Digital Commons Network^™

Exploring The Thinking Processes Of Female Students In Solving Number Pattern Problems Based On Self-Directed Learning, Lailia Maghfiroh, Yayan Eryk Setiawan, Sikky El Walida

Jurnal Pendidikan Sains

Problem solving in mathematics requires integrating conceptual understanding, procedural knowledge, and logical reasoning. This qualitative exploratory case study investigates the intuitive and analytical thinking processes of two female students with contrasting levels of Self-Directed Learning (SDL) in solving number pattern problems. Data were collected through written problem-solving tasks and semi-structured interviews and analyzed descriptively. In this case study, the student with high SDL tends to use analytical thinking, including systematic data organization, strategic planning, and reflective evaluation, while the student with low SDL tends to rely on intuitive thinking, using rapid pattern recognition and prior experience without systematic verification. These …

Augmented Reality In Physics Learning: Trends, Development Patterns, And Instructional Practices From Systematic Literature, Muhammad Musyaddad, Achmad Samsudin, Duden Saepuzaman, Ahmad Aminudin, Nisaul Afifah

Jurnal Pendidikan Sains

This study examines recent developments in the use of augmented reality (AR) in physics education within the evolving landscape of educational technology. The aim is to provide a comprehensive mapping of research trends, pedagogical approaches, and methodological characteristics of AR-based physics learning. Following PRISMA guidelines, a systematic search across four major databases identified 62 eligible studies published over the past five years. The findings reveal a consistent pattern in which AR is primarily used to support conceptual understanding of abstract physics topics, particularly electricity, mechanics, and magnetism, through inquiry-based and laboratory-oriented learning approaches. These implementations are commonly developed using systematic …

Reading Literacy Research Trends In Science Learning (2015–2025), Siwi Handayani, Siswanto Siswanto, Rina Rahayu

Jurnal Pendidikan Sains

This study aims to analyze research trends in reading literacy within science learning from 2015 to 2025 through a Systematic Literature Review (SLR) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The initial screening of 150 articles from the WoS Core Collection and SINTA databases yielded 29 articles that met the inclusion criteria. The analysis focused on publication dynamics, contributing countries, participant characteristics, research variables, and the science subjects investigated. The findings indicate that research on reading literacy in science learning remains limited and shows fluctuating development. Most publications originate from national journals indexed by SINTA, …

Cognitive Conflict In Iconic Visual Representations: Comparing Conventional And Ai Approaches, Endrayana Putut Laksminto Emanuel, Herfa Maulina Dewi Soewardini, Radhitya Duta Pradana, Sikky El Walida

Jurnal Pendidikan Sains

Background of study: The rapid integration of Artificial Intelligence (AI) in higher education has transformed mathematical problem-solving practices, yet it introduces potential discrepancies in students’ visual understanding, particularly in geometric contexts, leading to commognitive conflicts in visual mediators. Aims and scope of paper: This study aims to analyze and compare the sources of commognitive conflict in iconic visual representations when students solve flat shape problems using conventional methods versus AI-assisted approaches. Methods: A qualitative design was employed involving 20 first-year mathematics education students divided into two groups (AI-assisted and conventional). Data were collected through students’ written work and semi-structured interviews …

A Method To Accelerate Rock Breakage With Soundless Chemical Demolition Agents, Patrick A. Darko, Hani S. Mitri

Journal of Sustainable Mining

Soundless chemical demolition agents (SCDA) are recognized as a safe and sustainable alternative for rock breakage, with the potential to reduce reliance on explosives in mining. However, their mechanical performance is considerably hampered in the extreme cold climate, with slow or no rock fracturing observed. To overcome this deficiency, two methods were developed in recent studies by 1) electric heating the SCDA borehole with high-temperature wire, and 2) increasing the SCDA mixing water temperature. Good results were obtained with both methods. This paper explores the merits of a hybrid approach combining the two methods to further enhance the SCDA efficiency …

Improving Energy Reliability In Mining Areas Through Modelling And Control Of Resonance Phenomena In Solar-Integrated Power Systems, Yurii Papaika, Gennadiy Pivnyak, Maksim Malyshko, Yuliya Pazynich, Natalia Howaniec, Adam Smolinski

Journal of Sustainable Mining

This study addresses the emergency and operational transient processes that arise when integrating large-scale solar power plants (SPPs) into electrical load nodes, particularly under conditions of limited power system capacity. With the increasing penetration of distributed generation in Ukraine's energy landscape, challenges related to electromagnetic compatibility and harmonic resonance are becoming more prominent. The research builds upon the foundational work of leading Ukrainian scientific schools and focuses on the specific resonance phenomena observed during the operation of solar inverters in medium-voltage networks, especially in regions with high industrial and mining Energy demand. A novel algorithm is proposed for determining the …

Wettability Evaluation Via Contact Angle And Data Mining For Enhanced Oil Recovery, Syasya Wadihah Md. Ja’Afar, Bashir Busahmin

Journal of Sustainable Mining

The oil industry has used many enhanced oil recovery (EOR) strategies to increase crude oil production from reservoirs, particularly as production begins to fall. With a significant quantity of hydrocarbon reserves still existing in the reservoirs, improved oil recovery techniques have the potential to increase oil production, with wettability alteration playing a significant role. To date, there has been no comprehensive investigation of wettability in Brunei's sandstone reservoir. The goal of this research is to determine wettability by contact angle measurement and data mining approaches based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. …

Bridging The Cybersecurity Education Gap: The Role Of Open Educational Resources In Supporting Rural Cybersecurity Programs, Brittni Hardie

Theses and Dissertations

This study examines the intersection of cybersecurity education, open educational resources (OER), and rural higher education through a systematic review of current literature and an exploratory survey of rural community college faculty. The purpose of this research, consistent with the approved Institutional Review Board (IRB) protocol, was to understand how OER can be leveraged to design and deliver an affordable, high-quality System Security course within a rural higher-education environment. As cybersecurity workforce shortages continue to grow across the United States, rural institutions face persistent challenges in sustaining high-quality programs due to financial constraints, limited faculty capacity, and rapidly evolving curriculum …

Florence State Prepares For Jet Age Needs With New, Expanded Facilities, Tommy Hill

Documents

News article from The Birmingham News about the expanded facilities at Florence State College

Scientist Floyd Retires At Florence State, Jack Bridges

Documents

Dr. Homer H. Floyd's retirement from Florence State College

Empirical Benchmarks For Interpreting Effect Sizes In Violent Crime Interventions, Kohta James Matsukawa Hansen

Theses and Dissertations

Criminal justice researchers apply Cohen’s (1988) benchmarks to classify effect sizes as small, medium, or large, despite these standards never being meant for broad, decontextualized use (Cohen, 1988; Gies et al., 2024; Goulet-Pelletier & Cousineau, 2018; Lakens, 2013; Milner et al., 2023). Repeatedly doing so may weaken statistical validity, distort findings, and impede effective policymaking. This study introduces the first effect size benchmarks specifically designed for violent crime interventions.

Using a quasi-meta-analysis framework, 1,605 effect sizes from 104 violent crime intervention studies from the CrimeSolutions clearinghouse were converted to Cohen’s d. Three new discrete benchmarking methods were created using a …

Quantitative Analysis Of Cholera Dynamics And The Impact Of Integrated Control Measures., Kazeem Abidoye Odeyemi, Mutairu Kayode Kolawole

Tanzania Journal of Science

A comprehensive mathematical evaluation of cholera dynamics, emphasizing the crucial role of optimal control strategies which include vaccination, treatment in endemic region and environmental hygiene in controlling the disease spread in Western and Mid-Eastern regions of African setting. Through analysis of the local and global stability of the model and assessing the reproduction number (��0), this research highlights how targeted interventions can influence transmission rates of cholera disease. Sensitivity analysis identifies key parameters driving cholera dynamics, underlining the urgency of timely and adequate intervention. Utilizing the innovative Homotopy Perturbation Method for the numerical simulation, the study explores the interplay between …

Theory And Applications Of Natural And Nature-Based Solutions In Estuarine Environments, Elizabeth L. Royer

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on the theory and application of Nature-based Solutions (NbS), through three unique case studies. This study explores how the broad and interdisciplinary NbS framework can be applied to mitigate common coastal hazards. This framework considers the environment, economics, and social wellbeing, equally in the design and implementation of a solution. The current paradigm, working against nature, which is commonly seen in more passive approaches to hazard mitigation, like grey infrastructure (e.g. a seawall to protect a shoreline against wave action) does not accommodate the extremely dynamic environment. As perspectives shift to “working with nature” NbS can be …

Ais26s: Genai And Llms For Cybersecurity, Political Sciences, Transportation Security, And Resiliency, Latifur Khan

Paul English Applied Artificial Intelligence (AI) Institute Publications

This presentation examines the role of generative artificial intelligence (GenAI) and large language models (LLMs) in addressing complex challenges across cybersecurity, political science, and transportation security. Dr. Latifur Khan discusses how advanced AI methods can be applied to threat detection, data analysis, and decision-making in high-risk and data-intensive environments. The talk highlights interdisciplinary applications of LLMs, emphasizing their ability to extract insights from large-scale data, improve system resilience, and support intelligent infrastructure. Emerging research directions and practical implications for real-world deployment are also discussed.

Lay Summarization For Medical Patents, Manav Raja Vinotha

Miners Solving for Tomorrow Research Conference

Technical documents, such as medical patents, are often inaccessible to non-expert audiences due to specialized terminology. This paper presents a multi-agent system for automated lay summarization that optimizes both quality and computational efficiency through strategic task decomposition. The proposed pipeline utilizes four specialized stages: autonomous medical entity retrieval from the Unified Medical Language System (UMLS), context distillation into lay conceptualizations, and a structured writer-critic loop for iterative refinement. By distributing cognitive load across specialized agents, this architecture effectively leverages smaller, cost-effective language models while maintaining the performance of SOTA LLMs. Evaluated against single-agent baselines using an LLM as a Judge …

A Wearable Mxene-Based Sweat Sensor For Real-Time Monitoring Of Inflammatory Biomarkers, Ariel Pilger

Miners Solving for Tomorrow Research Conference

Many conventional biosensing approaches rely on invasive sampling or bulky benchtop instrumentation, limiting their use in continuous and portable applications. This project focuses on the development of wearable sweat-based biosensors that enable non-invasive, continuous, and portable monitoring of physical, chemical, and biological markers. The system will be designed to target markers present in sweat and transduce the biochemical interactions into measurable electrical signals. These signals will be processed through integrated electronics to produce clear, interpretable outputs for users and medical professionals. Supporting circuitry including filters, amplifiers, and an independent power supply will be implemented as necessary to ensure signal accuracy, …

Vortex Generation In Dipolar Supersolids, Jacob Harl

Miners Solving for Tomorrow Research Conference

Ultracold atomic gases offer highly tunable platforms for exploring complex quantum many-body phenomena. In addition, dipolar quantum gases of magnetic atoms featuring long-range anisotropic interactions are exquisite systems for realizing exotic phases-of-matter in an experimentally controlled way. A prototypical example is the supersolid state which simultaneously exhibits frictionless flow of superfuids and crystalline density modulation of solids. In this realm, we investigate the dynamical generation of vortex topological defects by imprinting a suitable phase jump in quasi-two-dimensional dipolar supersolids. This protocol enables the nucleation of a dark soliton which consecutively becomes unstable via the eponymous snake instability due to its …

Ai Models As Cultural Beings: Investigating Ai Cultural Biases And The Impact Of Cultural Alignment On Human-Ai Creative Collaboration, Choon Ngee Tan, Meng Han, Roy Y. J. Chua, Chi-Ying Cheng

Research Collection Lee Kong Chian School Of Business

Existing research on AI cultural biases predominantly focuses on Western models, overlooking critical gaps in non-Western models. We conduct a comparative analysis of AI models – ChatGPT (U.S. developed) and ErnieBot (China developed) – from different cultures to investigate how corresponding cultural biases manifest in their outputs. Additionally, we examine how cultural alignment between human users and AI models impacts their collaborative creative performance and the underlying psychological mechanisms. In Study 1, multi-choice prompt with zero-shot technique was used to evaluate cultural biases in four widely used AI models – ChatGPT-3.5/4, ErnieBot-3.5/4 – comparing their responses to established cultural psychometric …

Conflogger: Enhance Systems’ Configuration Diagnosability Through Configuration Logging, Shiwen Shan, Yintong Huo, Yuxin Su, Zhining Wang, Dan Li, Zibin Zheng

Research Collection School Of Computing and Information Systems

Modern configurable systems offer customization via intricate configuration spaces, yet such flexibility introduces pervasive configuration-related issues such as misconfigurations and latent softwarebugs. Existing diagnosability supports focus on post-failure analysis of software behavior to identify configuration issues, but none of these approaches look into whether the software clue sufficient failure information for diagnosis. To fill in the blank, we propose the idea of configuration logging to enhance existing logging practices at the source code level. We develop ConfLogger, the first tool that unifies configuration-aware static taint analysis with LLM-based log generation to enhance software configuration diagnosability. Specifically, our method 1) identifies …

Propaganda Ai: An Analysis Of Semantic Divergence In Large Language Models, Nay Myat Min, Long H. Pham, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large language models (LLMs) can exhibit concept-conditioned semantic divergence: common high-level cues (e.g., ideologies, public figures) elicit unusually uniform, stance-like responses that evade token-trigger audits. This behavior falls in a blind spot of current safety evaluations, yet carries major societal stakes, as such concept cues can steer content exposure at scale. We formalize this phenomenon and present RAVEN (Response Anomaly Vigilance), a black-box audit that flags cases where a model is simultaneously highly certain and atypical among peers by coupling semantic entropy over paraphrastic samples with cross-model disagreement. In a controlled LoRA fine-tuning study, we implant a concept-conditioned stance using …

Where Did It Go Wrong? Attributing Undesirable Llm Behaviors Via Representation Gradient Tracing, Zhe Li, Wei Zhao, Yige Li, Jun Sun

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their deployment is frequently undermined by undesirable behaviors such as generating harmful content, factual inaccuracies, and societal biases. Diagnosing the root causes of these failures poses a critical challenge for AI safety. Existing attribution methods, particularly those based on parameter gradients, often fall short due to prohibitive noisy signals and computational complexity. In this work, we introduce a novel and efficient framework that diagnoses a range of undesirable LLM behaviors by analyzing representation and its gradients, which operates directly in the model's activation space to provide a semantically meaningful signal linking …

Llmqua: Practical Backdoor Injection On Large Language Model Quantization, Xiangxiang Chen, Peixin Zhang, Jun Sun, Jin Song Dong, Wenhai Wang, Jingyi Wang

Research Collection School Of Computing and Information Systems

Quantization is widely used to enable local deployment of large language models (LLMs) on resource-constrained devices. Recent work (e.g., QuRA) shows quantization can be exploited via rounding manipulation to implant backdoors. However, such an attack has been evaluated only on small models and does not directly apply to LLMs due to three key constraints: (1) limited poisoning data from small, task-agnostic calibration sets; (2) layer-wise quantization restricting adversarial access to global representations; and (3) lack of gradient access in quantization pipelines, blocking gradient-based attacks.We propose LLMQuA, a practical quantization-phase backdoor attack tailored to the LLM setting. LLMQuA (i) injects backdoors …

Prompting Frameworks For Large Language Models: A Survey, Xiaoxia Liu, Jingyi Wang, Jun Sun, Xiaohan Yuan, Guoliang Dong, Peng Di, Wenhai Wang, Dongxia Wang

Research Collection School Of Computing and Information Systems

Since the launch of ChatGPT, a powerful AI Chatbot developed by OpenAI, large language models (LLMs) have made significant advancements in both academia and industry, bringing about a fundamental engineering paradigm shift in many areas. While LLMs are powerful, it is also crucial to best use their power where “prompt” plays a core role. However, the booming LLMs themselves, including excellent APIs like ChatGPT, have several inherent limitations: (1) temporal lag of training data, and (2) the lack of physical capabilities to perform external actions. Recently, we have observed the trend of utilizing prompt-based tools to better utilize the power …

Bridging Bug Localization And Issue Fixing: A Hierarchical Localization Framework Leveraging Large Language Models, Jianming Chang, Xin Zhou, Lulu Wang, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Automated issue fixing is a critical task in software debugging and has recently garnered significant attention from academia and industry. However, existing fixing techniques predominantly focus on the repair phase, often overlooking the importance of improving the preceding bug localization phase. As a foundational step in issue fixing, bug localization plays a pivotal role in determining the overall effectiveness of the entire process. To enhance the precision of issue fixing by accurately identifying bug locations in large-scale projects, this paper presents BugCerberus, the first hierarchical bug localization framework powered by three customized large language models. First, BugCerberus analyzes intermediate representations …

A Guide To Environmentally Sustainable Filmmaking In South Carolina, Christina Vera Dobrowolski

Senior Theses

The entertainment industry has a massive environmental impact. This information is not new in the filmmaking world, as multiple mainstream efforts exist to curb the material-intensive production of live action films and TV. One of these efforts is the use of voluntary sustainable guidelines, which serve as tools that outline sustainable best practices and resources that can help achieve them. However, current guidelines often lack practicality— focusing more on altruism rather than core economic factors that ultimately drive filmmaking. This project aims to address that gap for filmmakers in South Carolina by offering a website featuring attainable, state-specific resources for …

Fingerprinting Voice Commands Of Vpn-Protected Smart Speakers, Xiaoguang Guo, Keyang Yu, Qi Li, Dong Chen

Computer Science Faculty Research and Publications

Extensive recent research has shown that it is surprisingly easy to infer Amazon Alexa voice commands over their network traffic data. To prevent these traffic analytics (TA)-based inference attacks, smart home owners are considering deploying virtual private networks (VPNs) to safeguard their smart speakers. In this work, we design a new machine learning-powered attack framework—VoiceAttack that could still accurately fingerprint voice commands on VPN-encrypted voice speaker network traffic. We evaluate VoiceAttack under 5 different real-world settings using Amazon Alexa and Google Home. Our results show that VoiceAttack could correctly infer voice command sentences with a Matthews Correlation Coefficient (MCC) of …

Designing A Novel Inhibitor For Cobalamin-Independent Methionine Synthase, Damon Virgo

Undergraduate Honors Papers

Microbial infections are some of the leading causes of death in the population at large, and antimicrobial-resistant “superbugs” have become a growing issue, so there is a great need to explore new and alternative pathways for fighting these diseases. One method for doing this is by exploring Methionine synthase (MetSyn), which plays a critical role in cellular one-carbon metabolism, catalyzing the methylation of homocysteine to form methionine, an essential amino acid required for protein synthesis and methyl group transfer reactions. Key differences between the B12-independant fungal MetSyn enzyme and the B12-dependant mammalian form can allow for selective targeting of the …

Navigating Sustainable Burial In Southern Nevada, Kristen Gibson

Calvert Undergraduate Research Awards

Navigating Sustainable Burial in Southern Nevada investigates the accessibility of environmentally conscious end-of-life options for individuals throughout the region. Sustainable burial practices, including green burial, water cremation, and natural organic reduction, provide alternatives to traditional burial and cremation methods. Research was conducted through an analysis of Nevada statutes, local reporting, funeral service resources, and public discussions related to sustainable burial. The research revealed that although Nevada expanded legal recognition of sustainable burial by legalizing natural organic reduction, public access to clear information and available services remains limited. These findings informed the creation of a choose-your-own-adventure booklet designed to translate legal …

The Kaczmarz Algorithm In Hilbert C∗-Modules, Daniel Alpay, Chad Berner, Eric S. Weber

Mathematics and Statistics Faculty Research & Creative Works

The Kaczmarz algorithm in Hilbert spaces is a classical iterative method for stably recovering vectors from inner product data. In this paper, we extend the algorithm to the setting of Hilbert C^∗-modules and establish analogues of its effectiveness in both finite-dimensional and stationary cases. Consequently, we demonstrate that continuous families of elements in a Hilbert space can be uniformly recovered using the Kaczmarz algorithm. Additionally, we develop a normalized Cauchy transform for continuous families of measures and use it to provide sufficient conditions under which standard frames in Hilbert C(X)-modules can be generated by the Kaczmarz algorithm and …

Policy Design, And The Repeal Of Ghana’S Emissions Levy Act: A Multiple Streams Framework Analysis, Anthony Amotoe-Bondzie

Collected Student Papers

Ghana’s 2023 Emissions Levy Act (Act 1112) imposed charges on large industrial emitters and vehicles, intending to internalize pollution costs and raise government revenue. This study applies to Kingdon’s Multiple Streams Framework (MSF) to explain the policy’s rapid repeal. Drawing on legislative texts, official reports, and media accounts, it categorizes influences into the problem (rising living costs), policy (design features), and politics (stakeholder mobilization) streams. Key findings reveal that the levy’s mixed design, a per-ton CO₂ fee on industry combined with a flat vehicle ownership tax, undermined its climate rationale and appeared more as a revenue grab. Implementation gaps exacerbated …