Physical Sciences and Mathematics Commons^™

Human Subject Studies For The Alignment Of Llm-As-A-Judge Evaluation Metric For Science News, Gabriel Vega Osborne

Knowledge and Creativity Expo

Science news has become an important vehicle to disseminate scientific breakthroughs, discoveries, and technological innovations. With the advancement of large language models and related AI models, it is possible to automatically generate science news from scientific papers, extending the reader population from domain scientists to a broader scope. However, how to evaluate the quality of the generated news warrants research. Traditional token based metrics have been shown to fail to evaluate the semantics and nuances of science news. Inspired by the fact that a major goal of science news is to educate readers with new knowledge, we thus propose knowledge …

Bacillus Subtilis Parb C-Terminal Lysine Residues Are Essential For Dimerization And In Vivo Function, Indicating Their Roles In Dna Sliding, Aleksey Aleshintsev, Lindsey E. Way, Ngoc Khanh Lai, Bianca Guerra, Paloma Dorantes, Lois Akosua Serwaa, Sealtiel Anulao, Miranda Molina, Xindan Wang, Hyeongjun Kim

Physics & Astronomy Faculty Publications

The broadly conserved ParB protein performs crucial functions in bacterial chromosome segregation and replication regulation. The cellular function of ParB requires it to dimerize, recognize parS DNA sequences, clamp on DNA, then slide to adjacent sequences through nonspecific DNA binding. How ParB coordinates nonspecific DNA binding and sliding remains elusive. Here, we combine multiple in vitro biophysical and computational tools and in vivo approaches to address this question. We found that five conserved lysine residues in the C-terminal domain (CTD) of ParB play distinct roles. While the two central rigid lysine residues provide the structural platform for the CTD, the …

2025 San Diego Regional Climate Collaborative Annual Report, San Diego Regional Climate Collaborative

San Diego Regional Climate Collaborative

The San Diego Regional Climate Collaborative is a network for public agencies that serves the San Diego region to share expertise, leverage resources and advance comprehensive solutions to facilitate climate change planning. By partnering with academia, nonprofits, and businesses, the SDRCC also works to leverage the profile of regional leadership. This is the 2025 San Diego Regional Climate Collaborative Annual Report.

Being There For Mom: The Strengths Of Daughtering, Allison M. Alford, Kaitlin E. Phillips, Luke V. Stipanovic, Cayd A. Rocha-Barnette, Michelle Miller-Day

Communication Faculty Articles and Research

Objective

Daughters undertake daughtering, or the everyday role portrayal of contributing to a meaningful family relationship with their mothers, but the labor of it is often invisible.

Background

Using a strengths-based approach, we investigated what daughters do well in their relationships with mothers.

Method

We analyzed the responses of 1,444 women to the open-ended question, “What do you do well as a daughter?” to learn more about how women describe their daughtering. Utilizing the artificial intelligence of a large language model for data analysis, we supplied definitions and descriptions of 12 virtues and strengths from existing literature and created a …

Marine Vehicle Dynamics Using Koopman Operator Theory With Hybrid Observables, Mikhalib A L Green

LSU Master's Theses

Accurate modeling of marine vehicle dynamics remains challenging due to strong nonlinear hydrodynamic effects, environmental disturbances, and sensitivity to configuration changes, particularly for small-scale platforms. Classical physics-based models require extensive parameter identification and often exhibit degraded performance outside narrow operating regimes, while purely data-driven approaches may lack structure or impose high computational cost. This thesis presents a data-driven Koopman operator framework with hybrid observables for modeling the dynamics of unmanned marine vehicles. The proposed approach combines structured monomial observables with a learned neural network embedding to construct a lifted state representation in which the nonlinear vehicle dynamics are approximated by …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

Butte Priority Soils Operable Unit Final Butte Hill Revegetation Specifications, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Bres No. 30 – Atlantic 1 Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Butte Priority Soils Operable Unit (Bpsou) Insufficiently Reclaimed Sites Josephine Shaft (Bres No. 37) Remedial Action Work Plan (Rawp), Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Approval Letter For The Butte Priority Soils Operable Unit (Bpsou) Draft Final Rock Creek Cattle Company Borrow Submittal #2 (Dated February 4, 2026), Molly Roby

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Cdj-Pontryagin Optimal Control For General Continuously Monitored Quantum Systems, Tathagata Karmakar, Andrew N. Jordan

Mathematics, Physics, and Computer Science Faculty Articles and Research

The Chantasri-Dressel-Jordan (CDJ) stochastic path integral formalism (Chantasri et al. 2013 and 2015) characterizes the statistics of the readouts and the most likely conditional evolution of continuously monitored quantum systems involving a few qubits or quantum harmonic oscillators in Gaussian states. In our work, we generalize the CDJ formalism to arbitrary continuously monitored systems by introducing a costate operator. We then prescribe a generalized Pontryagin's maximum principle for quantum systems undergoing arbitrary evolution and find conditions on optimal control protocols. We show that the CDJ formalism's most likely path can be cast as a quantum Pontryagin's maximum principle, where the …

Best Integrated Writing Spring 2026 - Complete Edition, Wright State University School Of Humanities And Cultural Studies, Tracy Smith, Kristie Mckiernan

Best Integrated Writing

Best Integrated Writing includes excellent student writing from Integrated Writing courses taught at Wright State University.

Tornado Alley In A Changing Climate: The Eastward Shift, Kenny Reinhard

Best Integrated Writing

Reinhard argues that climate change is driving an eastward shift in Tornado Alley from the Great Plains toward the southeastern United States, increasing tornado frequency in more densely populated and less-prepared regions. It highlights how changing atmospheric conditions—such as increased moisture, instability, and wind shear—are contributing to this shift and emphasizes the need for improved preparedness and infrastructure in newly affected areas.

Enhancing Cyber Hygiene Among Communities Through Experiential Cyber-Security Awareness Programs, Dr Atul Bamrara, Partha Roy, Vishwanath Gargote, Khandu Thungon

Journal of Cybersecurity Education, Research and Practice

Human error remains the most frequently exploited vulnerability in the cyber-security ecosystem. Despite substantial investments in technical safeguards, cybercriminals increasingly rely on social engineering, misinformation, and emotionally manipulative tactics to compromise users. This study examines behavioral changes among participants who underwent structured cyber-security workshops addressing both conventional cyber hygiene practices and emerging digital threats. The training modules covered digital arrest scams, identity theft, sextortion, fake technical support fraud, fake social media profiles, online gaming related risks, and deep fake manipulation. The workshops were designed using interactive simulations, real world case studies, and hands on problem based exercises, with the explicit …

Assembly And Testing Of A De-202 Closed-Cycle Cryostat, Chris Maguire

SPARK Symposium Presentations

Closed-cycle cryostats, such as the Advanced Research Systems DE-202 model are commonly used in laboratories as they provide long-term, low-cost, low-maintenance operation. We will outline the assembly, testing processes, and benchmarking experiments associated with the setup of this instrumental accessory. Further, a technical manual was written for future students and faculty in undergraduate research. Assembly involved constructing a custom frame, and integrating vacuum, water, gas, and electrical systems. Testing confirmed the system did not leak gas. Thermal response was recorded during cooling and heating phases to verify functionality; a base temperature of 7.5 K was achieved in 65 minutes, surpassing …

Quantifying Global Internal Displacement Risk At The Hazard-Vulnerability-Conflict Nexus, Wenzhao Li, Surendra Maharjan, Rejoice Thomas, Hesham Morgan, Ali Elgendy, Susan Mikhail, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

This study introduces a novel, diagnostic hazard-specific displacement risk index that integrates high-resolution hazard data with social vulnerability metrics and historical displacement records, uncovering overlooked compound risks in conflict areas and links between environmental stress and instability. Our methodology combines historical displacement records with environmental, demographic, and socioeconomic indicators to identify high-risk regions and quantify interactions between hazards and conflict across six major hazard types. Results reveal floods as the primary displacement driver, particularly in South Asia (Bangladesh, India, Pakistan) and Sub-Saharan Africa (Nigeria, Ethiopia), where dense populations in flood-prone areas intersect with low socioeconomic resilience. Droughts disproportionately impact arid …

Dietary Inflammatory Index And Objective Disease Activity In Ibd: No Association Found, Rúbia Moresi Vianna De Oliveira, Ana Carolina Junqueira Vasques, Stefhani Andrioli Romero, Nitin Shivappa Mbbs, Mph, Ph.D., Michael David Wirth, James Hébert Scd, Glaucia Fernanda Soares Ruppert Reis, Cristiane Kibune Nagasako

Faculty Publications

Background

Inflammatory Bowel Disease (IBD) involves genetic and environmental factors, but the relationship between disease activity, adiposity, and diet remains unclear.

Objective

To investigate the association between endoscopic/radiological activity of IBD, body adiposity, and the Dietary Inflammatory Index with or without adjustment for energy density (E-DII or DII).

Method

An observational, cross-sectional study was carried out. Endoscopic activity was defined by an endoscopic Mayo score >2, Crohn’s Disease Endoscopic Index of Severity (CDEIS) > 5, and/or the presence of a deep ulcer in any intestinal segment. Body adiposity was estimated using the body mass index, waist circumference, and waist-hip ratio (WHR). …

An Age-Structured Model For The Effects Of Time Delay On The Dynamics Of Malaria In The Presence Of Temperature And Rainfall, Alanus Mapunda, Eunice Mureithi, Nyimvua Shaban

Tanzania Journal of Science

An age-structured model with a class of pregnant women that captures the impact of time-delay on the dynamics of malaria is established in this work. The main goal is to explore the transmission dynamics of malaria in natural setting which is comprised of pregnant women and other individuals. Some weather conditions are also considered in the model. The main importance is to investigate the evolution of various states involved in the transmission of malaria in the community. The model equilibria are analyzed to study their stability, and numerical solution of the system is presented and discussed. The result shows that …

Circulating Levels Of Insulin-Like Growth Factor I (Igf-I) And Risk Of Multiple Myeloma: An Observational And Mendelian Randomisation Study, Yolanda Benavente, Sara Hermosa, Nikos Papadimitriou, Alyssa I. Clay-Gilmour Ph.D., Elizabeth E. Brown, Jonathan N. Hofmann, Nathaniel Rothman, Qin Lan, Sonja I. Berndt, Demetrius Albanes, Mark Purdue, Mitchell J. Machiela, Stephen J. Chanock, Parveen Bhatti, Wendy Cozen, Aaron Norman, Susan L. Slager, James R. Cerhan, Vincent Rajkumar, Et. Al.

Faculty Publications

No abstract provided.

2026 April 23 - Tennessee Weekly Drought Summary, Tennessee Climate Office, East Tennessee State University

Tennessee Climate Office Weekly Drought Summaries

No abstract provided.

Advancing Environmental Justice Through Chemical Education: Perspective From New Orleans’ Water And Air Quality Challenges, Ja’Lynn Keller, Sita Aggarwal, Rami A. Al-Horani, Morewell Gasseller, Navneet Goyal

Faculty and Staff Publications

New Orleans, Louisiana, faces profound environmental vulnerabilities arising from the convergence of industrial pollution, urban runoff, and climate change–driven weather events. This paper examines systemic challenges affecting water and air quality in the region, with particular focus on low-income and marginalized communities disproportionately burdened by environmental degradation. Drawing on educational and community-based monitoring initiatives at Xavier University of Louisiana and Southeastern Louisiana University, we highlight the integration of student learning with real-time data collection of contaminants such as fecal coliforms, heavy metals, hydrocarbons, and polycyclic aromatic hydrocarbons (PAHs). The aftermath of Hurricane Katrina revealed significant fecal contamination in sediment layers, …

Analysis Of Novel Cannabis Products Labeled As Containing Thc-Jd, Kaelas R. Munger, Carlton C. B. Bone, Killian M. Anreise, Robert M. Strongin

Chemistry Faculty Publications and Presentations

Objective:

THC-JD (tetrahydrocannabioctyl) is a semi-synthetic cannabinoid with an eight-carbon C3 side chain, present in only trace amounts in hemp, requiring synthesis for commercial use. It has been promoted as potentially 19 times more potent than delta-9-THC, consistent with enhanced CB1 receptor affinities for cannabinoids with 7-8 carbon C3 side chains. While one peer-reviewed report identified THC-JD (tetrahydrocannabioctyl) in Japanese online products, an anecdotal report from the U.S. suggests mislabeling. Method: To inform regulatory efforts and consumer awareness and to address these discrepancies, this study chemically analyzed five U.S. commercial cannabis vape products marketed as containing THC-JD. Results: Utilizing GC-MS …

Analogy2kg: An Automatic Pipeline For Deriving Knowledge Graphs From Long-Text Analogies, Kara Combs, Lance E. Champagne, Bruce A. Cox, Christine M. Schubert Kabban, Trevor Bihl, Grace Lemming

Faculty Publications

Analogical reasoning is an increasingly popular, lightweight solution to enable large language model (LLM)-level reasoning without computational complexity. Still, it has yet to be adopted due to its reliance on strictly hand-formatted data. Therefore, we propose Analogy2KG (“Analogy to Knowledge Graph”), as an automatic pipeline that transforms text into a KG format via a fine-tuned version of information extraction (IE) algorithms for long-text analogies. The need to verify that the complex underlying analogical structure of the data is maintained was done via paired samples tests in the creation and validation of this pipeline. Graph density was used to evaluate the …

Addressing The Problems Of Data Variations, Quality, And Scarcity In Training Deep Neural Networks, Jian Sun

Electronic Theses and Dissertations

The performance of deep neural networks (DNNs) is strongly influenced by the characteristics and quality of the underlying datasets. This Ph.D. dissertation addresses three pervasive data challenges-imbalance, quality degradation, and scarcity-that commonly hinder the effectiveness of DNNs in computer vision (CV) and natural language processing (NLP) applications.

Class imbalance remains one of the most frequent causes of degraded model generalization. While Focal Loss effectively mitigates inter-class imbalance by assigning higher weights to minority classes, it struggles with intra-class imbalance, particularly in video datasets where longer clips dominate feature representation. To address this, I implement and utilize …

Secureai: Toward Experiential Security And Privacy Training For Ai Practitioners, Mujtaba Nazari, Eric Chan-Tin, Loretta Stalans, Mohammed Abuhamad

Computer Science: Faculty Publications and Other Works

The rapid adoption of artificial intelligence across industries has outpaced security and privacy training for AI practitioners. This paper presents methods, modules, and findings from an experiential training program designed to address security and privacy challenges in AI systems development and deployment. We conducted two program iterations: a comprehensive 12-workshop series (May-October 2024) and a condensed 6-workshop format (January-February 2025). The program combined expert-led panel sessions with hands-on laboratory activities, engaging 78 participants from diverse professional backgrounds. Evaluation through pre- and post-evaluation surveys and qualitative observations revealed improvements in cybersecurity knowledge and AI security awareness. Participants demonstrated enhanced ability to …

Deep Learning Based Routing And Clustering Approaches For Energy Optimization In 5g Wireless Sensor Networks: A Review, Ghufraan Ali Mohammad Jawad, Ahmed M. Al-Salih

Journal of Intelligent Informatics, Networking, and Cybersecurity

Next-generation Wireless Sensor Networks (WSNs), including 5G and Beyond-5G networks, have made significant progress. However, their development requires re-evaluating intelligent communication approaches to meet the growing demands for higher data transmission rates, more efficient spectral utilization, and reduced energy consumption. Scalability issues related to energy efficiency remain a critical concern for WSNs, which remain integral to the digital revolution. This paper investigates the role of Deep Learning (DL) methods in improving energy efficiency for routing and clustering in 5G WSNs. It is pertinent to note that Deep Q-Networks (DQNs) and their variants have significantly enhanced Cluster Heads (CHs) selection schemes …

Health Effects Of Cadmium And Manganese Exposure On Luteinizing Hormone Levels: A Pilot Study Of Female Infertility, Shatha F. Alhous, Anees A. Al-Hamzawi, Murtadha Sh. Aswood

Al-Bahir

Female infertility occurs for many reasons, some unknown. Exposure to heavy metals is a risk factor that negatively impacts on female reproductive system. Therefore, this study aimed to evaluate the effect of cadmium Cd and manganese Mn on luteinizing hormone LH levels for females’ infertility. This study included a healthy group (n = 20), primary infertile females (n = 40), and secondary infertile females (n = 20) collected from Al-Zahraa hospital in Najaf center, Iraq. The Atomic Absorption Spectroscopy (AAS) was used to measure the cadmium and manganese concentrations and LH levels were measured by a Maglumi 800 from Snibe …

Clinical Prediction Of Posttreatment Migraine Recurrence Using Biofeedback Data: A Machine Learning Framework For Enhanced Patient Stratification And Treatment Monitoring, Shibbir Ahmed Arif, Ferdib-Al-Islam, Mehidy Hasan Sium

School of Computing Faculty Scholarship and Creative Works

Migraine is a complex neurological disorder with significant implications for individual well-being and public health. Predicting migraine occurrences after treatment is crucial for evaluating therapeutic efficacy and enabling personalized care, yet remains largely underexplored. This study proposes a robust machine learning framework to predict posttreatment migraine headache occurrences using real-world headache log data collected from 133 patients undergoing biofeedback therapy. The methodology includes rigorous data preprocessing, outlier removal via the interquartile range (IQR) method, and class imbalance correction through the synthetic minority oversampling technique (SMOTE). A total of 10 classical and a hybrid ensemble machine learning models were developed and …

Understanding Key Lssues In Current Artificial Intelligence Development And Governance, Meng Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

At present, as global artificial intelligence advances vigorously, the industry is engaged in intense discussions about the future direction of its development and governance, where certain consensus has been reached alongside notable divergences and controversies. This study focuses on several key contentious issues in the current development and governance of artificial intelligence, analyzes their inherent logic by combining the laws of scientific and technological progress, and puts forward corresponding solutions. Regarding the potential bubble risk, it emphasizes the need to strengthen the application of artificial intelligence in the real economy to channel and guide its development momentum. For the future …

Comparative Study On Ai Talent Cultivation In China And The United States: Strategies And Recommendations, Lexuan Li, Ke Wen, Wenjie Liu, Wei Shen, Zhenguo Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI), as the core driving force of the new industrial revolution, has become a strategic pillar for enhancing national competitiveness, with the cultivation of AI talent being a decisive factor. This study systematically compares the AI talent cultivation systems of China and the United States from three perspectives: strategic planning, formal education, and practical domains. The findings reveal that the U.S. has continuously and systematically advanced AI talent cultivation plans at the national level, granted institutions substantial autonomy in talent cultivation with deep integration of industry-academia-research collaboration, and established a well-developed AI innovation and entrepreneurship environment. Accordingly, the …