Digital Commons Network^™

Regional Authority And Economic Interaction In The Eastern Lower Papaloapan Basin, Veracruz, Mexico, Shayna Skye Lindquist

Theses and Dissertations--Anthropology

This dissertation examines various networks of interaction underlying political and economic organization through a series of three papers that investigate the Eastern Lower Papaloapan Basin (ELPB), Veracruz, Mexico. The three papers are linked through their shared concern with a reliance on models of political economies that fall short in capturing the diverse and nuanced ways individuals and groups cooperated. While my primary focus is on the Early Classic period (AD 300-600), I engaged diverse datasets that provide context from which the ELPB’s Classic period societies emerged. The first two papers present the results of technological and geochemical analyses of obsidian …

Scalable Mental Health Analysis Using Big Data: A Demographic And Geographic Study Of Depressive Symptoms, Saikrishna Katamneni, Anitha Bommana, Naman Pandya, Gokul Kareti, Sangwhan Cha

Harrisburg University Other Works

This project explores the application of Big Data technologies for large-scale mental health analysis, focusing on the prevalence of depressive disorder symptoms across diverse demographic and geographic subgroups. Utilizing Apache Spark on Google Cloud Dataproc, the system efficiently processed millions of survey records stored in Hadoop Distributed File System (HDFS). Through comprehensive data preprocessing, aggregation, and visualization, the analysis revealed critical trends and disparities in mental health outcomes related to age, race, education level, gender, and state. Seasonal variations and subgroup-specific confidence intervals were also examined to identify high-risk populations and areas of measurement uncertainty. The results offer actionable insights …

Readmission Prediction For Diabetic Patients: A Scalable Big Data Approach For Resource-Constrained Hospitals, Rhema Ike, Minjae Kim, Bhushan Chindarkar, Sangwhan Cha

Harrisburg University Other Works

Hospital readmissions, particularly among diabetic patients, place a significant burden on healthcare systems by increasing operational costs and straining limited resources. This project presents a scalable, cloud-based solution that leverages machine learning and big data analytics to predict 30-day hospital readmissions. Utilizing a ten-year dataset of over 100,000 patient records, we implemented a Random Forest classifier trained on clinical, demographic, and hospitalization data. The system architecture integrates Google Cloud Platform services—including BigQuery, Vertex AI, and Looker Studio—with a custom Python/Flask web application for real-time data input and inference. Data preprocessing and feature engineering were conducted in Vertex AI Workbench, enabling …

Computational Bridges: Enhancing Natural Language Processing Of Swahili., Joyce Murungi

Harrisburg University Other Works

Swahili remains significantly underrepresented in natural language processing (NLP) despite being one of the most widely spoken languages in Africa. Computational Bridges: Enhancing Natural Language Processing of Swahili addresses this gap through computational linguistics, corpus creation, and large-scale analysis of Swahili syntax and lexical structure. Central to this study is GUMZO, a novel corpus developed from spontaneous conversational data collected from YouTube videos, television panel discussions, political speeches, religious discourse, and unscripted broadcasts. Unlike many existing datasets that rely on formal or translated text, GUMZO captures authentic language use and provides a stronger foundation for NLP research involving low-resource languages. …

A Critique Of Findings On Gun Ownership, Use, And Imagined Use From The 2021 National Firearms Survey: Response To William English, Deborah Azraek, Joseph Blocher, Philip J. Cook, David Hemenway, Matthew Miller

Faculty Scholarship

For a paper that has not yet been through peer review or even been formally published, William English’s “2021 National Firearms Survey” has been remarkably prominent in gun rights advocacy and scholarship. As of December 2024, it has been cited in roughly sixty-five briefs, invoked at oral argument in the Supreme Court and multiple courts of appeals, and regularly cited in public writings and published academic work.

This response is offered in the spirit of a peer review. Our focus is on methodological issues, questionable statistical results, and problematic conclusions. Because of serious methodological issues, English’s draft fails to provide …

A Power Dependence Model Of The Impact Of Leader Impostorism On Supervisor Support And Undermining: The Moderating Role Of Power Distance, Xueqi Wen, Zihan Liu, Feng Qiu, Keith Leavitt, Xingyu Wang, Ziyang Tang

Research Collection Lee Kong Chian School Of Business

Leaders, often perceived as possessing exceptional confidence and competence, are not immune to feelings of self-doubt. Leader impostorism describes the experience that one’s attributes, experiences, skills, and abilities fall short of the standards expected in the leadership role, resulting in a sense of deception in fulfilling leadership responsibilities. While existing research has examined the antecedents and individual outcomes of leader impostorism, its implications for leaders’ treatment of subordinates remain largely unexplored. In this research, we investigate the downstream consequences of leader impostorism on behaviors directed toward subordinates. Integrating research on leader impostorism with power dependence theory, we propose that for …

Rrh Library Newsletter, Winter 2025, Libraries At Rochester Regional Health

Rochester Regional Health authored publications and proceedings

Newsletter sections include: New Database: Embase; St. Lawrence Health New Database Access; EBSCOhost New Database User Interface; Discovery Tool Update

Parallel Algorithms For Large Scale Dynamic Graph Analysis, Arindam Khanda

Doctoral Dissertations

A complex system of interacting entities in contemporary scenarios, be it biological, technological, or social, can be represented using graphs. Dynamic graphs, unlike their static counterparts, are ones in which the underlying topology changes over time. These networks act as a model for numerous systems, from transportation to social interactions, capturing the ever-evolving nature of real-world phenomena. However, the inherent temporality of these networks presents a unique set of challenges and the traditional static graph algorithms often fall short in efficiency and applicability. In our research, we delve into the complexities presented by large dynamic networks and suggest various methodologies …

Thermal Hydraulics Investigation Of A Horizontal Dual Channel Setup Representing Micro Nuclear Reactors Using Integrated Advanced Measurement Techniques, Zeyad Zeitoun

Doctoral Dissertations

The urgency to reduce greenhouse gas emissions has spotlighted nuclear energy as a carbon-free solution. Despite its promise, nuclear safety concerns, highlighted by the 2011 Fukushima disaster, necessitate advancements in reactor technology. The United States has prioritized the development of safer, prismatic micronuclear reactors for the coming decade, focusing on the safe operation of these facilities. These reactors' design addresses potential hazards, particularly in loss of flow accident scenarios, making the understanding of natural circulation gas flow dynamics critical. Experiments on a horizontal dual-channel plenum-to-plenum facility (P2PF) were conducted to closely mimic the conditions within prismatic micronuclear reactors. This study …

Investigation Of The Transport And Plugging Behavior Of Preformed Particle Gels (Ppgs) In Fractures For Conformance Control In Carbonate Reservoirs, Abdulaziz A. Almakimi

Doctoral Dissertations

This study aims to systematically evaluate the performance of preformed particle gels (PPGs) in fractured carbonates mainly through a series of core flooding experiments. Carbonate reservoirs exhibit unique features of heterogeneity and pore geometry, while (PPGs) are known for their plugging efficiency, deformability, and dehydratable nature. The main goal of gel injection in fractured reservoirs is to place a gel pack appropriately into the fracture, diverting injected fluid to the oil-bearing matrix and reducing excessive water production. However, considerable dehydration of (PPGs) is possible at the adjacent matrix and within the fracture, which could lead to a filter-cake formation and/or …

Development Of Catalytic Membranes And Composites For Energy Storage Devices And Nonenzymatic Biosensors, Harish Singh

Doctoral Dissertations

The excessive use of fossil fuels has led to their rapid depletion, causing an energy crisis and environmental issues. Consequently, there's a growing focus on sustainable energy conversion. Electrocatalysts are pivotal in this development, particularly for fuel cells and solar fuel generators, necessitating high-efficiency, cost-effective catalysts for large-scale adoption. Transition metal chalcogenides have emerged as promising electrocatalysts due to their high efficiency, electrochemical tunability, and abundant active sites. This work is divided in two parts. In Part A, we have investigated transition metal chalcogenide and few atom clusters FACs as electrocatalysts for oxygen evolution and reduction, highlighting their structure-property relationships …

Preserving The Future: Recognizing Intergenerational Equity In United States Constitutional Jurisprudence In Light Of Evolving Climate Rights Litigation, Molly Morgan

Cardozo Journal of Equal Rights & Social Justice

Climate rights litigation is an essential part of holding states accountable for their climate change obligations. This type of litigation has increased across the globe, and domestic and international courts have issued landmark rulings that serve as precedent for reinforcing state obligations and protecting constitutional and human rights in the process. One focus of these cases is intergenerational equity, which implicates the theory that inadequate state action on climate change violates the rights of future generations. This Article explores the evolution of this theory in domestic and international law, illustrating its increasing importance in climate rights litigation and the necessity …

Equitable Incorporation: How History And Tradition Can Progressively Redefine The Fourteenth Amendment, Robert D'Alessandro

Cardozo Journal of Equal Rights & Social Justice

The Fourteenth Amendment, designed to ensure equality before the law, has been misinterpreted by the Supreme Court through its incorporation doctrine, leading to rulings that harm marginalized communities. The article advocates for "Equitable Incorporation," a doctrine requiring courts to consider the impact of their decisions on historically discriminated groups, ensuring the Amendment's purpose of equity and justice is upheld. This approach would necessitate the incorporation of unincorporated rights and reinterpret existing ones to reflect the Amendment's equitable intent.

Annotated Legal Bibliography

Cardozo Journal of Equal Rights & Social Justice

No abstract provided.

Table Of Contents - Cardozo Journal Of Equal Rights & Social Justice, Vol. 31, Iss. 2

Cardozo Journal of Equal Rights & Social Justice

No abstract provided.

Shining Light On Policy: The Case For Solar Panel Mandates On New Construction Projects, Emily Glazier

Cardozo Journal of Equal Rights & Social Justice

The note argues that solar panel mandates on new construction projects are a necessary and sensible approach to reducing greenhouse gas emissions and achieving climate goals, but their implementation must include provisions to protect vulnerable communities and address environmental justice concerns. While such mandates face legal and political challenges, the benefits of decreased emissions and energy independence outweigh the costs, particularly when paired with measures like community solar systems, incentives, and exemptions.

An Interpretative Phenomenological Study Of Sexual Assault Investigations In Sexual Assault Response Teams (Sarts), Caitlyn Marie Clark

Theses and Dissertations

This applied dissertation was designed to provide further insight into sexual assault investigations and response from the lived experiences of Sexual Assault Response Team and Task Force criminal justice professionals. There has been limited research on Sexual Assault Response Teams (SARTs), Multidisciplinary Teams (MDTs), and Sexual Assault Task Force (SATF) units, specifically encompassing the lived experiences of their members. This interpretative phenomenological study sought to explore these collaborative multidisciplinary teams from the lived experiences of the criminal justice professionals who represent them and to analyze the factors of sexual assault investigations and response that these members find significant.

In this …

Tap, Swipe, And Connect: Exploring Perceptions Of Inclusivity Through Smartphone Design And Accessibility, Carmen Van Ommen

Doctoral Dissertations and Master's Theses

There has been an increasing importance placed on customization of products and desire for the product to allow the consumer to express themselves or fit into desired social groups with their product choices. Designing products to be inclusive can encompass these desires, as well as ensure that a wide range of user groups can use the products well. In general, there is little research in the field of perceptions of inclusivity with consumer technology products. Previous research has validated a scale to measure Perceptions of Technology Product Inclusivity (PTPI) with different user groups and technology product groups, however no research …

Mini-Maps, Crucial Or Controversial: Assessing The Influence Of Perceived Spatial Orientation And Cognitive Mapping Performance On Video Game User Experience, Angela Le

Doctoral Dissertations and Master's Theses

Decades of game studies, also known as video games research, have revealed frequent video gameplay is positively correlated with enhanced cognition. An area of study that is limited, however, is the relationship between spatial cognition, specifically the domains of spatial orientation and cognitive mapping and their effects on video game user experience for games employing visual navigational aids. Video game user experience is influenced by many factors such as graphics and audio, gameplay mechanics, user interface design, etc. The present study aims to explore whether spatial abilities influence and are significantly correlated with user experience for games using heads-up displays …

The Effect Of Need For Cognition & Need For Affect On Human Reliance And Artificial Intelligence Interactions, Aliyah Mcgowan

Doctoral Dissertations and Master's Theses

Abstract

With the increased use of Artificial Intelligence (AI) automations in fields like medical diagnoses and mental health queries, there are concerns regarding an individual’s trust and reliance on the technology. Reliance on AI output may lead an individual to accept inaccurate or incorrect information without further analysis. Trust may influence reliance and trust formation may be a product of affective processing. This study investigated the relationship between Need for Affect (NFA), Need for Cognition (NFC), and trust and reliance on AI interactions. Participants were assessed on the NFA scale for willingness to approach or avoid emotional stimuli, the NFC …

Models Of Machine Learning To Diagnose Chronic Kidney Disease Using A  Weka-Based Classifier, Shaymaa Adnan Abdulrahman, Sameerah Faris Khlebis

Mesopotamian Journal of Artificial Intelligence in Healthcare

In the present day, humans are confronted with a variety of diseases as a result of their lifestyle and the current environmental conditions. Therefore, it is crucial to identify and predict these diseases in their early phases in order to prevent their severe manifestations. Manually identifying maladies is a challenging task for physicians on a regular basis. Predicting chronic illnesses is the aim of this article. This goal is applicable through a state-of-the-art approach to classification correctly identifies people with chronic illnesses. Predicting maladies is also a difficult endeavor. Therefore, disease prediction is significantly influenced by data mining. To get …

Biogpt: A Generative Transformer-Based Framework For Personalized Genomic Medicine And Rare Disease Diagnosis, Ghada Al-Kateb, Emine Cengiz, Murat Gök

Mesopotamian Journal of Artificial Intelligence in Healthcare

This paper introduces BioGPT, a generative transformer-based framework designed to advance personalized genomic medicine and rare disease diagnosis. Unlike conventional models that process either genomic sequences or clinical narratives in isolation, BioGPT employs a cross-modal architecture that effectively fuses both data streams, enabling precise classification and interpretable natural language generation. The model is pre-trained on large-scale genomic and electronic health record datasets and fine-tuned for rare disease tasks. Comprehensive experiments demonstrate BioGPT’s superiority over state-of-the-art biomedical models, including RarePT, BioBERT, and DNABERT, with improvements of up to 10% in F1-score and over 20 BLEU points in justification fluency. Ablation studies …

Prediction Of Cardiac Arrest Using A Hybrid Voting Classifier Combining Random Forest And Support Vector Machine, Ahmed Hamid Elias, Amr Badr, Raad S. Alhumaima

Mesopotamian Journal of Artificial Intelligence in Healthcare

Early and adequate identification of patients at risk of cardiac arrest remains a fundamental challenge in medical environments. Within this piece of work, we design and evaluate a hybrid machine-learning pipeline integrating Random Forest (RF) and Support Vector Machine (SVM) classifiers by soft voting to detect cardiac arrest occurrences from a publicly available dataset of 303 patient records with 13 clinical features. After median imputation and data cleaning, continuous features were normalized and the cohort was divided into 70 % training and 30 % test sets. RF scored 97 % with an AUC of 1.00 on its own, and SVM …

Advanced Machine Learning Models For Accurate Kidney Cancer Classification Using Ct Images, Dhuha Abdalredha Kadhim, Mazin Abed Mohammed

Mesopotamian Journal of Big Data

Kidney cancer, particularly renal cell carcinoma (RCC), poses significant challenges in early and accurate diagnosis due to the complexity of tumor characteristics in computerized tomography (CT) images. Traditional diagnostic approaches often struggle with variability in data and lack the precision required for effective clinical decision-making. This study aims to develop and evaluate machine learning (ML) models for the accurate classification of kidney cancer using CT images, focusing on improving diagnostic precision and addressing potential challenges of overfitting and dataset heterogeneity. Two ML models, Support Vector Machines (SVM) and Multi-Layer Perceptrons (MLP), were employed for classification. Key attribute extraction techniques, including …

Bridging Law And Machine Learning: A Cybersecure Model For Classifying Digital Real Estate Contracts In The Metaverse, Faris Kamil Mihna, Hazim Akram Sallal, Lobna Abdalhusen Al-Seedi, Hasan Ali Al- Tameemi, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Dheyaa A. Mohammed

Mesopotamian Journal of Big Data

The metaverse indicates an ever-evolving digital ecosystem where virtual real estate has now become an asset class. These properties, subject to smart contracts on the blockchain and represent as non-fungible tokens (NFTs), gives rise to new legal and cyber issues due to the decentralized and dematerialized nature of these digital assets .This paper proposes a machine learning approach to classify the digital real estate contracts into Ownership and Lease contracts. The study utilizes a dataset of one thousand digital real estate contracts collected from platforms such as Decentraland and The Sandbox. The dataset also included attributes such as plot size, …

Overview Of The Ciciot2023 Dataset For Internet Of Things Intrusion Detection Systems, Wisam Ali Hussein Salman, Chan Huah Yong

Mesopotamian Journal of Big Data

The rapid expansion of the use of the Internet of Things (IoT) has encouraged many attackers to exploit the vulnerabilities in these networks to violate data privacy or disrupt service; they are easy targets due to the diversity of devices within the network, which has led to the loss of unified security standards. intrusion detection system (IDS) play a pivotal role in securing IoT networks by monitoring inbound and outbound traffic to these networks and issuing a security alarm when there is an attack; moreover, they respond directly to these security threats to prevent them from harming the network and …

Enhancing Throughput In A Network Function Virtualization Environment Via The Manta Ray Foraging Optimization Algorithm, Sanaa S. Alwan, Asia Ali Salman

Mesopotamian Journal of Big Data

Network function virtualization (NFV) has emerged as a transformative paradigm in which traditional hardware appliances are replaced with virtual network functions (VNFs) running on commodity hardware. While NFV offers scalability and flexibility, it faces major challenges in sustaining high throughput and minimizing resource overhead under dynamic traffic conditions. In particular, flooding algorithm-based request propagation often leads to excessive redundancy, congestion, and resource waste. To address this limitation, this study applies Manta ray foraging optimization (MRFO), a swarm intelligence algorithm inspired by natural foraging behaviors, to optimize packet routing and resource allocation in NFV environments. The research employs a Barabási–Albert (BA) …

Recognition Of Alzheimer’S Disease Stages Via Inceptionv3 And Resnet50, Iman Aljubouri, Mostafa Ragheb, Mohamad Hamady

Mesopotamian Journal of Big Data

Early and precise detection of Alzheimer’s disease (AD) is essential for successful treatment. This research presents a system that autonomously detects and categorizes the phases of Alzheimer’s disease via brain scans and sophisticated deep learning techniques, including InceptionV3 and ResNet50. These models started with pretrained weights and were augmented by including bespoke classification layers, which consisted of dropout, batch normalization, and dense layers to increase performance and mitigate overfitting. The preprocessing processes included scaling the picture to 224 by 224 pixels, using average filtering for denoising, and converting the color space to guarantee compatibility with the models. Evaluations of the …

Concise Comparison Of Cnn Models On A Specified Dataset, Humam K. Yaseen, Saif S. Kareem, Bashar I. Hameed, Salam K. Abdullah

Mesopotamian Journal of Big Data

Recently, interest in Deep Learning (DL), which is a subset of Machine Learning (ML), has emerged. The most famous and used from the DL is the Convolutional Neural Network (CNN). CNN is particularly effective in image processing. There are many duties in image processing that CNN can do, i.e., segmentation, classification, object detection, facial recognition, etc. Image classification is one of the most important applications due to its relevance to various fields, including the healthcare industry and others. One of the challenges researchers face is selecting the appropriate algorithm for the classification task, particularly when dealing with binary or multi-class …

Optimized Wavelet Scatter Method For Accurate Classification And Segmentation Of Lung Nodules, Enas Hamood Al-Saadi, Ahmed Nidhal Khdiar, Nidhal K. El Abbadi

Mesopotamian Journal of Big Data

Lung cancer is a significant global health concern due to its high mortality rate. This is primarily due to the difficulty of identifying malignant growths in early-stage computed tomography (CT) images. The thing is, it's often hard to tell the difference between a benign nodule and a malignant one. This makes it tricky to figure out what's going on with the patient. That's where these computer-aided diagnosis (CAD) approaches come in. This study introduces a lightweight and interpretable framework for comprehensively analyzing lung nodules, including their detection, classification, and segmentation. This approach uses the wavelet scattering transform (WST) to extract …