing to internal delays relating to transfer of funds into the project account and hence hiring of a graduate student to work on the project. The PVAMU work will lead to a master’s thesis by the graduate student hired.

Project Related Extramural Funding Seeking Efforts The project team has submitted a preproposal for consideration for developing a full proposal for the 1890 Capacity Building Grant program. If selected, a full proposal will be developed and submitted to the grant program in August 2023.

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Broader Implications In this era of climate change sustainability is central to the discourse on agricultural production. N2O emission from the soil is the biggest source of greenhouse gas from agricultural activities as such this project has a potential to provide a nature-based solution for mitigating the environmental font print of sorghum agriculture which is ranked the fifth largest crop globally. Besides reducing the release of potent greenhouse gases and minimizing water pollution, the use of sorghum cultivars with better nutrient use efficiency will save farmers money on their fertilizer input.

Sakiko Okumoto, Ph.D. ASSOCIATE PROFESSOR Department of Soil and Crop Sciences College of Agriculture and Life Sciences Texas A&M University

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Mitigation of Food Safety Risks from Agriculture Fields Through Soil Microbiome Management

Javad Barouei, Ph.D. ASSOCIATE PROFESSOR Department of Agriculture, Nutrition, and Human Ecology College of Agriculture Prairie View A&M University

Introduction The Centers for Disease Control and Prevention (CDC) estimates that 48 million Americans experience foodborne diseases (FBD) each year resulting in an estimated of 128,000 hospitalizations and 3,000 deaths [1]. The economic burden of 15 leading foodborne pathogens (FBP) that account for >95% of the illnesses and deaths from FBD acquired in the United States was about $17.6 billion in 2018 dollars [2]. On-farm contamination of food with FBP can impose significant economic burden and impact sustainability of the farm. The use of manure-based biological soil amendments (MBBSA) in crop production has increased in popularity with the recent uptick in demand for organic foods. As an inexpensive fertilizer, manure contains nutrients that improve plant growth, and it also effectively maintains soil quality and health. However, livestock carry FBP such as Escherichia coli, Salmonella spp. and Listeria monocytogenes asymptomatically in their intestines. FBP can be shed in their feces, often in very large numbers. Therefore, manure is potentially a significant source of FBP which can pass to produce grown in the MBBSA amended soils, and subsequently cause FBD in humans. Once FBP contaminate fresh produce, removing or killing them is very challenging. Raw manure needs to be properly treated (e.g., composting) to reduce the level of FBP. However, raw manure is used by about 60% of US farmers growing produce that is typically consumed fresh [3]. To effectively minimize the risk of contamination by raw or undertreated MBBSA, survival and proliferation of FBP in soils must be minimized. Harnessing the benefits of native soil microbiome properties, e.g., competition and suppression of pathogens, could be an effective mitigation tool. Disease suppression properties of soil and plant microbiomes has been documented for many plant pathogens, and mostly linked to increasing microbiome diversity and beneficial microbes, including specific groups with pathogen suppression capabilities. Improving soil health has been noted to increase soil microbiome diversity and soil suppression properties, however, specific effects on food safety risk mitigation have not been investigated. We hypothesize that increasing soil microbiome diversity through soil health practices will minimize pathogen survival and proliferation. Thus, soil microbiome management strategies could serve as an effective tool for mitigating food safety risks. The objective of this study is to examine the survival and characterize surrogate Escherichia coli in soils under different soil health management practices. We also aim to establish these field trials as long-term field sites to continue this research theme and seek additional funding.

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Approach A field trial is being conducted at PVAMU Gov. Bill and Vara Daniel Farm and Ranch (Prairie View, TX). Randomized field trials have been set up with a plot size of 3 × 3 m2. There are three experimental treatments to establish levels of soil health practice and compare pathogen and soil microbiome interactions. The soil was artificially spiked with an E. coli surrogate to monitor their survival in the experimental plots. The treatments include: 1) CL, control plots without the surrogate E. coli; 2) CLEC, no manure control inoculated with surrogate E. coli (6 Log CFU/g); 3) DMEC, dairy commercial manure compost inoculated with the surrogate, (only a single application in Fall at 250 kg N ha-1 by surface broadcasting right before planting.; and 4) CREC, Cover-crop rotation with dairy manure compost inoculated with the surrogate. Strawberry is planted and grown in the experimental plots, and the field experiment is conducted in triplicates (total 12 plots).

Manure samples (before application) and soil samples (5 samples/plot, at bimonthly intervals for 12 months) and produce samples (5 samples/plot, in the middle of harvest season) are being analyzed for aerobic plate count (APC), coliforms, Salmonella, E. coli and Listeria monocytogenes using non-selective and selective culture methods. Microbiome of the samples will be also analyzed using bacterial 16S rRNA and ITS-gene sequencing. Concurrently, we isolate E. coli (including the inoculated E. coli which is naturally rifampicin resistant to easily monitor its survival by plating) and further identify and characterize them using biochemical, immunological, and molecular methods.

Results and discussion Due to the seasonal nature of this project and some extenuating circumstances including securing an IBC approval for the project because of involvement of potential human pathogens, and hiring grad students for the project, we are still in the process of collecting data from this project. With new students joining the project, we plan to conclude the project in summer 2024. The results will be reported upon completion of the data collection and analysis.

External Funding The project has already led to follow-on a USDA NIFA proposal which was successfully funded recently: USDA-NIFA 1890 CBG Program. $580,229 05/01/2023 – 04/30/2026 An Integrated Approach to Minimize Produce Food Safety Risks Associated With Manure Applications For Small-Scale Diversified Growers In Texas.

Barouei, J., Somenahally, A., Moussavi, M., Park, D., Kebrom, T., Harris, L. J., Jung, Y.

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Broader impact This Project has established a new research collaboration between PVAMU, Texas A&M AgriLife Research for exploring microbial food safety and soil health management, which are fundamental for sustainable agricultural production. The knowledge obtained from this work has the potential to translate into a better understanding of FBP behavior in production systems, providing novel evidence to support future recommendations and developing scientifically based food safety risk-reduction strategies for farms. Moreover, this project has provided opportunities for graduate and undergraduate students to participate in research, and co-curricular activities. Undergraduate student exposure and interaction with collaborating research-intensive institutions will provide a strong foundation for matriculation into professional and advanced degree programs in food safety, plant, and soil sciences.

References 1. CDC. Food Safety. https://www.cdc.gov/foodsafety/cdc-and-food-safety.html.

2. USDA Economic Research Service (ERS), Total cost of foodborne illness estimates for 15 leading foodborne pathogens. Last updated 3/10/2021. 2018.

3. Pires, A.F.A., et al. 2018. Food Protection Trends, 2018. 38(5): p. 347-362.

Anil Somenahally, Ph.D. ASSOCIATE PROFESSOR Department of Soil and Crop Sciences College of Agriculture and Life Sciences Texas A&M University

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Serverless Computing Risks Analysis and Mitigation

Suxia Cui, Ph.D. PROFESSOR Department of Electrical and Computer Engineering College of Engineering Prairie View A&M University

Project Introduction The new era of computing brought renovations of facilities to support the growing needs. Nowadays, PCs in forms of desktops or laptops are gradually replaced by mobile and embedded devices. Behind these portable, low cost, low latency devices, are the Internet and Cloud Computing infrastructure [1], which shift computational intensive tasks to remote data centers. This model will be more energy efficient in a long run. Recently, serverless computing has emerged in the market and evolved into an alternative to the traditional server-based computing where users submit functions for execution and pay only for the processing time and memory used by their functions [2, 3, 4, 5]. Amazon’s AWS Function, Microsoft Azure Functions, Google multiple Cloud Functions, and IBM Open Whisk are all developed under this scenario.

Accompanying advantages such as elasticity, ease of deployment, and fine-grain billing are security concerns throughout all layers of this architecture. Attackers will seek inherent vulnerabilities to achieve whatever malicious goal they have on the victim’s network, software, and databases. Yet, there is a lack of an analysis and estimation of the risks in the renovated serverless computing platform. Specifically, two questions must be investigated before technology moving forward: 1. How to efficiently model the risks in serverless computing?

2. How much will the load differences influence the evaluation of the risk mitigation strategies under new platform?

The overall goal of this project is to explore the different vulnerability features brought up by various user behaviors on serverless computing platform for an applicable risk mitigation solution.

Methods This study examines the security in the context of an application, which is defined as “a collection of software components interacting with each other and external clients to provide a certain business functionality [6].” Dissecting the definition, the traditional monolithic application can be established on algorithmic (software components), enterprise (interacting with each other and external clients), and fiscal dimensions (business functionality). As a homogeneous block, the application can be visualized occupying a space along these dimensions, forming a single structure and component attack surface.

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Applications in the serverless architecture are characterized by [7] peripheral interconnections, data access, and sequential calls for functions implementing some level of state [8]. Database access, HTTP user interface, and activation structure are components that allow for intricate compositions of code rivaling the most robust applications [9]. A security comparison with a monolithic application is challenging [10]. A monolithic application can be developed in the serverless architecture through a process called FaaSification [11, 12]. By implementing this code as fine grain, event driven, tasks, a flexible and scalable service is custom to the user [13]. Placed on the same dimensions as the monolithic application, a less cohesive, but wider attack surface emerges.

The contributions of this research are: Generate the formalized study of serverless security taking into account the academic literature while examining the capabilities of serverless architecture platforms in evolving uses.

Connect the heuristic categories of serverless security to explicit architectural features at the technologist, platform provider, and developer levels.

Define the prospective security environment in serverless architectures in terms of the previous objectives.

Risk mitigation methods, such as reinforcement learning and game theory were explored to obtain preliminary results.

Results The results have been published in publications [14, 15, 16]. A generalized form of serverless architecture security, from heuristic to detailed descriptions, is possible through formalized study of underlying technical characteristics. By adhering to a lexical and taxonomic structure, the security environment is scanned from its inception to programmatic realizations within serverless studies. Such a multi-dimensional structure exposes the prospective gaps in the future serverless structures. Without any particular order of priority, attack surfaces within the future of serverless computing were analyzed from the industrialist, provider, developer, and user’s perspectives.

Taking advantage of serverless, the application may have an instance of a scaling up or burst parallel routine. As such processing occurs, the necessary output of the application is then forwarded as appropriate.

The same logic and algorithmic processes, ideally, still take place that occur in the monolithic function, although in finer grained portions. Within the cloud container industry, security has been a parallel development with the advancing technology. The intended isolation mechanisms in conjunction with resource management and system limiting tools have a wide range of functionality. Lack of visibility on processes partitioning of memory elements resource limiting are expansive in their implementation techniques and strategies.

Discussion Continued expansion of the serverless architecture will be centered around providing greater utility in application development and accessibility. This will come from wider access point choices, responsiveness from the platforms, and composition optimizations. This project has shown that these improvements arise from imperative characteristics of the serverless architecture at multiple levels of stakeholder. As these components evolve, they will open security risks that cannot be entirely foreseen, contained, or eliminated due to the interests of all parties. The academic work on the serverless architecture, however, does provide

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a means to mitigate the damage and predict areas which provide for obstacles to malicious users. This can be found for both commercial and open-source platforms and in the general case as serverless platforms evolve.

External Funding A collaborative proposal submitted to NSF was a direct result from the PRISE support. The proposal was entitled “Collaborative Research: SaTC: CORE: Medium: Security-aware Resource Management for Serverless Platforms” with a total budget of $1,198,201. Although it was not funded, the team is going to revise and resubmit the proposal.

Broader Impact This project addresses the urgent need to advance cybersecurity research. The research benefit arises from novel approaches to identifying appropriate risk mitigation techniques on critical components in serverless computing platform. The research contributes to the academia and industry on increasing the trustworthy of emerging cloud computing platform. The broader impacts of this project include: 1) The proposed research activities enhanced cybersecurity research at PVAMU, and the research results were disseminated through publications. 2) The proposed work led to an increased awareness of cybersecurity challenges among the new generation. One Ph.D. student was supported by this project and will defense his dissertation in fall 2023.

References [1] H. Li, K. Ota, and M. Dong, “Learning iot in edge: Deep learning for the internet of things with edge computing,” IEEE Network, vol. 32, no. 1, pp. 96–101, 2018.

[2] N. C. Mendonca, P. Jamshidi, D. Garlan, and C. Pahl, “Developing self-adaptive microservice systems: Challenges and directions,” IEEE Software, vol. 38, no. 2, pp. 70–79, 2021.

[3] N. Mahmoudi and H. Khazaei, “Performance modeling of serverless computing platforms,” IEEE Transactions on Cloud Computing, pp. 1–1, 2020.

[4] Z. Xu, H. Zhang, X. Geng, Q. Wu, and H. Ma, “Adaptive function launching acceleration in serverless computing platforms,” in 2019 IEEE 25th International Conference on Parallel and Distributed Systems (ICPADS), 2019, pp. 9–16.

[5] E. Jonas, J. Schleier-Smith, V. Sreekanti, C.-C. Tsai, A. Khandelwal, Q. Pu, V. Shankar, J. Carreira, K. Krauth, N. Yadwadkar, J. E. Gonzalez, R. A. Popa, I. Stoica, and D. A. Patterson, “Cloud programming simplified: A berkeley view on serverless computing,” 2019.

[6] Lau, K.-K., Wang, Z.: Software component models. IEEE Transactions on Software Engineering 33(10), 709–724 (2007). https://doi.org/10.1109/TSE.2007.70726 [7] Kuhlenkamp, J., Werner, S., Tai, S.: The ifs and buts of less is more: A serverless computing reality check. In: 2020 IEEE International Conference on Cloud Engineering (IC2E), pp. 154–161 (2020). https:// doi.org/10.1109/IC2E48712.2020.00023 [8] Patil, R., Chaudhery, T.S., Qureshi, M.A., Sawant, V., Dalvi, H.: Server-less computing and the emergence of function-as-a-service. In: 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), pp. 764–769 (2021). https://doi.org/10.1109/ RTEICT52294.2021.9573962 [9] Sankaran, A., Datta, P., Bates, A.: Workflow integration alleviates identity and access management in serverless computing. In: Annual Computer Security Applications Conference. ACSAC’20, pp. 496–509. Association for Computing Machinery, New York, NY, USA (2020). https://doi. org/10.1145/3427228.3427665.

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[10] Marin, E., Perino, D., Pietro, R.D.: Serverless computing: a security perspective. Journal of Cloud Computing 11, 1–12 (2022) [11] Yussupov, V., Soldani, J., Breitenb ̈ucher, U., Leymann, F.: Standards-based modeling and deployment of serverless function orchestrations using bpmn and tosca. Software: Practice and Experience 52(6), 1454–1495 (2022) https://onlinelibrary.wiley.com/doi/pdf/10.1002/spe.3073. https://doi.org/10.1002/ spe.3073 [12] Lloyd, W., Vu, M., Zhang, B., David, O., Leavesley, G.: Improving application migration to serverless computing platforms: Latency mitigation with keep-alive workloads. In: 2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion), pp. 195–200 (2018). https://doi.org/10.1109/UCC-Companion.2018.00056 [13] Lee, H., Satyam, K., Fox, G.C.: Evaluation of production serverless computing environments. 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), 442–450 (2018) [14] S. Cui, and S. Homsi, “Deep Reinforcement for Co-resident Attack Mitigation in the Cloud,” Artificial Intelligence Annual Volume 2022, IntechOpen, October 2022, ISBN: 978-1-83768-947-7. DOI: 10.5772/intechopen.105991 [15] D. Alsup, M. Putluru, S. Cui, and Y. Zhang, “Cloud Security Game Theory Scoring from Prediction Models in Simulation,” Cluster Computing (2023), Springer, https://doi.10.1007/s10586-023-04067-x [16] D. Alsup, and S. Cui “A Security Evaluation Framework for Adopting Function-as-a-Service on the Serverless Cloud,” Journal of Cloud Computing (2023), Springer, submitted.

Dilma Da Silva, Ph.D. PROFESSOR Department of Computer Science and Engineering Ford Motor Company Design Professor II College of Engineering Texas A&M University

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Investigating the Effectiveness of Prediction Markets in Teaching Project Risks

Vahid Faghihi, Ph.D. ASSISTANT PROFESSOR Department of Construction Science College of Architecture Prairie View A&M University Project Introduction The effective teaching of project risks in undergraduate classes presents a significant challenge, as students often find the abstract and theoretical nature of probability theory concepts to be disconnected from real-world applications. This disconnection leads to a lack of engagement and hinders further topic exploration. Engaging students in classroom activities that accurately illustrate the behavior of engineering systems is crucial for their recruitment and retention in the field. With the growing complexity of designs and project requirements, engineering systems are becoming less predictable, requiring future engineers to develop probabilistic reasoning skills and understand concepts such as project risks. This research investigates the effectiveness of prediction markets as an engaging tool in teaching project risks, addressing the need for realistic and practical learning experiences. The research team considered the following main objective: 1. Measure the impact of prediction markets as a teaching tool on students’ engagement in the classroom.

• By integrating real project data and leveraging the social processing of private information, prediction markets provide a realistic and dynamic learning environment.

2. Evaluate the effects of market implementation on learning outcomes related to project risks.

• By actively participating in prediction markets, students can better understand probabilistic reasoning and improve their ability to effectively assess and manage project risks.

In the seed phase of this research project, as the focus of the Panther RISE grant, the following minor objectives were planned: 1. Establish a collaborative team comprising faculty members and graduate students with expertise in project management, education, and data analysis.

2. Improve and update the previously performed research and developed programming codes to expand the application.

3. Secure funding through the Panther RISE program to support the initial research phase and acquire necessary equipment and resources.

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Methods A comprehensive research methodology was employed to investigate the effectiveness of prediction markets in teaching project risks. Revealed and stated preference data, such as website access frequency, number of trades, self-reported surveys, exams, and in-class experiences, were collected to capture students’ engagement and performance throughout the project risk education. The collected data were subjected to rigorous analysis, including correlation analysis, development of generalized linear models, and hypothesis testing. These analytical approaches allowed for a comprehensive evaluation of the impact of prediction market implementation on students’ engagement and learning outcomes and the identification of individual learner differences that influenced their educational experiences. By employing this methodological framework, the research team was able to obtain valuable insights into the effectiveness of prediction markets as a teaching tool for project risks, informing future improvements and applications in engineering education (​ Damnjanovic, Faghihi, Scott, McTigue, & Reinschmidt, 2013)​.

Results Implementing prediction markets as a teaching tool for project risks yielded promising results. Through the use of real project data and students’ engagement in market-based activities, the research team observed a significant improvement in students’ class engagement and participation. Surveys, pre- and post-tests, and focus groups provided valuable insights into students’ perceptions and understanding of project risks. The results indicated a positive correlation between market implementation and learning outcomes, suggesting that using prediction markets enhanced students’ ability to comprehend and navigate project risks. Furthermore, the research team identified individual learner differences that influenced engagement and learning outcomes, contributing to a deeper understanding of the factors affecting student performance in project risk education.

These results demonstrate the effectiveness of prediction markets as an engaging tool in teaching project risks and provide valuable insights for future improvements and implementations in engineering education ​ (Damnjanovic, Faghihi, Scott, McTigue, & Reinschmidt, 2013)​.

Discussion The initial investigation into the effectiveness of prediction markets as a teaching tool for project risks yielded valuable insights and implications for engineering education. The study’s initial results indicated that integrating real project data and using prediction markets significantly enhanced students’ class engagement and learning outcomes. By providing a realistic and dynamic learning environment, the prediction market bridges the gap between theoretical concepts and real-world applications, allowing students to understand project risks better ​ (Damnjanovic, Faghihi, Scott, McTigue, & Reinschmidt, 2013)​.

External Funding This seed phase served as the foundation for subsequent research and enabled the development of a novel approach for teaching project risks using prediction markets. Leveraging the Panther RISE funding and the acquired equipment, the research team was able to refine their ideas and submit an expanded proposal through the UTC program. Although the program was awarded and received the funding to establish NCIT at PAVMU, this specific proposal was not selected to receive funding. However, using the Panther RISE award and seed funding, Dr. Faghihi developed a new research idea and got it awarded through the NCIT program for two years.

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Broader Impact The research on the effectiveness of prediction markets in teaching project risks has significantly broader impacts on engineering education and society. By developing and testing a new information technology tool, this research aims to promote the recruitment and retention of engineering students. The findings and outcomes of this study will contribute to developing innovative teaching materials that effectively communicate the concept of uncertainty and engineering risks to students. By integrating prediction markets into the curriculum, future engineers will gain a deeper understanding of probabilistic reasoning and the management of project risks, enhancing their preparedness for real-world challenges. Ultimately, this research has the potential to positively impact the engineering profession by equipping future engineers with the necessary skills and mindset to navigate uncertainty and ensure the successful execution of complex projects.

References ​​Damnjanovic, I., Faghihi, V., Scott, C., McTigue, E., & Reinschmidt, K. (2013, April). Educational Prediction Markets: Construction Project Management Case Study. Journal of Professional Issues in Engineering Education and Practice, 139(2), 134-138. doi:10.1061/(ASCE)EI.1943-5541.0000127

Ivan Damnjanovic, Ph.D. PROFESSOR Department of Civil and Environmental Engineering College of Engineering Texas A&M University

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Leveraging Geospatial Big Data and Artificial Intelligence (AI) to Improve Disaster Resilience in Vulnerable Communities

Thiagarajan Ramakrishnan (Ram), Ph.D. ASSOCIATE PROFESSOR Department of Accounting Finance & MIS College of Business Prairie View A&M University Project Introduction In the wake of the increasing incidence of natural hazards and population growth in hazard-prone areas worldwide, research and practices on improving disaster resilience have gained significant attention from various disciplines, government agencies, and the public [1]. Disaster resilience varies by location and can be vastly enhanced through effective management, i.e., rapidly identifying affected people, communities, and infrastructures and providing immediate assistance [2]. Disaster management relies heavily on realtime information describing on-site disaster impacts, which is difficult to obtain during natural hazards.

The increasingly growing geospatial big data, e.g., location-based social media, offer an innovative lens to observe disaster impacts in real-time. Social media provides a convenient platform where users can access, share, and exchange emergent information, ask for assistance and report local damages and conditions during disasters [3,4]. Responding agencies and volunteers can monitor disaster-related information and human reactions from social media to infer large-scale dynamic and heterogeneous disaster impacts in real-time and send help. Consequently, the popularity of incorporating social media data and platforms into disaster response has grown in recent years [5,6].

However, leveraging social media in disaster management and improve resilience has two challenges.

First, extracting accurate information from social media data for disaster management is difficult because of technical difficulties processing such big and noisy data. Second, vulnerable communities have limited access to social media platforms and little knowledge on using social media during disasters. Such digital divides hinder their effective use of social media to prepare for, respond to, and recover from disasters.

The objectives of this project included: 1. Developing AI-based methods to extract disaster-related information from social media.

2. Investigating the social and geographical disparities of disaster-related social media use during the different stages of disaster management.

3. Improving disaster resilience in vulnerable communities by conducting and organizing workshops on guidelines of using social media during disasters.

4. Developing a research proposal to submit for NSF grant.

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In this seed phase of our research, the immediate objectives were more modest: 1. Developing AI-based models for extracting disaster information from tweets 2. Categorizing the information based on the different stages of disaster management 3. Presenting the ongoing research at regional and international conferences to showcase and gain additional inputs on the collaborative work in the area of social media and disaster management 4. Participating in workshop to highlight the importance of the use of social media in disaster management 5. Producing convincing research proposals for submission to journals and NSF for obtaining funding.

Methods Dr. Zou has established a local server for Twitter data collecting, processing, and storage. This server has been used for collecting millions of twitter messages related to Harvey and Uri disasters. Using and analyzing these data, we will examine three hypotheses. (a) The digital divide in social media use during disasters exists that communities with lower socioeconomic conditions have less disaster-related social media use. (b) Under the same disaster threat and socioeconomic conditions, communities using social media more actively and efficiently during disasters suffer fewer damages and recover faster. (c) Welldesigned continuous modules and workshops education can effectively improve disaster-related social media use and disaster resilience in vulnerable communities.

Results • • • • • •

Twitter data related to Harvey and Uri has been collected and stored on the server Manual labeling based on the different disaster stages has been accomplished Presented the work in progress research to get additional feedback at a regional conference, Southwestern Decision Sciences Institute (SWDSI) 2023 in Houston Participated at the 2023 PVAMU Emergency Management Workshop Presented a poster at the International Conference of Information Systems in Crisis Response and Management (ISCRAM) 2023 in Omaha Presented an oral presentation at the Annual Meeting of the American Association of Geographers 2023 in Denver

Discussion Our team has collected 46 million Harvey-related and 1.6 million Uri-related tweet data. We have further developed a AI-based model to extract disaster information from these tweets using the steps shown below (Figure 1).

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Figure 1 This data has been analyzed to understand the different categories of rescue operations that has been requested. This data has been further analyzed to understand the emergence of tweets during the different phases of disaster management.

Further, based on this research an abstract titled “Examining the Use of Social Media and the Influence of Digital Divide to Improve Disaster Resilience in Vulnerable Communities” was submitted to the SWDSI 2023 and a presentation was made at this conference on Friday, March 10, 2023 in Houston, TX. We participated and shared our research through poster presentation with the researchers from Texas A&M, College Station and Prairie View A&M University through our poster on April 14, 2023 during the Faculty Research Day. Dr. Zou participated and provided a workshop on “Empowering Disaster Resilience Research and Practice with Geospatial Big Data” on Friday, April 21, 2023 as a part of the workshop organized on the topic of Emergency Management Faculty Development Workshop (Figures 2 & 3).

Dr. Zou attended the 2023 Annual Meeting of the American Association of Geographers. In this conference, Dr. Zou chaired a session on Geospatial Big Data and delivered an oral presentation on “Location-Based Social Media Analysis for Emergency Management: Challenges and Opportunities” on March 27, 2023, in Denver, CO.

Figure 2

Figure 3

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Dr. Ramakrishnan presented a poster titled “Investigating the Role of Digital Divide and Social Media Use (SMU) to Improve Disaster Resilience in Vulnerable Communities” at the International Conference of Information Systems in Crisis Response and Management (ISCRAM 2023) held in Omaha, Nebraska on May 30, 2023. (Figure 4)

Figure 4 Future Work Our team is currently working on journal submission and submitting a grant proposal to NSF for external funding. Tentatively, we plan to submit the paper and the grant during the Fall 2023 Semester.

Broader Impact The benefits and broader impacts of this project are three-fold. First, this project identifies the population that are vulnerable and affected disproportionately during disasters by leveraging geospatial big data and AI. The relief organizations and government agencies can use the techniques in this proposal to better manage disasters and carry out relief operations especially for vulnerable communities. Second, this project will examine the depth of digital divide that exists between vulnerable communities and general population and seek to minimize this gap by conducting workshops and continuous education in accessing and using social media tools. This will help vulnerable populations to leverage social media for seeking timely help and keeping themselves safe during natural disasters. Finally, the developed databases and AI algorithms will be open-sourced at GitHub to benefit researchers from various disciplines.

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References: [1] Cai, H., Lam, N.S., Qiang, Y., Zou, L., Correll, R.M. and Mihunov, V., 2018. A synthesis of disaster resilience measurement methods and indices. International journal of disaster risk reduction, 31, pp.844-855.

[2] Federal Emergency Management Agency (FEMA), E., 2003. Principles of emergency management.

Independent Study IS230.

[3] Zou, L., Lam, N.S., Cai, H. and Qiang, Y., 2018. Mining Twitter data for improved understanding of disaster resilience. Annals of the American Association of Geographers, 108(5), pp.1422-1441.

[4] Ngamassi, L., Ramakrishnan, T., & Rahman, S. (2016). “Use of Social Media for Disaster Management: A Prescriptive Framework,” Journal of Organizational and End User Computing 28(3), pp.

122-140.

[5] Zou, L., Lam, N.S., Shams, S., Cai, H., Meyer, M.A., Yang, S., Lee, K., Park, S.J. and Reams, M.A., 2019. Social and geographical disparities in Twitter use during Hurricane Harvey. International Journal of Digital Earth, 12(11), pp.1300-1318.

[6] Ramakrishnan, T., Ngamassi, L., & Rahman, S. (2022). “Examining the Factors that Influence the Use of Social Media for Disaster Management by Underserved Communities,” International Journal of Disaster Risk Science

Lei Zou, Ph.D. ASSISTANT PROFESSOR Department of Geography College of Arts & Sciences Texas A&M University

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Developing an Adaptive Toolkit for the Prevention of IPV and IPV-Related Mental Health Sequelae Among Black College Students

Temiola Salami, Ph.D. ASSOCIATE PROFESSOR Department of Psychology College of Arts & Sciences Prairie Vie A&M University Project Summary: As a result of socio-cultural factors (e.g., gender norms), limited access to services, and limited awareness of intimate partner violence (IPV) risk factors, Black American emerging adults may be particularly vulnerable to IPV and the resulting psychological sequelae of IPV (Barrick et al., 2013).

Further, given social distancing measures that have resulted from the COVID-19 pandemic, victims are less able to leave their abusers, increasing rates of IPV (Zero & Geary, 2020). Emerging adulthood is a particularly vulnerable period to experience IPV (Breiding et al., 2008), yet little is known regarding the various risk and protective factors of IPV during this period. We aim to identify risk and protective factors at different ecological levels (individual, relationship, community, societal) among Black American emerging adults attending college. Further, using information from the extant literature and through a focus group study, we aim to develop a toolkit. Thus, the proposed project will be beneficial by 1) providing a clearer picture of IPV among Black American emerging adults attending college in the wake of the COVID-19 pandemic, 2) providing an educational and skills-based toolkit that can be delivered remotely to help decrease IPV among this population, and 3) increasing awareness and decreasing the psychological impacts of IPV. Progress Update at the Different Participating Institutions: At Prairie View A&M University (PVAMU), the research project has made significant strides.

Specific steps initiated and completed by the research team are as follows: 1. Hired a PVAMU graduate student and assembled a team of students to work on the project 2. Developed and submitted IRB documentation (e.g., consent document, procedures, debrief process, resource list) for the initiation of the project.

3. Received IRB approval to commence the project.

4. Developed codebooks and Qualtrics surveys for both phases of data collection 5. Began assembling various components of the IPV toolkit 6. Collaborated with researchers at Sam Houston State University (SHSU) and Texas A&M University (TAMU)

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As a result of these initiatives, the project was launched at PVAMU, and the study made significant progress in the area of collecting data related to risk and protective variables among at-risk populations. Indeed, the researchers successfully collected data from 203 participants. During the data collection phase, there was an identified issue related to additional demographic data inadvertently included in the Qualtrics survey.

To ensure the highest research integrity, the research team promptly notified the IRB about this matter, and the project is currently on hold while the IRB determines the appropriate course of action regarding the mistakenly collected data. The PVAMU project team is actively collaborating with the IRB to find a solution, demonstrating their commitment to ethical standards.

At SHSU, a reliant agreement has been initiated as part of the collaborative research effort with PVAMU. Recognizing the necessity of adhering to strict ethical standards and research integrity, the agreement has been temporarily put on hold until the PVAMU IRB review is completed. Throughout this process, PVAMU and SHSU have maintained open and positive contact. They are dedicated to addressing any concerns that may emerge throughout the IRB evaluation.

The researchers at TAMU value the PVAMU IRB as an important resource. They have received copies of the IRB procedures from PVAMU and have initiated their institutional IRB process using these procedures. This step ensures procedural consistency between the two institutions, though TAMU will be working through its own IRB office.

External Grant Submission: In June 2022, the PVAMU and TAMU teams submitted an external grant proposal through the Office for Victims of Crime. The title of the funding announcement was “OVC FY 2022 Advancing the Use of Technology to Assist Victims of Crime” (see the announcement here: https://ovc.ojp.gov/funding/fy-2022/OOVC-2022-171237.pdf). The main aim of this grant was to expand on the work through the PVAMU internal grant by using funds from this grant to develop and assess an App-based psychoeducational and coping skills mobile App toolkit for Black college students susceptible to IPV. The effectiveness of the proposed toolkit was to be assessed using a randomized control trial design. We hypothesized that participants randomized into the 8-week intervention group would show reduced psychological distress and better educational outcomes than those in the waitlist control group.

Unfortunately, the grant was not awarded. However, it received fairly favorable reviews, with scalability being the main concern. The project team remains determined and plans to reapply for the grant, as well as explore other external grant mechanisms, to secure funding and continue the important work of this project.

Using the data collected through the PRISE grant as preliminary data and addressing the grant reviewer’s initial concerns, the project team aims to resubmit an external application in the summer of 2024.

Benefits and Broader Impact: The proposed project will be beneficial by 1) providing a clearer picture of IPV among Black American college students in the United States; 2) providing an educational and skills-based toolkit that can be delivered remotely to help decrease IPV and the deleterious consequences of IPV. Therefore, the proposed project aims to enhance IPV awareness, reduce mental health stigma, and provide relevant resources, coping

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skills, and insights for public policy. It also intends to provide research training to graduate students and increase research capacity at PVAMU. By addressing the mental health and well-being of Black college students, the project contributes to reducing health disparities and improving the overall campus climate.

References Barrick, K., Krebs, C. P., & Lindquist, C. H. (2013). Intimate partner violence victimization among undergraduate women at historically black colleges and universities HBCUs). Violence against women, 19(8), 1014-1033.

Breiding, M. J., Black, M. C., & Ryan, G. W. (2008). Chronic disease and health risk behaviors associated with intimate partner violence—18 US states/territories, 2005. Annals of epidemiology, 18(7), 538-544.

Zero, O., & Geary, M. (2020). COVID-19 and Intimate Partner Violence: A Call to Action. Rhode Island medical journal, 103(5), 57-59.

Noni Gaylord-Harden, Ph.D. PROFESSOR Department of Psychology & Brain Sciences College of Liberal Arts Texas A&M University

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Assessing Teachers’ and Leaders’ Perception and Application of Culturally Responsive Teaching and Culturally Sustaining Pedagogy

Beverly Sande, Ph.D. ASSOCIATE PROFESSOR Department of Curriculum and Instruction College of Education Prairie View A&M University Project Introduction Culturally responsive teaching (CRT) has been associated with increased student engagement and achievement. Its practice in classrooms, however, could be more optimal. Nonetheless, continuous exposure to these practices could result in sustaining CRT. Culturally sustaining pedagogy (CSP) is possible through constant predisposition to the malleable tenets of what makes a group of people unique.

Recruitment and retention challenges again lead to teacher shortages nationwide (Guha et al., 2017).

Especially in urban and rural school districts, low salaries and poor working conditions often contribute to the difficulties of recruiting and keeping teachers, as can the challenges of the work itself. Consequently, in many schools--especially those serving the most vulnerable populations--students often face a revolving door of teachers throughout their careers (Guha et al., 2017).

The teacher population in the state of Texas does not match its student population, and there is a need to not only increase the diversity of the teacher population but also use culturally responsive methods to teach our students. Prairie View A&M University (PVAMU), a Historically Black College and University (HBCU), and Texas A&M University, College Station, will lead innovative efforts to increase the number of diverse teachers to better meet PreK-12 needs of the State of Texas. The proposed study focuses on evaluating PreK-12 school districts and schools that work with predominant culturally, linguistically, ethnically, and economically diverse (CLEED) learners and their families in the State of Texas, assessing practices that focus on culturally responsive, respectful, relevant, and sustaining pedagogies. The study targets teachers’ and leaders’ development, mentoring, coaching, culturally responsive practices for social-emotional learning, empowerment, exposure, and support to develop models that revolutionize educational service delivery in Texas.

In this seed phase of the research, the proximate objectives were more modest 1. Assess teacher’s and leaders’ knowledge, perspectives, and attitudes toward the application of CRT and CSP 2. Use the results to develop assessment instruments and training modules to be used by stakeholders.

3. Engage critically in the cultural landscape of classrooms and teacher education programs.

4. Solicit external funding for a more extensive study.

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The current study uses quantitative and qualitative evidence to assess the teachers’ and teacher leaders’ knowledge, perspective, and attitude toward applying CRT in the classrooms. Using an assessment, the researchers are identifying the gap in knowledge and the kinds of attitudes that best promote CSP. Method Utilizing a mixed-method research approach, the researchers developed surveys, critical interviews, and focus groups to collect their data. The target population, sampled using purposeful and snowball approaches, includes preservice teachers, in-service teachers, and developing school leaders. The data collection phase is still ongoing. However, the researchers have used their ongoing pursuit of CRP and collaborative engagement to seek additional funding.

External Funding The researchers have continued to collaborate and from this collaboration, solicited external funding to support the development of training protocols for in-service and preservice teachers and leaders to prepare them to utilize and evaluate culturally sustaining pedagogy at their schools. The Team used the initial collaboration of the PRISE research to submit a multi-disciplinary proposal to the United States Department of Education (USDOE). In April 2022, the researchers partnered with other scholars and applied for a grant. Dr. Beverly Sande and Dr. Valerie Hill-Jackson led the submitted proposal. It included three Co-PIs from PVAMU and TAMU, including the TAMU faculty who contributed to the PRISE research. The proposal was titled “Leading Equity Across Diverse Environments with Revolutionary Synergy (LEADERS),” the budget was $12.2M. The Team was awarded funding for $12.2M in September 2022. The researchers also aim to examine the generalizability of these findings to inform teachers’ professional development and interventions.

Broader Impact The broader social impact is to provide opportunities to implement culturally responsive pedagogy (CRP), CRT, and CSP to better serve Texas’s culturally and linguistically diverse student population regarding academic learning, behavioral and social-emotional outcomes. The Team intends to develop an instrument to be used by teacher preparation programs, schools districts, and principal preparation programs to evaluate cultural consciousness inclusive of CRP, CRT, and CSP to empower educators in identifying mastery, vicarious, social persuasion, and emotional states (Bandura, 1977) driven by culturally and assess based pedagogies (Boykin & Bailey, 2000), to better support the cultural identities of learners of color and improve their school outcomes.

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References Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological Review, 84(2), 191.

1. Boykin, A. W., & Bailey, C. T. (2000). The role of cultural factors in School Relevant Cognitive Functioning: Synthesis of Findings on Cultural Contexts, Cultural Orientations, and Individual Differences. Report No. 42.

2. Guha, R., Hyler, M. E., & Darling-Hammond, L. (2017). The teacher residency: A practical path to recruitment and retention. American Educator, 41(1), 31.

Gwendolyn Webb, Ed.D. ASSOCIATE PROFESSOR AND ASSOCIATE DIRECTOR OF THE EDUCATION Department of Educational Administration and Human Resource Development College of Education and Human Development Texas A&M University

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Leveraging Machine Learning for Fundamental Investigation of Anomalous Transport Phenomena of Nano-Fluids under the Effect of External Fields for Energy Storage Applications

Shahin Shafiee, PhD ASSISTANT PROFESSOR Department if Mechanical Engineering College of Engineering Prairie View A&M University

Project introduction Nanoparticles are typically added to enhance material properties (i.e., strength, energy storage capacity).

Nanoparticles are an expensive and laborious proposition. In this simple invention – cheap additives generate nanoparticles in-situ, i.e., from conventional materials (such as, salts, soaps/ surfactants and bio-degradable materials such as cellulose) when heated at low temperatures (e.g., by direct heating, microwave, ultrasound, etc.). This increases the energy storage capacity (2X), power rating (1.5X), and lubrication (“smart grease”), while reducing corrosion/fouling and manufacturing costs (1000X cheaper).

Similarly, ferromagnetic materials can be deployed as additives to generate “ferro-nanofluids”. These fluids demonstrate magnetic properties, which can further enhance their operational characteristics and properties (e.g. enhanced convection heat transfer under the effect of external magnetic fields) as compared to ordinary nanofluids. Investigation of the modulation of the properties of ferro-nanofluids subjected to magnetic fields for enhancing cooling (heat flux) is the focus of this study. The project impacted the learning experiences of both PVAMU and TAMU students by involving them in state-ofthe-art nanofluids research. Students and faculty from both institutions interacted, and researchers gained experience with a wide range of facilities across the two campuses.

Methods Machine learning is a cost-effective powerful tool in prediction of thermophysical properties, the evaluation of thermo-hydrodynamic performance and simulating the behaviors of nanofluids in the unstudied combinations of operating conditions. Some of the ML techniques that can be used in the thermal analysis of nanofluids are presented in Table 1. The goal of the project is to explore, compare and contrast the efficacies of multiple machine learning techniques (e.g., ANN and SVM) along with experimental validation of the numerical predictions for investigating the fundamental mechanisms governing the anomalous transport phenomena of nano-fluids, especially when subjected to external fields and stimuli.

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Table 1. Features of Different Learning Algorithms Method Advantages Disadvantages Applicability Cases with available large ANN Ability to model complex Large samples required and samples high demand for computing nonlinear relationship resource and generalization CART Clear structure and easy Easy to overfit Cases with limited computing to understand. resources Cases with available large Large samples required and IANN Optimized network samples and requiring for better structures and parameters high demand for computing prediction performance of ANNs by intelligence resource algorithms Cases with limited computing RF Ability to model complex Risk of overfitting with highnoise data resources nonlinear relationship; stable performance; reducing risk of overfitting Sensitive to kernel function; dif- Cases with small samples SVM Acceptable with small ficult to deal with large samples samples; nonlinear regression; high-dimensional pattern recognition The specific aims of the project are listed below: Aim 1: Develop and acquire a simulation model based on machine learning techniques for convective heat transfer in nanofluids under different boundary conditions such as magnetic fields.

Aim 2: Perform experiments to investigate properties of different nanofluids (morphology, materials, synthesis protocols, etc.) and assess their heat transfer behavior. Perform experimental validation of numerical predictions for the properties and behavior (e.g., convective heat transfer) of nanofluid samples (e.g., Figures 1-6).

Aim 3: Perform experimental validation of numerical predictions for the anomalous transport phenomena of nano-fluids subjected to external fields.

Results The project is currently in progress with an approved no cost extension (NCE) till December 31, 2023.

In this study the anomalous enhancement of thermophysical properties of nanofluids (e.g., density) were measured experimentally and compared with the predictions from analytical models. The analytical models are based on the “nanoFin Effect (nFE)” which is governed by dominance of interfacial interactions between solid surface of the nanoparticles and the fluid molecules, that can be modeled using thermal impedances in a mixed circuit configuration (parallel and series connections), consisting of the following: (a) interfacial thermal resistance (also known as “Kapitza Resistance”, Rk); (b) thermal capacitor (due to surface-adsorption of fluid leading to formation of a semi-solid layer); and (c) thermal diode (due to competition between temperature gradient and concentration gradients from the semi-solid layer of adsorbed fluids on each nanoparticle surface and the bulk phase of the solvent). nFE (i.e., primarily the interfacial thermal diode effect) can also lead to preferential trapping of chemical species (e.g., ions) within the surface adsorbed thin film of solvent molecules that preferentially form on the nanoparticle surface, thus leading to very high concentration gradients (and formation of localized

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galvanic cells), which in turn, can lead to drastic reduction in corrosion in the vicinity of the nanoparticles, especially for nanoparticles precipitated on the containment walls (the nanoparticles precipitating on the containment walls form “nano-fins”, i.e., extended heat transfer surfaces on the micro/nano-scales). Hence, the project goals are: (a) to develop novel material models that capture the anomalous and anisotropic behavior of nanofluids, and (b) to generate feasibility data from experiments (for more sophisticated studies to enable development of machine learning algorithms).

The techniques and methodology utilized in this study confer several benefits, including: (1) Faster, cheaper, and simpler manufacturing technique (1000 times cheaper material costs compared with conventional approach of explicitly mixing nanoparticles); (2) Less laborious which enables rapid scale-up and large-scale industrial deployment; (3) Uses conventional materials (exotic or expensive nanoparticles are not required); (4) Amenable for alternate manufacturing techniques (e.g., radiation treatment, electromagnetic/microwave, ultrasound, laser, nuclear and electrical stimuli); (5) Improved energy storage capacity; (6) augmented power rating (by 20~120%), and (7) Reduced corrosion (by 50 ~ 100%); which (7) increases equipment life by 25~50%.

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Figure 2. Experimental Apparatus Used for Experimental Validation of Numerical Predictions Obtained for a Thermal Energy Storage Platform Using Machine Learning (ML) / Artificial Neural Networks (ANN)

Figure 3. Topology of the Multi-Layer Perceptron (MLP) Model used for this study for performing experimental validation of numerical predictions for thermal energy storage (TES) platform using Machine Learning (ML)

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As an outcome of this research collaboration and from allied research grants related to the tasks in this project, the following papers will be presented by the students (and eventually expected to be published in the conference proceedings) of the International Mechanical Engineering Congress & Exposition (IMECE 2023), which is organized by the American Society for Mechanical Engineers (ASME) at New Orleans, LA; from October 29 – November 2: 1. IMECE 2023-117109: Sai Sudhir, P.*, Ren, G.**, Thyagarajan, A.*, Shettigar, N.*, and Banerjee, D., “Exploring Efficacy of Machine Learning (Artificial Neural Networks) for Enhancing Reliability and Resilience of Thermal Energy Storage Platforms Utilizing Phase Change Materials for Sustainability and Mitigating Food-Energy-Water (FEW) Nexus”, Paper No. IMECE2023-117109 (American Society of Mechanical Engineers (ASME) Digital Collection), Proceedings of the International Mechanical Engineering Congress and Exposition (ASME-IMECE-2023), New Orleans, LA, Oct. 29 – Nov. 2, 2023.

2. IMECE 2023-117183: Shafer, J.*, Lee, J.*, Thyagarajan, A.*, and Banerjee, D., “Experimental Investigation of the Nano-Fin Effect (nFE) During Thin Film Evaporation from Nanopores Using Temperature NanoSensors”, Paper No. IMECE2023-117183 (American Society of Mechanical Engineers (ASME) Digital Collection), Proceedings of the International Mechanical Engineering Congress and Exposition (ASME-IMECE-2023), New Orleans, LA, Oct. 29 – Nov. 2, 2023.

3. IMECE2023-117254: Chavan, C.*, Zainab, A., and Banerjee, D., “Developing a Computational Model of Lungs for Patients With Acute Respiratory Distress Syndrome (ARDS)”, Paper No. IMECE2023-117254 (American Society of Mechanical Engineers (ASME) Digital Collection), Proceedings of the International Mechanical Engineering Congress and Exposition (ASME-IMECE-2023), New Orleans, LA, Oct. 29 – Nov. 2, 2023.

4. IMECE 2023- 117221: Bhattacharyya, R.*, Shettigar, N. *, Thyagarajan, A. *, Shafiee, S., and Banerjee, D., “Investigation of Nanofin Effect (nFE) for Resolving the Anomalous Properties of Nanofluids”, Paper No. IMECE2023-117221 (American Society of Mechanical Engineers (ASME) Digital Collection), Proceedings of the International Mechanical Engineering Congress and Exposition (ASME-IMECE-2023), New Orleans, LA, Oct. 29 – Nov. 2, 2023.

KEY: * : graduate student (M.S. or Ph.D.) ** : undergraduate student Discussion This study is leveraged to identify the dominant transport mechanisms that are responsible for anomalous behavior of nanofluids (e.g., for convective heat transfer enhancement of nanofluids). This fundamental study on nanofluids encompasses a variety of applications, including: (a) Power Generation (solar thermal, nuclear, geothermal, coal fired power plants, etc.); (b) Energy Storage (water heaters, fuel cell, electrical batteries, etc.); (c) Improving energy efficiency (e.g., refrigerators, HVAC, manufacturing, mining, etc.); (d) Thermal management (e.g., defense applications, cooling opto-electronic lasers, data centers, etc.); (e) Heat Transfer Fluids for heating and cooling applications (chemical processing, metallurgy, etc.); (f) Corrosion and fouling resistant (anti-stiction) coatings, processes, treatments and technologies; (g) Smart lubricants and smart materials (stimuli responsive lubricants and materials); and (h) Health Care (e.g., nanoparticle drug delivery, radiation therapy for cancer, radiation shields for interventional surgeons and for protecting astronaut health in deep space missions).

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External funding The PI teams has submitted two proposals to DOE (thermal management of data centers) and are currently working on a proposal to NSF. Projects on heat transfer enhancement (which is the focus of this project) are of great interest to NSF (Thermal Transport Processes – TTP program). Based on its application in energy systems, the project results may also be integrated with different thermal management systems that may be on interest for funding by DOD, NASA, Air Force Research Labs.

(AFRL), and industry sponsors/ partners (e.g., solar, nuclear, oil and gas, etc.).

Broader Impacts This project is a collaboration between Prairie View A&M University (an HBCU) and TAMU. The experimental design and instrumentation (done at PVAMU) introduced to underrepresented students in undergraduate and graduate level courses with the aim of providing an opportunity for engaging the students to research with joint mentorship from both PVAMU and TAMU faculties. PVAMU students gained access to specialized facilities (e.g. 3D printing) at TAMU related to the research goals of this project. Meanwhile, the experimental results were utilized to develop educational modules in thermal fluids classes, both for undergraduate and graduate students, to showcase the study of convection heat transfer and property changes involving nanofluids under different boundary conditions. Some of the project results are disseminated as conference publications. Students and researchers from both institutes will participate in the conference which provides them an opportunity to connect with stateof-the-art in similar areas.

Debjyoti Banerjee, PhD PROFESSOR Department of Mechanical Engineering College of Engineering Texas A&M University

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Examining Posttraumatic Stress Disorder Symptoms, Resilience, And Posttraumatic Growth in College Students with Disabilities During COVID-19 Pandemic

Beverly A Spears, Ph.D., MSW, BSW ASSISTANT PROFESSOR Department of Social Work College of Arts and Sciences Prairie View A&M University

Project Introduction: On March 15, 2020, the United States implemented a national shutdown to prevent the spread of the COVID-19 virus [1]. College campuses shut down and brought life changes to millions of students nationwide. Thus, COVID-19 led to numerous changes in the daily life of students, including social distancing, remote learning, job losses, loss of family members, and change in social life, which resulted in intensified stress, anxiety, and uncertainties. The new life changes led to decreased academic performance, financial instability, and social isolation [3, 4 & 6] This project aimed to identify the prevalence of COVID-19-related posttraumatic stress symptoms (PTS) among college students and determine the influence of sociodemographic, COVID-19, and mental health service utilization factors on PTS prevalence. We wanted to determine if there was a difference between disabled and non-disabled college students’ age, ethnicity/race, and PTSD symptoms. Also, were the PTSD symptoms influenced by their living status, hospitalization of family members, or disability or nondisability? Moreover, was there a difference between students who utilized mental health services on or off campus during the pandemic?

Methods: The ethical approval for the research project was obtained from the Institutional Review Boards of Texas A&M University and Prairie View A&M University. Participants were recruited through the university listserv, study flyers, student newsletters, and the university’s CANVAs platform announcement board. Data was collected via an online Qualtrics survey between October and December 2022. The participants completed a self-report PTSD Checklist (PCL-5), demographics information, and COVIDrelated and mental health service utilization questionnaires. As a result of completing the survey, participants received $5 gift cards upon completion. In this research project, 614 college students participated using an online survey. The chi-square test was used to compare the prevalence of PTS among different categorical variables. Hierarchical regression analysis was further performed to identify the influence of different factors on PTS.

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Results: The average age of 614 participants, who completed the survey, was 22.7 (SD=5.6). Most participants were female (73.5%), 45.3% were Black, 12.5% had disabilities, and 20.5% utilized the university mental health service at least once. Using the cut-off score of 32, 57.2% of college students reported PTS symptoms. In the chi-square test, age, disability status, COVID-19 infection, family hospitalization, and loss of a family member due to COVID-19, mental health service utilization on and outside the university significantly affected PTS symptoms. In hierarchical analysis, age, race, living status, hospitalization of family members, and utilization of mental health services significantly predicted the PTS score in the final model; when controlling for demographic and COVID-19-related information, disability status significantly predicted PTS symptoms in model 1 and model 2.

Discussion COVID-19 has dramatically impacted the mental health and well-being of a large vulnerable population of college students nationwide. This research project aimed to describe mental health issues as PTSD symptoms, a high level of anxiety, depression, psychosis, seizures, and suicidal behavior; symptoms leaving students to deal with acute stress, depression, and social isolation, significantly impacting daily life functions. Younger college students, disabled and non-disabled, are noted to have experienced more PTSD symptoms than older students can be explained due to their developmental stage [3].

Late adolescence and young adulthood are times of considerable developmental changes, including greater autonomy, identity discovery, socializing, and internalizing morality. In this stage, young adults process career choices through education and explore intimate relationships [5]. These changes were disturbed by the pandemic and its pressures, which occurred while these students were going through this developmental stage. Socialization and developing relationships are essential factors for most young people. Lifestyle changes, such as shelter-in-place order, social distancing, and mask-wearing, may have triggered college students’ anxiety and stress [6]. Disabled and non-disabled college students have been disproportionately hit by the economic consequences of the pandemic, with many facing job loss, decreased hours, or financial difficulties [7]. Uncertainty and stress from such volatility have been connected to developing PTSD symptoms of anxiety, depression, and suicide [1].

Furthermore, we found that many of these young people (disabled and non-disabled) experiencing PTSD had not used mental health services on or off campus. Many of the participants did not utilize the use of mental health services. However, we failed to ask questions to determine why these services were not utilized. Much of the literature revealed that the under-utilization of mental health services could be attributed to mental health stigma and beliefs, especially for minority students [1]. Students’ stigma was due to their perceptions, the severity of the problem, and ignoring mental illness symptoms. The lack of trust in counseling services, not feeling comfortable sharing personal feelings, mental health issues, and the fear of being misdiagnosed and judged are reasons students are reasons for not seeking mental health services. Thus, the lack of financial resources and insurance during the pandemic for many young college students may have caused them to feel that mental health services were not affordable. The lack of knowledge, marketing of health services, and resources may have been why college students did not utilize mental health services on campus.

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External Funding The two researchers from PVAMU and TAMU are seeking external funding to develop a culturally sensitive program for dealing with college students’ PTSD symptoms of stress, anxiety, and depression.

Broader Impact: The findings suggest that various factors increase the risk of PTS in (disabled and non-disabled) college students. The COVID-19 pandemic and its impacts are not going anywhere anytime soon; therefore, it is vital to understand how to best assist students with and without disabilities to navigate the pandemic and the post-pandemic experiences. Because only 20.5% of students reported utilizing university mental services, the findings could guide university administrators, student services, and educators in identifying strategies to encourage students at risk of PTS to use them to improve their mental health.

References.

[1] Andrade C., Gillen M., Molina J.A., Wilmarth, M. J, 2022. The Social and Economic Impact of Covid-19 on Family Functioning and Well-Being: Where do we go from here? J Fam Econ Issues.

2022;43(2):205-212. doi 10.1007/s10834-022-09848-x Epub 2022 May 27. PMID: 35669394; PMCID: PMC9136200.

[2] Centers for Disease Control and Prevention. (2021). Coping with stress. Retrieved from https://www. cdc.gov/coronavirus/2019-ncov/daily-life-coping/managing-stress-anxiety.html [3] Lee, J., Solomon, M., Stead, T., Kwon, B. & Ganti, L. (2021). Impact of COVID-19 on the mental health of US college students. BMC Psychology volume 9, Article number: 95 (2021).

[4] McMaughan, D J., Rhoads, K. e., Davis, C., Chen, X., Han, H, Jones, R. A., Mahaffey, C.C., & Miller, B.M. (2022). COVID-19 Related Experiences Among College Students With and Without Disabilities: Psychosocial Impacts, Supports, and Virtual Learning Environments. Front. Public Health, 10 December 2021, Sec. Public Mental Health Volume 9 – 2021 | https://doi.org/10.3389/ fpubh.2021.782793 [5] Newman, B.M. & Newman, P.R., (2017). Development Through Life a Psychosocial Approach. Ed: 13th Cengage Learning, Boston, MA [6] Son, C., Hegde, S., Smith, A., Wang, X., & Sasangohar, F. (2020). Effects of COVID-19 on college student’s mental health in the United States: Interview survey study. Journal of Medical Internet Research, 22(9), e21279. PMID: 32805704; PMCID: PMC7473764.

[7] Front. Public Health (2018). Perceived and Personal Mental Health Stigma in Latino and African American College Students. Sec. Public Mental Health, vol.6. https://doi.org/10.3389/ fpubh.2018.00049

Muna Bhattarai, Ph.D., RN ASSISTANT PROFESSOR College of Nursing, Texas A&M University

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Integrating Crime Pattern Theory & Spatial Analytic Techniques to Examine Youth Crime

Ling Wu, Ph.D. ASSOCIATE PROFESSOR Department of Justice Studies College of Juvenile Justice Prairie View A&M University

Project Introduction In light of a recent surge in crime in U.S cities, law enforcement authorities have implemented various strategies, such as increased patrols in ‘hot spots’—areas identified as having elevated crime rates using Geographic Information System (GIS) techniques. These spatial analytic methods offer data-driven approaches to crime reduction. However, the effectiveness of these techniques relies on combining empirical evidence with theoretical underpinnings to guide analysis and facilitate result interpretation. Among the theories that explore crime from a spatial perspective, Crime Pattern Theory (CPT) harmonizes well with existing GIS techniques.

Drawing elements from routine activities theory, rational choice theory, and the geometric theory of crime, CPT endeavors to comprehend the spatial patterns of criminal activities. It posits the existence of underlying geographic patterns of crime spread and anticipates a correlation between the location of offenses and crime generators or attractors. In this study, we leverage the CPT framework and employ spatial analytic techniques within the GIS environment to gain deeper insights into the geospatial patterns of youth crime. Supported by the PRISE Grant Award Letter, the allocated funds must be utilized by December 31, 2023. The research objectives encompass several key points: 1. To identify high-crime areas in Houston and examine community-related factors that act as crime generators or attractors.

2. To assess the adequacy of CPT in explaining the spatial distribution of crime through spatial analytic techniques.

The current phase of this research has accomplished the following objectives: 1. Conducted training sessions for faculty members, graduate, and undergraduate students in computational social science and spatial crime analysis.

2. Conducted comprehensive literature reviews, data collection, and model testing.

3. Fostered collaboration between two universities for multiple proposal submissions.

4. Advanced the research agenda of studying human behaviors within the context of an urbanizing world, marked by increasing vulnerability and inequality, utilizing crime and justice data in diverse formats.

5. Established reproducible and replicable data analytical procedures for the benefit of criminal justice scholars and students.

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6. Founded the Criminal Justice Data Analytics Lab, affiliated with the esteemed research network of the Center for Geographic Analysis at Harvard University. This lab is especially committed to actively involving minority student researchers from Historically Black Colleges and Universities (HBCUs) and Hispanic-Serving Institutions (HSIs).”

Overall, this research seeks to contribute significantly to the understanding of crime patterns and its underlying spatial dynamics, using innovative methodologies and theoretical frameworks. The establishment of the Criminal Justice Data Analytics Lab also reflects a commitment to inclusivity and diversity in the pursuit of scientific knowledge.

Methods and Results This project focused on constructing a spatial crime database for the Metropolitan Houston area. To achieve this, each crime spot was meticulously located and identified on the map, utilizing open data with corresponding geographical coordinates for analysis. Information pertaining to specific crime generators and attractors, including schools, parks, transportation hubs, and other points of interest (POI), was obtained from The City of Houston Geographic Information System’s topographical geo-database, Rice University’s Urban Data Platform at Kinder Institute, and the free OpenStreetMap service. These datasets consist of point features containing geographic coordinates and accompanying descriptions. In conjunction with openly available community data from sources like Census and federal/local agencies, these datasets were integrated and managed within the GIS environment to create a platform facilitating the visualization and analysis of youth offending in the context of neighborhoods. Our project’s integration of diverse theoretical perspectives, cutting-edge spatial analysis, and evidence-based approaches holds great promise for advancing our understanding of youth crime and informing practical strategies to build safer and more resilient communities.

Various geographical methods were employed to examine the spatial clustering patterns of crimes. Dr. Xinyue Ye, the project collaborator from TAMU, provided valuable guidance on advanced space-time analysis and the use of mobility data. Together with Dr. Ye, the analytical procedures were co-developed.

Informed by Dr. Ye’s recommendations, the project embraced cutting-edge technologies, such as volunteered geographic information (VGI), the global positioning system (GPS), and location-based service (LBS) human mobility data. Leveraging these data sources presented enhanced opportunities for estimating human mobility direction and distribution within the context of crime research. To analyze the crime data, the negative binomial model was chosen for its capabilities in addressing overdispersion, count data, and zero-inflation, along with its flexibility in incorporating covariates. This statistical model was deemed appropriate for the crime data due to its ability to handle the complexities and variations commonly found in such datasets. By combining extensive crime data, detailed geographic information, and innovative analytical techniques, this research project sought to enhance our understanding of spatial crime patterns in the Metropolitan Houston area, particularly focusing on youth offending in the neighborhood context. The collaboration with Dr. Ye and the adoption of state-of-the-art technologies reinforced the project’s scientific rigor and potential to make valuable contributions to the field of crime analysis and spatial research.

The following papers have been accepted or published: 2023 Wu, L. Peng, Q., & Lemke, M. Research Trends in Cybercrime and Cybersecurity: A Review Based on Web of Science Core Collection Database. International Journal of Cybersecurity Intelligence & Cybercrime. 6(1), 5-28.

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Ye, X. & Wu, L. Data - Local data warehouses. Encyclopedia of The World of Regional Science (Editors: Peter Nijkamp, Karima Kourtit, Kingsley Haynes, Zeynep Elburz)

2022 Wu, L., Peng, Q., Lemke, M., Hu, T., Gong, X. Spatial social network research: a bibliometric analysis. Computational Urban Science. doi: 10.1007/s43762-022-00045-y 2022 Ye, X., Wu, L., Lemke, M., Valera, P., & Sackey, J. Defining computational urban science. In New Thinking in GIScience. Higher Education Press and Springer. doi: 10.1007/978-981-19-3816-0_31 The conference presentations have also been accepted as below: 2022 Invited Panelist Talk, Capturing Inequality Conference at Rice University 2023 Estimating Cyber Victimization Risk in a Hybrid Physical-Virtual World: A Research Agenda. ASC Annual Meeting, Philadelphia.

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Triple Disadvantage, Human Mobility, and Crime. ASC Annual Meeting, Philadelphia.

Human Mobility Improves Crime Modelling. ASC Annual Meeting, Philadelphia.

Greener the safer? Effects of Urban Green on Street Crime and Safety Perception using Satellite Imagery. ASC Annual Meeting, Philadelphia.

Discussion In our research, the team explored potential extensions of the study to encompass the physical environment, virtual environment, and hybrid contexts. Specifically, we delved into the relationship between what we termed “triple neighborhood disadvantage” and different types of crime. Triple neighborhood disadvantage serves as a comprehensive measure of a neighborhood’s socioeconomic conditions, drawing on data concerning the daily movement of its residents. This measure takes into account various aspects of urban human mobility within the neighborhood, including commuting patterns and the flow of goods and services. To conduct this analysis, we utilized both SafeGraph mobility data and crime data. By incorporating these datasets, we were able to control for traditional neighborhood correlates of crime, such as poverty, population density, and education levels. The study’s findings provided compelling evidence that triple neighborhood disadvantage independently predicts various types of crime. Our research significantly contributes to the ongoing discourse surrounding the intricate relationship between socioeconomic conditions and crime. Moreover, it highlights the crucial role played by daily urban human mobility in shaping neighborhood disadvantage. By gaining a deeper understanding of the factors that contribute to crime in different neighborhoods, our work aims to facilitate the development of more effective strategies for reducing crime rates and enhancing overall community safety. As we continue to explore potential extensions of this research to include physical and virtual environments, as well as hybrid contexts, we strive to shed further light on the multifaceted interplay between neighborhood disadvantage and criminal activity. Ultimately, this knowledge has the potential to inform targeted interventions and policies, leading to safer and more resilient communities.

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External Funding Our team has achieved notable success in securing external grants for research endeavors. We were honored to receive two significant awards: “DEAP Institute in Research and Education for Science Translation via Low-Resource Neural Machine Translation,” sponsored by NASA, and “Semisupervised Fairness-Enhanced Knowledge Graph Construction on Social Media for AI- Enhanced Juvenile Justice,” supported by NSF. In addition to the awarded grants, we also made efforts to secure funding for a proposal titled “Center of Excellence for Disaster Resilient, Energy Efficient, Affordable Housing and Urban Management System (DREAMS),” submitted to the United States Department of Housing and Urban Development.

Although this proposal was not funded, the process of developing comprehensive community data for Metropolitan Houston has valuable implications for crime analytics and urban research.

Moving forward, we have ambitious plans for future research initiatives. One such proposal in the works is titled “Neighborhood effects and consequences of criminal justice contact: A study of juvenile cohorts in Harris County, TX using Criminal Justice Administrative Records System (CJARS).” We intend to submit this proposal to NSF Human Networks and Data Science – Infrastructure (HNDS-I) with a deadline of January 11, 2024.

Our dedication to pursuing innovative research projects underscores our commitment to advancing knowledge and addressing critical issues in our communities. With the support of these external grants and the potential for future funding opportunities, we aim to make significant contributions to the fields of data science, crime analytics, urban research, and juvenile justice. Through these endeavors, we aspire to enhance our understanding of complex social phenomena and work towards creating more resilient and equitable societies.

Broader impact Our project is firmly grounded in robust theoretical frameworks, supported by evidence from criminology theories, policing strategies, and computational spatial science. By incorporating our research findings into real-world practices, we hope to contribute to the betterment of society and forge strong collaborative ties within the research community. By leveraging these interdisciplinary approaches, we aim to optimize the allocation of limited police resources for effectively preventing youth crime. Specifically, our research seeks to target patrols at the micro level, where insights gained from this study are crucial for informed decision-making. The next phase of our inquiry will involve assessing virtual and social factors that elucidate and contextualize the observed patterns in offending. To tackle the complexity of these relationships, we plan to employ deep learning methods, which can unravel intricate and nonlinear associations between youth crime and various spatial and social determinants.

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The significance of this study lies in its potential to refine crime reduction strategies, enabling the intentional targeting of hotspot areas while addressing the underlying factors that contribute to crime in those locations. By shedding light on the complex interplay of spatial and social factors, we aspire to develop training modules for police, local non-profit organizations, and other stakeholders. These modules will incorporate our findings into their ongoing practices, leading to more effective crime prevention efforts. Moreover, this line of research has the potential to unlock numerous grant opportunities and enhance the research capacity at Prairie View A&M University.

By creating more research avenues, particularly for underrepresented minority students, we aim to foster inclusivity and diversity in the field of research. Additionally, this project fosters collaborative research activities between Prairie View A&M University and Texas A&M University, as well as partnerships with external agencies, fostering a collaborative and impactful research ecosystem.

Xinyue Ye, Ph.D. PROFESSOR Department of Landscape Architecture and Urban Planning College of Architecture Texas A&M University

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T H A N K

YOU

RESEARCH@PVAMU.EDU

WWW.PVAMU.EDU/RESEARCH

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