Digital Commons Network^™

Medulloblastoma Stem Cell Programs: Molecular Roadmaps Of Disease Progression, Tamra E Werbowetski-Ogilvie

Faculty, Staff and Students Publications

Over the last decade, an unprecedented number of sequencing studies have characterized the molecular landscape of pediatric brain cancers, including the highly heterogeneous tumor medulloblastoma (MB). Extensive MB profiling has enabled a much deeper understanding of the primitive neurodevelopmental programs that are hijacked during tumor progression. However, we have yet to successfully target and fully eradicate the putative stem and early progenitor cells that drive MB tumorigenesis. This goal will require better human models that faithfully recapitulate oncogenic events, a deeper understanding of the mechanisms governing cell fate decisions in the primary and metastatic compartments, and comprehensive validation studies of …

A Neutrosophic Homological-Gröbner Framework For Ai-Driven Media Education Evaluation, Mona Gharib, Hafiz Muhammad Athar Farid, José M. Merigó, Muhammad Riaz

Sustainable Machine Intelligence Journal

The integration of artificial intelligence (AI) with media technologies has transformed teaching in Journalism and Communication, introducing new challenges for quality evaluation. Traditional assessment methods fail to capture the uncertainty, inconsistency, and incompleteness inherent in modern educational data. This paper proposes a Neutrosophic Homological–Gröbner Framework, which unifies three perspectives: neutrosophic sets to encode uncertain evidence, homological algebra to analyze structural coherence, and Gröbner bases to systematically simplify interdependent evaluation rules.

Within this framework, diverse evidence is encoded into neutrosophic triplets, daggregate into course-level indicators, and organized into neutrosophic chain complexes, where Betti indices reveal coherence and fragmentation. Educational policies are …

High-Performance Deep Learning Techniques For Plant Disease Detection: Performance Analysis, Validation, And Applications, Doaa El-Shahat, Ahmed Elmasry

Sustainable Machine Intelligence Journal

The early detection of plant diseases is an indispensable task to improve crop yields and production quality. Crop disease observations by experienced pathologists are difficult and might take a long time. Therefore, deep learning (DL) techniques have been utilized to present an automated detection technique that could accurately and timely detect plant diseases. Several DL models in the literature were proposed, but no paper conducted a comparative study between those models to determine which of them was the best alternative for this task. Therefore, twenty-one DL models are compared in this review paper to show which of them could achieve …

Rf-Et-Ann: Hybrid Machine Learning Model For Forecasting Short-Term Photovoltaic Power Production, Walid Abdullah, Ahmed Ismail Ebada, Mohamed Abouhawwash

Sustainable Machine Intelligence Journal

Accurately forecasting photovoltaic (PV) power generation is a challenging problem due to the non-linear and highly variable characteristics of solar data, which is strongly influenced by several interdependent factors, such as environmental conditions, system characteristics, and technical aspects. In this study, a newly proposed machine learning (ML) technique based on integrating random forest (RF), extra trees (ET), and artificial neural networks (ANN) is presented to tackle this problem with better predictive accuracy, ensuring that solar energy is used more consistently, effectively, and economically; this model is dubbed RF-ET-ANN for short. Hybridization of these three models will help capture non-linear aspects …

Gnmsa: An Accurate Parameter Estimation Of Semi-Empirical Proton Exchange Membrane Fuel Cells Model Using A Hybrid Artificial Intelligence–Based Optimization Approach, Dina Atef, Doaa El-Shahat, Hafiz Burhan Ul Haq, Zaka Ur Rehman, Muhammad Nauman Irshad

Sustainable Machine Intelligence Journal

The PEMFC parameter estimation problem has recently piqued the interest of several researchers due to its significance in reaching better PEMFC modeling. However, existing PEMFC parameter estimation algorithms encounter significant difficulty in solving this problem because of its complex and highly nonlinear nature. Therefore, this paper presents a new hybrid approach, termed GNMSA, for addressing this problem more accurately. This hybrid approach combines the exploration operator of the generalized normal distribution optimization with the exploitation operator of the mantis search optimization (MSA), resulting in this strong variant that has high abilities to alleviate stagnation in local optima and expedite convergence …

An Enhanced Optimization Algorithm For Estimating Pem Fuel Cells Parameters: Performance Comparison And Real-World Applicability, Ibrahim Alrashdi, Karam M. Sallam

Sustainable Machine Intelligence Journal

Accurate modeling is critical for the efficient design, control, and optimization of a proton exchange membrane (PEM) fuel cell. This problem is regarded as a multi-modal, multi-variate, non-linear optimization problem, and several methods in the literature have been presented to tackle it. However, those methods still suffer from either being stuck into local optima or a slow convergence rate. To solve this problem more accurately in terms of final results and convergence speed, this study proposes a hybrid optimization algorithm based on enhancing the recently proposed crested porcupine optimizer (CPO) using two new optimization methods, namely ranking-based optimization and exploration-exploitation …

Deep Learning In Precision Agriculture: A Systematic Review Of Methods, Datasets, And Applications, Mariam Ahmed

Sustainable Machine Intelligence Journal

With the growing demand for food and water for the world population. Deep learning is one of the most powerful approaches for smart agriculture, offering the capacity for accurate and fast data manipulation and decision-making. To this end, this study contributes to the body of knowledge as follows. We present a comprehensive review of deep learning methods commonly applied in precision agriculture, categorized into three main categories: discriminative, generative, and hybrid models. Given the data-driven nature of DL, we provided a structured overview and tabulation of available datasets in precision agriculture, by categorizing them according to modality to guide dataset …

Vigilante Violence, The Rise Of The New Right, And The Persistence Of The Texas Farmworkers, 1975–1980, Brent M. S. Campney, Tim Bowman

History Faculty Publications

This study charts the ineffective vigilante violence perpetrated by growers in the Rio Grande Valley of Texas to suppress farmworker activism from the mid-to-late 1970s and their abrupt shift in tactics with a 1980 strike in Hereford, Texas, toward the adoption of strictly nonviolent and tediously legalistic new methods associated with the neoconservative backlash. It does so in two major sections. In the first, grower violence is chronicled in detail that underscores both its rage and ineffectiveness. The second section shows how the New Right usurped the longstanding usage of physical violence against ethnic Mexicans in Texas, prompting conservatives to …

Volumetric Wear Analysis Of Polyethylene Orthopedic Liners Using Point Clouds, Gabriel Landi

Dartmouth College Master’s Theses

In vivo wear of ultra-high molecular weight polyethylene (UHMWPE) orthopedic bearings represents a major problem in shoulder and knee arthroplasty, with significant amounts of devices failing due to the complication itself, and even more due to associated reasons for retrieval, including aseptic loosening. However, quantification of UHMWPE wear has largely been limited to linear and in vitro volumetric analyses, obscuring the understanding of the amount of clinically relevant material loss. This study proposes new methods to measure articular, rim, and backside surfaces of retrieved shoulder and knee UHMWPE liners, centered around the coordinate measuring machine (CMM)-based collection of high density …

Where The Government Steps Back, Business And Philanthropy Can Step Forward Together, Emily Brenner, Tory Martin, Anneliese Orr

Research, Reports, and Publications

No abstract provided.

Weighing The Power Of Ai Against Its Impact, Kallie Bauer, Emily Brenner

Research, Reports, and Publications

No abstract provided.

Innovation And Survival: The Charitable Sector Is Looking For New Ways To Work, Jeff Williams

Research, Reports, and Publications

No abstract provided.

We Need Data And We Need Context. Both Are At Risk., Joe Vandoornik, Kallie Bauer

Research, Reports, and Publications

No abstract provided.

Employee Well-Being At Risk During Times Of Hyper Disruption, Jill Roof, Anneliese Orr, Mandy Sharp Eizinger

Research, Reports, and Publications

No abstract provided.

Small Courageous Minds, Desirae Bach

Masters Theses

This thesis examines the barriers, agreements, and disagreements between mental health and Christianity, specifically as it relates to helping Christian children with anxiety. The following investigation explores how God defines and reacts to anxiety in the Bible and how anxiety is explained through scientific research. By explaining both scientific and biblical views of anxiety, the Christian community can grasp a better understanding of how to help children with anxiety. The research also builds a foundational investigation on how anxiety affects children and possible solutions for relieving anxiety in children. Educating children at a young age about mental health and offering …

Factors Influencing Sugary Drink Consumption Among Residential Students At Liberty University, Chinonso Felistas Eziechi

Masters Theses

Introduction/Background: Sugary drink consumption among college students remains a public health concern due to its association with obesity, diabetes, and cardiovascular disease. Despite high awareness of health risks, consumption among emerging adults remains elevated. Guided by the Social Cognitive Theory, this study examined the psychosocial, behavioral, and environmental determinants of sugary drink consumption among residential students at Liberty University. Objective: To identify key predictors of sugary drink consumption and evaluate how expectations, self-regulation, and environmental factors shape beverage behaviors. Design, Setting, Participants: This cross-sectional study used an anonymous Qualtrics survey distributed on campus. Of approximately 745 eligible residential students, 473 …

Reproducible Semantic Data Management Workflow For Materials Data Science: Generating Knowledge Graphs With Robust Fairifcation Pipelines, Kyle R. Henrikson, Van D. Tran, Meredith Francis, Isabella Giammattei, Quynh D. Tran, Laura S. Bruckman, Erika I. Barcelos, Roger H. French

Student Scholarship

Combining data from multiple sources is crucial for efficient knowledge aggregation in materials data science. FAIR data from ontology and Linked Data principles enable this. Semantic data management streamlines data exchange and aggregation, ensuring information is available and extractable. FAIRLinked and GraphDB provide solutions for consolidating, hosting, and extracting meaningful insight from multimodal data.

Rethinking The Refugee: From Migrant Hierarchies To Migration As A Human Right, Roni Amit

Seton Hall Law Review

No abstract provided.

In Defense Of Mapp - And The Good Faith Exception, Michael J. Zydney Mannheimer

Seton Hall Law Review

No abstract provided.

Cultural Superimposition In Humanitarian Protection, Jocelyn B. Cazares Willingham

Seton Hall Law Review

No abstract provided.

Board Of Officers - Vol. 56, No. 3 2026

Seton Hall Law Review

No abstract provided.

Go On Take Everything, Take Everything, I Want You To: Corporate Cash Accumulations, Broken Promises, And The Failure Of The Accumulated Earnings Tax, Meredith R. Conway

Seton Hall Law Review

No abstract provided.

Light On The Scale, Heavy On The Problems: How Telehealth’S Accessible Weight Loss Solution Has Compounded Its Risks, Wendy Xia

Seton Hall Law Review

No abstract provided.

Over The Line: Assessing The State Of Ballot-Based Associational Rights In A Post-Kim New Jersey, Josh Kutner

Seton Hall Law Review

No abstract provided.

Table Of Contents - Vol. 56, No. 3 2026

Seton Hall Law Review

No abstract provided.

Determination Of Sample Size In Vaccine Trials, Meghna Bose

Doctoral Theses

Sample size determination is an important as well as delicate problem in clinical trials. Often, prior information available before the beginning of the trial is used to estimate the sample size. Hence, it is of importance to quantify the prior information available in terms of an Effective Sample Size (ESS). The problem of finding Effective Sample Size (ESS) in Phase II clinical trials, where toxicity and efficacy are the two components of the treatment response vector, is considered. In particular, one of the components is assumed to be binary, and the other is assumed to be continuous. Theoretical expressions for …

Supporting Young Carers In Early Childhood: Mapping Power, Threat, Meaning, And Strengths: A Ptmf-Informed Qualitative Study, Carly Ellicott, Ali Bidaran, Felicity Dewsbery, Alyson Norman, Helen Lloyd

School of Psychology

Background/Objectives: This qualitative study examines strengths and strains faced by professionals working with young carers throughout the United Kingdom (UK) in the context of society’s youngest carers; young carers in early childhood (YCEC) (0–8 years). Methods: The Power Threat Meaning Framework (PTMF) was utilised to map key findings of three focus groups. This conceptual lens offers a narrative-based understanding of ways in which power operates in society. Increasingly applied to explore experiences of individuals, communities, and groups, the PTMF proposes that concepts of distress are founded in broader contexts of injustice and social inequalities. Twenty-four participants were recruited from throughout …

Midweek Memo, Georgia Southern University

Midweek Memo

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The George-Anne Daily, Georgia Southern University

George-Anne Media Group: Newsletters & Magazines

No abstract provided.

Algorithmic Editors: Section 230, Big Tech, And The Need For Clarity, Richard Gruters

Seton Hall Law Review

No abstract provided.