Digital Commons Network^™

Spatial Bias In Quality Of Estimated Delivery Date Records In The Electronic Health Record, Abigail Lewis, Randi Foraker, Min Lian, Min Zhao, Inez Oh, Aditi Gupta, Antonina Frolova, Philip R O Payne

2020-Current year OA Pubs

OBJECTIVES: This study evaluates the quality of estimated delivery dates (EDD) captured in electronic health record (EHR) systems, focusing on identifying and quantifying bias in these estimates for patients seeking prenatal care.

MATERIALS AND METHODS: We measured spatial and racial biases in the completeness and concordance of EDDs recorded in the EHR for patients seeking prenatal care. Element presence and agreement were compared across the catchment area to identify spatial patterns of bias in EDD records.

RESULTS: We obtained 38 477 unique pregnancy episodes in the EHR representing 33 081 unique patients between 2018 and 2022. Clear spatial and racial …

How Many Pieces Form A Forbidden Picture? Protecting Privacy Through The Mosaic Theory Of The Fourth Amendment, Sam Ciaccio

MC Law Review

No abstract provided.

The High Cost Of Low Pay: How Mississippi's Compensation System For Indigent Defense Violates The Sixth Amendment, Whitney L. Dowell

MC Law Review

No abstract provided.

Restoring The Right To Effectiveness: How An Unreasonably High Burden For Proving Ineffective Assistance Violates The Sixth Amendment, May Hebert

MC Law Review

No abstract provided.

Red, White, And Due: How The Due Process Clause Can Prevent Homeless Encampment Cleanouts From Sweeping Individual Rights Under The Rug, Codie Hicks

MC Law Review

No abstract provided.

Judges In Command: How Judicial Creativity In Interpreting The Ftca Harms Military Discipline, John Clyde Ready

MC Law Review

No abstract provided.

Closing Loopholes Or Creating Gaps: The Future Of Obstruction Of Justice Laws After Fischer V. United States, Helaina Theos

MC Law Review

No abstract provided.

Revisiting Water Use Household Behavior In Metro Manila: Evidence From The Almost Ideal Demand System Model, Albert E. Lamberte

Angelo King Institute for Economic and Business Studies (AKI)

This study revisits household water use behavior in Metro Manila by estimating residential water demand using the almost ideal demand system (AIDS) and the merged 2021 Family Income and Expenditure Survey-Labor Force Survey. The analysis examines how households respond to price, income, and cross-commodity changes under the two concessionaires, Manila Water (east zone) and Maynilad (west zone). Both have operational environments that differ substantially, particularly in non-revenue water (NRW), tariff structures, and service reliability. Results show that water demand is moderately price-inelastic in both zones. However, households serviced by Maynilad exhibit higher price and income elasticities, reflecting tighter expenditure constraints …

Iot-Enabled Smart Durian Harvesting: An Integrated Platform For Real-Time Orchard Monitoring And Sustainable Harvest Management, Norisan Abd Karim, Nur Carmilla Abdullah Muhammad Shafiq, Nur’Aina Daud, Syibrah Naim, Syaheerah Lebai Lutfi

Malaysian Journal of Computing (MJoC)

Durian harvesting requires timely monitoring to ensure that fallen fruits are collected promptly, thereby reducing quality deterioration and potential economic losses. Traditional orchard monitoring relies heavily on manual inspection, making the process time-consuming, labour-intensive, and inefficient, particularly in large orchards. The Internet of Things (IoT) technology has been widely used in smart agriculture to manage orchards, but there are very few existing systems that combine real-time durian fall detection with a mobile application that sends immediate notifications, automatic image capture of fall events, and orchard management functions. This study develops an Integrated IoT-Based Smart Orchard Platform for real-time durian harvest …

Benefit-Cost Analysis: A Practitioner’S Guide, Hue Vuong, Amir Abadi Ghadim, Paul Mattingley, Ross Kingwell, Vilaphonh Xayavong, Tafesse Estifanos, Dipesh Maharjan, Nicholas Thorsager, Christophe D'Abbadie, Johnny Machon, Sud Kharel

Farm Systems and Economics published reports

This guide provides government agency staff with a comprehensive, practical and nationally aligned framework for conducting Benefit-Cost Analysis (BCA) of public investments. BCA is a systematic approach to comparing the full range of expected costs and benefits of a proposed investment, expressed in monetary terms where possible. It helps determine whether an investment is likely to deliver net benefits to society and whether public resources are being directed towards initiatives that generate the greatest overall value. BCA is one of the primary and most widely recommended economic appraisal approaches used across Australian governments when assessing major public investments.

Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry

Engineering and Technology Management Faculty Publications and Presentations

This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …

The Effects Of Pluss: Adapting An Evidence-Based Reading Intervention For Multilingual Learners, Amanda K. Sanford, Christopher J. Pinkney, Sheldon Loman, Julie Esparza Brown, Theresa Just, Kendall Flori, Camila Swift, Holly Ramstead

Educator Licensure Faculty Publications and Presentations

The effects of an evidence-based literacy intervention with and without added supports to address the unique needs of multilingual learners (MLs) were assessed through an ABAB single-case withdrawal design. The dual dependent variables were reading fluency/accuracy and performance on the workbook that included spelling, vocabulary, and reading comprehension tasks. Three students (one each in second, third, and fourth grades) who qualified for English language development services and exhibited low reading performance (second and third graders) or were identified with a reading disability (fourth grader) participated. From the initial baseline phase (A1) to the final intervention phase (B2), adding supports specific …

Line Of Sight Lighting Effect In Dark Environment And Its Impact On Interactive Horror Game Experience, Hamzah Asyrani Sulaiman, Fuyen Heng

Malaysian Journal of Computing (MJoC)

Lighting effects in dark environments represent a fundamental psychological mechanism in horror game design, directly shaping player immersion, emotional tension, and the quality of interactive experience. Although Line of Sight (LoS) technology has been widely adopted across many game genres, empirical investigations examining how LoS-based dynamic lighting specifically affects player engagement, usability, and visual aesthetic perception within single-player horror puzzle contexts remain scarce. This paper presents the design, development, and user evaluation of “No One Left”, a three-dimensional top-down horror puzzle game developed using Unreal Engine 4, which systematically employs LoS-based lighting mechanics within a dark environment to restrict player …

Vision Transformer-Based Classification Of Cervical Cancer Images, Muhammad Daniel Alif Jumairi, Itaza Afiani Mohtar, Suraya Masrom, Masurah Mohamad, Lathifah Alfat

Malaysian Journal of Computing (MJoC)

Cervical cancer is a significant health concern as it is one of the most fatal causes of cancer deaths in women. Detection at an early stage is vital, but conventional diagnostic procedures via Pap smears relies heavily on manual cytopathology inspection, a process prone to human fatigue, diagnostic subjectivity, and severe practitioner shortages in low-resource medical environments. To address these challenges, this study presents an automated cervical cancer image classification framework utilizing advanced Vision Transformer (ViT) architectures. Three ViT variants: Data-efficient Image Transformer (DeiT), Swin Transformer, and Cross-Attention Multi-Scale Vision Transformer (CrossViT) were fine-tuned and evaluated on the benchmark SIPaKMeD …

Comparative Machine Learning Prediction Of Carbon Monoxide Levels And Contributing Factors In Shah Alam, Farzana Zubaidi, Norshahida Shaadan, Septia Devi Prihastuti Yasmirullah

Malaysian Journal of Computing (MJoC)

Carbon monoxide (CO) pollution is a major environmental and public health concern because it is colourless and odourless and is produced mainly by incomplete combustion from vehicles, industries, and household sources. Accurate prediction of CO levels and identification of key contributing factors can support early warning and targeted mitigation strategies. This study compared eight machine-learning regression models—Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), K-Nearest Neighbours (KNN), CatBoost, XGBoost, LightGBM, and Neural Network Regression (NNR)—using daily air-quality data from the Department of Environment Malaysia for 2018–2023. The dataset comprised 42 input features representing air pollutants, meteorological …

A Data Envelopment Analysis Approach To Benchmarking The Efficiency In Selected Malaysian Food Manufacturing Companies, Zuraida Alwadood, Natasya Syafiah Marlis Elias, Norlenda Mohd Noor, Bekzodjon Fayziev

Malaysian Journal of Computing (MJoC)

Food industry, which includes every stage of the production process, from farming and manufacturing to distribution, retail and services, is one of the biggest and most significant sectors in the global economy. The relative efficiency of Malaysian food manufacturing companies is still unclear since many of them lack benchmarking tools and struggle to allocate labor, energy and raw materials optimally. This study applies the Data Envelopment Analysis (DEA) method to examine the relative efficiency of Malaysian food manufacturing companies. Relative efficiency refers to the ratio of the weighted sum of the outputs to the weighted sum of the inputs. The …

Machine Learning-Based Mental Health Risk Prediction And Behavioural Clustering Among Malaysian Undergraduates, Norsyela Muhammad Noor Mathivanan, Debbie Yong Xi Foo, Gerard Soon Lee Chong, Siti Fazilah Shamsudin, Marizkays P. Jamison

Malaysian Journal of Computing (MJoC)

Mental health issues among undergraduate students have become increasingly prevalent, affecting emotional well-being, academic performance, and overall quality of life. The growing number of students experiencing stress, anxiety, and depression highlights the need for effective predictive models to support early risk identification and personalized intervention strategies. This study investigates the performance of several machine learning approaches, including Logistic Regression (LR), Random Forest (RF), Gradient Boosting, LightGBM, Extra Trees, XGBoost, Support Vector Classifier (SVC), and a stacked ensemble model for predicting mental health risk among Malaysian undergraduate students. The study utilizes a dataset comprising demographic, academic, lifestyle, and psychological assessment attributes …

Malaysia Tech Stock Forecasting Using Arima: A Comparative Analysis With Kalman Filter Noise Optimization, Sarah Nadirah Mohd Johari, Ahmad Irfan Mohd Sukeri, Restu Ananda Putra, Ahmad Nur Azam Ahmad Ridzuan

Malaysian Journal of Computing (MJoC)

Forecasting stock prices in Malaysia's technology sector is difficult due to market noise. To mimic market behavior, many models have been developed and gradually improved with new algorithms and machine learning techniques. Relying on a single model often has drawbacks, so hybrid models were introduced to improve prediction stability. This study used a time series method, Autoregressive Integrated Moving Average (ARIMA) that has been proven to be reliable in forecasting various types of data. However, the ARIMA model often struggles with handling nonlinear and noisy data; thus, hybrid integration of the Kalman Filter was introduced to help reduce noise. The …

Turning Ontologies Into Vectors: Embedding Malay Ontological Knowledge For Smarter Machine Learning, Zayanah Zafirah Zulkipli, Ruhaila Maskat, Noor Hasimah Ibrahim Teo, Jie Zhang

Malaysian Journal of Computing (MJoC)

This conceptual study proposes a three-layer methodological framework for representing Malay ontological knowledge in continuous vector spaces and integrating ontology-derived representations with machine-learning models. The framework comprises an Ontology Layer, an Embedding Layer, and an Integration Layer. Its contribution is not a new ontology-embedding algorithm rather, it provides a Malay-oriented methodological organization that links ontology construction, embedding-method selection, symbolic-subsymbolic fusion, ontology-guided consistency checking, and downstream evaluation. In the present study, the ontology layer is represented through a prototype Malay linguistic ontology and its class hierarchy and knowledge graph, whereas the embedding, integration, consistency-checking, and downstream evaluation stages are proposed components …

Assessment Of Age-Wise Diabetes Risk Using Biochemical Indicators And Machine Learning Classification Models, Farwa Afzal, Mohibullah Aziz, Shariffah Suhaila Syed Jamaludin

Malaysian Journal of Computing (MJoC)

Diabetes mellitus is a major metabolic disease across the world, leading to serious complications and healthcare challenges. Prediction of diabetes at an early stage with the help of biochemical as well as demographic factors can lead to better diagnosis and preventive measures for the same. This research paper aims to predict diabetes using biochemical parameters in combination with various machine learning classification techniques, stratified by age group. This study uses a dataset of diabetes data that includes demographic and clinical attributes like age, gender, body mass index (BMI), urea, creatinine, HbA1c, cholesterol, TG, HDL, LDL, and VLDL. Machine Learning classifiers, …

Arabic Clinical Mental Health Named Entity Recognition Via Distant Supervision: Recurrent Vs. Transformer-Based Architectures, Ali A. Jalil, Rabih Sbera

Polytechnic Journal

Arabic Natural Language Processing (NLP) has made great strides, but the accurate extraction of mental state and physiological symptoms from patient-generated clinical consultations in dialectal Arabic, which is plagued with significant morphological complexity, colloquial noise and an extremely limited number of manually annotated clinical benchmarks, remains a significant challenge. In this work, we propose an end-to-end token-level symptom extraction system that is systematic and grounded in distant supervision paradigm and a strict gold standard evaluation with human verification to ensure real-world clinical generalization. A large weakly labelled corpus of 46,873 Arabic patient questions was created based on a clinical lexicon …

Marital Status And Wellbeing Of Females In Egypt: Informing The Discourse And Exploring A New Framework, Hoda Rashad, Mohamed Hassan, Amr El Sayed, Rania Elsebaie

Faculty Books

Egypt is experiencing important changes in marriage formation and dissolution amid broader social, economic, and gender transformations. This study examines these changes by analyzing the recent patterns and social distribution of delayed and non-occurrence of marriage and divorce, their association with women’s empowerment, and their implications for women’s subjective wellbeing. Drawing on nationally representative quantitative data alongside exploratory qualitative evidence, the study also develops and investigates a “Conscious Decision Model” to examine how resources, agency, aspirations, and prevailing gender relations may shape women’s marital decisions and wellbeing. The analysis responds to public narratives that frequently attribute delayed marriage and divorce …

Using Electronic Health Record Audit Logs To Identify Pediatric Intensive Care Unit Teams, Liem M Nguyen, Stefanie S Sebok-Syer, Dane Jacobson, Karley Mariano, John Grunyk, Shelby Burk, Yasaman Nourkhalaj, Kimberley Kirk, Jochen Profit, Christian Rose, A Jay Holmgren, Thomas Kannampallil, Daniel Tawfik

2020-Current year OA Pubs

BACKGROUND: Teamwork influences patient outcomes, but electronic health records (EHRs) do not reliably capture patient care team members, limiting its study. EHR audit logs provide insights into healthcare workers' workflows and may help identify patients' care team members.

OBJECTIVE: To develop, validate, and compare algorithms utilizing EHR audit logs to identify patient-centric primary teams (bedside nurse, frontline clinician, and attending physician) in pediatric intensive care units (ICUs).

METHODS: We observed rounds for 1931 patient days (development = 678; validation = 1253) across pediatric (PICU), neonatal (NICU) and cardiovascular (CVICU) ICUs at a quaternary children's hospital and documented each patient's daytime …

A Tribute To Frank Delno Holland, Jr. (1924-2023), Sigma Gamma Epsilon National President (1970-1975), Paleontologist, And Friend, John M. Erickson

The Compass: Earth Science Journal of Sigma Gamma Epsilon

Frank Delno Holland, Jr., was born in Leavenworth, Kansas, on March 6, 1924. All family and friends, including his students and professional acquaintances, knew him as Bud. His parents were Frank D. Holland and Francys Bell Holland. His paternal grandparents, O.B. and Laura Holland were North Dakotans who had homesteaded in Sargent County during the 1880s, but had relocated to Kansas in 1903. Bud had a rural upbringing on the outskirts of Leavenworth, graduating in 1942 from Leavenworth High school. It seemed natural that he would follow in his father’s footsteps by attending the University of Kansas (KU) so Bud …

Tumor Cell-Derived Gremlin1 Correlates With Tumor Progression In Gastrointestinal Cancers, Amanda C. Bean

Summer Research Program Abstracts

No abstract provided.

Rent-A-Rag: Embedding-Space Watermarks For Auditing Third-Party Rag, Alexandr Goultiaev Tolstokorov, Kyriakos Mouratidis, Javad Dogani, Nikolaos Laoutaris

Research Collection School Of Computing and Information Systems

Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet later have no visibility into whether their documents are being reused without compensation. Auditing this misuse is difficult because the operator is non-cooperative, answers are paraphrased by the generator, and one response may combine evidence from many providers. We propose DirBucket, a provider-side semantic watermarking and black-box auditing framework for document-level reuse in multi-provider RAG. DirBucket watermarks documents by meaningpreserving paraphrases whose embeddings are biased toward provider-bucket secret directions, enabling detection from black-box answers while preserving retrieval utility. On a …

Characterizing And Repairing Obsolete Android Gui Tests Under Ui Evolution, Shiwen Song, Yiheng Xiong, Wenbo Guo, Manqi Sun, Jiaolong Kong, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Graphical user interface (GUI) tests are widely used in regression testing of mobile applications (apps) tovalidate app behavior from the user’s perspective. However, frequent app evolution, such as UI redesignsand feature updates, often renders existing GUI tests obsolete, even when underlying functionality remainsunchanged. Automatically repairing such tests is critical for maintaining test suites and reducing substantialmanual effort. Despite the practical importance, there is still a lack of a systematic understanding of thecharacteristics of obsolete GUI tests and a publicly available benchmark to support their study. To fill thisgap, we construct a benchmark comprising 736 obsolete GUI tests collected from 36 …

Are We Stuck? Modeling And Detecting Deadlocks In Multi-Autonomous Vehicle Systems, Mingfei Cheng, Xiaofei Xie, Lili Quan, Yuan Zhou

Research Collection School Of Computing and Information Systems

Autonomous driving system (ADS) testing is essential to ensure the safety and reliability of autonomous vehicles (AVs) prior to deployment. As ADSs are increasingly deployed in multi-AV traffic environments, it becomes crucial to assess their cooperative performance, particularly with respect to deadlock, a fundamental liveness issue in concurrent systems that can lead to traffic congestion and prolonged stalling. However, the analysis and testing of ADSs’ cooperative capabilities with respect to deadlock remain largely underexplored. In this work, we present the first systematic study of deadlock in multi-AV systems. We formalize deadlock in autonomous driving using a time-indexed wait-for relation grounded …

Compiling Code Llms Into Lightweight Executables, Jieke Shi, Junda He, Zhou Yang, Chengran Yang, Mykhailo Klymenko, Thong (James) Hoang, Sherry (Xiwei) Xu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

The demand for better prediction accuracy and higher execution performance in neural networks continues to grow. The emergence and success of Large Language Models (LLMs) have produced many cloud-based tools for software engineering tasks such as code suggestion. Although effective, cloud deployment raises concerns over privacy, latency, and reliance on network connectivity. Running LLMs locally on personal devices such as laptops would address these issues, because it enables offline use and reduces response time. However, local deployment is challenging, since commodity devices lack high-performance accelerators such as GPUs and are constrained by limited memory and compute capacity, which makes it …

When Going Alone Feels Right: The Effect Of Solo Cues On Interest In Attending Cultural Events, Inyoung Lee, Sang-Hoon Kim

Asia Marketing Journal

With the rise of solo consumers, cultural event marketers face a challenge: how can events appeal to solo consumers without marginalizing group consumers? To address this question, the impact of solo cues in marketing communication is examined through the lens of role and script theory. Two experiments demonstrate that solo cues, which signal the inclusion of solo consumers among attendees, increase solo consumers' attendance interest but do not affect group consumers. For solo consumers, the impact of solo cues is driven by anticipated enjoyment and is particularly pronounced for events entailing a high (vs. low) level of social interaction, where …