Digital Commons Network^™

Advancing Sustainable Tourism Behavioural Research: A Metacognitive Perspective, Arghavan Hadinejad, Kourosh Esfandiar, Liubov Skavronskaya

Research outputs 2022 to 2026

The current scholarly inquiry on sustainable tourism behaviour research predominantly relies on cognitive behavioural approaches, particularly exemplified by the widespread application of the theory of planned behaviour among other theories. Given recent advancements in psychology underscoring the necessity to expand beyond conventional cognitive frameworks, the current research aims to integrate metacognitive perspectives in sustainable tourism behaviour scholarship. This perspective explores the reflective awareness and control individuals exert over their cognitive processes which can uncover nuanced factors shaping sustainable tourism behaviour that may be overlooked in traditional cognitive models. Using a critical review approach, this research proposes the adoption of alternative …

Governing By Fragments: Soviet Ruins And The Architecture Of Unaccountability In Kazakhstan, Robert A. Kopack, Magdalena E. Stawkoski

Faculty Publications

This article examines two case studies in Kazakhstan with research into how Soviet institutional legacies underwrite extractive economies that render toxic costs invisible in post-Soviet setting. Through ethnographic work in Stepnogorsk (uranium processing) and the Semipalatinsk Test Site (nuclear weapons testing), we extend Gabrielle Hecht’s residual governance framework to demonstrate how inherited bureaucratic compartmentalization now serves market rather than state interests. We introduce the concept of functional nonreform to describe how distributed actors maintain Soviet-era boundaries that prevent accountability: Agencies monitor but don’t enforce, health authorities lack jurisdiction over contaminated sites, and environmental agencies measure radiation but cannot restrict land …

Barriers To Mental Healthcare In Rural Agricultural Communities, Monica Salonga

Research Honors

Mental health diagnoses have become increasingly concerning in the United States (US). In 2024, 60 million adults in the United States experienced a mental illness, and over 14 million adults stated they had suicidal thoughts (Reinert et al., 2025). The Center for Disease Control (CDC) reports around 4,000 suicide-related deaths in the United States every year (CDC, 2025). One population that suffers from disproportionate rates of suicide is farmers and farm families. Farmers are ranked #4 in the United States regarding suicide rates (Miller & Rudolphi, 2022). Between 2003 and 2018, there were 1,935 suicide-related deaths committed by farmers (Miller …

Front Matter, Editors Of Osgoode Hall Law Journal

Osgoode Hall Law Journal

Volume 62, Number 2

Front Matter, Editors Of Osgoode Hall Law Journal

Osgoode Hall Law Journal

Front Matter

Lindenwood University Employment Policies, 2026-2027, Lindenwood University

Employee Handbooks

In 2021-2022, Lindenwood University made the Employee Guidebook, online only. This is a compilation of the documents from all of the links in the 2026-2027 Employee Policies site.

A Light Unto My Path? Discerning Prophetic Words For The Latino/A Church Today, M. Daniel Carroll R. Ph.D

Apuntes: Reflexiones teológicas desde el margen hispano

No abstract provided.

Large Language Model-Assisted Research Question Development In Public Health: A Case Study In The Special Supplemental Nutrition Program For Women, Infants, And Children (Wic), Qi Zhang, Bidusha Neupane, Priyanka Patel, Futun N. Alkhalifah, Yi He, Leslie Hodges

Health Behavior, Policy & Management Faculty Publications

Objective:

To assess the feasibility of using large language models (LLMs) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages.

Design:

We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the USDA summary of the final public comments on the WIC revision. Five questions weekly for three weeks were generated using LLMs under two conditions: fed with or without relevant literature. The experiment generated 90 questions, which were evaluated using the FINER criteria (Feasibility, Innovation, Novelty, Ethics, …

Tuberculosis Presenting As Multiple Pulmonary Nodules Mimicking Malignancy, Abdel Martinez Borrego, Fatimah Bello, Brandon Cantazaro, Luisa Montoya, Frank Mediola

School of Medicine Publications

Multiple pulmonary nodules often raise concern for metastatic malignancy; however, the differential diagnosis is broad and includes infectious, inflammatory, granulomatous, vascular, and benign etiologies. Tuberculosis (TB), although uncommon, can present with multiple nodules that closely mimic metastatic disease on advanced imaging, requiring careful clinicoradiologic and microbiologic correlation. We describe a woman in her 50s who presented with dyspnea and new-onset heart failure, in whom imaging revealed mediastinal lymphadenopathy and bilateral pulmonary nodules concerning for malignancy. Evaluation was limited by concurrent COVID-19 infection, and CT-guided biopsy demonstrated necrotizing granulomas without evidence of malignancy or infection. She later re-presented with fever and …

An Educational Intervention On Mutual Goal Setting For Hospital Nurses: A Mixed-Methods Study, Teresa Jerofke-Owen, Kristi Opper, Joseph Beiler, Maharaj Singh

College of Nursing Faculty Research and Publications

Background:

Including patients in mutual goal setting is one mechanism to personalize care delivery and improve patient outcomes.

Objective:

The purpose of this study was to examine the impact of a nurse educational intervention about mutual goal setting on nurse engagement and self-efficacy for goal setting and the nurse/patient experience of goal setting.

Methods:

During this explanatory, sequential mixed-methods study, nurse engagement in goal setting and goal quality was assessed through chart audits 1-month pre-intervention (n = 173) and 1 and 3-months (n = 144, n = 137) post-intervention. Nurse self-efficacy was measured with the Appraisal Inventory Tool. Patient experience …

Missionary Body Politics: Physical Education In West China, 1905-1950, Cory Willmott

Asia Pacific Perspectives

Republican Era China saw a radical transformation in Chinese physical culture as it aimed to compete in the global geopolitics of nation-states. Employing an historical ethnographic approach, this paper outlines how the unique conditions of Sichuanese warlordism and secret societies affected missionary relations with the Guomindang (GMD) as they implemented increasingly militarized physical education programs. Before the 1928 GMD consolidation of power, missionaries and GMD educators aligned with the YM/ WCA in physical education programs. After the GMD moved their headquarters to Chongqing in 1938, relations soured between the GMD and the Sichuan missionaries. Although the latter had been the …

Pilot Study On The Use Of Multimorbidity Index In Patients Receiving Home Parenteral Nutrition, Michael M. Rothkopf, Mohan Pant, Zachary Rothkopf, Rebecca Brown Rdn, Jaime Haselhorst Rdn, Debbie L. Stevenson Rdn, Andrew Depalma Mba, Michael Saracco

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background

Home parenteral nutrition (HPN) patients often have multiple comorbidities. A validated multimorbidity index (MMI) could help determine resource needs and risks with HPN. We evaluated MMI scoring to determine if it predicted HPN resource use and outcomes.

Methods

We performed a secondary analysis of 60 HPN patients from a previous study based on Cumulative Illness Rating Scale (CIRS) scoring. We examined three variables: PN formula changes, hospitalizations, and hospital length of stay (LOS). Density plots were produced to select a CIRS score cutoff value. Spearman correlations among the three variables of interest were computed. The data were then subjected …

A Critical Perspective On The Society Of Environmental Toxicology And Chemistry's Adherence To Founding Principles— Opportunities For The Future, Barnett A. Rattner, Annegaaike Leopold, Carys L. Mitchelmore, Glenn W. Suter, Mark S. Johnson, Adriana C. Bejarano, Lawrence A. Kapustka, Niranjana Krishnan, Derek C. G. Muir, Beatrice O. Opeolu, Martha Georgina Orozco-Medina, April Reed, Bruce W. Vigon, Adam R. Wronski

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

The Society of Environmental Toxicology and Chemistry (SETAC) is a global organization whose mission is the advancement of environmental science and management through collaboration, leadership, communication and education. On SETAC's 45th anniversary, the following question was raised: Are the 1979 founding principles of SETAC, multidisciplinary approaches to solving environmental problems, multisector engagement and scientific objectivity, still useful, adequate and effective in fulfilling its mission? In a special session held at the 45th Annual Meeting in Fort Worth, Texas, United States, a critical evaluation of the founding principles was initiated by reviewing SETAC's history and ongoing activities, and recommendations were made …

The Crucial Role Of Machine Learning Models In Predicting Current Childhood Asthma: Model Comparison, Calibration, And Shap-Based Interpretation, Aditya Chakraborty, A. K.M. Raquibul Bashar

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Background: Asthma is one of the most prominent chronic diseases in children and one of the most challenging ailments to diagnose in infants and preschoolers in the United States. Predictive models can be instrumental in improving early diagnosis, personalized treatment strategies, and disease progression. By utilizing nationalized data, this study focuses on building and comparing high-performing analytical predictive models based on the relevant risk factors and identifying the most influential predictors.

Methods: We analyzed cross-sectional BRFSS Asthma Call-Back Survey data (2011-2020; N = 9,813) and randomly split participants into training and testing sets. An XGBoost model (hyperparameters tuned via grid …

Imperfections And Scars: The Aesthetics Of Curated Decay In Urban Conservation: The Aesthetics Of Curated Decay In Urban Conservation, Ioana Moldovan, Connell Vaughan, Michael O’Hara, Silivan Moldovan, Ioana Cecălășan

Research Outputs: 2025-Present

This paper explores the value of imperfections and curated decay in the conservation of architecture and public art as vehicles of cultural memory. While conventional heritage practice treats physical degradation as a threat, newer conservation ethics argue for embracing material impermanence within an aesthetics of care. We examine how acknowledging patina, weathering, and even structural decline can become an act of care, maintaining the “spirit” and authenticity of a place. The theoretical framework integrated the aesthetics of imperfection, including concepts like the Japanese wabi-sabi, which finds beauty in the incomplete and impermanent, critical heritage theory (which questions whose memories and …

Staphylococcus Aureus Strain Heterogeneity Modulates Virulence Traits Associated To Dual-Species Biofilms With Candida Albicans, Laure Nicolas Annick Ries, Beata Adrienn Brezniczky, Binduja Srelatha Pradeep, Esther Olabamiji, Lee Hutt, Glenn M. Harper, Alexander Strachan, Gyorgy Fejer

School of Biomedical Sciences

Staphylococcus aureus is a commensal Gram-positive bacterium and opportunistic pathogen that exhibits substantial variation in antimicrobial resistance and virulence traits. S. aureus is frequently co-isolated with the fungus Candida albicans, and together they represent the most common microorganisms recovered from biofilm-associated infections originating from both biotic and abiotic surfaces. Despite this clinical relevance, the impact of bacterial intra-species variation on interkingdom interactions remains poorly understood. This study therefore investigated how S. aureus strain heterogeneity influences C. albicans within dual-species biofilms. The majority of the 60 S. aureus strains tested suppressed fungal growth in a contact-dependent manner across different nutrient conditions …

Secure-Home: Detect And Redact Pii, Hajar Niroomand, Omar F. El-Gayar

Annual Research Symposium

Personally Identifiable Information (PII) leakage from home environments poses significant identity theft risks. While enterprise networks employ robust security measures, firewalls, intrusion detection systems, and access controls, these protections rarely extend to home settings, creating a critical security gap. Current firewall technologies lack the capability to detect and scrub PII from outbound traffic, leaving vulnerable populations such as children, elderly users, and remote workers exposed. This design science research proposes Secure-Home, a prototype outbound inspection tool that detects and redacts clear-text PII before data leaves home networks. Using rule-based detection and selective redaction, Secure-Home provides a practical last line of …

Comparative Analysis Of Shipping Costs Across Multiple Routes Using Fedex Api Data, Valerija Curikova, Andrew Kramer

Annual Research Symposium

Shipping costs are an important factor for both individuals and companies, especially in industries like healthcare where medical material should be delivered quickly and reliably. While working as an intern at Avera Health in the Supply Chain logistics team, I was introduced to the challenge of the high cost of delivering medical supplies to different patient locations. Shipping prices vary depending on distance, route direction, and service type. Using real-time data from the FedEx API allows for more accurate analysis of shipping costs and helps identify opportunities to optimize delivery decisions and reduce logistics expenses.

Post-Quantum Cryptography Secure Communication, Iot, And Blockchain, Nidhish Bhanse, Mark Spanier

Annual Research Symposium

Modern public-key cryptography, such as RSA and Elliptic Curve Cryptography (ECC), plays a crucial role in securing data. However, the development of quantum computing threatens the security of data encrypted with these methods. Data encrypted today might be decrypted in the future due to the increased power of quantum computers. To combat this, the National Institute of Standards and Technology (NIST) has developed new standards for post-quantum cryptography. The following research aims to provide an analysis of these NIST post-quantum cryptographic algorithms and their potential for use in various secure communication protocols and platforms.

Privacy-Preserving Synthetic Data Generation For Federated Learning In Imbalanced Credit Card Fraud Detection: A Comparative Analysis Of Smote Vs. Gan Approaches, Muhammad Bhutta, Abid Mehmood

Annual Research Symposium

Credit card fraud detection faces two critical challenges: extreme class imbalance with less than 0.2% fraud cases in typical datasets, and strict privacy regulations that prevent financial institutions from sharing sensitive transaction data for collaborative model training. This research addresses these challenges by investigating privacy-preserving synthetic data generation approaches in federated learning settings. The study compares two methods: Federated-SMOTE, which employs secure cross-bank nearest neighbor discovery to generate synthetic fraud cases through interpolation with strong privacy guarantees (ε=0.3), and Federated-GAN, which uses generative adversarial networks with differential privacy to synthesize realistic fraud patterns with relaxed privacy constraints (ε=0.7). Using the …

Pahina: Precision-Aware Hierarchical In-Network Aggregation For Edge Distributed Training, Yingpu Nian, Bo Yi, Qiang He, Xingwei Wang, Geyong Min, Keqin Li, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rise of edge intelligence is driving distributed machine learning toward a new paradigm of edge-collaborative computing. To overcome the severe communication bottleneck in this paradigm, In-Network Aggregation is a critical enabling technology. However, its effectiveness is fundamentally undermined by the profound resource heterogeneity of edge networks. Specifically, edge devices, adapting to hardware constraints, operate at varying numerical precisions, leading to significant data inflation as gradients are aggregated. Compounding this, unevenly distributed network resources and traditional, precision-oblivious routing strategies often misallocate critical, high-precision gradients to low-quality paths. This mismatch creates severe network congestion, crippling the efficiency of distributed training. To …

Fairrfl: Fair And Robust Federated Learning In The Presence Of Selfish Clients, Andrea Augello, Ashish Gupta, Giuseppe Lo Re, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL) is a paradigm that enables collaborative machine learning without disclosing the local data of the participants. However, in real-world FL deployment scenarios, some unscrupolous clients may alter the training process to skew the global model towards their local optimum, unfairly prioritizing their data distribution. Their influence can degrade overall model performance for normal clients and reduce fairness in the system. We call this novel category of misbehaving clients 'selfish'. This work proposes a Fair and Robust strategy for aggregation in the Federated Learning (FL) server to mitigate the effect of Selfish clients (FairRFL). FairRFL incorporates a novel …

Future Mining: Learning For Safety And Security, Md Sazedur Rahman, Mizanur Rahman Jewel, Sanjay Madria

Computer Science Faculty Research & Creative Works

Mining industry is rapidly transforming into an AI-driven cyber-physical ecosystem where safety and operational reliability depend on robust perception, resilient communication, trustworthy distributed intelligence and continuous monitoring of miners and equipment. Real-world mining environments impose severe constraints like poor illumination, dust, occlusion, GPS-denied conditions, irregular underground topologies, and intermittent connectivity. These factors degrade perception quality, disrupt situational awareness, impair trajectory prediction and weaken the reliability of distributed learning systems. Emerging cyber-physical threats, including backdoor triggers, sensor spoofing, label-flip attacks and poisoned model updates, further jeopardize operational safety, particularly as mines increasingly adopt autonomous vehicles, humanoid assistance, and federated learning for …

Qura: Reinforcement Learning Based Routing For Quantum Networks, Tasdiqul Islam, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Quantum routing deals with identifying a set of quantum repeaters to use to create entanglement between distant endpoints. Previous approaches proposed shortest-path and linear programming methods to find a solution to this problem. While the shortest path approach results in suboptimal performance, linear programming takes too long to find a solution as the network size and constraints increase. In this paper, we apply Deep Q-Reinforcement Learning (DQRL) to optimize routing in quantum networks both in terms of execution time and performance. The proposed Quantum Routing Algorithm (QuRA) first chooses which request to schedule among all requests. It then determines which …

Fleet: A Federated Learning Emulation And Evaluation Testbed For Holistic Research, Osama Abu Hamdan, Hao Che, Engin Arslan, Md Arifuzzaman

Computer Science Faculty Research & Creative Works

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network …

Feddot: Defending Federated Learning Against Overwhelming Targeted Attacks, Priyesh Ranjan, Ashish Gupta, Federico Coro, Sajal K. Das

Computer Science Faculty Research & Creative Works

Federated Learning (FL), which facilitates collaborative model training and protects users' privacy, has drawn great interest from the research community. With FL, participants train their models on local data and submit the corresponding updates for aggregation to a server. While concealing the identities of the participants, FL may attract adversaries in order to hamper the underlying model. In this paper, we propose an FL framework, FedDOT, to defend against adversaries performing targeted attacks. FedDOT incorporates two powerful defense algorithms, Maximum Spanning Tree based attacker detection (MSTAD) and Densest graph-based attacker detection (Density-AD), which leverage correlation between weight updates and graph …

You Only Need One Stage: Novel-View Synthesis From A Single Blind Face Image, Taoyue Wang, Xiang Zhang, Xiaotian Li, Huiyuan Yang, Lijun Yin

Computer Science Faculty Research & Creative Works

We propose a novel one-stage method, NVB-Face, for generating consistent Novel-View images directly from a single Blind Face image. Existing approaches to novel-view synthesis for objects or faces typically require a high-resolution RGB image as input. When dealing with degraded images, the conventional pipeline follows a two-stage process: first restoring the image to high resolution, then synthesizing novel views from the restored result. However, this approach is highly dependent on the quality of the restored image, often leading to inaccuracies and inconsistencies in the final output. To address this limitation, we extract single-view features directly from the blind face image …

Dynamic Hub-Aware Knowledge Distillation For Efficient Traffic Flow Forecasting, Xiangjie Kong, Can Shu, Wenchao Weng, Zhenzhen Zhao, Guojiang Shen, Lei Wang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Real-time traffic forecasting acts as a critical enabling service for IoT-driven Intelligent Transportation Systems (ITS). While existing Spatiotemporal Graph Neural Networks (STGNNs) achieve superior forecasting accuracy, their intensive computational complexity and high latency create a deployment bottleneck for resource-constrained IoT edge devices. To address this resource-accuracy mismatch, we propose a novel framework termed Dynamic Hub-Aware Knowledge Distillation (DHKD). Unlike traditional uniform distillation paradigms, DHKD introduces a topology-aware strategy to transfer knowledge from a complex teacher to a lightweight Spatiotemporal Multi-Layer Perceptron (STMLP) student model. Specifically, we design a dynamic hub-aware gating (DHAG) mechanism that adaptively identifies time-varying pivotal sensing nodes …

Utility-Preserving Federated Graph Learning With Dual-Perspective Fairness, Renqiang Luo, Huafei Huang, Shuo Yu, Fengqi Yu, Feng Xia, Sajal K. Das, Chengqi Zhang

Computer Science Faculty Research & Creative Works

Fairness-aware federated graph neural networks (FedGNNs) necessitate consideration of both the server and the clients. However, fairness-aware methods struggle to enhance dual-perspective (i.e., server and clients) fairness without sacrificing utility due to the distributed learning framework. As a consequence, the utility sacrifices of fairness-aware graph learning methods are even exacerbated in federated frameworks. In this work we propose F3GL, a dual-perspective fairness federated graph learning method that enhances both global (for the server) and local fairness (for clients) while preserving utility. Through theoretical analysis, we delineate the similarity between original sensitive features and those after convolution under different spectra. Our …

Explaining The Unseen: Multimodal Vision-Language Reasoning For Situational Awareness In Underground Mining Disasters, Mizanur Rahman Jewel, Mohamed Elmahallawy, Sanjay Kumar Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining disasters produce pervasive darkness, dust, and collapses that obscure vision and make situational awareness difficult for humans and conventional systems. To address this, we propose MDSE, Multimodal Disaster Situation Explainer, a novel vision-language framework that automatically generates detailed textual explanations of post-disaster underground scenes. MDSE has three-fold innovations: (i) Context-Aware Cross-Attention for robust alignment of visual and textual features even under severe degradation; (ii) Segmentation-aware dual pathway visual encoding that fuses global and region-specific embeddings; and (iii) Resource-Efficient Transformer-Based Language Model for expressive caption generation with minimal compute cost. To support this task, we present the Underground Mine …