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2026

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An Empirical Evaluation Of Machine Learning Methods And Text Classifiers For Sentiment Analysis Of Online Consumer Reviews, Pei Qin Lo, Sew Lai Ng, Li-Xian Jiao Feb 2026

An Empirical Evaluation Of Machine Learning Methods And Text Classifiers For Sentiment Analysis Of Online Consumer Reviews, Pei Qin Lo, Sew Lai Ng, Li-Xian Jiao

Journal of Informatics and Web Engineering

This study aims to identify the best predictive model for analysing online product reviews (OPRs) in the electronics industry, with a secondary focus on leveraging unstructured customer feedback to support product improvement. Using a dataset of 9,675 Oppo mobile phone reviews, this study employs three classification models—Random Forest, Support Vector Machine (SVM) and Logistic Regression–paired with Term Frequency-Inverse Document Frequency (TF-IDF) or bidirectional encoder representation transformer (BERT) as the embedding models to analyse customer sentiment and derive actionable insights. The methodology features a comprehensive analysis pipeline that includes text preprocessing with the Natural Language Toolkit (NLTK), feature extraction using) vectorization …

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A Conceptual Framework On Development Of Sign Language Chatbot For E-Commerce, Salma Jahan Nisha, Nabhan Salih, Wan-Noorshahida Mohd-Isa Feb 2026

A Conceptual Framework On Development Of Sign Language Chatbot For E-Commerce, Salma Jahan Nisha, Nabhan Salih, Wan-Noorshahida Mohd-Isa

Journal of Informatics and Web Engineering

This study proposes and validates a conceptual framework using a sign language (SL) chatbot in the e-commerce domain to improve accessibility. Currently, the use of SL is a less-explored area in online platforms. It is difficult for any communication-challenged person to interact online for buying products and services, using a chatbot, particularly for people using SL. The objective of this study is to introduce a novel hybrid architecture using SL Recognition with a conversational-based chatbot agent via a custom Application Programming Interface (API) in e-commerce platforms. Our work proposes a combined hybrid chatbot framework model using Convolutional Neural Network (CNN) …

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Dynamic Multi-Layered Hardware Obfuscation With Behavioral Drift Locking For Sat-Resistant Designs, Ahmed Yehia Salah Mohamed Emish Feb 2026

Dynamic Multi-Layered Hardware Obfuscation With Behavioral Drift Locking For Sat-Resistant Designs, Ahmed Yehia Salah Mohamed Emish

Theses and Dissertations

The globalization of the semiconductor supply chain has introduced critical vulnerabilities, including intellectual property (IP) piracy, reverse engineering, and hardware tampering. While traditional logic locking offers a baseline of defense, the emergence of powerful Boolean satisfiability (SAT) solvers has rendered many static obfuscation techniques ineffective. This work proposes a Dynamic Multi- Layered Hardware Obfuscation Framework that utilizes Behavioral Drift Locking (BDL) to provide a robust defense-in-depth against advanced adversarial models. The methodology integrates four synergistic layers: • Dynamic Key Management using a time-dependent rotation mechanism that updates keys every clock cycle to prevent static analysis. • Control Obfuscation through opcode …

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Editorial Preview For February 2026 Issue, Fong-Yee Lee, Su-Cheng Haw Feb 2026

Editorial Preview For February 2026 Issue, Fong-Yee Lee, Su-Cheng Haw

Journal of Informatics and Web Engineering

The February 2026 issue presents 17 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Web and Cloud Technologies, Recommender Systems and Software Engineering. The collection reflects JIWE’s ongoing effort to advance research in the informatics and web engineering domain. A special thematic section, guest-edited by Prof. Ts. Dr. Hairulnizam Bin Mahdin, centers on “Intelligent Systems and the Next Wave of Digital Innovation”. This section studies the pervasive role of AI and machine learning in multiple sectors of industrial automation and disaster preparedness. These studies also emphasize on trust, transparency, …

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Radiology Report Generation Using Deep Learning And Web-Based Deployment For Chest X-Ray Analysis, David Agbolade, Peyman Heydarian, Shakeel Ahmad Feb 2026

Radiology Report Generation Using Deep Learning And Web-Based Deployment For Chest X-Ray Analysis, David Agbolade, Peyman Heydarian, Shakeel Ahmad

Journal of Informatics and Web Engineering

The huge rise in the number of medical images has caused a major problem in radiology departments. Radiologists are now working harder than ever, which affects the quality of their diagnoses and patient care. It takes 15 to 30 minutes to write a manual radiological report for each case, and different people may see things differently. Modern departments process over 230 cases a week, which causes long delays in diagnosis. Automated report generation systems that are already in use have a lot of problems, such as not being able to be interpreted clinically, not having enough Digital Imaging and Communications …

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Deep Learning-Based Automatic Detection And Diagnosis Of Tuberculosis From Chest X-Ray Images: A Comprehensive Analysis, Paschal C. Ahanotu, Deborah A. Adedigba, Raza Hasan, Sellappan Palaniappan Feb 2026

Deep Learning-Based Automatic Detection And Diagnosis Of Tuberculosis From Chest X-Ray Images: A Comprehensive Analysis, Paschal C. Ahanotu, Deborah A. Adedigba, Raza Hasan, Sellappan Palaniappan

Journal of Informatics and Web Engineering

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infections and 1.3 million deaths globally. Conventional diagnostic approaches involving sputum smear microscopy, culture assays, and GeneXpert MTB/RIF are characterized by excessive turnaround times, elevated costs, and dependency on specialised infrastructure and trained personnel. These constraints are exacerbated in resource-poor countries, resulting in delayed diagnosis, delayed therapy initiation, and enhanced disease transmission. This work investigates the application of deep learning algorithms to automatically diagnose TB …

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Ontology-Based E-Commerce Recommender System: A Hybrid Semantic Filtering Approach, Jocelyn Pua, Su-Cheng Haw, Lucia Dwi Krisnawati, Shaymaa Al-Juboori, Gee-Kok Tong Feb 2026

Ontology-Based E-Commerce Recommender System: A Hybrid Semantic Filtering Approach, Jocelyn Pua, Su-Cheng Haw, Lucia Dwi Krisnawati, Shaymaa Al-Juboori, Gee-Kok Tong

Journal of Informatics and Web Engineering

The rapid growth of e-commerce has led to product overload, which has resulted in personalized product discovery becoming a crucial problem for consumers. Classic recommender systems (RS), which rely heavily on content-based filtering or collaborative filtering, tend to face well-known problems such as cold start, data sparsity, and ignorance of semantics. Such constraints frequently result in irrelevant or redundant suggestions, thereby lowering the user satisfaction and conversions. This study presents a hybrid ontology-based e-commerce recommender system that combines symbolic reasoning with deep semantic matching. The system is based on a Neo4j graph database to capture structured product relationships and is …

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Ai-Driven Malware Analysis And Detection: A Comprehensive Survey Of Techniques, Trends And Challenges, Salman Khan, Hasnat Raza, Mansoor Alam Feb 2026

Ai-Driven Malware Analysis And Detection: A Comprehensive Survey Of Techniques, Trends And Challenges, Salman Khan, Hasnat Raza, Mansoor Alam

Journal of Informatics and Web Engineering

Malware represents the most critical threat in cybersecurity, meant to compromise the security for any individual or any organization. These are covert software, designed to perform malicious act like data theft, data alteration, and to interrupt a normal operation of the services. The persistent evolution of malware has called for more sophisticated techniques in its detection and prevention, resulted into direct need of Artificial Intelligence in cybersecurity. Artificial intelligence, using machine learning techniques and rising concepts like neural networks has greatly improved the traditional static and dynamic ways of detecting malware. Advances in AI-driven solutions have made them much more …

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Optimised Data Integration Using Transformer Model And Resource Description Framework, Jerome Aondongu Achir, Muhammad Abdulkarim, Mohammed Abdullahi Feb 2026

Optimised Data Integration Using Transformer Model And Resource Description Framework, Jerome Aondongu Achir, Muhammad Abdulkarim, Mohammed Abdullahi

Journal of Informatics and Web Engineering

Organizations have become highly reliant on a range of data sources that span structured, semi-structured, and unstructured data types. These repositories allow large-scale storage for faster ingestion and analytics but pose tremendous challenges of integration owing to schema and contextual differences. Traditional data integration methods, such as the ontology-based Resource Description Framework (RDF), are often inadequate when dealing with these challenges. They specifically struggle with the dynamic evolution of the schema of data sources, context-aware interpretation, and achieving interoperability across heterogeneous data sources. This paper presents an integrated system that augments resource description knowledge with token embeddings using the attention …

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Xai -Driven Explainability For Cardiovascular Diseases Prediction, Jacqueline Dike, Jarutas Andritsch Feb 2026

Xai -Driven Explainability For Cardiovascular Diseases Prediction, Jacqueline Dike, Jarutas Andritsch

Journal of Informatics and Web Engineering

The adoption of artificial intelligence (AI) in cardiovascular disease prediction has significantly improved risk stratification, offering new avenues for early diagnosis and preventive care. With the growing availability of electronic health records and structured clinical datasets, machine learning (ML) and deep learning (DL) models have demonstrated strong predictive capabilities. However, despite their performance, its adoption in healthcare is often constrained by the lack of transparency and interpretability in many ML and DL models. This lack of explainability undermines clinical trust and raises ethical concerns. In high-stakes domains such as CVD prediction, clinicians require not only accurate outputs but also clear …

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Sentiment-Based Music Recommendation System Using Natural Language Processing For Emotion-Aware Song Suggestions, Yu Bui Xuan, S. Prabha Kumaresan, Naveen Palanichamy, Mohamed Uvaze Ahamed Ayoobkhan Feb 2026

Sentiment-Based Music Recommendation System Using Natural Language Processing For Emotion-Aware Song Suggestions, Yu Bui Xuan, S. Prabha Kumaresan, Naveen Palanichamy, Mohamed Uvaze Ahamed Ayoobkhan

Journal of Informatics and Web Engineering

Music plays a vital role in influencing emotions, mood, and mental health. However, classical music recommendation systems mostly rely on listening to history, genre preference, or popularity, ignoring the listener’s mood. Motivation to go further in this area gave birth to this study, where a new sentiment-based music recommendation system is being designed by incorporating Natural Language Processing (NLP) and Machine Learning (ML) techniques to provide emotion-aware song recommendations. The system collects various audio features such as valence, energy, tempo, and danceability from music distribution platforms such as Spotify, which are well-known indicators for classifying the emotional tone of a …

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A Unified Cloud-Based System For The Tyre Retail Industry: Design And Implementation Of Tracktive, William Theo Wei Loon, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong, Kuok-Kwee Wee Feb 2026

A Unified Cloud-Based System For The Tyre Retail Industry: Design And Implementation Of Tracktive, William Theo Wei Loon, Siew-Chin Chong, Kian-Ming Lim, Lee-Ying Chong, Kuok-Kwee Wee

Journal of Informatics and Web Engineering

In the Malaysian concurrent tyre retailing industry, most of the business activities are carried out in the traditional labour-intensive ways. This manual process is prone to inefficiencies, increases operating costs and increases the likelihood of error. The current paper outlines Tracktive, a unified and cloud-based solution that was conceptualized and built to automate the industry by centralizing the ordering of tyres, inventory and order management to a single cloud-based platform. Tracktive is built on microservice architecture and event-driven architecture and deployed with the help of a simulated cloud environment. All the core functional modules are independent, scalable modules, each having …

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Enhancing Fraud Detection In Financial Transactions Using Lightgbm And Random Forest, Wan-Ping Khor, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Riasat Khan Feb 2026

Enhancing Fraud Detection In Financial Transactions Using Lightgbm And Random Forest, Wan-Ping Khor, Kah-Ong Michael Goh, Check-Yee Law, Connie Tee, Yong-Wee Sek, Riasat Khan

Journal of Informatics and Web Engineering

In recent years, the frequency and complexity of financial fraud have been rising and have become an urgent challenge for the global financial system. Traditional rule-based detection methods struggle to cope with new types of fraud, especially in terms of real-time detection, generalization ability, and accuracy. To overcome these limitations, machine learning techniques have gradually emerged as a promising solution for identifying fraudulent transactions with better flexibility and scalability. Based on the publicly available European credit card fraud transaction dataset, this study proposes a hybrid model that combines the advantages of LightGBM and Random Forest, aiming to improve the accuracy, …

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Real-Time Posture Monitoring For Effective Exercise Using Mediapipe Python, Wan Izzul Wafiq Wan Noor Asmawi, S. Prabha Kumaresan Feb 2026

Real-Time Posture Monitoring For Effective Exercise Using Mediapipe Python, Wan Izzul Wafiq Wan Noor Asmawi, S. Prabha Kumaresan

Journal of Informatics and Web Engineering

Maintaining proper posture during exercise is crucial for preventing injuries and maximizing workout efficiency. This project aims to develop a real-time posture monitoring system using MediaPipe and OpenCV to provide instant feedback on the exercise form. The system captures video input through a webcam, processes it using OpenCV, and utilizes MediaPipe’s pose-estimation model to detect key body landmarks. By analysing the joint angles and comparing them to predefined optimal postures, the system evaluates the user’s form and provides corrective feedback in real time. This approach eliminates the need for expensive wearable sensors, making posture monitoring more accessible and user friendly. …

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Design And Implementation Of A Web-Based Lecture Timetable Scheduling System, Suraj Abubakar, Mohammed Kabir Dauda, Maryam Abdullahi Musa, Bala Muhammad Muhammad Feb 2026

Design And Implementation Of A Web-Based Lecture Timetable Scheduling System, Suraj Abubakar, Mohammed Kabir Dauda, Maryam Abdullahi Musa, Bala Muhammad Muhammad

Journal of Informatics and Web Engineering

Timetable management is a controversial but core administrative activity at tertiary learning institutions, normally saturated with timetabling clashes, resource wastage, and communication breakdowns. This study describes the development and validation of a Web-Based Lecture Timetable Scheduling System (WLTSS) for Abubakar Tafawa Balewa University (ATBU) Department of Computer Science. The system was implemented to automatically and effectively build, modify, and publicize lecture timetables. On Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), JavaScript, and React as frontend technologies, the system provides an adaptable interface with real-time data updates through the Reacts component model foundation. MySQL and Node.js are used to run …

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Test Case Prioritization Using Ant Colony Optimization To Improve Fault Detection And Time, Nurezayana Zainal, Muhammad Sh Salleh, Nur Atiqah Wahidah Sulaiman, Ammar Alazab, Nur Liyana Sulaiman Feb 2026

Test Case Prioritization Using Ant Colony Optimization To Improve Fault Detection And Time, Nurezayana Zainal, Muhammad Sh Salleh, Nur Atiqah Wahidah Sulaiman, Ammar Alazab, Nur Liyana Sulaiman

Journal of Informatics and Web Engineering

Regression testing plays a critical role in ensuring the reliability and quality of software following continuous integration and development. However, executing all test cases during regression testing can be time-consuming and resource-intensive. Test Case Prioritization (TCP) addresses this challenge by determining an optimal execution order of test cases that maximizes early fault detection while minimizing execution time. Optimization algorithms contribute significantly to enhancing the effectiveness of TCP while utilizing limited resources. This study proposes an Ant Colony Optimization (ACO) algorithm to address the TCP problem, leveraging its strength in navigating complex search spaces inspired by the foraging behavior of real …

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Flood Disaster Preparedness And Response Using A Web-Based Integrated Flood Management System (Ifms), Mohamed Abdiraman Abdirahim, Abd Samad Bin Hasan Basari, Malik Bader Alazzam, Muhammad Shukri Bin Che Lah, Rabiah Binti Ahmad, Aida Binti Mustapha Feb 2026

Flood Disaster Preparedness And Response Using A Web-Based Integrated Flood Management System (Ifms), Mohamed Abdiraman Abdirahim, Abd Samad Bin Hasan Basari, Malik Bader Alazzam, Muhammad Shukri Bin Che Lah, Rabiah Binti Ahmad, Aida Binti Mustapha

Journal of Informatics and Web Engineering

Floods are one of the most frequent and damaging natural hazards in Malaysia, especially in low-lying places, such as Batu Pahat and Johor. National flood monitoring systems, such as InfoBanjir and Malaysian Meteorological Department (MET) Malaysia, have disjointed data pools, late updates, and inadequate public access. In this paper, we present an IFMS, which is a contemporary web platform developed to integrate national flood management systems through data collection, automatic processing, dynamic visualization tools, and others. The system architecture consists of three main layers: IoT-enabled flood sensors, centralized web server, and responsive user interfaces. Backend processing is performed using Laravel, …

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Evaluating Accuracy Latency And Robustness Of Face Recognition Models For Real-Time Web Applications, Alani Fatini Sharzizi, Nur Afiqah Sahadun, Abdulkadir Hassan Disina, Harinda Fernando Feb 2026

Evaluating Accuracy Latency And Robustness Of Face Recognition Models For Real-Time Web Applications, Alani Fatini Sharzizi, Nur Afiqah Sahadun, Abdulkadir Hassan Disina, Harinda Fernando

Journal of Informatics and Web Engineering

Face-recognition technology is one of the most important advancements in the field of computer vision. They play a crucial role in many applications, including biometric authentication, surveillance, online security, and interactive web systems. The use of web-based solutions is increasing continuously. Therefore, accurate and fast recognition models employing few resources are required in real-world applications. However, because of the challenges related to such environments, including the lighting, occlusion, pose, and computing power of client devices, it is difficult to ascertain which model will be most successful in a real-life scenario. The purpose of this research is to compare four deep …

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An Enhanced Glowworm Swarm Optimization For Minimizing Surface Roughness In Die Sinking Electrical Discharge Machining, Nurezayana Zainal, Muhammad Ammar S.M. Shahrom, Mohamad Firdaus Ab. Aziz, Salama A. Mostafa, Anis Farhan Kamaruzzaman, Nor Bakiah Abd. Warif Feb 2026

An Enhanced Glowworm Swarm Optimization For Minimizing Surface Roughness In Die Sinking Electrical Discharge Machining, Nurezayana Zainal, Muhammad Ammar S.M. Shahrom, Mohamad Firdaus Ab. Aziz, Salama A. Mostafa, Anis Farhan Kamaruzzaman, Nor Bakiah Abd. Warif

Journal of Informatics and Web Engineering

Electric Discharge Machining (EDM) is a non-traditional machining process that utilizes electric sparks between an electrode and a workpiece submerged in a dielectric fluid to ablate a material. It is commonly used in die-making, aerospace, automotive manufacturing, and medical manufacturing, because it can machine hard and complex materials with high precision. In this work, a Surface Roughness Optimization for EDM (SRO-EDM) model is proposed to investigate the machining performance of the die-sinking EDM process of titanium alloys. A regression-based combined approach of Glowworm Swarm Optimization (GSO) and a Two-Factor Interaction (2FI) model has been proposed to investigate the impact of …

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The Language Matters In Restaurant Menus: The Saudi Arabian Marketing Perspective, Abdulaziz Khalid Alotaibi Feb 2026

The Language Matters In Restaurant Menus: The Saudi Arabian Marketing Perspective, Abdulaziz Khalid Alotaibi

UB Journal for Humanities

The study investigates the effect of variations of menu language on customer purchasing decisions in Saudi Arabian restaurants operating under fixed price systems. Furthermore, the study examines how different linguistic approaches used in menu descriptions impact customer ordering behaviors within the Saudi hospitality industry’s unique culture and multilingual setting. Thus, a cross-sectional quantitative survey employed experimental vignettes across four language conditions: basic descriptions, sensory-rich descriptions, culturally authentic words, and premium positioning phrases. Hence, through an application of stratified random sampling, data were collected from 447 restaurant customers living in Saudi Arabia’s five most significant metropolitan areas. The findings indicated that …

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Ring Learning With Errors: Quantum Safe Encryption For Space-Based Assets, Gage Hauptman, Beck Jorgensen Feb 2026

Ring Learning With Errors: Quantum Safe Encryption For Space-Based Assets, Gage Hauptman, Beck Jorgensen

Student Research Symposium (SRS)

Space-based assets rely on public key cryptography to ensure secure command, control, and telemetry operations. However, conventional algorithms like Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC) are susceptible to Shor’s algorithm when exposed to advanced quantum computing. In this poster, we focus on a quantum-resistant encryption methodology rooted in polynomial rings, specifically Ring Learning With Errors (RLWE). Rather than assessing its quantum resilience, we aim to evaluate throughput and timing variations across diverse telemetry data payloads on representative CPU hardware commonly used in space assets. We will analyze the encryption durations of telemetry payloads and project an experimental encryption rate …

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Reduction Of Lunar Dust Adhesion Via Biomimetic Surfaces, Alyssa Vega Feb 2026

Reduction Of Lunar Dust Adhesion Via Biomimetic Surfaces, Alyssa Vega

Student Research Symposium (SRS)

Lunar regolith poses significant risks to exploration machinery and equipment due to its highly abrasive, fine, angular, and electrostatic nature, as observed during the Apollo missions. A promising approach to mitigate these effects is the use of bio-inspired, or biomimetic, surfaces. These surfaces replicate natural micro and nanoscale textures to achieve functional properties such as reduced adhesion and enhanced durability. This study aims to quantify the correlation between surface morphology, surface energy, and adhesion in engineered biomimetic samples. Additively manufactured samples were investigated using optical profilometry to quantify surface roughness and topography, enabling correlation of morphology with adhesion and wettability …

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In Fact: The Age Of The Expert, Lee Gutkind Feb 2026

In Fact: The Age Of The Expert, Lee Gutkind

Creative Nonfiction Magazine

No abstract provided.

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A Proposed Research Into Parameter Variation For Stability, Abigail Butcher, Rogelio Gracia Otalvaro Feb 2026

A Proposed Research Into Parameter Variation For Stability, Abigail Butcher, Rogelio Gracia Otalvaro

Student Research Symposium (SRS)

The Oregonator model can be used to analyze nonlinear changes in systems and can predict the stability of the system. It is an autocatalytic reaction model and a Multiple Input Multiple Output case study that provides a reduced mathematical description of the Belousov–Zhabotinsky (BZ) reaction, a non-equilibrium oscillating chemical reaction. It captures the nonlinear chemical oscillations that arise from far-from-equilibrium kinetics. Bifurcation Analysis (BA) offers a mathematical approach to understanding how systems behave under changing conditions by examining nonlinear behaviors and transitional states, known as bifurcations. This poster demonstrates a novel framework that utilizes BA to assess stability, demonstrated through …

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Visualizing The Invisible: Simulating Electromagnetic Field In 3d Space With Matlab, Riley Esperanca, Aashman Gupta, Hailey Grabinski, Ameya Sute, Jacob Summerhays, Eliane Dean Feb 2026

Visualizing The Invisible: Simulating Electromagnetic Field In 3d Space With Matlab, Riley Esperanca, Aashman Gupta, Hailey Grabinski, Ameya Sute, Jacob Summerhays, Eliane Dean

Student Research Symposium (SRS)

Electromagnetic field behaviours in free space are defined by Maxwell’s Equations, which couple the temporal and spatial variations of electric and magnetic fields through partial derivatives. These derivatives quantify the rate of change of each field’s vector component with respect to position and time in 3D lattice, forming the basis for numerical field analysis. This research will develop a mathematical and computational framework using multivariable calculus to model, simulate, and visualize electromagnetic wave propagation in free space using MATLAB. Gradient, divergence, and curl operations are implemented to compute local field variations and energy transfer. The resulting data are used to …

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Constraining The Impact Threat From Small Bodies In Near Earth And Cislunar Space, Skylar G. Butler, Jarrett Dieterle, Henry Sanders, Aiden Kelleher Feb 2026

Constraining The Impact Threat From Small Bodies In Near Earth And Cislunar Space, Skylar G. Butler, Jarrett Dieterle, Henry Sanders, Aiden Kelleher

Student Research Symposium (SRS)

Understanding and mitigating the impact threat posed by small bodies in near-Earth and cislunar space is vital for planetary defense, spacecraft safety, and sustainable space operations. This research integrates dynamical evolution modeling, synthetic observations, and reprocessed infrared data to assess the origins and potential hazards of debris from young asteroid families. By modeling the trajectories and orbital evolution of fragments from the Datura, Emilkowalski, 1992 YC2, Lucascavin (Nesvorný & Vokrouhlický, 2006), and Veritas (Nesvorný et al., 2003) families, we constrain how these sources contribute to near-Earth debris populations. Reprocessing Infrared Astronomical Satellite (IRAS) data with Fourier-filtering techniques reveals fine-structure variations …

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Solar Wind–Magnetosphere Coupling During The September 2025 Cme: Multi-Spacecraft Analysis Using Mms And Omni Data, Skylar G. Butler, Margret Stanley Feb 2026

Solar Wind–Magnetosphere Coupling During The September 2025 Cme: Multi-Spacecraft Analysis Using Mms And Omni Data, Skylar G. Butler, Margret Stanley

Student Research Symposium (SRS)

A sequence of two coronal mass ejections (CMEs) impacted Earth in early September 2025, producing consecutive geomagnetic disturbances. The first CME, launched on 31 August 2025 and observed as a halo in SOHO/LASCO imagery, reached Earth on 1 September (~20:45 UTC), generating a clear interplanetary shock. OMNI 1-min solar-wind data show a sudden rise in |B| from ≈ 5 nT to > 25 nT, proton density and velocity increases (≈ 350 → 600 km s⁻¹), and a southward IMF Bz interval that drove enhanced coupling. The Kyoto Dst index recorded a sudden storm commencement followed by a minimum of −75 nT …

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How Hidden Companions Skew Stellar Ages: A Binary-Inclusive Analysis Of Ngc 2168, Skylar G. Butler Feb 2026

How Hidden Companions Skew Stellar Ages: A Binary-Inclusive Analysis Of Ngc 2168, Skylar G. Butler

Student Research Symposium (SRS)

We expand upon our previous analysis of NGC 2168 (M35), which investigated the precision of isochrone-based age determination for individual main-sequence stars using photometric modeling. Building on that earlier framework, we now assess how inferred stellar ages change when each object is modeled under the assumption that it may possess an unresolved binary companion. Using post burn in posterior samples from the same dataset, we computed the mean Stage 3 ages for more than 3,000 cluster members with binaries turned on, allowing a direct comparison to our prior results with binaries turned off. The resulting age distribution peaks near 2.1 …

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Periodic Pattern In Leidenfrost Bridges: Experiment And Theoretical Modeling, Diana Slavich, Denise Lesnichiy, Niyati Garg, Ian Holland Feb 2026

Periodic Pattern In Leidenfrost Bridges: Experiment And Theoretical Modeling, Diana Slavich, Denise Lesnichiy, Niyati Garg, Ian Holland

Student Research Symposium (SRS)

A system consisting of isolated liquid nitrogen Leidenfrost drops and tori boundaries exhibits bridge formation, displaying self-organized oscillations, traveling waves, and regular patterns associated with thermal energy, continuous evaporation, and vapor flow beneath the liquid bridge. To accomplish the liquid bridge formation, liquid nitrogen drops and a thermally controlled substrate were used within a curved boundary. The formation, dynamics, and stability of liquid bridges can be studied in an isolated environment through the Leidenfrost effect, while mathematical modeling can describe these bridges. Investigations into this system imply the formation of solitons, which are modeled using nonlinear differential geometry. Understanding this …

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Modeling The Secondary Catastrophic Disruption Of The Veritas Family, Jarrett Dieterle, Skylar G. Butler, Aiden Kelleher Feb 2026

Modeling The Secondary Catastrophic Disruption Of The Veritas Family, Jarrett Dieterle, Skylar G. Butler, Aiden Kelleher

Student Research Symposium (SRS)

The Veritas asteroid family, located in the outer main belt, is believed to have formed from the catastrophic breakup of a parent body approximately 8.3 million years ago (e.g., Nesvorný et al., 2003). Larger fragments remained in the main belt, while smaller particles evolved inward under radiation forces, forming a toroidal dust structure observable in infrared data as paired bands. Previous studies (e.g., Dermott et al., 2001) have shown that these bands can be linked to their parent families and modeled from their initial disruptions. We propose that the 10° dust bands associated with Veritas may record evidence of a …

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