Digital Commons Network^™

A Neutrosophic Approach For Evaluating The Multimedia-Aided College Piano Instruction Evaluation Techniques: Dombi-Based Heronian Mean Operators, Lan Yao

Neutrosophic Sets and Systems

No abstract provided.

Mr-Metric Spaces: Theory And Applications In Fractional Calculus And Fixed-Point Theorems, Abed Al-Rahman M.Malkawi, Ayat M. Rabaiah

Neutrosophic Sets and Systems

No abstract provided.

Neutrosophic Sets And Systems, Vol. 90,2025, Florentin Smarandache, Mohamed Abdel-Basset, Maikel. Leyva Vazquez

Neutrosophic Sets and Systems

No abstract provided.

Concerns Regarding The Standard Deviation Of Individual Responses For Assessing Treatment Response Heterogeneity, Aaron R Caldwell, David B Allison, Andrew W Brown, Gary L Gadbury, Thirupathi Reddy Mokalla, R Drew Sayer, Andrew D Vigotsky

Children’s Nutrition Research Center Staff Publications

The estimation of treatment response heterogeneity (TRH) is increasingly important as medicine moves toward personalized approaches. While various statistical methods have been proposed to quantify TRH in parallel-group trials, the standard deviation of individual responses (SDIR) has gained prominence within physiological research. This method is intended to quantify individual response variation by comparing standard deviations of change scores between intervention and control groups. We acknowledge that SDIR represents an improvement over many other flawed approaches that often involve responder counting. However, SDIR has critical limitations: 1) it cannot overcome the fundamental problem of causal inference because the correlation between potential …

The Impact Of Sanctions On Github Developers And Activities, Youmei Fan, Ani Hovhannisyan, Hideaki Hata, Christoph Treude, Raula G. Kula

Research Collection School Of Computing and Information Systems

The GitHub platform has fueled the creation of truly global software, enabling contributions from developers across various geographical regions of the world. As software becomes more entwined with global politics and social regulations, it becomes similarly subject to government sanctions. In 2019, GitHub restricted access to certain services for users in specific locations but rolled back these restrictions for some communities (e.g., the Iranian community) in 2021. We conducted a largescale empirical study, collecting approximately 156 thousand user profiles and their 41 million activity points from 2008 to 2022, to understand the response of developers. Our results indicate that many …

Sketch-Sparsenet: Sparse Convolution Framework For Sketch Recognition, Jingru Yang, Jin Wang, Yang Zhou, Guodong Lu, Yu Sun, Huan Yu, Heming Fang, Zhihui Li, Shengfeng He

Research Collection School Of Computing and Information Systems

In free-hand sketch recognition, state-of-the-art methods often struggle to extract spatial features from sketches with sparse distributions, which are characterized by significant blank regions devoid of informative content. To address this challenge, we introduce a novel framework for sketch recognition, termed Sketch-SparseNet. This framework incorporates an advanced convolutional component: the Sketch-Driven Dilated Deformable Block (SD3B). This component excels at extracting spatial features and accurately recognizing free-hand sketches with sparse distributions. The SD3B component innovatively bridges gaps in the blank areas of sketches by establishing spatial relationships among disconnected stroke points through adaptive reshaping of convolution kernels. These kernels are deformable, …

Sustainable Llm Inference For Edge Ai: Evaluating Quantized Llms For Energy Efficiency, Output Accuracy, And Inference Latency, Erik Johanne Husom, Arda Goknil, Merve Astekin, Lwin Khin Shar, Andre Kasen, Sagar Sen, Benedikt Andreas Mithassel, Ahmet Soylu

Research Collection School Of Computing and Information Systems

Deploying Large Language Models (LLMs) on edge devices presents significant challenges due to computational constraints, memory limitations, inference speed, and energy consumption. Model quantization has emerged as a key technique to enable efficient LLM inference by reducing model size and computational overhead. In this study, we conduct a comprehensive analysis of 28 quantized LLMs from the Ollama library, which applies by default Post-Training Quantization (PTQ) and weight-only quantization techniques, deployed on an edge device (Raspberry Pi 4 with 4GB RAM). We evaluate energy efficiency, inference performance, and output accuracy across multiple quantization levels and task types. Models are benchmarked on …

Enhancing Spatial Understanding In Mixed-Reality Presentations, Nam-Dang Vo, Van-Vinh Thai, Nam-Hoi Do, Viet-Tham Huynh, Anthony Tang, Khan-Duy Le

Research Collection School Of Computing and Information Systems

Mixed reality (MR) presentations often involve a presenter wearing a head-mounted display (HMD) and an audience watching via a large display, making it difficult for audiences to perceive spatial relationships between the presenter and virtual objects. We report two experiments testing three design variations: (1) scene camera placement (audience-aligned vs. opposite), (2) overlaying the presenter’s first-person view, and (3) highlighting objects in the presenter’s view. Results show that audience-aligned cameras and object highlighting improve spatial understanding, while combining third- and first-person views can further aid perception. We derive design guidelines for configuring MR presentations to better support audience comprehension.

Disc: Decentralized Identity System With Self-Sovereign Credential Aggregation, Yang Yang, Wai Keung Ching, Minming Huang, Supachate Innet, Guomin Yang, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

The evolution of decentralized identity (DID) and self-sovereign identity (SSI) frameworks, as endorsed by W3C Verifiable Credentials (VC) and eIDAS 2.0, underscores the need for secure, efficient, and privacy-preserving credential management. However, existing credential systems often depend on centralized issuers, lack efficient aggregation mechanisms, or fail to ensure unlinkability across authentication sessions. To address these challenges, we propose DISC (Decentralized Identity System with Self-Sovereign Credential Aggregation), a novel credential system that enables multi-authority credential issuance, user-controlled credential aggregation, and unlinkable authentication. DISC allows users to aggregate credentials from multiple issuers while maintaining constant-size authentication tokens and supporting batch verification for …

Addressing Sparsity For Knowledge Graph Completion: Data And Model Perspectives, Ran Liu

Dissertations and Theses Collection (Open Access)

Knowledge graphs (KGs) are powerful tools for structuring factual knowledge into relational triples, yet their practical utility is often adversely affected by data sparsity. Many entities and relations are associated with only a few observations, which limits the quality of learned embeddings and weakens generalization in downstream tasks. The problem of sparsity led to two interrelated challenges. Firstly, it restricts the informativeness of training samples: positive examples are scarce, and conventional negative sampling often produces trivial or redundant negatives that resulting in limited guidance. Secondly, in few-shot relation learning scenarios, sparsity worsens distribution shifts between training and test relations, as …

Multilevel Selection In The Margins: A Review Of Its Representation In Undergraduate Biology Textbooks, C. A. Greene, S. J. Mcpeek, L. Mitchem, A. B. Clark, Vincent A. Formica

Biology Faculty Works

Multilevel selection (MLS) is a foundational framework that expands the concept of natural selection beyond the traditional focus on individuals or genes to larger units, like groups. Despite strong empirical support and applications in a broad range of biological fields, MLS, often referred to as group selection, has a controversial history, which has impeded its wide use and led to incorrect inferences on the sources and strength of selection. Here, we explore how MLS is currently presented to undergraduate biology students by analyzing the content of nine commonly used Evolutionary Biology and Animal Behavior textbooks. We ask whether and how …

Women’S Unique Health Needs And The Sustainable Development Goals: A Meeting Report From The 79th United Nations General Assembly Science Summit, Irene Aninye, Sarah Chew, Nicole Althaus, Michele Barry, Ru-Fong Cheng, Carolyn Harris, Eleanor Nwadinobi, Beth Battaglino, Aisha Brooks, Marleen Temmerman

Centre of Excellence in Women and Child Health

Biological sex, hormones, and gender uniquely affect health and disease, often resulting in disparities for women across the lifespan and from different racial and ethnic groups, geographical locations, and socioeconomic backgrounds. Without intentional investment and infrastructure to support good health and wellbeing for women, half of the world’s population remains vulnerable to preventable morbidity and mortality. The Society for Women’s Health Research and ECHAlliance–The Global Health Connector convened a women’s health program as part of the 10th Annual Science Summit during the United Nations 79th General Assembly. This hybrid event was held in New York, NY, (USA) and virtually in …

Neural Network Ensemble For Computing Cross Sections Of Rotational Transitions In H2O + H2O Collisions, Bikramaditya Mandal, Dmitri Babikov, Phillip C. Stancil, Robert C. Forrey, Roman V. Krems, Naduvalath Balakrishnan

Chemistry Faculty Research and Publications

Water (H₂O) is one of the most abundant molecules in the universe and is found in a wide variety of astrophysical environments. Rotational transitions in H₂O + H₂O collisions are important for modeling environments rich in water molecules but they are computationally intractable using quantum mechanical methods. Here, we present a machine learning (ML) tool using an ensemble of neural networks (NNs) to predict cross sections to construct a database of rate coefficients for rotationally inelastic transitions in collisions of complex molecules such as water. The proposed methodology utilizes data computed with a mixed …

Attorneys And Ai: How Lawyers Use Artificial Intelligence And Analyze Its Impacts, Matthew I. Hall, Christian Turner, Eddie A. Gomez Schieber, Nathaniel Kite, Ari Schlesinger

Scholarly Works

AI systems are testing lawyers' professional ethics obligations of competence, confidentiality, and candor. In the legal profession, the widespread availability of AI systems presents opportunities, like improving the review of documents during the discovery stage of a lawsuit, and challenges, illustrated by the handful of high-profile incidents where lawyers submitted legal briefs in court citing and describing fictitious cases based on AI-generated output. We conducted interviews with 44 legal professionals in the U.S. to understand how attorneys are making sense of AI technology and the impacts these technologies are having on their profession, legal ethics, and legal institutions. We describe …

Development Of A Ux Effectiveness Evaluation Tool For Mental Health Mobile Applications, Stephanie Caridad Dr. Landicho, Maria Aura Teodora C. Matias

Journal Article

This study developed and validated a comprehensive User Experience (UX) evaluation tool specifically designed for mental health mobile applications. User experience plays a critical role in sustaining engagement and effectiveness in digital mental health interventions, yet existing evaluation tools are not tailored to the unique challenges of this domain. Through a multi-phase process, the research identified 12 key domains for evaluation and developed an initial 48-item instrument, which was refined through expert panel review for face and content validity, resulting in 50 items. Pilot testing with 19 participants demonstrated strong internal consistency (Cronbach's alpha = 0.972). Full-scale testing involved 205 …

Enhancing It Security Management With An Advanced Intrusion Detection System Based On Machine Learning And Explainable Ai, Hanan Aslam Al Zubaidi

Theses

The fast changing landscape of cyber threats continues to challenge the development of strong and reliable security frameworks for IT management systems. Traditional defense tools, such as Intrusion Detection Systems (IDS), often struggle to keep up with today's advanced and constantly evolving attack methods. This thesis explores these ongoing challenges and looks into how machine learning (ML) and explainable artificial intelligence (XAI) can be used to boost IDS performance.

The research outlines a smart, adaptive system that combines supervised learning for real-time threat detection, unsupervised models for anomaly analysis, and proactive defense strategies. The goal is to improve detection accuracy, …

Synthetic Data Generation Of Health And Demographic Surveillance Systems Data: A Case Study In A Low-And Middle-Income Country, Dorcas Mwigereri, Nigel Kamotho, Akbar Waljee, Ryan Rego, Eileen Weinheimer-Haus, Farhana Alarakhiya, Anthony Ngugi, Nicholson Price, Ji Zhu, Stephen Wong

Population Health, East Africa

Objective To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

Materials and Methods Three open-source models (CTGAN, TableGAN, and CopulaGAN) were used to generate synthetic data from the Kaloleni/Rabai HDSS dataset. To assess the quality of the synthetic datasets generated by each model, we performed fidelity, utility, and privacy tests.

Results CTGAN outperformed the other models, producing synthetic data that closely mirrored the statistical properties of the real dataset while preserving privacy. …

Substituted Piperidines As (Alpha4)3(Beta2)2 Nicotinic Acetylcholine Receptor Positive Allosteric Modulators: In Vitro Pharmacology, Nataly Sanchez, Josue Gaona, Wilder Felix, Nathalia Menegasso, Brisa Cea, Ganesh Thakur, Ayman K. Hamouda

School of Medicine Student Publications and Presentations

Nicotinic acetylcholine receptors (nAChRs) play a crucial role in the pathogenesis of neurodegenerative diseases such as Parkinsonʼs disease (PD). In PD, degeneration of dopaminergic and cholinergic neurons impairs neurotransmitter release, leading to cognitive and motor dysfunction. Current treatments provide only symptomatic relief, highlighting the need for novel therapeutic strategies. The (α4)₃(β2)₂ subtype of nAChRs is expressed on affected neurons, making it a promising druggable target for enhancing neurotransmitter release and providing neuroprotection. To explore the therapeutic potential of positive allosteric modulators (PAMs) of (α4)₃(β2)₂ nAChRs, we evaluated chemical derivatives of CMPI [3-(2-chlorophenyl)-5-(5-methyl-1-(piperidin-4-yl)-1H-pyrazol-4-yl)isoxazole]. We used two-electrode voltage-clamp recordings from Xenopus laevis …

Probabilistic Template Matching For Detecting Resting-State Functional Mri Language Network In Brain Tumor Patients, Jian Ming Teo, Vinodh A Kumar, Alexander M Khalaf, Kyle R Noll, Sherise D Ferguson, Chibawanye I Ene, Sujit S Prabhu, Max Wintermark, Ho-Ling Liu

Faculty, Staff and Student Publications

Background: Intersubject variation among patients with brain tumors complicates the template matching process for detecting the resting-state (rs) functional MRI (fMRI) language network when using independent component analysis (ICA).

Purpose: This study aimed to develop methods that use a probabilistic language atlas to incorporate intersubject variation in brain tumor patients into the template matching process.

Methods: This retrospective study included 79 patients with brain tumors (average age, 50 ± 15 years) who underwent presurgical task-based (tb)-fMRI and rs-fMRI at clinical 3T scanners. At varying template generation thresholds (τ), binary and probabilistic templates were obtained from the language atlas. A binary …

Fixed Point Theorems For Multivalued Mappings In Neutrosophic Fuzzy Metric Spaces, K. Dinesh, R. Suganya, V. Banu Priya, M. Suresh, A. Atkinswestley

Neutrosophic Sets and Systems

No abstract provided.

A Novel Approach For Neutrosophic Complete Graph And Neutrosophic Excellent Domination, Murugan Raji, Ponnaiah Tharaniya, Surapati Pramanik, G. Prabhu

Neutrosophic Sets and Systems

No abstract provided.

Novel Dynamic Multi-Depth Neutrosophic Coordinate Model For Teaching Performance Analysis In College Electronic Piano Courses With Numerical Examples And A Mathematical Framework, Xinyuan Zhang

Neutrosophic Sets and Systems

No abstract provided.

Structured Α-Neutrosophic Symbolic Conformance: A Formal Design Quality Evaluation Model For Digital Cultural And Creative Products Based On Paper Cutting Art, Fengxia Bi

Neutrosophic Sets and Systems

No abstract provided.

The 2-Refined Neutrosophic Quaternions Numbers, Iqbal Ahmed Musa, Yaser Ahmad Alhasan, Ahmad Abdullah Almekhlef

Neutrosophic Sets and Systems

No abstract provided.

A Practice-Based Framework For Teaching Geography At The Secondary Level, Kyle F. Tredinnick

Department of Geography: Dissertations, Theses, and Student Research

Further work is necessary to carry out the goals set forth in geography education initiatives like the Geography for Life: National Geography Standards and Road Map for 21^st Century Geography Education Project. Methods to communicate the focus of geographic lessons at the secondary level can help guide teachers who are otherwise unaware of the ability for geography to answer questions and develop solutions to problems. Research and development of effective instructional tools to teach geography is needed. This research aims to provide a practice-based model for lesson-development in geography, as supported in academic literature. Input from higher-education faculty …

Building Confidence For Class Participation, Tamas Makany, Ivy Seow

Research Collection Lee Kong Chian School Of Business

What happens to students’ critical thinking when half the class filters their thoughts through AI? During a recent debate on AI policy in education, one student mentioned they routinely run their ideas through ChatGPT before speaking up. When I asked who else did the same, more than half the class raised their hands.

Spatio-Temporal Dynamics Of Malaria Vector Niche Overlaps In Africa, Eric Ali Ibrahim, John Odindi, Mark Wamalwa, Henri E.Z. Tonnang

All Peer-Reviewed Publications

Malaria remains a significant public health challenge in sub-Saharan Africa, with transmission heightened by the dynamics of primary and secondary mosquitoes infected with Plasmodium parasites. Regions where both vector types co-exist face heightened likelihood of intensified malaria transmission. Hence, understanding vectors' ecological interactions, especially their niche overlaps in geographic or environmental space, is crucial for targeted malaria control and elimination strategies. We employed a dynamic cellular automata (CA) model to map niche overlaps among primary (Anopheles gambiae complex, An. funestus group) and secondary (An. pharoensis, An. coustani) malaria vectors across African, using open-access environmental and vector occurrence datasets sourced from …

Intelligent Method Of Dynamic Control For A Class Of Stochastic Nonlinear Systems, Isamidin Khakimovich Siddikov, Davronbek Abdalimovich Khalmatov, Gulchekhra Rakhimjanovna Alimova, Dilnoza Rakhmanovna Khushnazarova

Chemical Technology, Control and Management

The paper considered the problems of researching the stabilisation system and backstepping control of stochastic nonlinear systems. The characteristics of stochastic nonlinear dynamic control systems are random signals with normal lawful distribution, which significantly complicates task control. In stochastic control, it is necessary to determine the trajectories of the control variables in order to achieve the desired control objective at minimum cost. Since the mathematical equations of stochastic nonlinear systems are not always constant, not every model-based controller can be accurate. Therefore, in this work, a neuro-fuzzy network is used to evaluate the parameters of the control system with backstepping, …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Virtual Reality In Esl Teaching And Learning In A Hybrid Learning Environment In The United Arab Emirates, Basma Abdulla Abri

Dissertations

This study examines the impact of Virtual Reality (VR) on the oral proficiency of EFL learners in the UAE context, with exploring the instructors and students’ perspectives on using VR as a language learning tool. The study involved 44 Emirati higher education students enrolled in an English Communications Program divided into a control group using traditional methods, and an experimental group using VR tools for communicative activities. Data collection employed mixed-methods research through questionnaire surveys involving college students and interviews with EFL instructors over a ten-week period. According to the findings, the experimental group demonstrated higher oral proficiency than the …