Physical Sciences and Mathematics Commons^™

Impact Of Data Quality Types On Computational Time In Data Source Selection Using Ant Colony Optimization, Nor Amalina Mohd Sabri, Abd Samad Hasan Basari, Nurul Akmar Emran

Journal of Informatics and Web Engineering

Data quality varies dramatically from source to source, even within the same domain. Given these challenges, data source selection has emerged as a crucial step in information integration. It demands efficient and scalable approaches that can handle massive data volumes while ensuring the quality of results. Adapting the ACO algorithm to solve the data sources selection problems may lead to inconsistent computational time if the data sources provided are vary in quality. These challenges bring the issues of time consuming in selecting the required data sources. However, how much the computational time needed in solving the data sources selection is …

Lightweight String Similarity Approaches For Duplicate Detection In Academic Titles, Fahrudin Mukti Wibowo, Muhammad Zidny Nafan, Muhamad Azrino Gustalika, Harinda Fernando, Muhammad Hussain, Nur Afiqah Sahadun

Journal of Informatics and Web Engineering

This study addresses the critical challenge of detecting duplicate final year project (FYP) titles in academic institutions, where minor variations like reordering, synonyms, and paraphrasing often obscure plagiarism. We systematically evaluate four string similarity algorithms - Jaro-Winkler, Levenshtein Edit Distance, TF-IDF with Cosine Similarity, and Jaccard Similarity - using a synthetic dataset of 250 title pairs representing common duplication patterns. Our experiments reveal that character-based methods (Jaro-Winkler and Edit Distance) achieve perfect detection (F1-score=1.0) for literal matches, including typographical variations and phrase reordering. At the same time, TF-IDF demonstrates strong semantic capability (F1-score=0.95), albeit with some false positives. Jaccard Similarity …

Improving Code Effectiveness Through Refactoring: A Case Study, Abdullah Almogahed, Manal Othman, Mazni Omar, Samera Obaid Barraood, Abdul Rehman Gilal

Journal of Informatics and Web Engineering

Software refactoring is a crucial practice in modern software development methodologies, such as Agile and DevOps, as it enables teams to iteratively improve and evolve their codebases while minimizing the risk of introducing bugs or regressions. It fosters a culture of continuous improvement and code hygiene, ultimately leading to more robust, maintainable, and scalable software systems. However, research examining the impact of refactoring on code effectiveness is scarce. This study, therefore, seeks to investigate the impact of refactoring methods on the code’s effectiveness. The study was carried out in four distinct phases: refactoring methods selection, case study selection, software metric …

Societies’ Funds Management System Using Blockchain, Jun Xiang Lau, Nur Ziadah Harun, Suziyanti Marjudi, Abd Samad Hasan Basari, Nur Amlya Abd Majid, Roziyani Setik

Journal of Informatics and Web Engineering

Lack of transparency of the funds and lack of immutability of the funds’ records are large problems today, especially in the societies’ funds. Leveraging the Ethereum Blockchain, the system ensures complete transparency and security in recording and accessing financial transactions for any society. Advanced encryption and blockchain consensus protocols guarantee data privacy and resilience against fraud or tampering. The information entered in the system helps the treasurer to effectively manage funds and accurately and transparently pass appropriate financial transactions. This project meets the challenges of transparency and immutability in society’s fund records, providing an assurance system and promoting growth for …

Implementation Of Conjugate Gradient Method For Estimating Inflation Rate In Malaysia, Shin Yi Wong, Nur Syarafina Mohamed, Norhaslinda Zullpakkal

Journal of Informatics and Web Engineering

Optimization methods are valuable for making decisions and identifying the most suitable alternative based on a given objective function. One of the mathematical optimization methods is Conjugate Gradient (CG) method which is commonly used to solve large-scale unconstrained optimization systems with less storage space. Recently, various optimization methods have been studied and used in economics estimating. However, just a few studies have predicted inflation rate using modified CG method. Random initial points are tested on New Three-Terms (NTT) which are modified Rivaie-Mustafa-Ismail-Leong (RMIL+) and Umar Mustapha Waziri (UMW) CG method with ten optimization test functions suggested by Andrei using MATLAB. …

Conjugate Gradient Methods In Fitting Precipitation Of Rainfall Data In Malaysia, Hua Ru Tang, Nur Syarafina Mohamed, Nurul Hajar Mohd Yusoff, Norhaslinda Zullpakkal

Journal of Informatics and Web Engineering

Conjugate gradient method (CGM) is one of the most efficient numerical methods for solving unconstrained optimization problems. It is also known as an iterative method with simple formulation. The classical CGM has always been an interest to the current researchers in improving the formulation which are categorized into three-term (TTCGM), spectral (SCGM), hybrid and scaled CGM. Although there are many variations of the CGM available, choosing the most efficient and effective one for a particular problem can be a time-consuming process. In this study, spectral Hestenes-Stiefel (sHS) CGM with the greatest NOI and central processing time per unit (CPU time) …

Enhancing Imbalanced Data Augmentation: A Comparative Study Of Ganified-Smote And Latent Factor Integration, Rusma Anieza Ruslan, Nureize Arbaiy, Pei-Chun Lin

Journal of Informatics and Web Engineering

One such serious problem in machine learning (ML) is imbalanced datasets. Minority class samples are usually sparse but hold significant meaning. The model can become biased toward the majority class due to unbalanced class distribution. This results in fraudulently high accuracy without being able to detect minority cases. This bias is also most perilous in critical applications, where ignoring minority cases can be highly destructive. To overcome this problem, the Synthetic Minority Oversampling Technique (SMOTE) is one of the most widely used. SMOTE creates balanced class distribution by interpolating between existing minority samples. It creates samples that are too close …

Re: Comments On The Draft Final Butte Mine Waste Repository Geotechnical Investigation Work Plan (Dated September 23, 2025), Emma Rott

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Needs Analysis Of Interactive Learning Media For Teachingheat Transfer In Physics: Students’ And Teachers’Perspectives, Rizki Eka Shintya, Ika Mustika Sari, Ida Kaniawati

Jurnal Pendidikan Sains

Heat transfer material in physics is known to be abstract. This study specifically analyzes the conceptual difficulties, and visualization needs specific to this material, which is different from the analysis of physics needs in general. This analysis does not only look for the need for traditional media but explicitly looks for the need for interactive media (simulation, animation, virtual laboratory). Therefore, this study aims to identify the need for interactive learning media in physics education, specifically in the teaching of heat transfer, from the perspective of students and teachers in West Java, Indonesia. Using a quantitative descriptive survey, data were …

Efficacy Of Teaching At The Right Level Approach Combined With The Problem-Based Learning Model On Science Learning Outcomes At Middle School, Heri Syahputra Azhar Panjaitan, Ryan Dwi Puspita

Jurnal Pendidikan Sains

This research seeks to evaluate the efficacy of the Teaching at the Right Level (TaRL) strategy in conjunction with the Problem-Based Learning (PBL) model on the scientific learning outcomes of eighth-grade students at middle school Islam Terpadu Khairul Imam. This research addresses the inadequate scientific problem-solving abilities and science learning outcomes of Indonesian students, necessitating a more interactive and individualized instructional technique. The methodology used is a quasi-experimental design with a nonequivalent control group. The sample included two purposively chosen eighth-grade classes: one experimental class (n=25) underwent the TaRL–PBL intervention, whereas the control class (n=25) simply used the lecture approach. …

Tableau Part Ii - October 2025, Rubab Shahzad

Day Family Research Lab Workshop Series

Part Two of Introduction to Tableau. Learn to make cool visualizations using Tableau. A hands-on opportunity where we will go over calculated fields, hierarchies, unions, dashboards, and stories.

Prior experience with Tableau is recommended

Skin Cancer Image Classification Using Deep Learning With Data Segmentation Technique, Akhilesh Kumar Shrivas, Hema Vastrakar

Karbala International Journal of Modern Science

The human skin is an impressive organ and structural element often impacted by a diverse range of recognized and unknown diseases. Diagnosing disorders that affect the outermost layer of the body is the most uncertain and difficult component in the scientific field. Dermatological diseases are one of the most significant health concerns in the 21st century since their identification is challenging and costly, plagued with challenges and the subjectivity that comes with human interpretation. The main objective of this piece of research work is to develop a robust model for the classification of skin cancer diseases using deep convolution neural …

Arizona’S Experiential Learning Opportunities: Regional Security Operations Centers And Cybersecurity Clinics, Joshua Kipers, Paul Wagner, Robert J. Honomichl

Journal of Cybersecurity Education, Research and Practice

The increasing frequency, sophistication, and economic impact of cybersecurity incidents have intensified the global demand for a skilled cybersecurity workforce. Traditional academic programs often fail to provide the applied experience necessary to prepare graduates for the rapidly evolving threat landscape. This paper examines Arizona’s innovative approaches to experiential cybersecurity education through the establishment of Regional Security Operations Centers (RSOCs) and the Arizona Cybersecurity Clinic. These initiatives integrate Kolb’s Experiential Learning Theory and the NICE Cybersecurity Workforce Framework to align academic preparation with real-world practice. The RSOCs, supported by the Arizona Department of Homeland Security, provide paid student internships focused on …

Second Moment Of Degree Three L-Functions, Sampurna Pal

Doctoral Theses

Let $F$ be a Hecke-Maa\ss\  cusp form for $SL(3,\mathbb{Z})$. In this dissertation, we obtain a non-trivial upper bound of the second moment of $L(F,s)$ in the $t$-aspect: $$ \int_{T}^{2T}|L(F,1/2+it)|^2 dt\ll_{F,\epsilon} T^{3/2-3/32+\epsilon}.$$ Immediate corollaries include improvements over the existing results on the Subconvexity bound for self-dual $GL(3)$ $L$-functions in the $t$-aspect and for self-dual $GL(3)\times GL(2)$ $L$-functions in the $GL(2)$ spectral aspect, the error term in the Rankin-Selberg problem, and the zero density estimate for $GL(3)$ $L$-functions.

An Integrated Pcb-Based Heating And Auto-Ranging Platform For Volatile Organic Compound (Voc) Detection Using Carbon Nanotube Based Sensors, Thomas Kalach

USF Tampa Graduate Theses and Dissertations

This thesis presents the development, characterization, and integration of a novel low-cost, high-dynamic-range sensor platform for the detection of volatile organic compounds (VOCs), leveraging the unique electrical properties of carbon nanotube (CNT) thin films. The platform introduces a fully integrated auto-ranging analog front-end circuit capable of real-time resistance measurement spanning over eight orders of magnitude ranging from tens of ohms to hundreds of megaohms, without compromising signal resolution or precision. This was achieved through a digitally controlled, multi-path feedback architecture and controllable current source.

To further enhance sensor performance, the system incorporates a copper trace heater beneath the sensor array, …

Development Of Natural Product Analogs As Therapeutic Agents, Jacob Mara

Theses and Dissertations

Cancer stem cells (CSC) are cancer cells in a tumor mass that may self-renew and develop into various cell types, resulting in a heterogeneous tumor. CSCs have been shown to have a role in many facets of cancer formation, including tumor genesis, proliferation, and metastatic activity. They are also implicated in chemotherapeutic treatment resistance and the recurrence of some malignancies. Based on these capacities, CSCs have been identified as the next target for cancer therapy and control. Salinomycin's potential for therapeutic use based on ongoing research, as well as the synthesis of its derivatives and their biological activity, will also …

10.13.2025 Ored Connect, Liz Williamson

ORED Newsletter

• ORED Small Grants Program RFP
• Mississippi Impact Grants RFP
• Contest to Promote Lab Practices
• Field Fest
• AAAS Membership for Faculty, Staff, and Students 

Draft Final 2024 Residential Metals Abatement Program (Rmap) Baptist Student Union (Former Mckinley School) Soil Remedial Action Construction Completion Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Prompt Engineering For Genai In Cybersecurity Incident Response: A Multi-Platform Evaluation Based On Nice Pr-Ir-001, Yuanyuan Liu

Journal of Cybersecurity Education, Research and Practice

This study explores the application of prompt engineering in cybersecurity education, mainly by evaluating the performance of different generative artificial intelligence (GenAI) platforms when performing tasks consistent with the NICE framework role pr-ir-001 - Network Defense Incident Responder. The study employed structured prompts designed for a medical technology environment compliant with HIPAA and NIST SP 800-53, while the tasks of the three GenAI models (GPT-4, Gemini, and DeepSeek) were to generate event response scenarios. Their outputs will be evaluated from four aspects: accuracy, relevance, clarity and completeness.

The results show that the three models differ in depth and consistency, but …

Empirical Methods For Support System Design In Deep Serpentinite Excavations: A Critical Evaluation, Anas Driouch, Redouane Oubah, Abdelaziz Lahmili, Latifa Ouadif

Journal of Sustainable Mining

Underground mining excavations in fragile serpentinite rock masses represent a significant challenge today. The complicated features of these rock formations, especially at great depths, make it difficult to classify them using commonly employed methods. This paper focuses on the application of empirical methods for the classification of serpentinite rock masses and the design of support systems in underground mining excavations. Specifically, it concerns an access tunnel excavated entirely in serpentinite at a depth of 460 m. Geomechanical classification systems indicate that the Tunnelling Quality Index (Q-system) and Rock Mass Rating (RMR) evaluate serpentinites as rock masses of exceptionally poor to …

Building Resilient And Sustainable Supply Chains: A Distributed Ledger-Based Learning Feedback Loop, Tan Gürpinar, Mehmet A. Gulum

Computer Science Faculty publications

Global supply chains face increasing disruptions from cyber threats, geopolitical instability, extreme weather events, and a range of economic, social, and environmental sustainability challenges. As these disruptions intensify, enhancing Supply Chain Resilience (SCR) has become a strategic priority. This study investigates how Distributed Ledger Technology (DLT) can contribute to SCR by mitigating vulnerabilities and strengthening key capabilities within global supply chains. A qualitative research approach is employed, utilizing expert evaluations to examine DLT’s impact on supply chain vulnerabilities and capabilities. Five workshops were conducted with 25 industry professionals from logistics, IT, procurement, and risk management. Experts examined how DLT could …

Bifusenet: A Multimodal Network For Estimating Blood Alcohol Concentration Via Bidirectional Hierarchical Fusion, Abdullah Tariq, Arooba Maqsood, Martin Masek, Syed Zulqarnain Gilani

Research outputs 2022 to 2026

Drunk driving remains a significant public safety challenge, demanding innovative alternatives to conventional methods such as field sobriety tests and breathalysers. Estimating a driver's level of intoxication through facial cues is particularly challenging due to the subtle and person-specific nature of alcohol-induced behaviours. In this paper, we present BiFuseNet, a 3D spatio-temporal multi-modal network designed to classify alcohol impairment levels into three categories: sober, moderate, and severe. Unlike prior approaches that rely on either uni-modal RGB video or hand-crafted facial features, our method exploits complementary physiological cues from RGB and infrared (IR) facial videos. We introduce a Bi-directional Hierarchical Fusion …

Cortenmm: Efficient Memory Management With Strong Correctness Guarantees, Junyang Zhang, Xiangcan Xu, Yonghao Zou, Zhe Tang, Xinyi Wan, Kang Hu, Siyuan Wang, Wenbo Xu, Di Wang, Hao Chen, Lin Huang, Shoumeng Yan, Yuval Tamir, Yingwei Luo, Xiaolin Wang, Huashan Yu, Zhenlin Wang, Hongliang Tian, Diyu Zhou

Michigan Tech Publications

Modern memory management systems suffer from poor performance and subtle concurrency bugs, slowing down applications while introducing security vulnerabilities. We observe that both issues stem from the conventional design of memory management systems with two levels of abstraction: a software-level abstraction (e.g., VMA trees in Linux) and a hardware-level abstraction (typically, page tables). This design increases portability but requires correctly and efficiently synchronizing two drastically different and complex data structures, which is generally challenging.We present CortenMM, a memory management system with a clean-slate design to achieve both high performance and synchronization correctness. Our key insight is that most OSes no …

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

The Effect Of Renewable Energy Consumption, Economic Growth, And Energy Use On Co₂ Emissions In Indonesia: An Ardl Analysis, Raihan Ahmad Mustofa, Mariam Kamila, Rininta Nurrachmi, Syifa Shafnastiara

Jurnal Kebijakan Ekonomi

Abstract This study examines the dynamic relationship between renewable energy consumption, total energy use, economic growth, and CO₂ emissions in Indonesia over the period 1990–2021 using an ARDL approach. The results show a stable long-run relationship with quick adjustment after shocks. In the long run, energy consumption is associated with higher CO₂ emissions, reflecting the continued dominance of fossil fuels. Renewable energy helps reduce emissions in the short term, but its long-term impact remains statistically insignificant. GDP does not show a significant effect, suggesting that meaningful decarbonization will require a more intensive shift in the energy mix.

Keywords: ARDL, renewable …

Discriminative Accuracy Of Cha2ds2-Vasc Score, And Development Of Predictive Accuracy Model Using Machine Learning For Ischemic Stroke Risk In Cardiac Amyloidosis And Atrial Fibrillation, Waqas Ullah, Abhinav Nair, Eric Warner, Salman Zahid, Mansoor Rahman, Palwasha Khan, Indranee Rajapreyar, Sridhara S. Yaddanapudi, M. Chadi Alraies, Said Ashraf, Jeffery Van Hook, Yegeny Brailovsky

Department of Medicine Faculty Papers

BACKGROUND: CHA2DS2-VASc score in cardiac amyloidosis (CA) with atrial fibrillation (AF) is believed to underestimate ischemic stroke risk, necessitating a better predictive model.

METHODS: Data were obtained from the National Readmission Database (NRD). Outcomes between CA-AF and no-CA-AF were compared using multivariate regression analysis to calculate adjusted odds ratios (aORs). AutoScore, an interpretable machine learning framework, was used to develop a stroke risk prediction model, and its predictive accuracy was evaluated with an area under the curve (AUC) using the receiver operating characteristic analysis.

RESULTS: A total of 11,860,804 (CA-AF 22,687 (0.19%) and no-CA-AF 11,838,117) patients were identified from 2015 …

Butte Priority Soils Operable Unit (Bpsou) 2025 Monthly Progress Report. Consent Decree For The Butte Priority Soils Operable Unit. Civil Action No. Cv 89-039-Bu-Seh, Josh Bryson

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Hawc Performance Enhanced By Machine Learning In Gamma-Hadron Separation, R. Alfaro, C. Alvarez, A. Andrés, E. Anita-Rangel, M. Araya, J. C. Arteaga-Velázquez, N. Ghosh, P. Hüntemeyer, K. Leavitt, M. Najafi, Et. Al

Michigan Tech Publications

Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With more than a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich data set, we introduce a machine learning approach for gamma-hadron separation. A multilayer perceptron shows the best performance, surpassing traditional and other machine learning-based methods. This approach shows a notable improvement in the detector’s sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19% increase in significance for the Crab …

Factors Influencing Plant Community Structure And Composition Of Restored Tamaulipan Thornscrub Forests, Jerald T. Garrett, Audrey J. Hicks, Christopher A. Gabler

School of Earth, Environmental, & Marine Sciences Faculty Publications

The Lower Rio Grande Valley (LRGV) of Texas is a biodiversity hotspot due to its high alpha, beta, and gamma diversity and high regional endemism, which are at high risk of degradation. The region has lost 95% of its native thornforest habitat primarily due to agricultural and urban expansion. This study aims to evaluate the current vegetative structure and composition of restored thornforest sites located in the LRGV to identify restoration methods and site characteristics that affect forest restoration outcomes. Twelve restored thornforest sites were selected for this study that varied in time since restoration, patch size, degree of isolation, …

Development Of Students Worksheet Based On Project-Based Learning To Train Critical Thinking Skills At Vocational High School, Dia Ayu Permatasari, Harun Nasruddin

Jurnal Pendidikan Sains

Critical thinking is a cognitive skill that helps identify problems and generate solutions, leading to logical decisions or considerations for solving those problems. Students' critical thinking skills can be trained through learning activities to further improve students' critical thinking skills with learning tools. Therefore, this research intends to produce a student worksheet based on project-based learning to train students' critical thinking skills on the colligative properties of solutions. The development research uses the 4D development model from Thiagarajan, limited to the development stage. A limited trial was conducted with 30 students of industrial chemical engineering expertise at Vocational High School …