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Full-Text Articles in Physical Sciences and Mathematics

Metarag: Identifying Website Owner Using Meta-Path-Guided Dynamic Graph Retrieval-Augmented Generation, Cheng Tu, Yunshan Ma, Bingyang Guo, Qianyu Li, Yang Li, Min Zhang, Fan Shi, Xiang Wang Jul 2028

Metarag: Identifying Website Owner Using Meta-Path-Guided Dynamic Graph Retrieval-Augmented Generation, Cheng Tu, Yunshan Ma, Bingyang Guo, Qianyu Li, Yang Li, Min Zhang, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to link websites to their real-world owners, which is crucial for credibility assessment and information provenance in information retrieval and vital for applications in cybersecurity, Internet governance, and digital regulation. Existing approaches for website owner identification primarily rely on querying infrastructure registration records or analyzing webpage content. However, these methods often fail due to incomplete or outdated registration records and sparse webpage content. We observe that inter-website relationships, derived from shared infrastructure data such as primary domains, IP blocks, and geolocations, can provide valuable but underutilized ownership cues. To exploit this insight, we propose MetaRAG, a …

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Stprompt++: Prompting Vision-Language Models For Weakly Supervised Video Anomaly Detection And Fine-Grained Localization, Peng Wu, Chengyu Pan, Guansong Pang, Xiangteng He, Zhiwei Yang, Peng Wang, Yanning Zhang Jul 2028

Stprompt++: Prompting Vision-Language Models For Weakly Supervised Video Anomaly Detection And Fine-Grained Localization, Peng Wu, Chengyu Pan, Guansong Pang, Xiangteng He, Zhiwei Yang, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Traditional weakly supervised video anomaly detection (WSVAD) tasks typically rely on coarse-grained frame-level labels for training. Although this approach reduces annotation costs, it results in weak semantic understanding and spatial localization capabilities due to the absence of fine-grained annotations, hindering precise pixel-level anomaly detection and localization. Thanks to the success of vision-language models (VLMs), e.g., CLIP, recent approaches leveraging large VLMs focus on exploiting their strong semantic understanding capabilities, but they typically feed only keyframes or short video segments into the models, without supplying sufficient prior contextual information (e.g., contextual frames around anomalies, zoomed-in anomaly regions, and detailed anomaly descriptions), …

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Purification Of Protoporphyrin Ix Using The Fplc, Ken Overway, Laura Narby May 2027

Purification Of Protoporphyrin Ix Using The Fplc, Ken Overway, Laura Narby

Honors Projects

Protoporphyrin IX (PPIX) is the main pigment in brown eggshells, and it is embedded in the shell gland of hens during the final stages of the egg-making process. This natural dye can be used as a substitute for organic sensitizers in dye-sensitized solar cells (DSSCs). One benefit of PPIX is that it is a natural resource and has a high absorption coefficient in the visible and near-infrared region (NIR). Purified PPIX is extremely helpful in dye-sensitized solar cells because it improves electron injection efficiency, can be chemically modified to improve its absorption efficiency, and can be mixed with nanomaterials such …

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Ai Failures In The Eyes Of The Downstream Developer: A First Look At Concerns, Practices, And Challenges, Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin, James C. Davis, Christoph Treude Feb 2027

Ai Failures In The Eyes Of The Downstream Developer: A First Look At Concerns, Practices, And Challenges, Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin, James C. Davis, Christoph Treude

Research Collection School Of Computing and Information Systems

With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with lower training cost. However, their adoption also introduces failure modes such as data leakage and biased outputs, that may require careful handling by downstream developers. While previous research has proposed taxonomies of these technical concerns and various mitigation strategies, how downstream developers address these issues during the development of general AI-based software when reusing PTMs remains unexplored. Understanding downstream developers’ perspectives is essential because they …

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Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño Jan 2027

Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Most students lack awareness regarding the labor market outcomes for their chosen college majors. This study aims to answer what factors affect the career expectations of graduating students at Quezon City University and how these expectations align with the prevailing labor market situation. It employed descriptive, causal, and explanatory research using a sample of 108 respondents from fourth-year information technology students for the school year 2021 to 2022. Eight of the nine null hypotheses were rejected by employing multinomial logistic and linear regression. Student fixed effects and other labor market outcomes significantly predicted salary, estimated stability, and estimated skills in …

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Logupdater: Automated Detection And Repair Of Specific Defects In Logging Statements, Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, Michael R. Lyu Jan 2027

Logupdater: Automated Detection And Repair Of Specific Defects In Logging Statements, Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Developers write logging statements to monitor software runtime behaviors and system state. However, poorly constructed or misleading log messages can inadvertently obfuscate actual program execution patterns, thereby impeding effective software maintenance. Existing research on analyzing issues within logging statements is limited, primarily focusing on detecting a singular type of defect and relying on manual intervention for fixes rather than automated solutions.To address the limitation, we initiate a systematic study that pinpoints four specific types of defects in logging statements (i.e., statement code inconsistency, static dynamic inconsistency, temporal relation inconsistency, and readability issues) through the analysis of real-world log-centric changes. We …

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Generalizing Unsupervised Lidar Motion Estimation Via Patch-Aware Feature Reweighting Under Snowy Conditions, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Tongpo Zhang, Shaojing Song, Qinge Wu, Hui Kong Jan 2027

Generalizing Unsupervised Lidar Motion Estimation Via Patch-Aware Feature Reweighting Under Snowy Conditions, Beibei Zhou, Zhiyuan Zhang, Zhenbo Song, Jianhui Guo, Tongpo Zhang, Shaojing Song, Qinge Wu, Hui Kong

Research Collection School Of Computing and Information Systems

Unsupervised LiDAR motion estimation has attracted significant attention in pattern recognition and robotic vision. However, ensuring robust performance under adverse conditions — particularly structured noise like heavy snowfall — remains a challenging problem. Existing models struggle with domain generalization due to the sensitivity of feature extractors to spatial outliers, limiting their reliability in complex real-world scenarios. In this work, we present an unsupervised LiDAR motion estimation framework via patch-aware feature reweighting to mitigate the domain shift between clear and snowy conditions. Our approach prioritizes reliability-aware reweighting to reduce the influence of snowflake noise and outlier points on pose estimation, while …

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Push-Pull Farming System Helps Insure Smallholder Maize Production Under Climate Change, Yann Clough, Wilhelm May, Timothy Luttermoser, Dickens Nyagol, Nikolaos Alexandridis, Frank Chidawanyika, Mattias Jonsson, Charles A.O. Midega, Katja Poveda, Zeyaur R. Khan Jan 2027

Push-Pull Farming System Helps Insure Smallholder Maize Production Under Climate Change, Yann Clough, Wilhelm May, Timothy Luttermoser, Dickens Nyagol, Nikolaos Alexandridis, Frank Chidawanyika, Mattias Jonsson, Charles A.O. Midega, Katja Poveda, Zeyaur R. Khan

All Peer-Reviewed Publications

Agroecological approaches harnessing in-field biodiversity have been highlighted as a sustainable way to reduce yield gaps, but long-term assessments of their ability to buffer production from weather extremes and increasing interannual and intraseasonal climate variability are scarce. Here, we analyze a 12-year dataset from 642 control vs. push-pull maize field pairs in western Kenya, where long-term climate trends include increasing temperatures and intensifying drought and rainfall. In push-pull, maize is intercropped with a fodder legume that improves soil fertility, suppresses weeds, and deters pests, and is bordered with fodder grasses attracting and suppressing the pests. We assess interactions between push-pull …

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Toward Mapping Multiphase Multicomponent Mixtures With Neural Networks, Kristen L. Hallas, Melissa De Jesus, Christine J. Wu, Jianzhi Li, Jason Bernstein, Philip C. Myint Dec 2026

Toward Mapping Multiphase Multicomponent Mixtures With Neural Networks, Kristen L. Hallas, Melissa De Jesus, Christine J. Wu, Jianzhi Li, Jason Bernstein, Philip C. Myint

School of Mathematical & Statistical Sciences Faculty Publications

Equation of state (EOS) tables are commonly used in hydrodynamic simulations of high-pressure, high-temperature phenomena in fields like planetary science, astrophysics, and high-energy-density science. However, generating and storing EOS tables for multiphase, multicomponent mixtures over a wide range of pressures and temperatures is computationally infeasible due to their memory-intensive nature. To address this issue, we have developed a neural network-based machine learning model to predict new EOS tables for binary mixtures. In particular, a deep feedforward neural network trained on a set of ten EOS tables at particular mixture compositions is able to predict nine new (hold-out) EOS tables at …

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Hed-Id: A Federated Expert System For Interpretable And Resource-Adaptive Intrusion Detection On Edge Devices, Kushboo Nasir, Sahar K. Badri, Asad Khattak, Daniyal M. Alghazzawi, Mohammed Yahya Alghamdi, Mona Alkhozae, Abeer Almakky, Rania M. Alhazmi, Muhammad Zubair Asghar Dec 2026

Hed-Id: A Federated Expert System For Interpretable And Resource-Adaptive Intrusion Detection On Edge Devices, Kushboo Nasir, Sahar K. Badri, Asad Khattak, Daniyal M. Alghazzawi, Mohammed Yahya Alghamdi, Mona Alkhozae, Abeer Almakky, Rania M. Alhazmi, Muhammad Zubair Asghar

All Works

The proliferation of edge computing in industrial and IoT networks necessitates expert systems that support accurate, interpretable, and resource-efficient intrusion detection under strict privacy and computational constraints. This paper presents HED-ID, a fully integrated federated expert system designed for real-time, edge-oriented anomaly detection. The system combines: (i) a stacked bidirectional GRU (BiGRU) architecture with hybrid temporal–spatial attention for sequential and feature-level anomaly modeling, (ii) a resource-adaptive Grey Wolf Optimizer (A-GWO) that modulates exploration–exploitation behavior based on real-time CPU utilization and Bayesian epistemic uncertainty, with formal convergence proof under bounded resource shifts (Appendix A), (iii) federated SHAP, which aggregates local explanations …

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Fine-Tuning Large Language Models For Binary And Multiclass Target-Specific Hate Speech Detection, Sanaa Kaddoura, Sumaia Al-Kohlani Dec 2026

Fine-Tuning Large Language Models For Binary And Multiclass Target-Specific Hate Speech Detection, Sanaa Kaddoura, Sumaia Al-Kohlani

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Social media platforms are widely used by individuals from diverse demographic backgrounds to share their opinions and daily experiences. The anonymity provided by these platforms has increased the prevalence of hate speech, causing significant psychological harm to targeted groups. Existing hate speech detection approaches predominantly rely on binary classification, which fails to capture the lexical and contextual diversity of hate categories, leading to the misclassification of hate against minority groups. This study addresses this limitation by evaluating the effectiveness of five large language models (LLMs) for target-specific multiclass hate speech detection across six classes. The evaluation uses a dataset of …

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Revisiting Ulam Stability For Boundary Value Problems, Martin Bohner, Snezhana Hristova, Agnieszka B. Malinowska, Ewa Girejko Dec 2026

Revisiting Ulam Stability For Boundary Value Problems, Martin Bohner, Snezhana Hristova, Agnieszka B. Malinowska, Ewa Girejko

Mathematics and Statistics Faculty Research & Creative Works

The main goal of this paper is to apply Ulam stability theory to boundary value problems for dynamic equations, while addressing several common misconceptions found in the existing literature. We identify the key issues that arise when applying Ulam stability to such problems and propose three distinct approaches to overcome them. To enhance clarity and accessibility, we begin with nonlinear ordinary differential equations and subsequently extend the analysis to nonlinear dynamic equations on time scales. Since a time scale is defined as any nonempty closed subset of the real numbers, our results are applicable to dynamic equations on continuous, discrete, …

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A Projector-Rank Partition Theorem For Exact Degrees Of Freedom In Experimental Design, K. G. Nagananda Dec 2026

A Projector-Rank Partition Theorem For Exact Degrees Of Freedom In Experimental Design, K. G. Nagananda

Mathematics and Statistics Faculty Publications and Presentations

In many experimental designs—split-plots, blocked or nested layouts, fractional factorials, and studies with missing or unequal replication—standard ANOVA procedures no longer tell us exactly how many independent pieces of information each effect truly contributes. We provide a general degrees of freedom (df) partition theorem that resolves this ambiguity. For N observations, we show that the total information in the data (i.e., N −1 df) can be split exactly across experimental effects and randomization strata by projecting the data onto each stratum and counting the df each effect contributes there. This yields integer df—not approximations—for any mix of fixed and random …

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Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi Dec 2026

Quantifying Customer Sentiment For Automobile Brand Perception Analysis Using Machine Learning On Twitter, Sujith Samuel Mathew, Kadhim Hayawi, Neethu Venugopal, May El Barachi

All Works

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter(or X). The research models the ’Brand Polarity Score’, which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet’s influence, characterized by the engagement metrics of the tweet and the author’s follower count. We also …

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Dblp: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification, Chihan Huang, Belal Alsinglawi, Islam Al-Qudah Dec 2026

Dblp: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification, Chihan Huang, Belal Alsinglawi, Islam Al-Qudah

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Recent advances in deep neural networks (DNNs) have led to remarkable success across a wide range of tasks. However, their susceptibility to adversarial perturbations remains a critical vulnerability. Existing diffusion-based adversarial purification methods often require intensive iterative denoising, severely limiting their practical deployment. In this paper, we propose Diffusion Bridge Distillation for Purification (DBLP), a novel and efficient diffusion-based framework for adversarial purification. Central to our approach is a new objective, noise bridge distillation, which constructs a principled alignment between the adversarial noise distribution and the clean data distribution within a latent consistency model (LCM). To further enhance semantic fidelity, …

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X-Threat Framework For Adaptive And Explainable Deep Learning-Based Minority-Class And Zero-Day Cyber Threat Detection, Samina Naz Qaisarani, Sahar K. Badri, Asad Khattak, Daniyal M. Alghazzawi, Mohammed Yahya Alghamdi, Mona Alkhozae, Abeer Almakky, Rania M. Alhazmi, Muhammad Zubair Asghar Dec 2026

X-Threat Framework For Adaptive And Explainable Deep Learning-Based Minority-Class And Zero-Day Cyber Threat Detection, Samina Naz Qaisarani, Sahar K. Badri, Asad Khattak, Daniyal M. Alghazzawi, Mohammed Yahya Alghamdi, Mona Alkhozae, Abeer Almakky, Rania M. Alhazmi, Muhammad Zubair Asghar

All Works

The dynamic nature of cyber threats—particularly minority-class and zero-day attacks—poses significant challenges to existing intrusion detection systems (IDS), which often lack adaptability, interpretability, and robustness. This paper presents X-THREAT, an adaptive and explainable deep learning framework designed to improve detection of rare and previously unseen cyber threats. Unlike prior approaches that treat detection, augmentation, and explainability separately, X-THREAT integrates these components within a unified pipeline. A hybrid VAE–GAN module enhances minority-class representation during training, while a CNN–BiLSTM–attention model performs threat detection, and a context-aware SHAP mechanism provides interpretable insights into model decisions. Experimental results on three benchmark datasets—CICIDS2017, UNSW-NB15, and …

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Reduced Mediators Released By Cyanobacteria During Exoelectrogenesis Detected Using Differential Pulse Voltammetry, Laura T. Wey, Monica Brachi, Yagut Allahverdiyeva, Shelley D. Minteer Dec 2026

Reduced Mediators Released By Cyanobacteria During Exoelectrogenesis Detected Using Differential Pulse Voltammetry, Laura T. Wey, Monica Brachi, Yagut Allahverdiyeva, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Cyanobacteria generate electrical current through exoelectrogenesis (extracellular electron transfer) downstream of photosynthesis, yet the identity of the endogenous redox mediator(s) responsible remains unresolved. Here, we apply differential pulse voltammetry (DPV) to detect and characterise redox-active species released by Synechocystis sp. PCC 6803 under illumination. To enable sensitive detection, we developed an electrolyte intermediate between BG11 (Blue-Green-11) growth medium and MOPS (3-(N-morpholino)propanesulfonic acid) buffer that minimises background electrochemical interference while maintaining short-term cellular functionality. DPV revealed multiple light-enhanced oxidation peaks (0.1–0.65 V vs. saturated calomel electrode (SCE)) that were not resolvable using cyclic voltammetry, providing evidence that cyanobacteria release reduced compounds …

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Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu Dec 2026

Generative Ai Adoption And Solvers' Popularity On Supply-Driven Crowdsourcing Platforms: The Dual Role Of Price Signals, Zimeng Zhu, Carol Hsu, Fiona Fui-Hoon Nah, Na Liu

Research Collection School Of Computing and Information Systems

Purpose – We investigate the effect of solvers’ adoption of Generative AI (GenAI) on their popularity in a supply-driven crowdsourcing platform. We also examine the impact of price signals as well as their heterogeneous impact based on the solvers’ membership duration on the platform. Design/methodology/approach – Our analysis focuses on solvers who adopt GenAI for design-related gigs on the supply-driven crowdsourcing platform. By combining propensity score matching (PSM) with multi-period difference-in-differences (DID), we examine how GenAI adoption impacts solvers’ popularity and how price signals affect this main effect. Findings – Our findings reveal that solvers who adopt GenAI tend to …

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Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel Dec 2026

Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

All Works

Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing …

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Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi Dec 2026

Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

All Works

Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

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Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki Dec 2026

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

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Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

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An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi Dec 2026

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

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Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

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Fair And Explainable Educational Recommendations With A Hybrid Graph-Gru Framework, Edmund Evangelista, Syed M.Salman Bukhari Dec 2026

Fair And Explainable Educational Recommendations With A Hybrid Graph-Gru Framework, Edmund Evangelista, Syed M.Salman Bukhari

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Artificial Intelligence (AI) recommender systems are increasingly used in education to personalize learning and help students navigate large collections of digital learning resources. However, many existing approaches emphasize predictive accuracy over fairness, robustness, diversity, and transparency. This creates an important educational challenge. The students with limited participation histories may receive less reliable support, while highly popular resources may dominate recommendation lists and limit access to other useful learning materials. To address this challenge, this study aims to develop and evaluate a responsible educational recommender framework that supports personalized learning resource navigation while making recommendation behavior more fair, stable, diverse, and …

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Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak Dec 2026

Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

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The rise of Large Language Models (LLMs) has transformed how Natural Language Processing (NLP) and its subdomains are approached. Recent technological advancements have driven this transformation. This study offers researchers a detailed overview of LLMs, comparing them with traditional rule-based systems, statistical techniques, machine learning, neural networks, and the rise of transformer-based architectures. From a wider perspective, language models such as GPT, BERT, T5, PaLM, and LLaMA have facilitated the transformation of entire sectors, including healthcare and business, due to their highly scalable nature. Despite their wide range of applications, LLMs face numerous challenges, such as output biases, limited interpretability, …

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Long-Term Analysis Of Pm2.5 Elemental Composition And Source Contributions In Coastal Urban Environments Of Corpus Christi And Houston In Texas, Sai Deepak Pinakana, Jeremy A. Sarnat, Amit U. Raysoni Dec 2026

Long-Term Analysis Of Pm2.5 Elemental Composition And Source Contributions In Coastal Urban Environments Of Corpus Christi And Houston In Texas, Sai Deepak Pinakana, Jeremy A. Sarnat, Amit U. Raysoni

School of Earth, Environmental, & Marine Sciences Faculty Publications

Long-term assessments of fine particulate matter play a key role in identifying persistent and emerging emission contributors. Coastal urban areas are highly susceptible to transboundary dust and wildfire events in addition to local pollution sources. Multi-decadal assessments in these regions using chemical speciation data could offer insights into major pollutant sources and their trends. This study utilizes nearly two decades of PM2.5 elemental composition data at two major coastal cities in Texas: Houston and Corpus Christi. Fourteen trace elements were analyzed to characterize the seasonal variability and long-term source evolution using a combination of Spearman’s correlation analysis, principal component analysis, …

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How Leaders Build Employee Trust In Artificial Intelligence: Voice Opportunities, Humility, And Trust Transfer, Jack Mcguire, David De Cremer, Devesh Narayanan Dec 2026

How Leaders Build Employee Trust In Artificial Intelligence: Voice Opportunities, Humility, And Trust Transfer, Jack Mcguire, David De Cremer, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Artificial intelligence is increasingly central to organizational work, yet employee trust in AI remains fragile. Although prior research has primarily explained trust in AI through technological characteristics such as transparency, reliability, and accuracy, we argue that trust in AI is also shaped by the social context in which employees encounter these systems. Drawing on affect-as-information theory and social information processing theory, we develop and test a model in which leader-provided voice opportunities reduce employees’ negative affect about AI-related work experiences, thereby enhancing perceptions of leader trustworthiness and, in turn, trust in AI. We further propose that this indirect effect depends …

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The Downstream Effect Of Gender Bias On Academic Performance And Career Aspirations Amongst Female Students In Stem Via Gender-Professional Identity Integration (G-Pii), Chi-Ying Cheng, Shuna Shiann Khoo, Shih-Fen Cheng, Yeow Leong Lee, Vandana Ramachandra Rao Dec 2026

The Downstream Effect Of Gender Bias On Academic Performance And Career Aspirations Amongst Female Students In Stem Via Gender-Professional Identity Integration (G-Pii), Chi-Ying Cheng, Shuna Shiann Khoo, Shih-Fen Cheng, Yeow Leong Lee, Vandana Ramachandra Rao

Research Collection School of Social Sciences

Background Gender imbalance in STEM, characterized by a significant underrepresentation of women, remains a significant challenge. Although gender bias is a well-known contributor to women’s attrition from STEM, the psychological mechanisms linking gender bias to departure are less well understood. Our research investigates early antecedents of attrition and the psychological processes that precede leaving the STEM pathway in tertiary education. Drawing upon identity integration research, we propose that perceived gender bias exerts undermines female STEM students’ academic performance and career aspirations by reducing their Gender-Professional Identity Integration (G-PII), a construct that captures individual differences in the perceived compatibility between a …

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Evaluating The Transformation Of Land Cover, Land Surface Temperature, And Urban Heat Island In Sylhet Division Through Geospatial Analysis, Md Fazle Rabbi Joy, Md Abdur Rahim, Md Mahfuzar Rahman, Sumiaya Amin Preota, Mohammed Mahathee Hossain Dec 2026

Evaluating The Transformation Of Land Cover, Land Surface Temperature, And Urban Heat Island In Sylhet Division Through Geospatial Analysis, Md Fazle Rabbi Joy, Md Abdur Rahim, Md Mahfuzar Rahman, Sumiaya Amin Preota, Mohammed Mahathee Hossain

School of Earth, Environmental, & Marine Sciences Faculty Publications

Rapid and unplanned urbanization has significantly transformed land use/land cover (LULC), contributing to increased land surface temperature (LST) and the emergence of urban heat islands (UHIs), especially in ecologically fragile regions. This study focuses on the Sylhet Division of Bangladesh, assessing LULC changes, LST dynamics, and UHI expansion from 1994 to 2024 using Landsat satellite imagery, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) indices, and field-based LST validation. Unsupervised classification reveals a 191.75% increase in settlement areas and a 25.65% loss in forest cover. Correspondingly, the highest LST zones (> 30 °C) were absent in 1994 but …

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The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej Dec 2026

The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej

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This study investigates green technology adoption (GTA) among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE), focusing on the influence of corporate sustainability goals (CSG) and sustainability motivation (SM). Utilizing institutional theory, the theory of planned behavior (TPB), and resource-based view (RBV), the research highlights how SMEs integrate environmental, social, governance (ESG) and economic considerations into their CSG to enhance GTA. Addressing a gap in prior research that has largely emphasized external drivers of adoption while underexploring internal organizational mechanisms, the study conceptualizes CSG as strategic intent and models SM as a second-order construct . Based on …

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From Post-Hoc To Pre-Assessment Evaluation: Llms As Collaborative Partners For Simulating Student Performance, Siaw Ling Lo, Ping Fan Ke, Yi Meng Lau Dec 2026

From Post-Hoc To Pre-Assessment Evaluation: Llms As Collaborative Partners For Simulating Student Performance, Siaw Ling Lo, Ping Fan Ke, Yi Meng Lau

Research Collection School Of Computing and Information Systems

Ensuring the quality of multiple-choice questions (MCQs) remains a complex challenge in higher education, as traditional post-hoc psychometric analyses often identify flaws only after students have been negatively impacted. While Large Language Models (LLMs) offer potential for simulating student performance to validate items before delivery, they are frequently hindered by a “high-accuracy bias” that prevents larger models from realistically mimicking student errors. This paper proposes the Triadic Pedagogical Fit (TPF) framework, which triangulates Model Scale, Task Complexity via Revised Bloom’s Taxonomy, and Instructional Persona to optimize pre-assessment evaluation. Through two studies, we demonstrate that while Mid-scale (12B) models effectively mirror …

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