Digital Commons Network^™

A Novel Deliberative Multicriteria Evaluation Approach To Ecosystem Service Valuation, Georgia Mayrommati, Mark E. Borsuk, Richard B. Howarth

New Hampshire EPSCoR

Although efforts to address ecosystem services in decision making have advanced considerably in recent years, there remain challenges related to valuation. In particular, conventional economic approaches have been criticized for their inability to capture the collective nature of ecosystem services, for their emphasis on monetary metrics, and the difficulty of assessing the value of ecosystem services to future generations. We present a deliberative multicriteria evaluation (DMCE) method that combines the advantages of multicriteria decision analysis with a deliberation process that allows citizens and scientists to exchange knowledge and evaluate ecosystem services in a social context. Compared with previous applications we …

Metarag: Identifying Website Owner Using Meta-Path-Guided Dynamic Graph Retrieval-Augmented Generation, Cheng Tu, Yunshan Ma, Bingyang Guo, Qianyu Li, Yang Li, Min Zhang, Fan Shi, Xiang Wang

Research Collection School Of Computing and Information Systems

Website owner identification aims to link websites to their real-world owners, which is crucial for credibility assessment and information provenance in information retrieval and vital for applications in cybersecurity, Internet governance, and digital regulation. Existing approaches for website owner identification primarily rely on querying infrastructure registration records or analyzing webpage content. However, these methods often fail due to incomplete or outdated registration records and sparse webpage content. We observe that inter-website relationships, derived from shared infrastructure data such as primary domains, IP blocks, and geolocations, can provide valuable but underutilized ownership cues. To exploit this insight, we propose MetaRAG, a …

Ai Failures In The Eyes Of The Downstream Developer: A First Look At Concerns, Practices, And Challenges, Haoyu Gao, Mansooreh Zahedi, Wenxin Jiang, Hong Yi Lin, James C. Davis, Christoph Treude

Research Collection School Of Computing and Information Systems

With the advancement of AI models, more software systems are adopting AI as a component to facilitate automation. Pre-trained models (PTMs) have become a cornerstone of AI-based software, allowing for rapid integration and development with lower training cost. However, their adoption also introduces failure modes such as data leakage and biased outputs, that may require careful handling by downstream developers. While previous research has proposed taxonomies of these technical concerns and various mitigation strategies, how downstream developers address these issues during the development of general AI-based software when reusing PTMs remains unexplored. Understanding downstream developers’ perspectives is essential because they …

Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Most students lack awareness regarding the labor market outcomes for their chosen college majors. This study aims to answer what factors affect the career expectations of graduating students at Quezon City University and how these expectations align with the prevailing labor market situation. It employed descriptive, causal, and explanatory research using a sample of 108 respondents from fourth-year information technology students for the school year 2021 to 2022. Eight of the nine null hypotheses were rejected by employing multinomial logistic and linear regression. Student fixed effects and other labor market outcomes significantly predicted salary, estimated stability, and estimated skills in …

Constitutional Limits On Occupational Licensing Bans As Collateral Consequences Of A Criminal Conviction, Imran Rabbani

Student Works

No abstract provided.

Beyond The Cash Kickback: Contractual Control As Remuneration In Private-Equity-Backed Physician Practices, Maxwell Dandrea

Student Works

No abstract provided.

Originalism’S Impact On The Sovereignty Of Indian Nations, Julian Allber

Student Works

No abstract provided.

Bodily Autonomy And Involuntary Servitude: Applying The Thirteenth Amendment To Restrictions On Abortion In The Case Of Pregnancy Induced By Rape, Joseph Innocente

Student Works

No abstract provided.

Multidisciplinary Practices (Mdps) In Elder Law, Bryan Koslow

Student Works

No abstract provided.

Imsa 2027 Profile, Office Of College And Academic Counseling

IMSA Profile

The nationally-ranked Illinois Mathematics and Science Academy (IMSA) develops creative, ethical leaders in science, technology, engineering and mathematics. As a teaching and learning laboratory created by the State of Illinois, IMSA enrolls academically talented Illinois students in its tuition-free residential Academy for grades 10-12. Students are challenged with a rigorous curriculum designed to develop them into problem solvers and critical thinkers.

Notable IMSA students include YouTube Co-Founder Steve Chen, PayPal Co-Creator Yu Pan, Yelp Co-Founder Russell Simmons, SparkNotes and OkCupid Co-Founder Sam Yagan, and Hearsay Social Founder Clara Shih.

Logupdater: Automated Detection And Repair Of Specific Defects In Logging Statements, Renyi Zhong, Yichen Li, Jinxi Kuang, Wenwei Gu, Yintong Huo, R. Michael Lyu

Research Collection School Of Computing and Information Systems

Developers write logging statements to monitor software runtime behaviors and system state. However, poorly constructed or misleading log messages can inadvertently obfuscate actual program execution patterns, thereby impeding effective software maintenance. Existing research on analyzing issues within logging statements is limited, primarily focusing on detecting a singular type of defect and relying on manual intervention for fixes rather than automated solutions.To address the limitation, we initiate a systematic study that pinpoints four specific types of defects in logging statements (i.e., statement code inconsistency, static dynamic inconsistency, temporal relation inconsistency, and readability issues) through the analysis of real-world log-centric changes. We …

Faculty Handbook, Georgia Southern University

Faculty Handbooks

Faculty Handbook for Georgia Southern University for the 2026-2027 academic year. The Faculty Handbook is published online by the Office of the Vice President for Academic Affairs and archived in Digital Commons@Georgia Southern.

Optimizing Human Fcrn Mouse Models To Improve Pharmacokinetic Evaluation Of Antibody Drug Candidates., Gregory J. Christianson, Zachary M Howard, Samantha Kenney, Emily Lowell, Nijaguna Bethur, Derry C. Roopenian, Elena Gonzalo-Gil

Faculty Research 2026

The use of animal models that can reliably predict drug performance in human patients is critical to antibody therapeutic development. Along with assessing toxicity and efficacy, determining the pharmacokinetic (PK) properties of therapeutics in Tg32 and Tg276 mice is essential to preclinical characterization. While Tg32 mice have been well established as indispensable in their ability to model the PK properties of antibody therapeutics, their intact immunity leaves them capable of mounting anti-drug antibody responses that interfere with PK interpretation. Here, we demonstrate the negative impact anti-drug responses can have on PK parameters derived from Tg32 mice, and provide strong evidence …

Optimizing Human Fcrn Mouse Models To Improve Pharmacokinetic Evaluation Of Antibody Drug Candidates., Gregory J. Christianson, Zachary M Howard, Samantha Kenney, Emily Lowell, Nijaguna Bethur, Derry C. Roopenian, Elena Gonzalo-Gil

Faculty Research 2026

The use of animal models that can reliably predict drug performance in human patients is critical to antibody therapeutic development. Along with assessing toxicity and efficacy, determining the pharmacokinetic (PK) properties of therapeutics in Tg32 and Tg276 mice is essential to preclinical characterization. While Tg32 mice have been well established as indispensable in their ability to model the PK properties of antibody therapeutics, their intact immunity leaves them capable of mounting anti-drug antibody responses that interfere with PK interpretation. Here, we demonstrate the negative impact anti-drug responses can have on PK parameters derived from Tg32 mice, and provide strong evidence …

Limosilactobacillus Reuteri Promotes Melatonin Release From Human Intestinal Organoids Via 5′Ectonucleotidase Activity, Micah D Forshee, Erika J Nachman, Esha R Shenoy, Heather A Danhof, Ludwig Ermann Lundberg, Stefan Roos, Robert A Britton

Faculty, Staff and Students Publications

Strains of Limosilactobacillus reuteri have been used to prevent or treat various conditions; however, the mechanisms by which they exert beneficial effects are not completely understood. Infant colic is one example in which L. reuteri DSM 17938 reduces clinical symptoms. While the etiology of colic is unknown, abnormal melatonin levels in infants have been suggested as a possible contributor. L. reuteri DSM 17938 has been shown to produce adenosine from AMP via production of the extracellular enzyme 5′ectonucleotidase (5′NT). Adenosine is a potent signaling molecule that impacts several important aspects of host physiology, including the release of melatonin from the …

Using Randomization To Compare Ai And Expert-Generated Formative Assessment Questions In Medical Education, Akshat Kumar, Katie Stinson, Litao Wang, Elizabeth Nugent, Yongsu Lee, Mark Hormann, Latanya Jones Love, Toufeeq Ahmed Syed

Faculty, Staff and Student Publications

Background: AI-generated content is being used across the education spectrum and is beneficial for creating complex multiple-choice questions, such as those used in medical education. However, evaluating AI-generated content is challenging, and existing testing and evaluation methods are falling short. This study uses randomization to compare medical students' performance on and subjective evaluation of AI versus expert-generated questions. We hypothesized that there would be no difference in student performance or subjective evaluation of AI vs expert-generated questions.

Methods: We designed a single-center, randomized study where medical student participants received either one AI- or expert-generated question per day over four weeks. …

Steroid Receptor Coactivator 3-Deficient Regulatory T Cells Eradicate Multiple Solid Tumors In Syngeneic Mouse Models, Nuri Sung, Eunsu Kim, Yosef Gilad, Yuri Park, Adam M Dean, Yan Xia, Jianming Xu, Clifford C Dacso, David M Lonard, Sang Jun Han

Faculty, Staff and Students Publications

Steroid receptor coactivator 3 (SRC-3) is highly expressed in regulatory T cells (Tregs) and is important for their immunosuppressive activity. Recently, we demonstrated that disrupting SRC-3 expression in Tregs eliminates triple-negative breast cancer (TNBC) and prostate cancer in syngeneic animal models by generating an anti-tumor immune microenvironment without inducing immune-related adverse events (irAEs). Further analysis of these mice revealed that SRC-3 knockout (KO) Tregs infiltrated breast tumors and facilitated the infiltration of CD8

Comparative Assessment Of Agmd And Vmd Systems Configured With Flat Sheet And Hollow Fibre Membranes: An Energy And Exergy, Thermo-Economic, And Environmental Analysis, Hamed Kariman, Mehdi Khiadani

Research outputs 2022 to 2026

Membrane Distillation (MD) systems, particularly Air Gap Membrane Distillation (AGMD) and Vacuum Membrane Distillation (VMD) in Flat Sheet (FS) and Hollow Fibre (HF) configurations, demonstrate strong potential for freshwater production, Yet, there remains a lack of comparative studies evaluating their energy, exergy, economic and environmental performance under similar conditions. This study presents a comparative analysis of four MD systems under similar operating conditions: FS-AGMD, HF-AGMD, FS-VMD and HF-VMD, and considers their respective costs and operation in Western Australia. The analysis was performed for single-stage and multi-stage configurations of up to ten modules, both with and without Heat Recovery (HR), to …

Social Procurement As A Catalyst For Dei In Construction: Some Takeaways For Hrm?, Jennifer Waterhouse, Robyn Keast, Judy Lundy, Martin Loosemore

Research outputs 2022 to 2026

Purpose: Diversity, equity and inclusion (DEI) initiatives aim to foster workplaces where all employees feel valued and can add value. Increasingly, the success of these initiatives is influenced by environments and interactions beyond organisational boundaries, including recent backlash. Despite this, most DEI research remains focused on intra-organisational aspects and human resource management (HRM) activities. The purpose of this paper is to introduce social procurement as a novel and promising framework for advancing DEI in theory and practice through a collaborative, inter-organisational approach that has the potential to overcome institutional barriers and mitigate backlash. Design/methodology/approach: Data were collected through 15 in-depth …

Public Procurement’S Role In Shaping Supply Markets: A Taxonomy Of Local Contracting Authorities’ Approaches Using The Awareness-Motivation-Capability Framework, Steven Borobia, Frederik G.S. Vos, Louise Knight, Esmee Peters, Fons Wijnhoven, Niels Uenk

Business Faculty Publications and Presentations

Purpose: Public procurement is increasingly recognised as a tool for governing and shaping supply markets. Yet we still know little about how local authorities engage in market shaping through contracting practices, especially in newly decentralized markets. This study investigates the role of awareness, motivation and capabilities (AMC) in how municipalities influence (or fail to influence) social care markets, and how these dynamics manifest in contracting practice. Design/methodology/approach We combine secondary data with semi-structured interviews conducted with procurement professionals across municipalities, regional consortia, and expert organizations. The case context is Dutch social services procurement, which transitioned to a decentralised market in …

Firms’ Darq Readiness And Marketing Implications: A Systematic Literature Review, Ali N. Tehrani, K. Roy, Richard Gruner

Research outputs 2022 to 2026

Purpose – This study aims to systematically review literature across diverse disciplines, including marketing, management and information systems, to synthesise insights on firms’ readiness to adopt and implement DARQ technologies and their marketing implications. DARQ technologies are emerging and cutting-edge technologies, including Distributed Ledger Technology, Artificial Intelligence, Extended Reality and Quantum Computing (hence, DARQ). Design/methodology/approach – Through a systematic review of over 2, 500 papers across three databases, this study distilled a focused sample of 88 papers. It comprehensively explores the reported dimensions, antecedents, outcomes and marketing implications of firms’ readiness for DARQ technologies. Findings – This study identified three …

Long Range Battery-Free Wireless Power Transfer Testbed For Underground Mines Iot And Lpwan Devices, Anabi Hilary Kelechi, Samuel Frimpong, Sanjay Madria

Mining Engineering Faculty Research & Creative Works

Underground mines are susceptible to occasional roof falls and cave-ins, temporarily destroying the existing wireless communications and telemetry infrastructure. During this temporary outage, intermittent provision of electrical energy wirelessly to the already deployed low-power wireless area networks (LPWAN) and Internet of Things (IoT) devices assumes a fundamental requirement. In this article, we propose and design a long-range far-field radio frequency (RF) wireless power transfer (WPT) testbed to power LPWAN and IoT devices at 35 m in an underground mines facility. Class AB external power amplifier (PA) was introduced to achieve a long-distance RF WPT, in the 880 MHz band. Thus, …

Evaluating The Impact Of Research Equipment Availability On Scientific Rigor, Reproducibility, And Innovation Across Stem Disciplines, Lyndi G. Hraban

Honors Program: Senior Projects (Public)

Research infrastructure plays a central role in shaping the quality and innovative capacity of scientific research. This study evaluates how the availability of research equipment influences scientific rigor, reproducibility, and innovation across STEM disciplines. Drawing on literature from science-to-science research, policy studies, and discipline-specific analyses, this project synthesizes existing scholarship to explore the relationship between access to infrastructure and key research outcomes. Findings across the literature indicate that access to advanced and well-maintained equipment can strengthen scientific rigor by enabling more precise measurements, improved experimental control, and the implementation of standardized research protocols. Shared and centralized facilities are also associated …

Methods For Tagging Whale Sharks: Insights Into Performance And Best Practices With A Focus On Clamp Attachments, Freya C. Womersley, Sofia Green, Alberto Garcia-Baciero, Ronan Conlon, Amy L. Jeffries, Matt J. Waller, Sara S. Ratão, Nuno Queiroz, Pedro Afonso, Gonzalo Araujo, Adam Barnett, Christine Barry, Michael L. Berumen, Farukhkha Bloch, Ginevra Boldrocchi, Camrin D. Braun, Ryan Caillouet, Constance Chapman, Jesse E.M. Cochran

Faculty Publications

Background: Biologging and telemetry have transformed our understanding of marine megafauna movement ecology. Yet, methodological constraints continue to limit data quality and deployment duration. Devices recording whale shark (Rhincodon typus) behaviours and movements have been used for decades, but they remain challenging to deploy and vary in success. Recently, spring-loaded clamp-based systems have emerged as one of the most widely used approaches to attach electronic tags to the fins of this globally endangered species. Currently, however, no consensus guidelines exist as to how to optimise this approach, potentially leading to continued underperforming deployments limiting analysis potential. Here, we synthesise experiences …

Smart Medical System Integrating Clinical Workflows For Robust Skin Cancer Detection Across Heterogeneous Pathologies, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Skin cancer is among the most prevalent and life-threatening dermatological diseases worldwide, with melanoma responsible for a substantial proportion of skin cancer–related deaths due to delayed and unreliable diagnosis. Conventional clinical screening based on visual inspection and expert interpretation is inherently subjective and often affected by inter-observer variability, lesion heterogeneity, and imaging artifacts, highlighting the need for accurate and generalizable automated diagnostic systems. This study proposes a novel hybrid deep learning architecture for skin cancer classification that integrates an attention-guided autoencoder with a transformer-inspired global context modeling module, forming a unified and robust representation learning framework. The encoder–decoder structure is …

Scalable And Interpretable Fraud Detection In Fintech: Evidence From A Hybrid Tabnet–Graph Neural Network Framework, Haseeb Ahmad, Istiaque Bhuiyan, Tanvir Bhuiyan, Ariful Hoque

Research outputs 2022 to 2026

This study proposes a fraud detection framework for FinTech that integrates tabular deep learning with relational graph reasoning. We address a key limitation of state-of-the-art tabular fraud models: their assumption that transactions are independent, despite evidence that modern fraud often arises from coordinated infrastructure reuse (e.g., shared devices, email domains, accounts, and addresses). Using the IEEE-CIS Fraud Detection dataset (≈590,540 e-commerce transactions), we develop a hybrid architecture that combines TabNet, chosen for its built-in interpretability on tabular data, with a GraphSAGE encoder trained on a leakage-free heterogeneous entity graph constructed strictly from historical training transactions. Unseen entities in validation and …

A Deep Learning Ensemble Framework For Multi-Subtype Renal Tumor Classification Using Contrast-Enhanced Ct, Hisham Abdeltawab, Ahmed Alksas, Mohamed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Rasha T. Abouelkheir, Ahmed Elmahdy, Mohamed Abou El-Ghar, Sohail Contractor, Ayman El–Baz

All Works

Renal cell carcinoma (RCC) is considered the most aggressive and common form of renal cancer. Therefore, early detection is crucial to ensure appropriate and effective treatment planning. In our study, we propose a novel computer-aided diagnostic (CAD) approach which incorporates a deep learning ensemble to differentiate between five renal tumor subtypes, utilising the modality of contrast-enhanced computed tomography (CE-CT). The addressed renal lesions are malignant tumors (chromophobe RCC (chRCC), papillary RCC (pRCC), and clear cell RCC (ccRCC)) and benign tumors (renal oncocytoma (RO) and angiomyolipoma (AML)). Our study includes 280 patients who underwent renal biopsy, 112 patients were diagnosed with …

Integrating Machine Learning And Explainable Ai For Employee Attrition Prediction In Hr Analytics, Maytha Al-Ali, Majed Alwateer, Shatha Abed Alsaedi, Hossam Magdy Balaha, Mahmoud Badawy, Mostafa A. Elhosseini

All Works

Employee attrition poses significant challenges to organizations, impacting productivity, morale, and financial stability. Predicting attrition and understanding its underlying drivers are critical for implementing effective retention strategies. In this study, we propose a comprehensive framework that utilizes advanced machine learning techniques to predict employee attrition and job change likelihood. The framework integrates robust preprocessing pipelines, state-of-the-art predictive models, and explainability tools such as SHAP (SHapley Additive exPlanations) to ensure transparency and fairness in HR analytics. By addressing key challenges such as class imbalance, feature selection, and model interpretability, our approach provides actionable insights for proactive talent management. We evaluate the …

Engineering Sulfur Concrete Mixes: A Data-Driven Approach To Aggregate Packing And Binder Efficiency, Vasilia Al Khaldi, Abdel Hamid I. Mourad, Maisa El Gamal

All Works

Processing window (mass loss < 3%). A first-order, order-of-magnitude carbon-footprint estimate—presented with explicit caveats rather than as a full life-cycle assessment—indicates that replacing Portland cement with by-product sulfur is the dominant contributor to a substantial reduction in embodied CO₂ per cubic metre relative to equivalent-strength Portland cement concrete. The valorization of steelmaking by-products aligns with circular economy principles, enabling high-performance, recyclable, and low-carbon construction materials without chemical binder modification.

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

All Works

Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …