Digital Commons Network^™

Ps, David L. Groves

Visions in Leisure and Business

No abstract provided.

Developing And Implementing A Primary Source Literacy Case Scenario For The Undergraduate Business Classroom, Annette Bochenek, Megan Troyer

Libraries Faculty and Staff Scholarship and Research

This article discusses the development and implementation of a case-based learning scenario activity intended to enhance primary source literacy among undergraduate business students. The activity was delivered in the context of a large Midwestern university’s undergraduate business course. This case is based on the ACRL RBMS-SAA Guidelines for Primary Source Literacy and gives students an opportunity to use digitized primary archival sources as part of the research process in connection with a modern business-related scenario in which they can apply primary source literacy skills. This guided activity also explores affective experiences as they occurred in student responses to this activity …

Standardizing Assessment Of Clinical Performance In Physical Therapy: From Good Intentions To Shared Standards, Arvie Vitente, Carmina Rafael, Rolando Lazaro

Philippine Journal of Physical Therapy

Clinical education is the crucible in which physical therapy students integrate knowledge, skills, and professional judgment into safe, ethical, and person-centered practice. Yet globally, the assessment of clinical performance remains highly variable, shaped by disparate tools, rubrics, and levels of assessor preparation. While several countries have adopted nationally standardized approaches to clinical assessment, many contexts—including the Philippines—continue to rely on locally developed or institution-specific systems, resulting in inconsistent expectations, inequitable student experiences, and limited capacity for benchmarking, research, and quality improvement. This editorial argues that good intentions alone are insufficient and that the profession must move toward shared, trustworthy standards …

Precision Education In Health Sciences Education: A Call-To-Action For Developing An Interprofessional Strategy, Arvie Vitente, Jennifer Bosworth, Robert Sweet, Rebecca Cozzi, Christopher Galloway, Piotr Szczurek, Melanie Obispo-Young, Eron Bozec, Erica Brkovic, Tina Bobo, Jennifer Lubinski

Philippine Journal of Physical Therapy

Introduction. Growing learner diversity, expanding biomedical knowledge, and rapid clinical digitization have exposed the limits of on-size-fits-all health sciences training. Competency-Based Education (CBE) has sharpened the focus on outcomes, yet many programs still move learners through fixed sequences, infrequent assessments, and retrospective remediation. Precision Education (PE) offers a complementary, data-enabled approach that uses learning analytics, artificial intelligence (AI), and continuous feedback loops. Embedded within CBE, PE converts competencies from static milestones into dynamic trajectories that can be measured, visualized, and adjusted in real time across the curriculum. This abstract highlights the need for an interprofessional strategy to design, fund, and …

Comparing The Effectiveness Of Eggshell Spectra From Laser-Induced Break-Down Spectroscopy And Near-Infrared Spectroscopy Using Principal Compo-Nent Analysis To Determine The Authenticity Of Organic Eggs, Ahmad Qusthalani, Rara Mitaphonna, Muliadi Ramli, Rajibussalim Rajibussalim, Kurnia Lahna, Nasrullah Zaini, Nasrullah Idris

Makara Journal of Science

This study aimed to explore the potential of modern spectroscopy in the authentication of organic and non-organic chicken eggs using near-infrared spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS) spectra. A total of 175 eggs were analyzed, which were grouped into seven categories based on the source of feed given: 100% organic, 100% non-organic, 75% organic, 75% non-organic, 50% organic, free-range chickens, and eggs obtained from the local traditional market. Each group consisted of 25 eggs. NIRS spectra were recorded in the wavelength range of 350–2500 nm, whereas LIBS spectra were recorded in the range of 200–900 nm. A total of …

Factors Influencing The Success Of Student Veterans In Higher Education

Faculty Publications and Presentations

A case study was conducted at a private institution of higher education to gain a deeper understanding of the factors that support student veterans, a nontraditional student demographic in a private institution of higher education. The article explains the interplay and relationship between what nontraditional students bring to the academic experience, the role of educators, and factors contributing to or detracting from their success in higher education. Analysis of key issues related to combat student veterans in higher education revealed common themes and contextual information from which the lessons learned were developed. The results of the study indicate that the …

Investigation Of Regulated Cell Death (Rcd) Modalities Induced By Copper(Ii), Manganese(Ii) And Silver(I) Complexes Containing Bridging Dicarboxylate And 1,10-Phenanthroline Ligands, Ella O'Sullivan

Doctoral

With the increasing global cancer burden in recent years, there is an urgency to develop novel chemotherapeutic agents. Transition metal complexes represent an attractive class of candidates, owing to their unique electronic and stereochemical properties, as well as their capacity for ligand exchange, redox activity, and catalytic reactivity. These characteristics enable cellular signalling mechanisms that lead to tumour cell cytotoxicity via regulated cell death (RCD) mechanisms. The conventional RCD mechanisms of apoptosis and autophagy are attractive targets for the development of novel complexes because tumour cells evade apoptosis but use autophagy as a survival mechanism when under stress. Therefore, novel …

Community And Academic Synergy For Cancer Survivorship Care Delivery Enhancement (Project Cascade): A Study Protocol For A Pragmatic, Stepped-Wedge, Cluster-Randomized Trial In Texas Primary Care Community Health Centers, Mary-Louise E Millett, Lauren Q Malthaner, Katharine Mccallister, Derek W Craig, Rebecca Eary, Melissa A Valerio-Shewmaker, L Aubree Shay, Suja S Rajan, Hilary Y Ma, Samiran Ghosh, Benjamin F Crabtree, Simon J Craddock Lee, Bijal A Balasubramanian

Faculty, Staff and Student Publications

Background: In the United States, over 18 million individuals are living with cancer. The majority of these cancer survivors also manage other chronic conditions and receive care from multiple specialists, including oncology, cardiology, and primary care clinicians. However, it remains unclear who holds primarily responsibility for coordinating their care across specialties. Because of its generalist nature, primary care is uniquely suited to deliver whole-person and coordinated care for all conditions for cancer survivors. However, primary care teams experience many challenges delivering high-quality survivorship care. While integrating care for all conditions including cancer is a core principle of high-quality primary care, …

Developing A Vietnamese Text Summarization Large Language Model On Limited Hardware, Tin Pho

Master's Theses

Text summarization models have achieved significant growth during the last few years because of major Large Language Model (LLM) technological advancements. The application of LLMs are widely used in news distribution (TL;DR news), translation tools (DeepL Translate), or virtual assistants (ChatGPT, DeepSeek, Claude, etc.). However, the progress has not yet reached all languages equally. The Vietnamese language is used by more than 90 million people, but the language is not as highly developed for LLM as it has many homophones, five different tones that affect meaning of words, and irregular grammar compared to other languages (e.g. English, Spanish, etc.). Also, …

Loss Of The Maternal Effect Gene Nlrp2 Impairs Embryonic And Extra-Embryonic Development, Revealing A Novel Genetic Cause Of Congenital Anomalies†, Momal Sharif, Zahra Anvar, Imen Chakchouk, Sara H El-Dessouky, Roni Zemet, Eric C Kao, Wessam E Sharaf-Eldin, Ying-Wooi Wan, Zhandong Liu, Pengfei Liu, Michael Jochum, Ignatia B Van Den Veyver

Duncan NRI Faculty and Staff Publications

Maternal-effect genes (MEGs) play a crucial role in early mammalian development, and their dysfunction can lead to severe embryonic and extra-embryonic abnormalities. NLRP2, a MEG that encodes a subcortical maternal complex (SCMC) protein, has been implicated in preimplantation development, but its role after implantation remains underexplored. In this study, we investigated the developmental consequences of maternal Nlrp2 loss-of-function in a maternal knockout (KO) mouse model at embryonic day 11.5. Embryos derived from Nlrp2-KO females have abnormal yolk sac vasculature, increased embryonic resorption, craniofacial abnormalities, neural tube defects, and congenital heart defects. Placental architecture is disrupted with an altered junctional zone …

Context Matching Is Not Reasoning When Performing Generalized Clinical Evaluation Of Generative Language Models, Andrew Wen, Qiuhao Lu, Yu-Neng Chuang, Guanchu Wang, Jiayi Yuan, Jiamu Zhang, Liwei Wang, Sunyang Fu, Kurt D Miller, Heling Jia, Steven D Bedrick, William R Hersh, Kirk E Roberts, Xia Hu, Hongfang Liu

Faculty, Staff and Student Publications

Current discussion surrounding the clinical capabilities of generative language models(GLMs) predominantly centers around multiple-choice question-answer(MCQA) benchmarks derived from clinical licensing examinations. While accepted for human examinees, characteristics unique to GLMs bring into question the validity of such benchmarks. Here, we validate five benchmarks using eight GLMs, ablating for parameter size and reasoning capabilities, validating via prompt permutation three key assumptions that underpin the generalizability of MCQA-based assessments: that knowledge is applied, not memorized, that semantic consistency will lead to consistent answers, and that situations with no answers can be recognized. While large models are more resilient to our perturbations compared …

Context Matching Is Not Reasoning When Performing Generalized Clinical Evaluation Of Generative Language Models, Andrew Wen, Qiuhao Lu, Yu-Neng Chuang, Guanchu Wang, Jiayi Yuan, Jiamu Zhang, Liwei Wang, Sunyang Fu, Kurt D Miller, Heling Jia, Steven D Bedrick, William R Hersh, Kirk E Roberts, Xia Hu, Hongfang Liu

Faculty, Staff and Student Publications

Current discussion surrounding the clinical capabilities of generative language models(GLMs) predominantly centers around multiple-choice question-answer(MCQA) benchmarks derived from clinical licensing examinations. While accepted for human examinees, characteristics unique to GLMs bring into question the validity of such benchmarks. Here, we validate five benchmarks using eight GLMs, ablating for parameter size and reasoning capabilities, validating via prompt permutation three key assumptions that underpin the generalizability of MCQA-based assessments: that knowledge is applied, not memorized, that semantic consistency will lead to consistent answers, and that situations with no answers can be recognized. While large models are more resilient to our perturbations compared …

Nathaniel "Jj" Davis

African American Funeral Programs

Program for the funeral of Nathaniel "JJ" Davis, held at Clear Spring Baptist Church on December 27, 2025.

Intrusion Detection System For Iot/Cloud Networks Using Federated Learning And Lightweight Cryptography, Ayad Al-Adhami, Rajaa K. Hasoun, Sanaa Ali Jabber, Soukaena H. Hashem

Baghdad Science Journal

This study presents a secure solution that utilizes lightweight cryptography (LWC) and intrusion detection systems (IDS) to safeguard cloud networks and Internet of Things (IoT) from cyberattacks. Federated Learning (FL) is suggested for identifying zero-attacks to guarantee the security of different local IoT networks connected to global server of cloud. The proposed federated learning utilizing data from all local IoT network devices to create a generalized Intrusion Detection System (IDS). Local IoT networks consist of clients that communicate updates to their parameters with a central server located in the global cloud. This server integrates these changes and deploys an improved …

Enhanced Network Anomaly Detection Using Hybrid Deep Learning Network Based On Interactive Threshold, Maythem S. Derweesh, Sundos A. Hameed Alazawi, Anwar H. Al-Saleh

Baghdad Science Journal

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. …

Optimizing Runtime Memory Size Of Smith-Waterman Algorithm For Long Sequences Alignment, Imad Qasim Habeeb, Zeyad Qasim Habeeb, Hanan Najm Abdulkhudhur

Baghdad Science Journal

Sequence alignment is used to help researchers see areas of similarity between two sequences. Hence, it is a key component of many applications, such as DNA matching, plagiarism detection, and spelling correction. The Smith-Waterman algorithm (SWA) is widely used to calculate the sequence alignment because it is guaranteed to find an optimal solution. This algorithm creates a matrix of the size n * m where the symbols n, m refers to the lengths of two sequences needed to be aligned. Therefore, it requires impersonal hardware with a large amount of main memory at runtime to align long sequences. Furthermore, it …

Information System To Elucidate Dna Sequencing Based On Machine And Deep Learning Techniques, Suhiar Mohammed Zeki Abd Alsammed

Baghdad Science Journal

The elucidation of DNA sequencing provides great importance in increasing the comprehension of organisms' genomic functions. However, the investigation of concealed structural information preserved within Deoxyribonucleic Acid (DNA) sequencing represents an outstanding challenge. Recently, machine and deep learning have become the techniques of preference for various tasks of genomics modeling, involving the prediction of genetic variation influence on the mechanisms of gene regulation like DNA receptivity and splicing. Therefore, this paper presented an information system for elucidating DNA sequencing data using diverse machine and deep learning techniques. In this system, two encoding methods are utilized for modifying the DNA sequencing …

Enhanced Pothole Detection In Urban Environments Using Yolo-Nas With Adaptive Image Augmentation Techniques, Saluky, Aisya Fathimah, Onwardono Rit Riyanto

Baghdad Science Journal

Pothole detection in urban environments is a critical task for maintaining road infrastructure and ensuring vehicular safety. Recent advancements in deep learning, particularly the YOLO (You Only Look Once) framework, have shown promise in object detection tasks. However, achieving high accuracy and robustness in real-world conditions remains a challenge. This study introduces an enhanced pothole detection system utilizing the YOLO-NAS (Neural Architecture Search) model, optimized through adaptive image augmentation techniques. The YOLO-NAS model is fine-tuned on a custom dataset of urban road images, with various augmentation strategies applied to simulate different environmental conditions such as shadows, low light, and occlusions. …

Carolyn Baker-Lee

African American Funeral Programs

Funeral program for Carolyn Baker-Lee. Service was held on December 27, 2025 in Register, Georgia.

Amino Acid Signaling In Skeletal Muscle Is Blunted By Prematurity In A Piglet Model, Antonio C Ramos Dos Santos, Agus Suryawan, Ki Beom Jang, Rosemarie D Parada, Mahmoud A Mohammad, Marta L Fiorotto, Teresa A Davis

Faculty, Staff and Students Publications

Background: Preterm (PT) infants are at increased risk for reduced postnatal lean mass accretion. We established that the feeding-induced stimulation of protein synthesis in skeletal muscle is blunted in piglets born PT compared with those born at term.

Objectives: We evaluated the extent to which key components of the amino acid-sensing pathways that regulate mechanistic target of rapamycin complex 1 (mTORC1) activation contribute to anabolic resistance in skeletal muscle of piglets born PT compared with those born term.

Methods: Piglets delivered by cesarean section 10 d PT (n = 23) or at term (n = 22) were administered total parenteral …

Spinal Cord Injury In The Context Of Major Motor Vehicle Collision Trauma: A Retrospective Ecological Analysis Of Global Estimates Across Income Groups, Tim Nutbeam, Jessica Caterson, Colleen J. Saunders, Hendry R. Sawe, Sabariah Faizah Jamaluddin, Ian Roberts, Jason E. Smith, Paulus Ambunda, Willem Stassen

Peninsula Medical School

Introduction: Road traffic injuries (RTIs) are a leading cause of death globally, especially in low- (LICs) and middle-income countries (LMICs). Despite this burden, post-crash care remains underdeveloped. Many clinical principles of post-crash care focus on spinal cord injury (SCI), yet its incidence is poorly understood. The aim of this study was to describe the incidence of death and non-fatal RTI with a specific focus on SCIs using Global Burden of Disease (GBD) 2019 data. Methods: A retrospective ecological analysis was conducted using GBD 2019 data for 204 countries and territories (2012–2019). We examined the MVC-related mortality, SCIs, and major non-fatal …

A Spatiotemporal Model Of Cxcl10 As A Master Regulator Of Immune Evasion And Metastasis In Osteosarcoma, Benjamin B Gyau, Tsz-Kwong Man

Faculty, Staff and Students Publications

The C-X-C motif chemokine ligand 10 (CXCL10) is implicated in the progression of osteosarcoma (OS), the most aggressive pediatric bone malignancy. However, its role often presents a profound clinical paradox: although high circulating levels are strongly linked to poor prognosis, its canonical function is to recruit anti-tumor immune cells. This review unravels these contrasting roles by proposing a novel spatiotemporal model. We argue that in the early stages, immune-evading OS cells initiate the formation of a pre-metastatic niche (PMN) in the lungs, creating a localized inflammatory environment that becomes the primary source of elevated circulating CXCL10. As the disease progresses, …

Collagen Type Ii-Targeting Lentiviral Gene Therapy For Mucopolysaccharidosis Iva, Betul Celik, Sampurna Saikia, Shaukat Khan, Krishna Sai Musini, Shunji Tomatsu

Department of Pediatrics Faculty Papers

Mucopolysaccharidosis (MPS IVA) is caused by pathogenic variations in the GALNS gene, leading to the accumulation of glycosaminoglycans in tissues and causing progressive skeletal lesions. While conventional lentiviral vectors (LVs) provide long-term stable expression, they do not deliver therapeutic levels to bone and cartilage. We hypothesized that engineering the LV envelope with a collagen type II-targeting peptide (WYRGRL) increases the binding affinity of the LVs for bone and cartilage. These modified vectors carrying the CBh and COL2A1 promoters delivered the GALNS gene to MPS IVA newborn mice via intravenous (IV) or intraarticular (IA) administration. The peptide-modified LVs exhibited markedly increased …

Supply Chain Network Based On Blockchain And Intelligent Agent, Hiba Hamdi Hassan, Rana Fareed Ghani

Journal of Soft Computing and Computer Applications

In agricultural supply chains, the complexity and indeterminacy pose serious challenges to traceability, reliability and confidence today. This challenge is especially acute in the olive oil industry where adulteration, wrong labeling, and uneven chemical quality threaten the actual well-being of producers and consumers. The project aims to design a blockchain-based hybrid architecture with intelligent agents (FNNs) to enhance transparency, reliability and responsiveness in the olive oil supply chain. The Blockchain component enables a completely open, tamper-proof ledger to be built in a very decentralized way and preserved as an archive of every account of its transactions. The intelligent agents contribute …

Intelligent Extensible Markup Language Encryption Using Type-2 Fuzzy Logic, Faiez Musa Lahmood Alrufaye, Seham Ahmed Hashem

Journal of Soft Computing and Computer Applications

Financial and commercial institutions increasingly rely on Extensible Markup Language (XML) files as a standard means of exchanging data. However, this extensive use has created serious security challenges due to the fact that these files contain sensitive information such as bank card numbers and expiration dates. Relying on traditional full file encryption methods achieves a high degree of security, but it causes problems related to the large file sizes that consume memory and the long encryption and decryption times, which reduces the efficiency of systems when dealing with a large number of daily transactions. Methods based on Type-1 Fuzzy Logic …

Enhanced Generative Convolutional Networks: A Hybrid Algorithm For Refinement Video Classification, Dalal Thair Mahjoub, Hala Bahjat Abdulwahab, Kesra Nermend

Journal of Soft Computing and Computer Applications

Video classification is a vital area of research due to the growing volume of video content in various applications. Accurate category across various resolutions poses challenges, which include adapting to scaling, resizing, and compression. Therefore, this paper introduces an innovative Generative Convolutional Network (GCN) set of rules tailored for multi-resolution video classes. The proposed GCN model utilizes Convolutional Neural Networks (CNNs) combined with generative modeling to enhance the extraction of functions across varying video resolutions, which is crucial for maintaining class robustness in the face of common video adjustments, such as scaling, resizing, and compression. In contrast, traditional fashions frequently …

Review Of Video Steganography By Using Deep Learning Methods: Datasets, Techniques, And Evaluations, Noor Fahem Sahib, Soukaena Hassan Hashem, Ekhlas Falih Naser

Journal of Soft Computing and Computer Applications

The growing prevalence of cyber threats, including fraud and attacks, has intensified the demand for secure methods of safeguarding confidential information exchanged between users. As telecommunications increasingly rely on multimedia data, video steganography has become a prominent technique to address these concerns. By embedding sensitive data within video files, this approach enhances protection against unauthorized access and common internet-based attacks, offering a robust layer of security in an era of escalating digital risks. With the introduction of Deep Learning (DL) steganography methods recently, video steganography can be defined as a rapidly developing subject within information security. This study provides a …

Real-Time Hand Gesture Recognition System For Abductees Rescue Using Deep Learning Techniques, Aws Saood Mohamed, Nidaa Flaih Hassan, Abeer Salim Jamil

Journal of Soft Computing and Computer Applications

Hand gesture recognition is a challenging problem in computer vision, particularly in terms of security surveillance applications. This study presents the first efficient system for abduction-related hand gesture real-time detection based on deep learning. The most critical problem is to detect and recognize hand gestures in real surveillance conditions and to be computationally effective for real-time multi-hand tracking in various lighting situations while allowing reliable surveillance beyond the 1–4 meters limitation. The proposed system consists of three main parts: The adaptive hand tracking algorithm, which has been used to create the Abductees-Rescue dataset. Introduced pose estimation You Only Look Once …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

The Tax Adviser, Volume 8, Number 6, June 1977, American Institute Of Certified Public Accountants

Tax Adviser

No abstract provided.