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Sociological Theory Meets Generative Ai: A Pedagogical Guide, Kimberly Murray Oct 2026

Sociological Theory Meets Generative Ai: A Pedagogical Guide, Kimberly Murray

The Journal of Public and Professional Sociology

This paper considers the relationship between sociology and generative AI (GenAI) as a phenomenon that may be studied using common sociological concepts and theories, including the Sociological Imagination, Structural Functionalism, Conflict Theory, and Symbolic Interactionism, which are considered foundational to the discipline of sociology. Within each section are contemporary examples of GenAI and how they connect to sociology. This paper benefits a wide audience, as students, faculty, and those who use these theories and concepts in practice settings may adopt them within a variety of contexts, including the classroom, a scholarly presentation, a workshop, or a training opportunity. Sources include …

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Mutation-Based Multi-Agent Test Case Update, Dawei Tian, Jiakun Liu, Yun Peng, Yichen Zhang, Jianlei Chi, Jun Sun, Xiaohong Su Oct 2026

Mutation-Based Multi-Agent Test Case Update, Dawei Tian, Jiakun Liu, Yun Peng, Yichen Zhang, Jianlei Chi, Jun Sun, Xiaohong Su

Research Collection School Of Computing and Information Systems

Modern software systems evolve rapidly under CI/CD practices, where tests are critical for quality. However, substantial code changes often render existing test cases obsolete, causing pipeline disruptions, reduced productivity, and compromised quality. Recent automatic test update approaches leverage LLMs to refine test cases via execution feedback and exact-matching context retrieval, prioritizing executability and line coverage but suffering three limitations: (1) neglecting test assertion adequacy, weakening fault detection; (2) relying on coarse line coverage instead of specific uncovered lines/branches; (3) using exact-matching retrieval, which fails for LLM hallucinated queries. To address these, we propose MuMuTestUp, a mutation-guided multi-agent framework with three …

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Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair, Qinyan Zhou, Peixin Zhang, Jun Sun, Haonan Zhang, Dongxia Wang Oct 2026

Ddor: Delta Debugging For Explainable Overrefusal Testing And Repair, Qinyan Zhou, Peixin Zhang, Jun Sun, Haonan Zhang, Dongxia Wang

Research Collection School Of Computing and Information Systems

While safety alignment and guardrails help large language models (LLMs) avoid harmful outputs, they can also induce overrefusal, i.e., unwarranted rejection of benign queries that merely appear risky. We present DDOR (Delta Debugging for OverRefusal), a fully automated and explainable framework for overrefusal testing and repair in a black-box setting, where only model inputs and outputs are accessible and internal safety mechanisms remain opaque. DDOR applies delta debugging to localize minimal refusal-triggering fragments (mRTFs) that provide phrase-level, explainable evidence for why a refusal occurs. Conditioned on these mRTFs, DDOR generates diverse, context-rich prompts and performs multi-oracle validation to filter intrinsically …

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¡Somos Más! The Latino Vote And The Strength Of Democracy In Massachusetts, Phillip Granberry, Rafael M. Pérez Medina, Carlos Muñoz-Cadilla, Fabián Torres-Ardila, Karla Corres Oct 2026

¡Somos Más! The Latino Vote And The Strength Of Democracy In Massachusetts, Phillip Granberry, Rafael M. Pérez Medina, Carlos Muñoz-Cadilla, Fabián Torres-Ardila, Karla Corres

Gastón Institute Publications

This report identifies important characteristics of Latinos, including their age, citizenship, registration, and turnout. Because data on Latino voters in Massachusetts are limited, the report highlights opportunities for future qualitative research to understand Latino voters' motivations. The first of several reports on this research will soon follow, examining the ecosystem of Latino-serving civic organizations and the grassroots efforts that support Latino voter turnout.

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Size-Tunable Tellurium Quantum Dots By Glancing Angle Deposition, S. M. Sayem, Salim Hussain, Fernando Maia De Oliveira, Ranjitha Kumarapuram Hariharalakshmanan, Gregory Guisbiers, Tansel Karabacak Oct 2026

Size-Tunable Tellurium Quantum Dots By Glancing Angle Deposition, S. M. Sayem, Salim Hussain, Fernando Maia De Oliveira, Ranjitha Kumarapuram Hariharalakshmanan, Gregory Guisbiers, Tansel Karabacak

Faculty Scholarship

Tellurium has a unique helical crystal arrangement and pronounced anisotropy that influence its electronic and optical properties at the nanoscale. This study reports the synthesis of pure tellurium quantum dots (Te QDs) on silicon wafer using glancing angle deposition (GLAD). Quasi-hemispherical dots with lateral sizes ranging from 9 to 28 nm and vertical dimensions of 6 to 10 nm were produced as a function of the deposition duration. A comparison of the dot sizes with the Bohr radii of the charge carriers indicated a regime of strong to intermediate confinement. Structural analyses confirmed the polycrystalline nature and trigonal phase of …

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Quantum Mechanics, Non-Locality, And The Space Discreteness Hypothesis, Wilson A. Zuniga-Galindo Oct 2026

Quantum Mechanics, Non-Locality, And The Space Discreteness Hypothesis, Wilson A. Zuniga-Galindo

School of Mathematical & Statistical Sciences Faculty Publications

The space discreteness hypothesis asserts that the nature of space at short distances is radically different from that at large distances. Based on the Bronstein inequality, here, we use a totally disconnected topological space X as a model for the physical space at short distances. However, we consider the time as a real variable. In this framework, the Dirac–von Neumann formalism can be used. This discreteness hypothesis implies that given two different points in space, there is no continuous curve (a world line) joining them. Consequently, this hypothesis is not compatible with the theory of relativity. We propose R×(R×X)3 as …

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State: The Magazine Of Illinois State University, Fall 2026, Illinois State University, University Marketing And Communications Oct 2026

State: The Magazine Of Illinois State University, Fall 2026, Illinois State University, University Marketing And Communications

State: The Magazine of Illinois State University (1999-present)

Illinois State University alumni magazine

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Study Of A Nonlinear Delayed Parabolic Model For Prion Disease Dynamics With The Unfolded Protein Response, Gangadhara Boregowda, Laurent Pujo-Menjouet, Zhaosheng Feng, Michael R. Lindstrom Oct 2026

Study Of A Nonlinear Delayed Parabolic Model For Prion Disease Dynamics With The Unfolded Protein Response, Gangadhara Boregowda, Laurent Pujo-Menjouet, Zhaosheng Feng, Michael R. Lindstrom

School of Mathematical & Statistical Sciences Faculty Publications

Prion diseases are neurodegenerative disorders characterized by the dynamic spread of misfolded toxic proteins in the brain. In this process, the normal cellular prion protein (PrPC) produced by neurons misfolds into a toxic form known as scrapie prion protein (PrPSc). These misfolded proteins propagate through the brain by converting healthy prions into their toxic form. This biological mechanism can be modeled by a system of nonlinear parabolic partial differential equations, accompanied by a nonlinear delayed integral boundary condition. Our primary objective is to establish the existence of nonnegative classical solutions to this system. Furthermore, we derive a priori estimates for …

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Adolescent Health Across Asia Pacific, 2000–23: A Systematic Analysis For The Global Burden Of Disease Study 2023, Gbd 2023 Asia Pacific Adolescent Health Collaborators, Karly I. Cini, Dorothea Dumuid, Iffat Abbsar, Luo Li, Diandra Amandita Priambodo, Bhoomadevi A, Muhammad Ahmed Abdullah, Abdullah, Richard Gyan Aboagye, Swetha Acharya, Isaac Yeboah Addo, Nurudeen A. Adegoke, Ripon Kumar Adhikary, Usha Adiga, Mohd Adnan, Qorinah Estiningtyas Sakilah Adnani, Obed Adonteng-Kissi, Ebenezer Afrifa-Yamoah, Fransisca Handy Agung, Aqeel Ahmad, Khabir Ahmad, Naved Ahmad, Tauseef Ahmad, Jahanzaib Mian Ahmed, Mushood Ahmed, Shahzaib Ahmed, Kasuni H.M. Akalanka, Muhammad Nadeem Akhtar, Wole Akosile Oct 2026

Adolescent Health Across Asia Pacific, 2000–23: A Systematic Analysis For The Global Burden Of Disease Study 2023, Gbd 2023 Asia Pacific Adolescent Health Collaborators, Karly I. Cini, Dorothea Dumuid, Iffat Abbsar, Luo Li, Diandra Amandita Priambodo, Bhoomadevi A, Muhammad Ahmed Abdullah, Abdullah, Richard Gyan Aboagye, Swetha Acharya, Isaac Yeboah Addo, Nurudeen A. Adegoke, Ripon Kumar Adhikary, Usha Adiga, Mohd Adnan, Qorinah Estiningtyas Sakilah Adnani, Obed Adonteng-Kissi, Ebenezer Afrifa-Yamoah, Fransisca Handy Agung, Aqeel Ahmad, Khabir Ahmad, Naved Ahmad, Tauseef Ahmad, Jahanzaib Mian Ahmed, Mushood Ahmed, Shahzaib Ahmed, Kasuni H.M. Akalanka, Muhammad Nadeem Akhtar, Wole Akosile

Peninsula Medical School

Background: The Asia Pacific region is home to more than half of the world's 1·93 billion adolescents (aged 10–24 years). Addressing adolescent health in this region is of global importance, but to date a systematic analysis of key contributors to disease in adolescents has not been done, which is a barrier to responsive action. This systematic analysis of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 aims to provide a comprehensive assessment of adolescent health across the Asia Pacific region, at both the subregional and national levels, encompassing burden of disease, mortality, and prevalence of adolescent …

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Blue Carbon Ecosystems As Climate Solutions: Sequestration Across Coastal And Marine Environments, Allison K. White, Richard J. Kline, Md. Saydur Rahman Oct 2026

Blue Carbon Ecosystems As Climate Solutions: Sequestration Across Coastal And Marine Environments, Allison K. White, Richard J. Kline, Md. Saydur Rahman

School of Earth, Environmental, & Marine Sciences Faculty Publications

Blue carbon ecosystems (BCEs) such as mangroves, salt marshes, and seagrasses, sequester significant amounts of CO2 from the atmosphere. These ecosystems capture and store carbon in biomass and sediments over long periods. Other marine ecosystems, including the open ocean, kelp forests, bivalve reefs, and coral reefs, have been excluded from the BCEs, but they have the potential to serve as crucial blue carbon sinks or to support adjacent BCEs. Climate change, however, poses severe threats to these ecosystems. Rising sea levels, increasing temperatures, and ocean acidification can lead to habitat loss, reduced biodiversity, and impaired carbon sequestration capacity. Consequently, anthropogenic …

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Microwave Spectrum And Iodine Nuclear Quadrupole Coupling Constants Of Difluorodiiodomethane, Cf2i2, Michael J. Carrillo, Michael J. Tubergen, Garry S. Grubbs, S. A. Cooke, Stewart E. Novick Oct 2026

Microwave Spectrum And Iodine Nuclear Quadrupole Coupling Constants Of Difluorodiiodomethane, Cf2i2, Michael J. Carrillo, Michael J. Tubergen, Garry S. Grubbs, S. A. Cooke, Stewart E. Novick

Chemistry Faculty Research & Creative Works

The high-resolution rotational spectrum of difluoro diiodomethane is reported for the first time. A molecular-beam broadband Fourier transform microwave spectrometer recorded the spectrum over the frequency range of 5.9–18.1 GHz. The rotational constants, centrifugal distortion constants, nuclear quadrupole coupling constants, and nuclear spin-rotation constants for the two equivalent iodine nuclei were determined from fitting 1378 hyperfine transitions of 163 rotational transitions: A = 2743.79691(18)MHz, B = 587.83103(5) MHz, C = 528.72470(5) MHz, χaa = −1213.287(6) MHz, χbb – χcc = −885.210(5) MHz, and χab = 1437.721(5) MHz. 31 dipole-forbidden/quadrupole-allowed transitions, arising from the large nuclear quadrupole coupling effect of iodine, …

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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 Oct 2026

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 …

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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 Oct 2026

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.

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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 Oct 2026

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 …

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Line Of Sight Lighting Effect In Dark Environment And Its Impact On Interactive Horror Game Experience, Hamzah Asyrani Sulaiman, Fuyen Heng Oct 2026

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 …

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Vision Transformer-Based Classification Of Cervical Cancer Images, Muhammad Daniel Alif Jumairi, Itaza Afiani Mohtar, Suraya Masrom, Masurah Mohamad, Lathifah Alfat Oct 2026

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 …

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Comparative Machine Learning Prediction Of Carbon Monoxide Levels And Contributing Factors In Shah Alam, Farzana Zubaidi, Norshahida Shaadan, Septia Devi Prihastuti Yasmirullah Oct 2026

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 …

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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 Oct 2026

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 …

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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 Oct 2026

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 …

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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 Oct 2026

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 …

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Turning Ontologies Into Vectors: Embedding Malay Ontological Knowledge For Smarter Machine Learning, Zayanah Zafirah Zulkipli, Ruhaila Maskat, Noor Hasimah Ibrahim Teo, Jie Zhang Oct 2026

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 …

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A Fuzzy Logic Decision Support System For University Student Dropout Assessment, Adam Aiman A Hamid, Harliza Mohd Hanif, Siti Rahimah Batcha, Nur Zafirah Mohd Sidek, Noratika Nordin, Norain Alwi Oct 2026

A Fuzzy Logic Decision Support System For University Student Dropout Assessment, Adam Aiman A Hamid, Harliza Mohd Hanif, Siti Rahimah Batcha, Nur Zafirah Mohd Sidek, Noratika Nordin, Norain Alwi

Malaysian Journal of Computing (MJoC)

Student dropout continues to be an issue in universities, which has a negative impact on the academic performance of students and the reputation of educational institutions. Since the dropout decision is affected by multiple factors like academic performance, financial situation, stress, and self-motivation, the status of these factors can be highly uncertain, subjective, and conventional assessment methods cannot consider this impreciseness. This study designed a fuzzy logic–based decision support system approach to account for the risk of student dropout in a flexible and human-like way. The purpose of this study was to assess the potential risk of a student dropout …

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Lung Cancer Mortality In Malaysia Through Mortality Dynamics Models, Suraya Fadilah Ramli, Norkhairunnisa Redzwan, Azizi Yahaya, Juliana Martika Mansor Oct 2026

Lung Cancer Mortality In Malaysia Through Mortality Dynamics Models, Suraya Fadilah Ramli, Norkhairunnisa Redzwan, Azizi Yahaya, Juliana Martika Mansor

Malaysian Journal of Computing (MJoC)

Over the last several decades, lung cancer remains one of the most frequent causes of cancer mortality across the world with significant implications for public health planning. Accurate forecast of the mortality rate and life expectancy is necessary for guiding health care resources. This research aims to analyze the historical trend in lung cancer mortality rates, evaluate and compare the accuracy of the three mortality forecasting models; Lee Carter, Age Period Cohort (APC) and Cairns-Blake-Dowd (CBD), forecast lung cancer mortality rate from 2021 to 2031 and compute life expectancy at birth. The lung cancer mortality data for Malaysia covering from …

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Assessment Of Age-Wise Diabetes Risk Using Biochemical Indicators And Machine Learning Classification Models, Farwa Afzal, Mohibullah Aziz, Shariffah Suhaila Syed Jamaludin Oct 2026

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, …

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Modelling Malaysian Mortality With Cause-Of-Death: A Compositional Data Approach To Lee-Carter Extension, Saiful Azril Ishak, Norazliani Md Lazam, Shamshimah Shamsuddin, Nurin Haniah Asmuni, Nordiana Rosdi Oct 2026

Modelling Malaysian Mortality With Cause-Of-Death: A Compositional Data Approach To Lee-Carter Extension, Saiful Azril Ishak, Norazliani Md Lazam, Shamshimah Shamsuddin, Nurin Haniah Asmuni, Nordiana Rosdi

Malaysian Journal of Computing (MJoC)

Forecasting cause-specific mortality is at the core of actuarial valuation, annuity pricing, and long-term public health planning. In traditional extrapolation frameworks such as the Lee-Carter and Li-Lee models, probability leakage is a structural flaw which causes the independently projected cause death rates to not sum to total mortality and results in implausible trajectory crossovers. To overcome these shortcomings, this study explores the application of Compositional Data Analysis (CoDA) extensions namely CoDA Lee-Carter (CDLC) and CoDA Li-Lee (CDLL) to historical Malaysian cause of death data. We transform cause proportions to the unit simplex using centred log-ratio coordinates. This transformation imposes exact …

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Field Assessment Of Power Link Budget Accuracy In A Residential Ftth Gpon Deployment, Arnita, Zulfadli, Tiara Resti Indah Rahmawati, Aswan Muhammad Azizi, Sangkot Wahyu Pulungan Oct 2026

Field Assessment Of Power Link Budget Accuracy In A Residential Ftth Gpon Deployment, Arnita, Zulfadli, Tiara Resti Indah Rahmawati, Aswan Muhammad Azizi, Sangkot Wahyu Pulungan

Malaysian Journal of Computing (MJoC)

Fiber to the Home (FTTH) based on Gigabit Passive Optical Network (GPON) is widely used for residential broadband access because of its high bandwidth capacity, scalability, and efficient use of passive optical infrastructure. During FTTH network planning, Power Link Budget (PLB) analysis is commonly applied to estimate cumulative optical losses and received optical power. However, differences may occur between theoretical PLB calculations and actual field measurements due to practical deployment conditions. This study assesses the consistency of PLB predictions using field measurements from a residential FTTH GPON deployment. Measurements were conducted at two Fiber Access Terminal (FAT) points using an …

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Arabic Clinical Mental Health Named Entity Recognition Via Distant Supervision: Recurrent Vs. Transformer-Based Architectures, Ali A. Jalil, Rabih Sbera Oct 2026

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 …

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Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc Oct 2026

Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc

Journal of Global Hospitality and Tourism

This study investigates consumer responses to low-anthropomorphic service robots in restaurant front of-house roles using the AIDUA (Artificially Intelligent Device Use Acceptance). Data from 1,268  participants were analysed using PLS-SEM. The results revealed that social impact and  anthropomorphism significantly influenced both performance and effort expectancy, while hedonic  motivation influenced only performance expectancy. Performance expectancy strongly influenced  emotions, which in turn significantly influenced both the willingness to use service robots and objections  to their use. However, effort expectancy did not significantly influence emotions. The findings validate the  AIDUA model in this context and offer practical insights for robot design and implementation.

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Implementation Of The Problem-Based Learning Model On The Topic Of The Immune System To Enhance The Critical Thinking Skills Of Grade Xi B Students At Sman 1 Tojo, Nani Sofyanti, Sutrisnawati Sutrisnawati, Vita Indri Febriani, Mohammad Jamhari, Bustamin Bustamin, Raya Agni Sep 2026

Implementation Of The Problem-Based Learning Model On The Topic Of The Immune System To Enhance The Critical Thinking Skills Of Grade Xi B Students At Sman 1 Tojo, Nani Sofyanti, Sutrisnawati Sutrisnawati, Vita Indri Febriani, Mohammad Jamhari, Bustamin Bustamin, Raya Agni

Jurnal Pendidikan Sains

Critical-thinking skills are essential competencies for students in 21st-century learning. Preliminary classroom observations in Class XI B at SMAN 1 Tojo indicated that instruction remained predominantly teacher-centered and provided limited opportunities for students to engage in activities involving interpretation, analysis, evaluation, and inference. This study aimed to improve students’ critical-thinking skills through the implementation of Problem-Based Learning (PBL) on the immune system topic. The study employed a Classroom Action Research (CAR) design conducted over two cycles, involving 28 students in Class XI B. Data were collected using a teacher activity observation sheet and critical-thinking skills tests assessing four indicators: interpretation, …

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Bioherbicidal Potential Of Noni Fruit (Morinda Citrifolia) Extract Against Water Hyacinth (Eichhornia Crassipes), Nur Rahma Afriani, Paulus Hengky Abram, Vanny M Agustina Tiwow, Reny Reny Sep 2026

Bioherbicidal Potential Of Noni Fruit (Morinda Citrifolia) Extract Against Water Hyacinth (Eichhornia Crassipes), Nur Rahma Afriani, Paulus Hengky Abram, Vanny M Agustina Tiwow, Reny Reny

Jurnal Pendidikan Sains

Noni fruit (Morinda citrifolia) contains secondary metabolites that may have potential bioherbicidal activity. This preliminary laboratory experiment aimed to identify flavonoids and tannins in noni fruit extract and to describe the phytotoxic responses of water hyacinth (Eichhornia crassipes) exposed to different extract concentrations. Noni fruit was extracted with 96% ethanol by maceration and concentrated using a rotary evaporator. The extract was qualitatively screened for flavonoids and tannins and applied to water hyacinth at concentrations of 0%, 10%, 20%, 30%, and 50% (v/v), with three plants per treatment. Plant responses were observed for 25 days and analyzed …

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