Digital Commons Network^™

Multi-Agent Ai Systems For Detecting Emerging Therapeutic Targets And Intervention Patterns In Neuroplasticity Research, Raza Hasan, Salman Mahmood

Journal of Informatics and Web Engineering

The exponential growth of neuroplasticity research presents profound challenges to the manual synthesis of literature, hindering the identification of emerging therapeutic targets and intervention patterns. While multi-agent Artificial Intelligence (AI) systems have been proposed conceptually to address this, concrete implementations demonstrating end-to-end utility are scarce. This paper details the successful implementation and application of a novel four-agent AI system designed to automate the discovery, extraction, analysis, and validation of patterns in neuroplasticity literature. Our platform consists of four parts: a Literature Discovery Agent (LDA) for corpus gathering, a Concept Extraction Agent (CEA) with a multi-level NLP strategy fallback mechanism for …

Synthetic Data Generation For Healthcare And Wellness: Methods, Applications, And Future Directions, Sellappan Palaniappan, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung, Sumit Dhariwal

Journal of Informatics and Web Engineering

Artificial intelligence in healthcare relies heavily upon the availability of high-quality datasets, but very rigid privacy regimes, institutional silos, ethical issues, and heterogeneous data still serve to limit the availability of real-world clinical data. To combat these limitations, you focus on synthetic data generation as a privacy-preserving and scalable alternative to traditional data generation for healthcare and wellness studies. More relevant is a work on a broad and clear framework of synthesis methods based on statistical modelling, rule-based generation and domain-specific clinical logic, in contrast to recent studies that mainly target sophisticated methods for the deep learning architectures. The paper …

Ai-Powered Continuous Health Monitoring For Chronic Disease Management In Malaysia: Real-Time Risk Prediction Using Wearable Sensors And Digital Biomarkers, Umi Najiah Ahmad Razimi, Yong Yoke Leng, Nor Hapiza Mohd Ariffin, Mohammed Hazim Alkawaz

Journal of Informatics and Web Engineering

Chronic Non-Communicable Diseases (NCDs), such as diabetes mellitus, hypertension, and cardiovascular disease, make up a significant portion of the mortality cases in Malaysia, as well as impose consistent demands for costs related to health care expenditure. According to the cost reports at the national level, major NCDs (cardiovascular disease, diabetes, and cancer) are responsible for RM 9.65 billion annually as direct healthcare cost [3]. However, the current approach to managing chronic diseases focuses heavily on periodic assessments in clinic setting at weekly or monthly intervals. Here, a predictive analysis framework based on AI technology, leveraging continuous monitoring with patient data …

Intelligent Telemedicine Systems For Contactless Heart Rate Estimation Using Deep Learning-Based Rppg, Do Duc Thinh, Nguyen Duc Manh, Nguyen Thi Bich Ngoc, Vu Van Huan

Journal of Informatics and Web Engineering

Over the last ten years, telemedicine has undergone significant developments, from simple communication-based healthcare to smart and data-driven solutions. The solutions are now powered by Artificial Intelligence (AI), Machine Learning (ML), and Internet of Medical Things (IoMT) technologies. Physiological monitoring is traditionally done with contact-based sensors. The sensors include Electrocardiogram (ECG) and pulse oximeters. The sensors have various disadvantages, including hardware costs, difficulty in continuous usage, and discomfort for patients. Photoplethysmography (rPPG), a remote method of physiological monitoring, is a breakthrough technology. The method is used to estimate physiological signals, including Heart Rate (HR), from video streams captured by standard …

Synthetic Data Generation For Healthcare Machine Learning: A Case Study In Vital Signs And Diagnostic Predictions, Sellappan Palaniappan, Rajasvaran Logeswaran, Kasthuri Subaramaniam, Oras Baker, Bui Ngoc Dung

Journal of Informatics and Web Engineering

Healthcare Machine Learning (ML) applications face significant challenges in accessing high-quality training data due to stringent privacy regulations, institutional data silos, and concerns over patient confidentiality. This paper explores synthetic data generation as a viable and privacy-preserving alternative to real patient data for developing ML models in healthcare settings. We present techniques for generating realistic vital signs data including body temperature, blood pressure, heart rate, respiratory rate, and oxygen saturation according to appropriate statistical distributions. In addition, we demonstrate how synthetic datasets generated can be used to train diagnostic prediction models. The generated datasets were applied to multiple diagnostic prediction …

Ai-Powered File Security System With Facial Biometrics, Qr Code, And Otp Verification, Xinyi Loi, Ming Man Chan, Yuk Yii Chan

Journal of Informatics and Web Engineering

Commonly used file-sharing systems suffer from impermissible security risks such as impersonation, credential theft, and QR code exploitation, which can expose sensitive information. This paper describes and evaluates an AI-driven secure file-sharing system designed to alleviate these risks through a multi-layered authentication strategy. The complete system involves AES-128 encryption, unique QR code referencing, live facial recognition, and One-Time Password (OTP) confirmation. In our proposed workflow, the sender encrypts a reference to the file and links it to facial data of the recipient, if applicable, and embeds this information into a QR code, that is dynamically generated during the send operation. …

An Integrated Yolov8 Based System For Real Time Vehicle Tracking And Identification Using Cctv Footage, Riya Jain, Piyush Kumar, Ashwani Kumar Dubey, Angela Amphawan, Neo Tse Kian

Journal of Informatics and Web Engineering

Vehicle identification and tracking play a critical role in security and surveillance, supporting the efforts of law enforcing authorities to prevent crime, monitor traffic, and enforce the vehicle rules and regulations. This research presents a proof-of-concept system that employs YOLOv8 for real time vehicle detection and car type classification using live or recorded CCTV footage. YOLOv8 was chosen after rigorous comparison, it was chosen for its ease of use and efficient image training capabilities, making it well suited for rapid prototyping that the concept needed. The system is designed to extract key vehicle features, including colour, model of the vehicle, …

Enhancing Air Traffic Management Using Spatio-Temporal Deep Learning Predictions, Madan Singh, Azween Abdullah

Journal of Informatics and Web Engineering

Modern air traffic management faces growing challenges, particularly at busy airports that must handle high flight volumes in limited airspace corridors. The study is especially pertinent for such airports, where daily operations must navigate intricate air traffic networks. To overcome these challenges, innovative approaches are needed to predict and manage congestion effectively which remains a major challenge. The paper presents a Spatio-temporal analytical framework for air traffic flow forecasting, utilizing a dual deep-learning architecture that combines network topology evaluation with time-series analysis. The model contains two major components. The first component employs Graph Neural Networks (GNNs) to model spatial relationships …

Analysis And Predictive Modelling Of Ev Charging Patterns And User Behaviour, Yi Xuan Law, Kah Ong Michael Goh, Check Yee Law, Connie Tee, Yong Wee Sek, S M Hasan Mahmud

Journal of Informatics and Web Engineering

As more Electric Vehicles (EVs) are released, the ability to predict energy consumption and charging duration becomes crucial in building optimal infrastructure and a proper system of managing energy. This paper proposes a machine learning model that predicts these two important parameters using a real-world dataset. The dataset consists of 1320 EV charging sessions made between January and February 2024 on Kaggle. The data set includes vehicle specifications, time stamps of sessions, environmental conditions, and user behaviour. Feature engineering tasks followed a thorough preprocessing procedure where missing values were imputed, outliers were removed, and the type of data was converted, …

Machine Learning For Employee Attrition Prediction: A Comparative Study, Kai Jia Low, Sook Ling Lew, Sri Winarno

Journal of Informatics and Web Engineering

Employee turnover remains a long-standing issue that can lead to significant economic losses and a decline in workplace productivity. Traditional methods for predicting natural attrition, such as surveys and rumour assessment, usually fail to capture the complexity and multi-factorial nature of natural attrition. To address this limitation, this paper proposes to develop an advanced prediction model using Machine Learning (ML) algorithms to enhance the accuracy and reliability of export prediction. These methods are applicable to a full set of data covering parameters such as demographics, workplace performance and survey scores. The objective of this model is to provide a data-driven …

Gamification By Design: Methodological Foundations Of The Gamespro Speaking Model For Primary Education, Mohd Zulhairi Che Abd Rahman, Hasiah Mohamed, Siti Zulaiha Ahmad, Aurelio P. Vilbar

Journal of Informatics and Web Engineering

This study presents the development and validation of the GAMESPro model, a structured gamification model designed to enhance English-speaking proficiency among Malaysian primary school students. Despite national reforms and policy initiatives aimed at improving communicative competence, oral English skills remain underdeveloped, particularly in rural and resource-constrained schools. Existing gamification practices in Malaysian classrooms often rely on isolated digital tools and lack pedagogical coherence. Addressing this gap, the study employed the Design and Development Research (DDR) methodology, encompassing three phases: Needs Analysis, Design and Development, and Evaluation. A Systematic Literature Review identified key gamification principles, while validation of ten experts confirmed …

The Development Of Decentralized Ticketing System Using Web3, Yu-Xian Cheng, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Md Ismail Hossen

Journal of Informatics and Web Engineering

Traditional ticketing systems often suffer from major drawbacks such as ticket fraud, duplication, inflated resale prices, lack of transparency, and centralized control over transactions. These issues result in reduced trust and limited flexibility for both event organizers and ticket buyers, especially in unregulated secondary markets. To address these gaps, this paper presents the design and development of a Decentralized Ticketing System (DTS) using Web3 technologies. The system leverages Ethereum blockchain, smart contracts written in Solidity, and NFT-based ticket issuance to ensure security, transparency, and verifiable ownership. Features include wallet-based login via MetaMask, multi-ticket purchasing, QR-based validation, controlled resale pricing, and …

Editorial: Artificial Intelligence In Healthcare And Wellness — Emerging Frameworks, Intelligent Monitoring, And Precision Medicine, Sellappan Palaniappan

Journal of Informatics and Web Engineering

AI is changing our understanding of health and health-care systems in ways that go beyond the traditional one-click-fits-all approach to care. It is also making care more personalised than ever before, with delivery occurring on an ongoing basis and at all times. The six papers in this special issue of the Journal of Informatics and Web Engineering will give you a clear view of where we are headed. They examine several different areas, including: monitoring chronic illness through wearable technology; using deep learning techniques for contactless heart monitoring via rPPG; developing new cancer risk models; identifying neuroplasticity therapeutic pathways using …

An Ai-Based Framework For Heart–Brain–Body Coherence In Wellness Monitoring: A Longitudinal Simulation Study, Asiah Lokman, Akalpita Tendulkar, Yujiao Zhang, Mantena Sireesha

Journal of Informatics and Web Engineering

Present wellness monitoring systems primarily emphasize single-modality metrics, neglecting the intricate, interdependent interactions among cardiac, neural, and behavioral regulatory systems. This paper introduces a machine learning framework that implements heart–brain–body coherence as a dynamic, longitudinally evaluated wellness metric. In contrast to previous simulation-based studies that utilized independently generated, cross-sectional data categorized by deterministic IF-THEN rules, the current study adopts a longitudinal simulation design featuring 80 virtual subjects monitored across 30 consecutive daily profiles (N=2,400 records). This design incorporates physiologically realistic inter-variable correlations and probabilistic coherence labelling derived from a continuous risk-scoring function. The subject-level train/test split (56 training subjects, 1,680 …

A Poet Bears Witness: An Interview With M. L. Williams On The Function Of Literary Studies In Troubled Times, Leah Toth, M. L. Williams

Critical Humanities

Dr. M. L. (Marty) Williams is a poet and professor of literature at Valdosta State University, a comprehensive university in south-central Georgia. He is the author of Game (What Books Press 2021), Other Medicines (Redbone Chapbooks 2008) and co-editor of How Much Earth: The Fresno Poets (Roundhouse Press 2001). The focus of the interview that follows is the future of literary studies in the modern neo-liberal university, a daunting topic in 2026, in the face of what many administrators refer to as the "right-sizing" of literature programs nationwide in both the U.S. and the U.K. Anybody paying attention to the …

Human Wisdom And Artificial Intelligence: Role Of Liberal Arts Education In A Neoliberal World, Purna Chandra Bhusal

Critical Humanities

Neoliberalism is an economic and political ideology that originated in the 1930s and 40s, gained momentum in the late 70s and 80s, and peaked in the 90s and 2000s. Since its inception, neoliberalism has promoted a free-market economy with minimal state intervention, granting individuals the freedom to compete and succeed. Since the last quarter of the twentieth century, the neoliberal system has invested in, incorporated, and promoted Artificial Intelligence (AI), valuing an AI-enhanced corporate ethos. As a result, the success of higher education is generally measured by the quantity of technologically sound, market-ready, AI-trained human capital it produces for industry. …

Enhancing Access And Social Mobility: Metro State University's Liberal Arts Mission In Action, Kristen Lillvis, Kate Ries

Critical Humanities

This article examines how a liberal arts framework has guided Metropolitan State University’s responses to political, financial, and social pressures on higher education. Drawing on the university’s history as an access-oriented, urban institution, the authors present two case studies—the North Star Promise scholarship and emergency grant funding—to demonstrate how mission-driven investments in students’ basic needs advance social mobility and strengthen institutional sustainability. The authors argue that prioritizing student supports, including those available through short-term or contingent opportunities, yields long-term benefits for learners, their communities, and the university.

Submitted: April 13, 2026

Accepted: May 21, 2026

Literature Carries Philosophy’S Open Secret: On Derrida’S Reading Of Romeo And Juliet, Rajesh Sampath

Critical Humanities

This paper attempts a close reading of Derrida’s texts, particularly with a focus on his brief but significant reading of Shakespeare’s The Tragedy of Romeo and Juliet in “Aphorism Countertime” (originally published in French in 1986). We compare this short work with his more sustained engagement with the philosophy of death in Aporias (1993), which itself is an attempted critique of Heidegger’s Being and Time (1927); the latter is, arguably, the most influential work of twentieth-century continental European philosophy, particularly on issues of time, death, and authentic life. The paper tries to show that a fruitful engagement between philosophy and …

Defending The Humanities: Introduction To The Special Issue On “Why The Humanities Matter”, Megan Hutchinson

Critical Humanities

Introduction to the Special Issue

Defending the Humanities: “why the Humanities Matter”

Evaluating The Impact Of Early Contractor Involvement On Enhancing The Performance Of Construction Projects In Egypt, Samah Saleh Abdelraof, Ayman Ahmed Ezzat Othman, Lamees Mostafa Ahmed

Mansoura Engineering Journal

The construction industry constitutes a critical pillar of Egypt’s national economic growth and sustainable development. Despite its strategic importance, the sector continues to face persistent challenges, including chronic cost overruns, schedule delays, and inadequate coordination among stakeholders. These inefficiencies are largely attributed to the dominance of the traditional Design–Bid–Build (DBB) system, which enforces a rigid separation between design and construction, fostering adversarial relationships and project disputes, which highlights the need for alternative procurement approaches. This research investigates the impact of Early Contractor Involvement (ECI) as a collaborative procurement alternative to enhance project performance. Case studies show that ECI improves early-stage …

Semantic Directing: A Human-Ai Workflow For Interative Virtual Cinematography, Lejie Liu

Dartmouth College Master’s Theses

This thesis presents AI Director, a Unity-based prototype for semantic directing in virtual cinematography. The system enables creators to describe cinematic intent in natural language, receive editable shot-strategy plans generated by a large language model, and refine specific shots through text and visual references. By separating semantic planning from deterministic camera execution, the workflow keeps user authorship centered on shot intent while making the resulting camera plans inspectable, editable, and executable in real time. This work explores semantic directing as a human-AI workflow for iterative cinematic authoring in dynamic 3D scenes.

Policy Solutions In The Ev Battery Supply Chain Addressing Unethical Social And Environmental Practices, Mateo Mazariego-Halpern

Undergraduate Theses, Capstones, and Recitals

California should adopt a phased supply chain accountability framework for electric vehicles sold in the state, combining the immediate implementation of a voluntary Fair Trade Decal program with the mandatory adoption of COFFEE (Corporate Operations Free From Environmental Evil) Standards based on CAFÉ standards, by 2035. This thesis identifies a critical gap in U.S. EV policy: current federal legislation regulates where batteries are sourced but not how cleanly or ethically they are produced. The consequences of this regulatory gap are severe, with emissions simply shifting further down the supply chain rather than being eliminated, child labor and indigenous displacement going …

Rapid-Prototyping Nanofabrication: Lcd-Based Projection Lithography And Physical Vapor Deposition, Sabeel Saleem Mohammmed

University Honors Theses

The semiconductor industry's continued growth, driven in large part by demand for artificial intelligence hardware, has highlighted the need for greater workforce development in regions adjacent to major fabrication centers like Oregon's Silicon Forest. This capstone project lays the groundwork for a small scale and student led semiconductor fabrication lab at Portland State University by demonstrating two of the core steps in chip manufacturing: photolithography and thin film deposition. Rather than relying on conventional fixed reticles, this work explores a unique, low cost approach to patterning that uses an ultraviolet-compatible liquid crystal display (LCD) as a programmable reticle, allowing arbitrary …

The Role Of Urban Forests In Mitigating Flooding Effects: A Case Study In Savannah, Georgia, Gillian Bush

Undergraduate Theses, Capstones, and Recitals

Flooding in coastal communities is shaped by the interaction between natural systems and urban development. Chatham County, a low-elevation region in the Savannah, Georgia metropolitan area along the eastern coast of the United States, is prone to coastal storms and frequently affected by flooding. It serves as a useful case study to better understand the factors behind flooding and the areas that are most at risk. This study uses GIS-based analysis to examine the overlap between flood risk, urban forest cover, and impervious surfaces. The results indicate elevated vulnerability along major transportation corridors and barrier islands, where high-risk flood zones …

A Point-Of-Care Hormone Monitoring Platform For Postpartum Health Assessment, Ava Mcdonald, Avary Torres, Frances Warneke

Bioengineering Senior Theses

Our point-of-care hormone monitoring device for postpartum health assessment uses paper-based microfluidics to provide an accessible and affordable hormone monitoring tool, tracking biomarkers correlated with postpartum depression (PPD). The device features a 3D-printed spring-box housing and platform containing chromatography paper testing sheets that are lined with polydimethulsiloxane (PDMS) and wax paper, embedded with specific reagents that generate colorimetric hormone readouts from saliva samples. This device was motivated by the need to address critical shortcomings in PPD care, where societal stigma surrounding mental health and reliance on self-reporting diagnostic methods prevent mothers from truthfully reporting symptoms and receiving timely support and …

Universal Communications Module: Converting Ieee2030.5 Xml Files To Ansi/Cta-2045, Wyatt Bilodeaux

University Honors Theses

The goal of this thesis is to discuss the method and organization of the Universal Communication module capstone project. This project was part of the Portland State University Electrical engineering degree program. While this thesis is part of the University honors curriculum. This thesis goes into detail about team structure and how a 9 person engineering capstone project was managed and organized. It looks at how we broke down the project into sections to be able to properly distribute our resources. This was one of the earliest and most important challenges that this capstone team had to tackle. This was …

Fluvial-Tidal Controls On Dune Morphology And The Effects Of Nature-Based Morphology In Mitigating Storm Impacts, Abiola A. Adebiyi

Electronic Theses and Dissertations 2020 - Present

Nature-based morphological features in coastal areas, such as dunes, coral reefs, and seagrass, play dual roles as both receivers of threats from increasing human activity and climate change and defenders that mitigate potential impacts. Therefore, it is imperative to improve our understanding of how these features may affect hydrodynamic processes under various conditions. To address this need, the objectives of this research are two-fold: 1) to enhance our knowledge of changes of bedform morphology in response to different fluvial-tidal controls in the Lower Columbia River (LCR), and 2) to investigate how natural-based features may mitigate tropical storm impacts using a …

Sediment: A Magical Realist Road Novel, Nicolas Victor Rago

Dartmouth College Master’s Theses

This magical realist novel follows G and Louie, two strangers, as they road trip west to east across the continental United States. As they travel, they drift through time, exploring historical and personal pasts through surreal atmospheric prose.

Fluorescence Cryotomography For Preclinical Validation: Applications In Gene Therapy And Fluorescence-Guided Surgery, Caleb Y. J. Kwon

Dartmouth College Ph.D Dissertations

Fluorescence imaging has become a powerful tool in biomedical research and clinical practice, offering molecular specificity and high sensitivity for visualizing biological processes in real time, However, the successful translation of novel fluorescent probes or medicines from preclinical development to clinical applications requires rigorous validation of their biodistribution and target specificity. Traditional methods for assessing performance, such as bulk excised organ analysis or two-dimensional histology, provide limited information as they cannot capture the complete, organism-wide distribution of these probes or medicines. This thesis aims to improve this analysis pipeline through the application and optimization of fluorescence cryotomography for quantitative, high-resolution …

Deciphering Small Sequence Differences In T Cell Receptor-Antigen Pairing, Yi Han, Yuqiu Yang, James Zhu, Farjana J Fattah, Mitchell S Von Itzstein, Minying Zhang, Casey Bermack, Peixin Jiang, Shailbala Singh, Yanhua Tian, Yifei Hu, Yafang Deng, Xiongbin Kang, Donghan M Yang, Jialiang Liu, Yaming Xue, Chaoying Liang, Indu Raman, Chengsong Zhu, Olivia Xiao, Jonathan E Dowell, Jade Homsi, Sawsan Rashdan, Ke Pan, Shengjie Yang, Mary E Gwin, David Hsiehchen, Yvonne Gloria-Mccutchen, Fangjiang Wu, John V Heymach, Don Gibbons, Junzhou Huang, Chao Cheng, Jianjun Zhang, Cassian Yee, Alexandre Reuben, David E Gerber, Tao Wang

Faculty, Staff and Students Publications

T cells have important functions in development and disease processes through T cell receptor (TCR)-dependent activities. Many tools were developed to predict the binding between TCRs and antigens. However, one of the uncertainties is whether such tools can decipher how small changes in the TCRs or antigenic peptides contribute to binding. We develop a deep learning model, pMTnet-omni, which not only predicts the binding vs. non-binding of TCRs towards pMHCs, but also distinguishes the stronger vs. weaker binding of TCRs similar in sequence. We leverage this capability to interpret the biological rules that govern TCR-antigen pairing. This also enables pMTnet-omni …