Digital Commons Network^™

Advancing Blended Education: Caribbean Lecturers’ Reflective Narratives Of Caution, Creativity, And Change, Mia A. Jules, Donna-Maria B. Maynard, Grace A. Fayombo, Jason E. Marshall, Tanya Newton, Kamilah Hutson, Amanda Kellman, Cherise Bynoe, Mikaila Collymore, Jo-Ann Prosper-Chase, Laura Lee Foster, Adicia Clarke

Journal of Global Education and Research

Blended teaching requires lecturers to constantly self-reflect on their pedagogical practice to enhance student learning. However, there is yet to be a significant corpus of literature that highlights the cognitive resources that lecturers in higher education should possess to effectively use blended learning strategies. It is important to understand the intellectual resources required for blended pedagogy so that such capabilities can be fostered during faculty-training programs; ultimately resulting in innovative strategies to ensure quality learning outcomes and the advancement of university-level blended teaching mandates. This qualitative case study explored how twelve Caribbean lecturers experienced and navigated the teaching process in …

Did Seismic Events Trigger The Growth Of Aragonitic Speleothems In Mawmluh Cave, North-East India?, Dildi Dildi, Dana Riechelmann Dr., Michael Weber Dr., Anne Jantschke Dr., Regina Mertz-Kraus Dr., Denis Scholz Dr.

International Journal of Speleology

Mawmluh Cave, north-east India, is located in a seismically very active region within the Himalayas with frequent earthquakes of varying magnitude. So far, speleothems from the cave have mainly been used to reconstruct past monsoon variability. The potential effect of seismic events on speleothem formation in Mawmluh Cave, however, has not been studied in detail yet. We investigated three stalagmites from Mawmluh Cave, each predominantly consisting of calcite overlain by substantially younger aragonite at the top (i.e., an aragonite ‘cap’). 230Th/U-dating shows that the initiation of aragonite growth occurred between 1859 and 1950 CE in agreement with previous studies. The …

Service Robots With Low Anthropomorphism In Restaurants: Consumer Reactions And Implications, Ferhat Eren, Volkan Genc

Journal of Global Hospitality and Tourism

This study investigates consumer responses to low-anthropomorphic service robots in restaurant front of-house roles using the AIDUA (Artificially Intelligent Device Use Acceptance). Data from 1,268  participants were analysed using PLS-SEM. The results revealed that social impact and  anthropomorphism significantly influenced both performance and effort expectancy, while hedonic  motivation influenced only performance expectancy. Performance expectancy strongly influenced  emotions, which in turn significantly influenced both the willingness to use service robots and objections  to their use. However, effort expectancy did not significantly influence emotions. The findings validate the  AIDUA model in this context and offer practical insights for robot design and implementation.

Hands-On Ransomware: An Experiential Wannacry Case Study For Undergraduate Cybersecurity Education, Eli Creek Richmond, Thomas R. Devine

Military Cyber Affairs

Ransomware represents one of the most disruptive threats in the cyber landscape, yet hands-on malware analysis remains rare in undergraduate cybersecurity curricula. This paper presents the design, implementation, and evaluation of an experiential learning module centered on the WannaCry ransomware case study, deployed in a senior-level course at West Virginia University. Students performed static and dynamic analysis using industry-standard tools. Pre- and post-module assessments demonstrated measurable gains in self-reported competency across seven technical dimensions. The module's competencies align directly with DoD Cyber Workforce Framework Work Role 212, Cyber Defense Forensics Analyst, supporting education-to-workforce pipeline development.

From Framework To Toolchain: Implementing Zero Trust Architecture In Cloud-Native Environments For Dow Compliance, Shelby C. Snyder

Military Cyber Affairs

Federal agencies face a fiscal year 2027 target for enterprise-wide Zero Trust deployment, but NIST SP 800-207A defines logical components without identifying the Kubernetes technologies that implement them. This paper proposes a three-tier mapping of the Policy Engine, Policy Administrator, and Policy Enforcement Point to service mesh, microsegmentation, and perimeter tooling, stating the criteria by which each component is classified. It then applies a defined rubric to six Zero Trust vendors across component alignment, Kubernetes capability, federal authorization posture, and evidence quality, finding that no single vendor covers all three tiers. The mapping is a testable architectural proposition; a Stage …

Characterizing Advanced Persistent Threats With Cyber Attack Flow Metrics, Tyler Miller, Caleb Chang, Shouhuai Xu

Military Cyber Affairs

Cyber attack campaigns vary not only in scale but in structure, yet conventional characterizations often reduce them to a single dimension such as technique count or impact severity. In this paper we extend the concept of cyber attack flows by defining three new metrics, novelty, technique complexity and flow complexity. Then we characterize the attack flows of three advanced persistent threat campaigns using these metrics and draw insights regarding their capabilities. Our findings include that low novelty does not equate to low attack capabilities and that exploitation of an internet-facing appliance is a common initial attack vector.

Semantic Shields: Automating Critical Infrastructure Defense Via Nlp-Driven Ransomware Profiling, Henry Trowbridge, Ian Zalcberg, Ryan Schley, Carter Yagemann, Natasha Phan, Srikar Maduposu, Vimal Buck

Military Cyber Affairs

Ransomware poses a growing threat to critical infrastructure, where successful attacks can disrupt operational technology (OT) and industrial control systems (ICS) with significant public safety consequences. However, attributing ransomware incidents to specific threat actors remains challenging due to ransomware-as-a-service ecosystems, actor rebranding, and the obfuscation of traditional indicators of compromise. This paper presents Semantic Shields, an NLP-driven attribution framework that leverages BERT-generated semantic embeddings and DBSCAN clustering to profile ransomware actors through the linguistic characteristics of ransom notes. Using a dataset of 295 ransom notes from 189 distinct threat groups, the framework achieved an 87.2% true positive clustering rate and …

Closing The Interpretability Gap: Explainable Ml-Based Malware Detection For Defensive Cyberspace Operations, Tashi Stirewalt, Sean Hodgson, Puumaaya Tahiru, Assefaw Gebremedhin

Military Cyber Affairs

This paper presents an end-to-end, explainable malware triage pipeline designed for defense-oriented cyber operations. It combines high-performance static detection methods with analyst-centered interpretability. Utilizing the EMBER 2024 Windows PE subset, we train and evaluate four classifiers and select LightGBM as the production model based on its predictive performance, inference efficiency, and compatibility with exact tree-based attribution. The deployed system consists of four sequential components: PE feature extraction, malware probability scoring, dual explainability (using SHAP and LIME), and large language model (LLM) report generation, all integrated within a Flask web interface. On a temporal test set of 1,080,000 samples, LightGBM achieves …

Llm-Generated Countermeasures For Iot Cyberattacks, James Alger, Michael Tu

Military Cyber Affairs

The rapid expansion of the Internet of Things (IoT) has introduced significant cybersecurity challenges, particularly for resource-constrained devices that traditional intrusion detection systems often fail to protect effectively. This paper proposes a novel, two-phase autonomous security pipeline designed to bridge the gap between probabilistic threat detection and deterministic network enforcement. The framework first utilizes a custom Time Series Transformer (TST) to classify multivariate network traffic and identify specific attack vectors, such as ransomware, SQL injections, and malicious file uploads. In the second phase, an agentic AI layer, comprising a locally hosted Large Language Model (LLM) orchestrated via LangGraph, processes the …

The Fossil Pearls Of The Million-Pearls Flowstone, Fairgrounds Room, Fort Stanton Cave, New Mexico Usa, Victor J. Polyak, Paula P. Provencio, Yemane Asmerom

International Journal of Speleology

‘Fossil’ cave pearls in the Fairgrounds Room of Fort Stanton Cave form two distinct layers in a flowstone sample, FS-14-2. The flowstone in the Fairgrounds Room covers a mud-silt bank and the extinct streambed that extends across the room. Uranium-series dates of two pieces of flowstone from this room, one piece from the higher end of the flowstone (FS-14-1, devoid of pearls), and another piece from the lower end of the flowstone (FS-14-2, rich in pearls), show that the flowstone was deposited during the Last Glacial period (60,000 to 11,000 yr BP).  The two cave-pearl layers in FS-14-2 yielded U-series …

Sedimentary Dynamics In Tubular Sandstone Caves, Porto União, Southern Brazil, Leandro Bianchini, Julio Cesar Paisani

International Journal of Speleology

Clastic sedimentation in sandstone caves remains poorly understood, particularly in systems with tubular morphology. This study investigates sedimentation, reworking, and sediment removal dynamics in the Legru Cave system, southern Brazil, based on the integrated analysis of sedimentary facies, microstructures, and microfeatures, combined with optically stimulated luminescence dating. The results indicate that conduit infilling is dominated by high-density flows, including cohesive debris flows, partially diluted multiphase flows, and flows with intermediate rheological behavior between mud flow and hyperconcentrated flow, in addition to shallow flow alternations. Sediment supply is predominantly allochthonous, related to hillslope-derived material delivered by overland flow and mud flows, …

Market Orientation And Strategic Performance In Global Firms: A Sequential Mediation Model Of Innovation And Supply Chain Resilience, Meisam Karami

Journal of Global Business Insights

This study examines how market orientation is associated with strategic performance in globally operating firms through the sequential mediating roles of innovation and supply chain resilience. Drawing on the Resource-Based View and Dynamic Capabilities Theory, the research proposes a capability-sequencing framework in which market orientation reflects a sensing capability, innovation represents a seizing capability, and supply chain resilience embodies a reconfiguring capability that stabilizes value creation under environmental disruption. Using cross-sectional survey data from 356 manufacturing and service firms engaged in international activities, partial least squares structural equation modeling was employed to test the hypothesized relationships. The results indicate that …

Technological Appropriation As A Strategic Policy In Smes: Building Anticipatory Capabilities For Digital Transformation, Hernán Cornejo

Journal of Global Business Insights

Abstract: Technological appropriation has become a critical strategic issue for small and medium-sized enterprises (SMEs) undergoing digital transformation. While many SMEs adopt digital tools to address immediate operational needs, fewer succeed in embedding them into organizational routines, strategic decision-making, and long-term adaptation. This study examines technological appropriation as a key mechanism through which SMEs convert digital adoption into strategic organizational capability. Drawing on the literature on technological appropriation, dynamic capabilities, and strategic foresight, the paper proposes and empirically tests an integrated model. A mixed-methods design was employed, combining a survey of 198 SMEs from Argentina, Chile, and Colombia with 15 …

Reinterpreting The Grand Ethiopian Renaissance Dam Dispute In The Nile Basin Through The Game Theories Of Attrition And Bargaining, Olileanya Amuche Ezugwu, Felix C. Chidozie, Adeola A. Adebajo

Journal of Strategic Security

Why have more than a decade of negotiations, mediation efforts, and diplomatic interventions failed to produce a binding agreement on the Grand Ethiopian Renaissance Dam (GERD)? This article examines the hydro-politics of the Nile Basin through the lens of game theory, focusing on how bargaining behavior, strategic persistence, and competing perceptions of gains and losses shape state interactions. Drawing on the game of attrition and the bargaining game, the study analyzes the evolving strategies of Ethiopia, Egypt, and Sudan in the context of dam construction, filling, and operation. Rather than viewing the dispute solely as a contest over water allocation, …

Theoretical Model For A Poroelastic Membrane Under Uniform Load, Minh Duc Nguyen

OneUSF Summer Undergraduate Research Symposium

Flexible membrane wings appear throughout both natural and engineered flyers — parachutes, sails, birds, and bats — and are known to confer favorable aerodynamic characteristics through deformation in response to the surrounding flow. To date, flexibility and porosity have largely been studied in isolation, yet in a poroelastic membrane the two are coupled: as the membrane extends under load, its pores stretch with the surface. This study takes the first step toward analyzing that coupling by characterizing how the effective elasticity of the membrane depends on its porosity under uniform static pressure. Circular clamped membranes were simulated across a range …

Effects Of A Low-Dosage Bilingual Oral Language Intervention On Language Outcomes Among Dual Language Learners, Dana S. Melik

OneUSF Summer Undergraduate Research Symposium

Dual language learners (DLLs) represent one of the fastest-growing populations in the United States, and many continue to receive English-language instruction with limited bilingual language support (Lindholm-Leary, 2022). Because oral language development is associated with later academic outcomes (Spencer & Petersen, 2020), evidence-based bilingual oral language interventions are needed, particularly in underserved schools where staffing and resources are limited. The present study examined the effects of Story Champs Bilingual Edition 2.0 (SCBE2.0), a narrative-based Spanish-English oral language intervention, on language outcomes among Spanish-English DLLs when delivered by trained nonteacher personnel at a low dosage. English and Spanish oral language outcomes …

Open-Source Software For Computing Code Automorphisms, Mateo Fernandez-Tyson, Gianna Baker

OneUSF Summer Undergraduate Research Symposium

An error-correcting code is a mathematical structure that models the flow of information through a noisy communication channel and seeks to mitigate errors through redundancy. These codes have a wide range of uses spanning from satellite transmissions to cryptographically secure communication schemes. An automorphism of a code is a permutation of the coordinate positions that preserves the code, and these symmetries are useful in helping to answer questions of code equivalence, classification, and decoding. Our work focuses on implementing and testing algorithms for code automorphism computation using GAP and the GUAVA package. We present an efficient algorithm for generating all …

Wavelet-Based Multiscale Analysis Of Cave Co₂ Concentration In Response To Short-Term High-Intensity Tourism Activities: Spatiotemporal Heterogeneity And Lag Characteristics, Mingda Cao, Wenwen Song, Yan Zhang, Jie Zhang, Zhiqiang Yao

International Journal of Speleology

High-intensity tourism activities can cause a significant increase in cave air CO₂ concentration, thereby affecting the cave micro-environment and secondary carbonate deposition. During the 2023 National Day Golden Week, high-frequency continuous monitoring of cave air CO₂ partial pressure (PCO_2(A)) and visitor numbers was conducted in Dawang Cave, Anhui Province. Wavelet transform and cross-correlation analyses were used to reveal the multi-scale response characteristics of CO₂ concentration to tourism activities. The results show that: (1) PCO_2(A) exhibited clear diurnal variations (higher during the day, lower at night) and decreased spatially with enhanced ventilation, controlled jointly by …

Bidirectional Associations Between Social-Cognitive Processes And Mathematics Achievement In Autistic Children, Carole M. Wadie, Conner Peltier, Quinn Slyker

OneUSF Undergraduate Research Conference

Mathematics achievement is a critical component of academic functioning, strongly predicting later educational and life outcomes (Kaskens et al., 2022; Tonizzi & Usai, 2024). Although prior research has examined mathematics achievement and general cognition, limited work has investigated the role of social-cognitive processes in mathematical development among autistic children. Skills such as theory of mind (ToM), affect recognition, and inferencing may support mathematical learning by enabling children to interpret instructions, integrate information, and apply conceptual understanding (Kloo et al., 2022; Kim et al., 2021). Developmental cascade theory suggests that functioning in one domain may influence development in another, with bidirectional …

Improving Cancer Diagnosis And Patient Outcomes With Deep Learning Models, Mariana Arriz-Jorquiera

USF Tampa Graduate Theses and Dissertations

Cancer care depends on timely and reliable decisions, from detection and diagnosis to treatment planning and patient monitoring. These decisions are often made under uncertainty because medical images and healthcare data may be noisy, incomplete, or difficult to interpret. In breast cancer imaging, ultrasound is widely used because it is safe, accessible, and complementary to other imaging modalities. However, variations in image quality, acquisition conditions, and noise can obscure lesion boundaries and texture, affecting human interpretation and artificial intelligence reliability. This dissertation develops deep learning, image-analysis, and optimization methods to improve healthcare decisions under imperfect information. Its primary focus is …

From Shortage To Strategy: Optimizing Scarce Resource Allocation In Healthcare, Daniela Duran Cantarino

USF Tampa Graduate Theses and Dissertations

The allocation of scarce medical resources presents one of the most consequential challenges in health care, requiring decisions that balance equity and efficiency under pervasive uncertainty. This dissertation develops optimization-based frameworks for two critical domains, organ transplantation and emergency medical services, and demonstrates how operations research methods can improve upon current practice along both dimensions simultaneously.

In the domain of deceased-donor kidney allocation, two complementary frameworks are proposed. The first is a bi-objective stochastic optimization model that jointly maximizes post-transplant survival (efficiency) and prioritizes candidates with the highest pre-transplant mortality risk (equity), subject to regional chance constraints on graft failure …

Toward Intelligent Machines: Conscious Learning And Hardware-Accelerated Ai, Pavia Bera

USF Tampa Graduate Theses and Dissertations

Artificial intelligence systems excel at narrow, well-defined tasks but remain brittle at theboundaries of their training distributions: they cannot quantify uncertainty, adapt continuously to non-stationary data, or operate efficiently on energy-constrained hardware. This dissertation addresses these limitations through four coordinated contributions spanning probabilistic learning algorithms, biologically inspired temporal memory, cross-entity warning propagation via distributed associative memory, and spintronic processing-in-memory.

The first contribution introduces Boosted Bayesian Neural Networks (BBNNs), which extend standard mean-field variational inference by iteratively constructing a mixture posterior through Boosting Variational Inference. On five clinical medical classification datasets, BBNNs achieve superior uncertainty calibration—lower Negative Log-Likelihood and Expected Calibration …

Necessary Digital Competencies Of Preschool Teachers In Austria, Walter Fikisz

Journal of Global Education and Research

The aim of this paper is to identify specific digital competencies that preschool teachers require, compared with schoolteachers, to extend the Austrian digital competence model for teachers, digi.kompP. Methodologically, the study is based on interviews with Austrian experts in early childhood education, media pedagogy, computer science, and preschool teacher education. The expert interviews addressed the digital competencies required by preschool teachers, differences compared with schoolteachers, the timing and nature of competence development, and the institutions involved. As a result of this study, sixteen new can-do statements were developed for the expansion of the existing digi.kompP model, which maps the specific …

Autoencoders As Classifiers Trained On Single Sources Of Radiographic Images For Generalizability Across Unseen Sources, Ryan M. Putney

USF Tampa Graduate Theses and Dissertations

Artificial neural networks trained to classify X-ray images according to disease state will learn to distinguish between technical or procedural variations in the images rather than features relevant to the disease. For example, the model might learn to recognize the machine that captured the image or to distinguish an image taken while the patient is lying supine or standing upright. The present work is aimed at using autoencoders as a generalizable classifier to detect COVID vs pneumonia (PNA) X-ray images. The first experiment was to find an architecture for a fully convolutional autoencoder (CAE) and for a convolutional autoencoder with …

Multi-Modal Survival Prediction On Breast Cancer Mammograms And Enabling Multi-Institutional Collaborations With Federated Learning, Nikolas Koutsoubis

USF Tampa Graduate Theses and Dissertations

Cancer remains one of the leading causes of mortality worldwide, and machine learning is well positioned to leverage the large volumes of routinely collected clinical and imaging data to improve patient outcomes. Realizing this potential at scale requires three capabilities that current practice does not yet deliver in combination: models that integrate the multiple data modalities clinicians use, training procedures that respect institutional data-sharing constraints, and tooling that enables sharing of de-identified data when federated approaches are not sufficient. This dissertation develops methods and infrastructure addressing each of these capabilities in the context of breast cancer and oncology more broadly. …

Efficient, Secure Learning And Resource Management For Wireless Networks, Jiahao Xue

USF Tampa Graduate Theses and Dissertations

The rapid advancement of wireless networks has enabled a wide range of applications such as federated learning (FL) and Internet of Things, with supporting technologies like radio frequency identification (RFID). However, resource management and security challenges in wireless networks remain insufficiently addressed for these representative applications. In this dissertation, we first investigate wireless resource management for FL, then study novel attack and defense mechanisms for RFID systems. Finally, we analyze the performance and security of artificial intelligence (AI)-enabled next generation wireless networks.

First, we investigate the client assignment and bandwidth allocation problem for FL in a multi-provider wireless setting. Unlike …

Building Trustworthy Information Systems: A Unified Framework For Comparative Risk Detection, Parisa Momeni

USF Tampa Graduate Theses and Dissertations

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score …

Seeing Around Corners: An Indirect Light Transport Decomposition Framework For Fusing Physics And Learned Priors, Fadlullah Raji

USF Tampa Graduate Theses and Dissertations

Non-line-of-sight (NLOS) imaging, or seeing around corners, is the ability to recover information about objects hidden from direct view and remains one of the most compelling challenges in computational imaging. Existing approaches fall broadly into two categories: active and passive. Active time-resolved methods achieve impressive performance but require specialized pulsed laser hardware and ultrafast detectors, making them expensive and limited by slow measurement acquisition. Passive NLOS methods, which instead exploit the subtle soft shadows (penumbrae) cast by a hidden scene onto a visible matte surface, have recently emerged as a practical alternative. However, existing passive approaches are largely limited to …

Language Models For Oncology Clinical Text: How Model Architecture And Data Strategies Shape Tumor Phenotype Extraction And Disease Progression Detection, Thanh Duong

USF Tampa Graduate Theses and Dissertations

The rapid growth of electronic health records (EHRs) has created new opportunities to apply machine learning to clinical data.However, a large portion of important clinical information is still stored in unstructured text, such as pathology reports, radiology reports, and longitudinal clinical notes.These documents contain key details about tumor characteristics, diagnoses, treatments, and patient outcomes.Extracting structured and useful information from this text is challenging due to complex medical language, varied document formats, and the need to combine information across multiple reports over time.This dissertation studies how language models can be designed and adapted to better extract and use oncology-specific information from …

Mapping Seafloor Habitats And Characterizing Demersal Reef Fish Communities On The West Florida Shelf Across Multiple Spatial Scales Using Multibeam Sonar And Underwater Video, Alexander R. Ilich

USF Tampa Graduate Theses and Dissertations

In this dissertation, I examine the role of spatial scale in deriving seafloor terrain attributes, modeling benthic habitat, and analyzing ecological patterns of the demersal reef fish communities on the West Florida Shelf (WFS), with implications for improving predictive habitat mapping and understanding fish-environment relationships. Spatial scale plays a central role in benthic habitat mapping and ecological inference, influencing how terrain attributes are derived, how habitats are modeled, and how ecological responses are manifested. This work integrates multiscale terrain analysis, acoustic remote sensing, and community ecology to evaluate how spatial scale influences both physical habitat representation and ecological analysis.

Chapter …