Physical Sciences and Mathematics Commons^™

Insights On Ai-Supported Uncrewed And Autonomous Systems Education, Brent A. Terwilliger Ph. D, John Faraca

Publications

Artificial Intelligence (AI) related technology is reshaping the educational experience in programs focused on uncrewed and autonomous systems, aviation, robotics, and aerospace, with growing implications for workforce readiness and cross-sector innovation. Early survey data, capturing student, educator, and employer perspectives, reveals that AI-supported tools are notably changing student engagement, communication, and skills development. Initial indications underscores the importance of AI proficiency and technological familiarity in hiring and workforce development, particularly in technical and operational roles. Key areas of focus include the use of AI to strengthen outreach and interactivity; enrich instruction through intelligent simulations; inform curricular improvements using data analytics; …

U.S. Water Infrastructure: Diving Into Needs And Recent Policy Developments, Joseph Kane

Brookings Scholar Lecture Series

As part of the Brookings Scholar Lecture Series, Brookings Mountain West presents a lecture titled, "U.S. Water Infrastructure: Diving Into Needs and Recent Policy Developments" by Brookings fellow in Brookings Metro, Joseph Kane. Whether providing drinking water, treating wastewater, or managing stormwater, water infrastructure in the United States spans a wide range of human-made and natural systems. In turn, the enormous scale and variety of these systems is leading to an array of challenges: aging and vulnerable infrastructure; fragmented governance and siloed planning across different geographies; a lack of financial, technical, and managerial capacity; difficulties adapting to new industrial and …

Large Language Models As Information Providers For Appropriate Antimicrobial Use: Computational Text Analysis And Expert-Rated Comparison Of Chatgpt, Claude And Gemini, Marcello Di Pumpo, Maria Rosaria Gualano, Danilo Buonsenso, Francesca Raffaelli, Daniele Donà, Vittorio Maio, Patrizia Laurenti, Walter Ricciardi, Leonardo Villani

College of Population Health Faculty Papers

OBJECTIVES: Antimicrobial resistance is a critical public health threat. Large language models (LLMs) show great capability for providing health information. This study evaluates the effectiveness of LLMs in providing information on antibiotic use and infection management.

METHODS: Using a mixed-method approach, responses to healthcare expert-designed scenarios from ChatGPT 3.5, ChatGPT 4.0, Claude 2.0 and Gemini 1.0, in both Italian and English, were analysed. Computational text analysis assessed readability, lexical diversity and sentiment, while content quality was assessed by three experts via DISCERN tool.

RESULTS: 16 scenarios were developed. A total of 101 outputs and 5454 Likert-scale (1-5) scores were obtained …

Topology Across Scales On Heterogeneous Cell Data, M. Torras-Pérez, Iris Yoon , '13, P. Weeratunga, L.-P. Ho, H. M. Byrne, U. Tillmann, H. A. Harrington

Mathematics & Statistics Faculty Works

Multiplexed imaging allows multiple cell types to be simultaneously visualised in a single tissue sample, generating unprecedented amounts of spatially-resolved, biological data. In topological data analysis, persistent homology provides multiscale descriptors of “shape" suitable for the analysis of such spatial data. Here we propose a novel visualisation of persistent homology (PH) and fine-tune vectorisations thereof (exploring the effect of different weightings for persistence images, a prominent vectorisation of PH). These approaches offer new biological interpretations and promising avenues for improving the analysis of complex spatial biological data especially in multiple cell type data. To illustrate our methods, we apply them …

Intrinsic Defects (Vacancies And Antisites) In Neutron Irradiated Cdsip2 Crystals, Timothy D. Gustafson, Elizabeth M. Scherrer, Nancy C. Giles, Kevin T. Zawilski, Peter G. Schunemann, Jonathan E. Slagle, Kent L. Averett, Larry E. Halliburton

Faculty Publications

Cadmium silicon phosphide (CdSiP₂) is a nonlinear optical material widely used in optical parametric oscillators. Intrinsic defects (vacancies and antisites) are responsible for unwanted broad optical absorption bands in these crystals that degrade the performance of the devices. In the present work, optical absorption and electron paramagnetic resonance (EPR) spectra are acquired (at room temperature and 12 K, respectively) from a neutron-irradiated CdSiP₂ crystal. After the irradiation, the crystal is highly absorbing from the band edge near 600 nm to beyond 1.3 μm because of overlapping defect-related absorption bands. Heating to 550 °C removes nearly all the …

Turning Waste Into Fertilizer: Aloe Vera Leaf Shavings Improve Plant Growth And Support Soil Fertility In Organic Systems, Isaiah Edward Jaramillo, Carine Cocco, James Jihoon Kang, Chu-Lin Cheng, Engil Pereira

School of Earth, Environmental, & Marine Sciences Faculty Publications

The Aloe vera industry discards large amounts of outer leaf tissue (“shavings”), creating an opportunity to repurpose this byproduct as a sustainable fertilizer. This study evaluated whether aloe shavings can serve as a plant-based alternative to compost in organic Aloe vera production. A field trial in the Lower Rio Grande Valley of Texas tested three treatments: aloe shavings (applied to supply 39 kg N ha−1), organic compost (39 kg N ha−1), and a non-fertilized control. Laboratory incubations further assessed nitrogen mineralization and microbial respiration. Aloe shavings significantly enhanced vegetative growth: leaf number increased from 5.7 to 12.3 leaves per plant …

Park Reclamation: Tackling Invasive Species At The Oconee River Greenway In Milledgeville, Ga, Christine Mutiti

Academic Affairs

This project focused on the ecological restoration of a kudzu-invaded section of the Oconee River Greenway while integrating high-impact learning into the Environmental Methods and Measurements (ENSC 2000) course at GCSU. Kudzu (Pueraria montana), a Category I invasive species, has significantly disrupted native plant communities at the site, making it an ideal setting for applied learning and ecological intervention. The project engaged students in authentic environmental research by involving them in every stage of the process including problem assessment, methodology design, field implementation, data collection, and reflection.

Students initiated the first phase of invasive plant removal using mechanical …

Clark Tailings Consolidated Waste Management Area (Ctcwma) Site Investigation Draft Data Summary Report 2024 Surface Water, Groundwater, And Soil Characterization, Woodard & Curran

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Graphrag-Enabled Local Large Language Model For Gestational Diabetes Mellitus: Development Of A Proof-Of-Concept, Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma

All Works

Background: Gestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations. While generative artificial intelligence (AI) and large language models (LLMs) have shown promise in health care, their application in GDM management remains underexplored. Objective: This study aimed to investigate whether retrieval-augmented generation techniques, when combined with knowledge graphs (KGs), could improve the contextual relevance and accuracy of AI-driven clinical decision support. For this, we developed and validated a graph-based retrieval-augmented generation (GraphRAG)–enabled local LLM as a clinical support tool for GDM management, assessing its performance against open-source LLM …

Microclimatic Conditions In Shulgan-Tash Cave (Southern Ural, Russia): Implications For The Preservation Of Eastern Europe’S Largest Paleolithic Art Collection, Olga Chervyatsova, Rayan Akhmedyanovb, Nailya Saifullina, Ludmila Kuzmina, Mihael Kotov, Yuri Dublyansky

International Journal of Speleology

This study presents the results of a long-term investigation of the microclimatic conditions in Shulgan-Tash Cave (Southern Urals, Russia), which hosts the largest assemblage of Upper Paleolithic paintings (ca. 16.3–19.6 ka) in Eastern Europe. The cave exhibits two distinct seasonal ventilation regimes – winter and summer – differentially influence its two levels of galleries. In the Lower Level, which contains 83% of the images, near-atmospheric CO₂ concentrations persist year-round, and temperature variations associated with seasonal infiltration events are minimal (1.0–1.3°C). By contrast, the Upper Level, including the decorated Hall of Paintings, is affected by downdrafts of CO₂-rich …

Confinement-Induced Resonances For The Creation Of Quasi-One-Dimensional Ultracold Gases Of Alkali–Alkaline-Earth Dimers, Lorenzo Oghittu, Premjith Thekkeppatt, Nirav P. Mehta, Seth T. Rittenhouse, Klaasjan Van Druten, Florian Schreck, Arghavan Safavi-Naini

Physics and Astronomy Faculty Research

We theoretically investigate the role of confinement-induced resonances (CIRs) in low-dimensional ultracold atomic mixtures in the formation of weakly bound dimers. To this end, we examine the scattering properties of a binary atomic mixture confined by a quasi-one-dimensional (quasi-1D) potential. In this regime, the interspecies two-body interaction is modeled as an effective 1D zero-range pseudopotential, with a coupling strength g1D derived as a function of the three-dimensional scattering length a. This framework enables the study of CIRs in harmonically confined systems, with particular attention paid to the case of mismatched transverse trapping frequencies of the two atomic species. Finally, we …

Cv: Teresa Bixby (Chemistry), Teresa Bixby

Chemistry Department Faculty Curricula Vitae

No abstract provided.

Electron Yields Of Lunar Regolith Simulant Layers, Christopher Vega, Matthew Robertson, Thomas Keaton, Heather Allen, Jr Dennison

Journal Articles

The charging of bulk lunar regolith has been recognized since the Apollo era as an immediate and critical issue facing our return to the moon. Accurate electron yield (EY) measurements of bulk highly insulating granular materials—which largely determine how such particles charge through interactions with space environments—are lacking due to many experimental complexities that have led to a critical knowledge gap. Such knowledge is essential for addressing fundamental science and myriad important lunar applications and simulations related to lunar dust and regolith electrostatic charging. The few prior EY studies of lunar dust were limited due to severe charging effects and …

Cloud Seeding: Enhancing Winter Snowpack To Bolster Utah's Water Supply, Lily Lambert, Kendall Becker, Binod Pokharel, Scott Hotaling

All Current Publications

As Utah’s climate warms, the state is working to combat the decline in winter snowpack caused by rising temperatures. Cloud seeding is a safe technology that, under the right conditions, can boost snowfall. In this fact sheet, we describe what cloud seeding is, where it is currently used in Utah, and the safety measures and management practices that ensure its responsible use.

Eutrophication, Hope Northagen, Sarah Erwin, Ethan Gilliam

All Current Publications

Eutrophication refers to the ecological state of a water body after excessive nutrient pollution. The external introduction of nutrients (primarily nitrogen and phosphorus) accelerates the growth and reproduction of plants, algae, and microbes. This fact sheet explains where eutrophication comes from, its impacts, how it is treated, and what you can do to help restore and protect our lakes, rivers, and oceans.

Barriers To Machine Learning Adoption In Regulated Electric Utilities, Donald R. Shiflet Jr.

USF Tampa Graduate Theses and Dissertations

Machine learning (ML) technologies have the potential to revolutionize regulated electric utilities by improving operational efficiency, enabling predictive maintenance, and optimizing energy management. Despite these advantages, the adoption of ML in this sector lags other industries due to technical, organizational, and regulatory barriers. This research, grounded in the Technology-Organization-Environment (TOE) framework, explores these barriers to uncover actionable solutions for integration. The study identifies key challenges, including explainability, cybersecurity, workforce resistance, and regulatory ambiguity to ML adoption in electric utilities. Utilizing an exploratory qualitative methodology, this approach integrates insights from the literature and industry interviews to rank barriers by frequency, severity, …

Re: Butte Priority Soil Operable Unit (Bpsou) Final Insufficiently Reclaimed Sites Field Sampling Plan (Fsp): Bres No. 91 – Robert Emmett Dump, Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Re: Butte Priority Soil Operable Unit (Bpsou) Final Bres No.94 – Rialto Dump Reclamation Improvement (Ri) Field Sampling Plan (Fsp), Mike Mcanulty

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Editorial Preview For October 2025 Issue, Su-Cheng Haw, Hui-Ngo Goh

Journal of Informatics and Web Engineering

The October 2025 issue of the Journal of Informatics and Web Engineering (JIWE) concludes the journal’s triannual publication cycle and precedes its transition to a quarterly schedule commencing in 2026. This issue presents 21 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Software Engineering, Recommender Systems, Cybersecurity, and Web Technologies. The collection reflects JIWE’s ongoing effort to advance research in the informatics and web engineering domain. A special thematic section, guest-edited by Prof. Ts. Dr. Hairulnizam Bin Mahdin, centers on “AI-Enhanced Computing and Digital Transformation”. The featured studies examine …

Pavement Distress Analysis In Malaysia: A Novel Deepseg-Cracknet Model For Crack Detection And Characterization Using Real-World Data, Arselan Ashraf, Ali Sophian, Teddy Surya Gunawan, Syed Asif Ahmad Qadri

Journal of Informatics and Web Engineering

Pavement distress analysis plays a big role in keeping roads in good shape, especially in busy spots like Selangor and Kuala Lumpur, where heavy traffic and tropical weather make them wear out fast. This work introduces DeepSeg-CrackNet, a fresh hybrid deep learning model that uses Deep Gradient ResNet to spot cracks and a Residual block with a Modified Attention Mechanism to sort them into types, making it simpler to detect and label pavement damage. The model was trained on real data collected from Malaysian roads, with the CRACK500 dataset added in to cover more situations, and captured using a GoPro …

Zero Trust - The Future Of Cybersecurity, Danial Haider, Shougfta Mushtaq, Hasnat Ali, Mazliham Mohd Su’Ud

Journal of Informatics and Web Engineering

The recent Zero-Trust Architecture (ZTA) is progressively adopted to the develop network security by assuming no implicit trust within or outside an organization’s boundary. Though, ZTA faces substantial challenges in detecting sophisticated and developing cyber threats, particularly due to its trust on traditional security mechanisms that struggle to manage internal threats and sophisticated attack techniques. To report these shortcomings, the proposed study discovers the combination of advanced machine learning (ML) and deep learning (DL) performances to improve the anomaly detection proficiencies within ZTA environments. The study develops the CICIDS2017 dataset, which contains diverse and realistic network traffic patterns, to assess …

The Role Of Generative Ai In E-Commerce Recommender Systems: Methods, Trends And Insights, Kai-Ze Liau, Heru Agus Santoso

Journal of Informatics and Web Engineering

Recommender systems have existed for decades, shaping how people consume digital content, receive information, and engage in day-to-day activities, among others. Undoubtably, recommender systems also play a crucial role in e-commerce applications as well, with industry players like Amazon, AliBaba, eBay using recommender systems within their ecosystems to give suitable and value-driven insights. However, recommender systems face some main concerns such as data sparsity, cold-start problems and so on. As a result, research is currently ongoing to solve these issues and provide high-quality recommendations to consumers. This review aims to identify prevailing gaps surrounding these issues by analysing existing research …

Training The Brain: A Machine Learning Approach To Predicting Wellbeing Through Intentional Thought Pattern Modification, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung

Journal of Informatics and Web Engineering

This study provides a quantitative framework for wellbeing outcome prediction through intentional cognitive pattern alteration. We demonstrated 81.67% accurate prediction of wellbeing states, in a three-level classification (Low, Medium, High), using a Random Forest classifier with 16 features from psychological, physiological, and behavioural metrics. Our model singles out the gratitude cultivation (21.3%) and peace duration (23.7%) as the strongest predictors of positive well-being outcomes, which provides empirical support to traditional approaches of cognitive training with empirical evidence. Analysis of 1,000 synthetic cases shows that consistent practice of positive thought patterns over 3-6 months can strongly shift wellbeing states, with key …

Hybrid-Based Movie Recommender System: Techniques, Case Studies, Evaluation Metrics, And Future Trends, Cheng-Yung Lai, Noramiza Hashim, Lucia Dwi Krisnawati

Journal of Informatics and Web Engineering

The necessity for sophisticated recommender systems in the movie recommendation sphere has become particularly pronounced, generating a more personalized movie recommendation due to people nowadays who like to watch movies online. Efficient recommender systems make use of advanced machine learning (ML) techniques in the pursuit of accurate and meaningful recommendations. This paper endeavours to give a comprehensive overview of technologies known as recommender systems, concentrating on ML methods found at the base. Different strategies have been applied in this work, which include collaborative filtering (CF), content-based filtering (CB), hybrid approaches, Generative AI and so on. The merits and demerits of …

Hybrid Filtering For Personalized And Health-Conscious Recipe Recommendations In Unieats, Hui Lek Liew, Xinying Chew, Khai Wah Khaw, Shiuh Tong Lim

Journal of Informatics and Web Engineering

This research paper introduces UniEats, a recipe recommendation website designed to help university students better organize their meals and adopt healthier eating habits. The system aims to address common challenges students face, including time constraints, limited cooking skills, and insufficient nutritional awareness, which often lead to unhealthy food choices. At the core of UniEats is its recommendation engine, which employs a hybrid filtering approach, combining content-based and collaborative filtering techniques to provide personalized recipe suggestions based on users' dietary preferences, rating history, and recipe attributes. UniEats offers a range of features, including recipe search, weekly meal planning, nutritional analysis through …

Implementation Of Lightweight Machine Learning Models For Real-Time Text Classification On Resource-Constrained Devices, Marwah Zaid Mohammed Al-Helali, Naveen Palanichamy, K. Revathi

Journal of Informatics and Web Engineering

This paper addresses the growing need for implementing intelligent Natural Language Processing (NLP) systems on low-power, memory-limited devices such as Raspberry Pi, mobile phones, and IoT edge hardware. As edge computing and smart devices proliferate, there is an urgent need for more advanced NLP technology that does not require constant cloud access and is efficient in computing and provides results in real time. While deep learning and cloud-based models typically offer high text-classification accuracy and have demonstrated exceptional performance across a range of NLP tasks, they are often too resource-intensive for real-time deployment in constrained environments. To overcome these limitations, …

Review On Secure And Efficient Iot-Based Healthcare System With The Integration Of Machine Learning And Firewalls, Muhammad Awais, Syeda Samar Fatima, Jawaid Iqbal

Journal of Informatics and Web Engineering

The integration of the Internet of Things (IoT) into healthcare would mean a revolutionized approach in patient monitoring, diagnosis, and treatment, making this quite some development in healthcare delivery. This review has focused on how the integration of IoT with Machine Learning (ML) and stringent security measures tackle the challenging situation of data privacy and cyber threats in healthcare. Current methodologies point toward how essential advanced sensors, cloud computing, and wireless technologies for IoT-based healthcare systems necessary to secure patient data. patient record kept in files and now forward to the cloud database system so that in any case of …

Addressing Iot Security Challenges Through Advanced Machine Learning And Encryption, Ahmad Aziem Khushairi Anuar, Ahmad Anwar Zainuddin, Ahmad Adlan Abdul Halim, Dek Rina

Journal of Informatics and Web Engineering

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main …

Research Article Recommender Systems: A Comprehensive Review Of Models, Approaches And Evaluation Metrics, Sir-Yuean Lim, Noramiza Hashim, Lanh Le Thanh

Journal of Informatics and Web Engineering

With the advent of the current digital era, individuals across the developed world are commonly equipped with devices that can access vast amounts of information at their fingertips. What was considered an impossible feat was realized through remarkable technological advancements. This positive transformation has had a profound impact on education, where traditional knowledge management, such as libraries, are no longer a primary determinant of a student’s academic success. Instead, it has been replaced by the internet as a medium for learning, practicing, and topic exploration. However, the sheer volume of the ever-increasing information available online can easily overwhelm a user, …

Optimised Heterogeneous Handover In Mobile Ipv6 Using Enhanced Predictive Fast Proxy With Media Independent Handover (Mih) Support, Aliyu Aminu Abdulhadi, Nura Mukhtar

Journal of Informatics and Web Engineering

In wireless networks, handover performance is essential for enabling real-time traffic applications. Long handover delays make it impossible for a Mobile Node (MN) to send and receive data packets, which is very undesirable for real-time applications like video conferencing and VoIP. Therefore, in order to guarantee better handover performance, decreasing handover duration is crucial. The Internet Engineering Task Force (IETF) has standardized Fast Proxy Mobile IPv6 (FPMIPv6) as an enhancement to the novel Proxy Mobile IPv6 (PMIPv6) to attain better handover performance. FPMIPv6 functions in two modes: predictive and reactive, using a link-layer triggering mechanism. The predictive mode uses link-layer …