Physical Sciences and Mathematics Commons^™

Community Perception And Attitudes Toward Plastics And Microplastic Pollution: A Case Study In The Red River Delta, Vietnam, Dang Hai Mai

Applied Environmental Research

Plastics and microplastic contamination pose increasing threats to human health, food safety, and environmental sustainability. In Vietnam, rapid urbanization, economic growth, and population expansion have contributed to a surge in plastic waste, intensifying pollution across regions such as the Red River Delta. This study adopts the theory of planned behavior (TPB) to explore community perceptions, attitudes, and behavioral intentions regarding plastic and microplastic pollution. A quantitative research design was employed, with data collected through structured questionnaires administered to 1,305 respondents via a stratified multistage sampling strategy across four provinces in the Red River Delta. Descriptive statistics and multiple linear regression …

Bibliography For Love Data Week 2025, Arianna Tillman, Isabella Piechota, Annikah Carpio

Library Displays and Bibliographies

A bibliography created to support a display about research data and Love Data Week during January/February 2025 at the Leatherby Libraries at Chapman University.

Clea Labs For Stellar And Cosmological Astronomy Bottled For Silicon Macintosh, Hansika Reddy Bandela, Yuktha Shetty, Jack C. Straton

Physics Faculty Publications and Presentations

Project CLEA -- CONTEMPORARY LABORATORY EXPERIENCES IN ASTRONOMY -- develops laboratory exercises that illustrate modern astronomical techniques using digital data and color images.

These Labs were developed for Windows, but the authors have bottled them for Silicon Macintosh computers.

Clea Labs For Planetary, Stellar, And Cosmological Astronomy Configured For 64-Bit Windows 11+ Machines, Itan Altamirano, Jack C. Straton

Physics Faculty Publications and Presentations

Project CLEA -- CONTEMPORARY LABORATORY EXPERIENCES IN ASTRONOMY -- develops laboratory exercises that illustrate modern astronomical techniques using digital data and color images.

These Labs were developed for 32-bit Windows machines but PSU student Itan Altamirano reconfigured installation programs to bypass steps that ask for DVDs and allowing these 32-bit programs to work on 64-bit Windows 11+ computers.

Synthesis And Characterization Of Doped Rare-Earth Zinc Alloys, Partha Das

Graduate Theses/Dissertations

Rare-Earth-Zinc (RE-Zn) alloys doped with manganese represent a promising class of materials with diverse applications in magnetic, electronic, and thermoelectric devices. These alloys hold significant potential owing to the unique combination of rare-earth elements' properties with the versatility of zinc, augmented by manganese doping. In this study, I present the successful synthesis of ErMn_0.2Zn_11.8 alloys, achieved through self-flux method. The synthesized crystals were characterized using electron dispersive spectroscopy (EDS) and single crystal X-ray diffraction (XRD). Additionally, magnetic measurements were performed to investigate the magnetic properties. Future plans include DFT calculations and comparison study between Rare-Earth-Zinc alloys with …

Balancing Performance And Efficiency: An Autoencoder Approach To Api-Based Malware Detection, Selma Bouraoui

Graduate Theses/Dissertations

The constant evolution of malware presents a critical challenge to today's interconnected world. It poses an increasing threat on different scales, spanning from individuals, organizations to critical infrastructures such as government’s security. Hackers continuously develop new techniques to evade detection methods. When confronted with the high volume and variety of malware, conventional approaches tend to struggle to perform in robust, accurate and timely manner. This thesis explores the application of deep learning methods to improve malware detection and classification techniques. By analyzing API call sequences, the proposed approach leverages Autoencoders to compress high-dimensional malware data into more optimized representations that …

Evaluation Of Practical Methods To Determine If A Karst Creek Is Gaining Or Losing: Case Study Of Leith Creek, Elizabeth Jones

Graduate Theses/Dissertations

Karst landscapes are abundant in Missouri, with features such as caves, springs, and sinkholes that form through the dissolution of limestone. Leith Creek is a small stream in Polk County, Missouri fed by two springs and the shallow unconfined Springfield Plateau aquifer, a highly karstified aquifer which is made up of limestone and minor interbedded shale-mudstone units. To determine if Leith Creek is gaining or losing, stream flow, water chemistry and temperature sensors were monitored. Stream flow results required multiple visits to take measurements while temperature sensors required two visits, one to install the dataloggers and another to remove the …

The Stochastic Occupation Kernel (Sock) Method For Learning Stochastic Differential Equations, M. L. Wells, Kamel Lahouel, Bruno Jedynak

Mathematics and Statistics Faculty Publications and Presentations

We present a novel kernel-based method for learning multivariate stochastic differential equations (SDEs). The method follows a two-step procedure: we first estimate the drift term function, then the (matrix-valued) diffusion function given the drift. Occupation kernels are integral functionals on a reproducing kernel Hilbert space (RKHS) that aggregate information over a trajectory. Our approach leverages vector-valued occupation kernels for estimating the drift component of the stochastic process. For diffusion estimation, we extend this framework by introducing operator-valued occupation kernels, enabling the estimation of an auxiliary matrix-valued function as a positive semi-definite operator, from which we readily derive the diffusion estimate. …

Optical Coherence Tomography Harmonization With Anatomy-Guided Latent Metric Schrödinger Bridges, Shuwen Wei, Blake E. Dewey, Samuel W. Remedios, Bruno Jedynak, Shimeng Wang, Peter Calabresi, Shiv Saidha, Aaron Carass, Multiple Additional Authors

Mathematics and Statistics Faculty Publications and Presentations

Medical image harmonization aims to reduce the differences in appearance caused by scanner hardware variations to allow for consistent and reliable comparisons across devices. Harmonization based on paired images from different devices has limited applicability in real-world clinical settings. On the other hand, unpaired harmonization typically does not guarantee anatomy consistency, which is problematic because anatomical information preservation is paramount. The Schrödinger bridge framework has achieved state-of-the-art style transfer performance with natural images by matching distributions of unpaired images, but this approach can also introduce anatomy changes when applied to medical images. We show that such changes occur because the …

Recontextualizing Engagement With Data Visualizations In The Context Of The Socio-Ecological, Eva Thanheiser, Laurie Rubel, Beth Herbel-Eisenmann, Courtney Koestler

Mathematics and Statistics Faculty Publications and Presentations

We explore how data visualizations (DVs) are representational tools that can be used to narrate certain aspects of a context but silence others. Using multiple DVs allows for contrasting the DVs and likely provides opportunities for critical reading and exploring the entanglement of political and ecological dimensions of an issue. We start by integrating two sets of practices, Notice, Wonder, Feel, Act and Reimagine (NW-FAR) (Kahn, et al., 2022) and critical graph reading, Framing, Formatting, Narrating (FFN) (Rubel, et al., 2021) towards taking a socio-political stance and supporting students to read data visualizations and develop critical data literacy. We look …

Highly Sensitive Diffractive Optical Structures For Detection Of Volatile Organic Compounds, Faolan Radford Mcgovern

Doctoral

From aerospace to agriculture, sensors play a fundamental role in many aspects of modern life. Sensors form an integral part of the complex systems and devices required for the continued functioning and development of services and industries across society. The use of sensors is paramount in areas affecting human health, one such area being the monitoring of indoor air quality, in particular the detection of volatile organic compounds (VOCs). Human contact with VOCs has been associated with many health complications, including skin and eye irritation, cardiovascular damage, and cancers. Optical, electrical, gravimetric, and chemical sensors have been developed for VOC …

Surface Modifications Of Titanium Alloys For Biomedical Applications, Justynne Fabian

Doctoral

Hip implant failure remains a critical concern in healthcare, primarily driven by poor bone integration and bacterial infection. These issues contribute to a rising number of revision surgeries, posing significant challenges that must be addressed, particularly in light of an ageing global population. Titanium surface anodisation emerges as a promising solution for biomedical applications, offering a cost-effective, controllable, and efficient process. The anodisation technique results in the formation of titanium dioxide nanotubes (TiO2 NTs) on the metal surface. These nanostructures offer several advantages, including enhanced mechanical properties, and potential for surface modification through the incorporation of antibacterial and osteoinductive properties. …

Classification Of Indonesian Sign Language (Sibi) Using Data Mining Algorithms K-Nearest Neighbor And Random Forest, Muhammad Zaki Wirawan, Achmad Afif, Anik Nur Handayani, Imanuel Hitipeuw, Osamu Fukuda

Knowledge Engineering and Data Science

This study aims to address the communication hallenges faced by the Indonesian deaf community by developing an automatic classification model for Sistem Bahasa Isyarat Indonesia (SIBI) using data mining techniques. The main objective is to identify a practical algorithm for recognizing SIBI hand gestures to enhance accessibility and inclusiveness in digital communication. A comprehensive dataset consisting of 32,850 gesture samples representing SIBI alphabet signs was collected and processed through feature extraction, data cleaning, and normalization using Z-Transform and Min-Max methods. Two classification algorithms, K-Nearest Neighbor (KNN) and Random Forest, were implemented and evaluated using metrics such as accuracy, precision, recall, …

A Comparative Study Of Machine Learning Models For Javanese Wuku Classification: Exploring Svm, Naïve Bayes, And Cnn For Cultural Texts, Danang Arbian Sulistyo, Aji Prasetya Wibawa, Didik Dwi Prasetya, Fadhli Almu'iini Ahda, Agung Bella Putra Utama

Knowledge Engineering and Data Science

This study rigorously evaluates machine learning models for classifying culturally significant Javanese Wuku texts from the “Keagamaan atau Spiritual” category, a domain challenged by unique linguistic nuances and limited digitized resources. We compared Support Vector Machine (SVM), Naïve Bayes, and Convolutional Neural Network (CNN) on texts from five pivotal Wuku types (Sinta, Galungan, Kuningan, Sungsang, Warigalit) sourced from sastra.org, aiming to identify the most effective computational approach. The dataset comprises N = 1419 documents (T = 751.290 tokens), with per-class document counts reported for all five Wuku types. Our evaluation uses accuracy, precision, recall, F1-score, and …

Analysis And Research On The Guiding Role Of Xi Jinping Thought On Socialism With Chinese Characteristics For A New Era In The Discipline Of Information Resources Management, Sanhong Deng, Yiqin Zhang, Hao Wang

Journal of Scientific Information Research

[Purpose/significance]This paper explores the guiding role of Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era in the development of the Information Resource Management discipline with Chinese characteristics, providing significant insights for the innovative advancement of China's Information Resource Management discipline and strengthening the discourse power of Chinese social sciences. [Method/process]This paper systematically reviews the core elements of the development philosophy of the Information Resource Management discipline within Xi Jinping Thought on Socialism with Chinese Characteristics for a New Era from a holistic perspective,elucidates the logical system of the development of the discipline from the diverse perspectives …

Extraction Of Fine-Grained Research Methods In The Field Of Information Science, Jiayi Hao, Yuzhuo Wang, Chengzhi Zhang

Journal of Scientific Information Research

[Purpose/significance]Research methods in information science are one of the critical research directions in this field. Constructing a fine-grained research method corpus and extracting research method entities can help scholars quickly understand the research methods in this field, explore the evolution of methods and their future development trends, and lay the foundation for the service and application of the research method corpus in the subsequent digital wave. [Method/process]Firstly, based on academic articles published in the Journal of the China Society for Scientific and Technical Information from 2000 to 2023, this study randomly selected 50 articles and manually annotated the research methodology …

Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang

Journal of Scientific Information Research

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …

Identifying Research Paths Based On Main Path Analysis: A Case Study Of Knowledge Graphs, Ruibin Wei, Yidan Wang, Yan Xu

Journal of Scientific Information Research

[Purpose/significance]The main path analysis of citation networks can be used to identify important literature in specific fields and can achieve the extraction of mainstream research threads. This paper will use the main path analysis method to analyze the research path of knowledge graphs and sort out the context of their research development. [Method/process]This paper firstly obtains research papers in the field of knowledge graphs from the Web of Science platform, then uses the HistCite software to generate a direct citation network of the literature, and then imports the data into Pajek to generate multiple main paths of the dataset, and …

The Evolution Of Research Methods In The Digital Humanities Perspective: A Quantitative Analysis Based On Cnki Data And A Large Language Model, Guangyao Sun, Dongbo Wang

Journal of Scientific Information Research

[Purpose/significance]This paper aims to explore the evolution trend of research methods in the field of digital humanities with the help of large language model technology. [Method/process]This paper mainly focuses on the data of CNKI journal articles, selects the general Chinese large language model GLM-4, uses prompt engineering and chain of thought to extract and cluster the abstract data, of papers and analyzes its evolution trend through quantitative processing. [Result/conclusion]The study shows that GLM-4 can well identify and extract research methods from complex abstract data. Analyzing the evolution trend in chronological order, it is found that research methods such as "interview …

Research On Automated Generation And Evaluation Of Patent Claimsbased On Gpt-4, Junhua Li, Qian Yuan, Xiang Yan, Changhong Lv

Journal of Scientific Information Research

[Purpose/significance]This study aims to automatically generate claims using the GPT-4 model, in order to reduce the writing difficulty for inventor and improve the work efficiency and quality. [Method/process]The article constructs Prompts suitable for automatically generating patent claims and implements four prompting strategies: ZeroShot, Exact-Drafting, Stepwise-Claim, and Exact-Step Claim. By inputting patent specifications and technical disclosure documents into the GPT-4 model and using Prompts to guide its output, the automated generation of patent claims is achieved. The ROUGE and BERTScore evaluation metrics were used to assess the quality of the text, and the generated text was analyzed in comparison with the …

Research On Emerging Technology Topic Identification Based On Bertopic, Dakun Wang, Bolin Hua

Journal of Scientific Information Research

[Purpose/significance]Identifying and foreseeing emerging technologies, bring technological first-mover advantages to enterprises and governments, and grasp technological development trends in a timely manner. [Method/process]This study uses BERTopic's topic modeling method to obtain domain topic distribution, and merges paper and patent topics based on the cosine similarity of topic vectors to identify emerging topics. [Result/conclusion]Using the BERTopic topic modeling method combined with index evaluation can effectively identify emerging topics and emerging terms.Taking the field of new energy vehicles as an example to carry out empirical research, using two methods: divided verification period and data verification method, 12 of the 16 identified topics …

An Explainable Ai And Optimized Multi-Branch Convolutional Neural Network Model For Eye Anemia Diagnosis, Kamel K. Mohammed, Nadia Dahmani, Rania Ahmed, Ashraf Darwish, Aboul Ella Hassanien

All Works

This paper proposes a novel, non-invasive approach to diagnosing eye anemia using deep learning techniques. Traditional methods, reliant on invasive procedures like venipuncture, are costly and can cause patient discomfort. Our model leverages a multi-branch convolutional neural network (CNN) architecture, incorporating the Hippopotamus Optimization (HO) algorithm and multiclass support vector machines (SVMs) for enhanced accuracy. To address data imbalance, we employ the Synthetic Minority Oversampling Technique (SMOTE) and data augmentation. The model is trained and evaluated on a dataset of 211 eye images. The model achieves a remarkable 97.06% accuracy, with a Receiver Operating Characteristic (ROC) curve demonstrating an Area …

Digital Transformation Of Education: An Integrated Framework For Metaverse, Blockchain, And Ai-Driven Learning, Mousa Al-Kfairy, Omar Alfandi, Ravi S. Sharma, Saed Alrabaee

All Works

The integration of Metaverse, Blockchain, and Artificial Intelligence (AI) has the potential to revolutionize the educational landscape by providing immersive, secure, and personalized learning environments. This study proposes a conceptual framework that combines these technologies to address the key challenges faced by contemporary education systems, including accessibility, engagement, security, and personalization. The Metaverse serves as the immersive platform, offering virtual classrooms, interactive simulations, and gamified learning experiences. Blockchain provides the foundation for secure and transparent academic records, enabling tamper-proof credential verification and decentralized data management. AI enhances the educational experience by powering adaptive learning systems, predictive analytics, and intelligent tutoring …

Leveraging Sentiment Analysis Of Food Delivery Services Reviews Using Deep Learning And Word Embedding, Dheya Mustafa, Safaa M. Khabour, Mousa Al-Kfairy, Ahmed Shatnawi

All Works

Companies that deliver food (food delivery services, or FDS) try to use customer feedback to identify aspects where the customer experience could be improved. Consumer feedback on purchasing and receiving goods via online platforms is a crucial tool for learning about a company’s performance. Many English-language studies have been conducted on sentiment analysis (SA). Arabic is becoming one of the most extensively written languages on the World Wide Web, but because of its morphological and grammatical difficulty as well as the lack of openly accessible resources for Arabic SA, like as dictionaries and datasets, there has not been much research …

Deep Learning Approaches For Eeg-Based Biometrics: A Systematic Review, Ali E. Albaiati, Muhammad Firdaus Akbar, Murtadha D. Hssayeni, Ashraf Khalil, Mohd Nadhir Ab Wahab, Sundus Sulaiman Weli, Enas A. Raheema

All Works

Biometics such as fingerprint, face, and iris are vulnerable to spoof attacks. The unique characteristics of Electroencephalography (EEG) make it a promising biometric modality especially because of its resistance to spoofing attacks. Many deep learning methods have been proposed for EEG-based biometric systems. This systematic review examines these methods in terms of their feature extraction ability and authentication performance. We follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search IEEE Xplore, PubMed, Web of Science, ScienceDirect, and Springer databases. Initially, we identified 285 relevant articles published between 2018 and 2024. After removing duplicates and applying …

Quantitative Preventive Approaches To Diabetes: Mathematical Modeling And Analysis, Rushi P. Bhatt

Theses, Dissertations and Culminating Projects

The rising prevalence of diabetes presents a pressing global health concern, necessitating effective control strategies. This study aims to construct a mathematical model to analyze the influence of diverse factors on blood sugar levels, with a focus on identifying optimal methods for maintaining healthy glucose levels. Employing ordinary differential equations (ODE), the model investigates variables including leptin resistance, fat mass, glucose, insulin resistance, beta cell mass, daily physical activity, and dietary intake. Utilizing parameter estimates from existing literature, the model’s framework is established, and simulation results elucidate the intricate interplay between lifestyle choices and blood glucose dynamics. Furthermore, the model …

Exploring Coupled Land Use -- Climate Change Impacts In The Northeast United States From 2020-2025 With Implications For Bombus Habitat Suitability, Archana Prasad

Theses, Dissertations and Culminating Projects

The combined impacts of land use and climate change pose significant challenges to bumblebee populations in the Northeast United States (NEUS), a region characterized by dense urbanization, diverse landscapes, and exposure to climate extremes. As urban and suburban areas expand, the loss of agricultural lands and open spaces alters habitat availability for bumblebees, which rely on grasslands, meadows, and forest edges for nesting and foraging. Simultaneously, the NEUS is experiencing an increase in extreme climate events, including prolonged heatwaves, heavy precipitation, and shifts in temperature patterns. Using CMIP6 climate model projections, we analyzed trends in nine climate indices. Our results …

The Future Of Ai Regulation In Drug Development: A Comparative Analysis, Gabriela Lenarczyk, Timo Minssen, Nicholson Price, Arti Rai

Faculty Scholarship

As artificial intelligence (AI) transforms drug development, regulatory frameworks are evolving to oversee its implementation, particularly at the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA). This paper makes three contributions to understanding emerging regulatory approaches. First, we offer a comparative analysis of how these agencies have responded to AI-driven advances, incorporating new US executive orders and the European Union (EU)’s AI Act. Second, we propose a novel analytical framework to understand regulatory divergence: the FDA’s flexible, dialog-driven model contrasts with the EMA’s structured, risk-tiered approach, reflecting broader institutional and political-economic differences. While the former encourages …

Artificial Intelligence And Procedural Due Process, Brandon L. Garrett

Faculty Scholarship

Artificial intelligence (AI) violates procedural due process rights if the government uses it to deprive people of life, liberty, and property without adequate notice or an opportunity to be heard. A wide range of government agencies deploy AI systems, including in courts, law enforcement, public benefits administration, and national security. If the government refuses to disclose the reasons why it denied a person bail, public benefits, or immigration status, serious due process concerns arise. If the government delegates such tasks to an AI system, the due process analysis does not change. One asks whether a person received adequate notice and …

Augmenting, Not Replacing: The Role Of Llms In Human-Centric Formal Re: Supplemental Material, Sonora Halili, Paola Spoletini, Alicia M. Grubb

Computer Science: Faculty Publications

This repository contains the supplemental information for the RE'25 paper entitled "Augmenting, Not Replacing: The Role of LLMs in Human-Centric Formal RE" and the Smith College Departmental Honors Thesis entitled "The LTL Whisperer: Prompting AI to Explain Temporal Logic: Supplemental Information". This work investigates how and to what extent generative AI with large language models (LLMs) can assist practitioners and novices in interpreting formal requirements expressed in Linear Temporal Logic (LTL).