Digital Commons Network^™

Bio-Inspired Visual Intelligence: Improving Efficiency And Robustness Through Foveal-Peripheral Sampling And Learned Saccades, Jiayang Liu

Dissertations - ALL

Deep neural networks have achieved state-of-the-art performance across a wide range of computer vision tasks, yet their deployment in real-world and safety-critical domains remains limited by two major challenges: vulnerability to adversarial perturbations and high computational cost. While adversarial training can improve robustness under certain threat models, it is computationally expensive and often generalizes poorly to black-box or transferable attacks. At the same time, conventional vision pipelines process uniformly sampled high-resolution images, introducing substantial data redundancy, latency, and energy overhead. In contrast, the human visual system achieves efficient and robust perception through space-variant foveal-peripheral sampling, attention-guided saccadic eye movements, and …

Rapid Synthesis And Processing Of High-Capacity Layered Oxide Cathode Materials For Lithium And Sodium Batteries, Hansheng Li

Dissertations - ALL

Lithium-Manganese-Rich-Oxide (LMRO) is a type of layered oxide cathode active material with high energy densities of up to 900 - 1,000 Wh kg-1, competitive in capacity-demanding rechargeable lithium batteries. The increased energy density is beneficial for the range and recharging interval of electric vehicles. Based on layered Ni/Mn/Co oxide cathode materials, a high Mn content is chosen for this project as being abundant, while increased Ni concentration trades off thermal stability with specific capacity, while Co is carcinogenic and sourced less sustainably. Lithium content is increased along with Manganese content stoichiometrically as a second phase is introduced. Traditional synthesis routes …

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. …

Instructions To Authors, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Keynote Address And Meeting Program, Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Business Meeting Report (Secretary's And Treasurer's Report), Academy Editors

Journal of the Arkansas Academy of Science

No abstract provided.

Analysis Of Coal Seam Water Sources Based On Hydrochemistry And Water Isotopes, Wei Dong, Wang Hao, Wang Chungang, Wang Xiaodong, Liu Wei, Wang Qiangmin, Liu Quanhui, Zhou Zhenfang, Wang Yutong

Coal Geology & Exploration

Background The northern Yuheng mining area exhibits the widespread occurrence of water-bearing coal seams. This unique hydrogeological phenomenon poses a serious challenge to efficient mine roadway construction and safe coal mining in the mining area. The recharge sources of the coal seam water remain poorly understood, representing a primary bottleneck in the prevention and control of coal seam water hazards. Methods This study aims to reveal the recharge sources and evolutionary mechanisms of coal seam water in the northern Yuheng mining area. By integrating methods including hydrochemical analysis, isotope analysis, and inverse hydrogeochemical simulation, this study systematically investigated the hydrogeochemical …

Advances In Research On Methods For Intelligent Identification Of Seismic Facies, Liu Xingye, Yu Peilin, He Hengjun

Coal Geology & Exploration

Background The intelligent identification of seismic facies can significantly improve the efficiency of sedimentary system characterization and hydrocarbon reservoir interpretation. However, influenced by factors such as non-stationary geological bodies, high costs of sample labeling, and limited training samples, conventional methods for intelligent identification are generally insufficient to achieve high identification accuracy and widespread application concurrently. Advances This study presents a systematic review of three types of technologies for the intelligent identification of seismic facies, namely unsupervised, supervised, and semi-supervised learning, with each type including deep learning methods. The three technological types are comparatively verified using 3D seismic data from a …

A Method For 3d Model Reconstruction Of Large-Diameter Rescue Wells Based On Multiple Cameras, Gu Hairong, Wang Boyang, Yang Wenjuan, Tong Yubo, Wang Jiaxi, Sun Lishun

Coal Geology & Exploration

Objective Surface drilling for mine rescue represents a critical technique in the emergency rescue system against mine disasters. To accurately assess the passability of large-diameter rescue wells during rescue operations, it is essential to build precise 3D models of the rescue wells for rapid reconstruction of rescue scenarios. Methods Based on the theory of multi-camera-based 3D model reconstruction, this study investigated the method of arranging four Intel D435i cameras within a large-diameter rescue well. Accordingly, a multi-camera-based 3D model reconstruction system for a large-diameter rescue well was established, involving the joint calibration of the camera array and the selection of …

Methods For Assessing The Circulation Depth Of Geothermal Water In A Moderate- To Low-Temperature Convective Geothermal System And Their Applications, Li Yiman, Xia Haoran, Gao Binbin, Sun Wenjie, Pang Zhonghe

Coal Geology & Exploration

Background The circulation depth of geothermal water is an important parameter for investigating geothermal systems or fields. It determines the thermal energy intensity and mineral composition of geothermal water, emerging as a critical factor influencing the efficient and sustainable development and utilization of geothermal resources.Progress This study presents a summary of the characteristics of typical moderate- to low-temperature convective geothermal systems from the perspective of sources (heat and fluid sources), pathways, geothermal reservoirs, cap rocks, and the geothermal water upwelling process and its impacts. Furthermore, this study systematically reviews suitable methods for assessing the circulation depth of geothermal water, …

Research On Optimization Modeling Method For Eye Tracking In Solfeggio Cognitive Simulation, Kun Zhang, Jiajie Qian, Shuhong Ma, Zengxu Zhao, Yuchen Pan, Yaoqi Tang

Journal of System Simulation

To address the fixation offset problem caused by head movement in music solfeggio teaching simulation and the lack of system-level simulation validation in existing methods, this paper proposed a fixation accuracy optimization method integrating image semantic understanding, temporal trajectory modeling, and solfeggio cognitive simulation. With Vision Transformer as the core, after preprocessing via Mahalanobis distance, sliding window, and region of interest, position offset perception, offset residual regression, and dual-pathway fusion were introduced to achieve offset modeling and correction under unlabeled conditions. Simulation results indicate that the error of this method decreases by 43.9% compared with the original value error; removing …

Recent Advances In Research On Intelligent Technologies And Equipment For Underground Geological Exploration Of Coal Mines In China, Zhang Jianming, Li Quanxin, Liu Fei, Fang Jun

Coal Geology & Exploration

Background Intelligent coal mine construction represents an important measure to promote the high-quality development of the coal industry. Developing intelligent technologies and equipment for geological exploration is the key to both transparent geological guarantee and the efficient prevention and control of major disasters in coal mines. Advances This study presents a systematic analysis of the latest advances in intelligent technologies and equipment for underground geological exploration of coal mines in China, focusing on intelligent drilling and geophysical prospecting. In terms of intelligent drilling, this study introduces intelligent drilling rigs equipped with multiple functions, including adaptive control, remote monitoring, and fault …

Nozzle Erosion Reconstruction Model For Data Analysis In Rocket Engines And Correlation With Chamber Pressure, Ryan J. Thibaudeau, Stephen A. Whitmore

Mechanical and Aerospace Engineering Student Publications and Presentations

Graphite nozzles remain the dominant choice for small hybrid and solid rocket motors operating on laboratory and university budgets, owing to their low cost, ease of machining, and rapid turnaround during iterative design campaigns. These same programs, however, must contend with the fact that graphite erodes through coupled thermochemical and mechanical mechanisms when exposed to the oxidizing species generated by high-energy propellant combustion, and the resulting throat-area growth fundamentally alters the time histories of chamber pressure, thrust, and delivered specific impulse. This paper presents a nozzle-erosion reconstruction model that extracts the time-resolved throat area from coupled thrust and chamber-pressure measurements …

A Cross-Dataset Vision Transformer Study For Brain Tumor Mri Image Classification, Sharon Kawira Mungania

Masters Theses

Brain tumor MRI classification is an important medical-imaging task because MRI scans contain complex anatomical patterns that can be time consuming to interpret manually. This study evaluates whether a pre-trained Vision Transformer can classify brain tumor MRI images consistently across datasets with different class structures. Three publicly available Kaggle datasets were used: Nickparvar, Br35H, and Figshare. Nickparvar and Figshare were treated as multi-class classification tasks, while Br35H was treated as a binary tumor/no-tumor task. Images were converted to three-channel format, resized to 384 × 384 pixels, normalized using ImageNet statistics, and augmented during training. The selected model was ViT-Base Patch …

Tea Polyphenol-Assisted Green Synthesis Of Silver Nanoparticle-Boron-Doped Diamond Nanoparticles For Theophylline Electrochemical Detection, Prastika Krisma Jiwanti, Adira Ainun Tsalsabilla, Angellita Wijaya Gunawan, Siti Wafiroh, Mohammad Kalimanjaro, Mai Tomisaki

Makara Journal of Science

Drug theophylline is a bronchodilator used to treat asthma and chronic obstructive pulmonary disease (COPD), but precise monitoring is required to avoid side effects. Therefore, this study aimed to develop a screen-printed electrode (SPE) modified with a composite of green-synthesized silver nanoparticles and boron-doped diamond nanoparticles using tea polyphenols (Ag-BDDTP/SPE) for detecting theophylline. Formation of AgNPs was confirmed by UV-Vis spectroscopy, which showed a localized surface plasmon resonance peak at ~450 nm. Transmission electron microscopy revealed polydisperse nanoparticles with an average diameter of 39.94 nm. During the electrochemical detection of theophylline, square-wave voltammetry (SWV) was employed to evaluate signal-to-background (S/B) …

Lafe0.925ti0.075o3 Perovskite Coated With Ethyl Cellulose: A High-Stability, Low-Hysteresis Platform For Advanced Humidity Sensing, Akhmad Futukhillah Fataba Alaih, Djoko Triyono, Rifqi Almusawi Rafsanjani

Makara Journal of Science

Humidity sensors are pivotal in numerous sectors, such as environmental monitoring, industrial automation, and health-care. This study presents the fabrication and detailed evaluation of an advanced humidity sensor using ethyl cellulose (EC)-coated LaFe_0.925Ti_0.075O₃ (LFTO) perovskite nanoparticles synthesized via the sol-gel method. The LFTO nanopar-ticles exhibited a mesoporous structure with an enhanced surface area, which significantly improved their moisture adsorption capabilities. The innovative application of EC coating addresses critical substrate adhesion issues, enhancing mechanical stability without compromising sensor sensitivity. Comprehensive performance evaluations revealed minimal hysteresis (

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 …

Tidal River Flood Risk Highest During Intermediate Rather Than Peak River Discharge, Leicheng Guo, Adam T. Devlin, Ton Hoitink, Ian Townend, David A. Jay, Zheng Bing Wang, Chunyan Zhu, Hamed R. Moftakhari, Qing He, Fan Xu, Multiple Additional Authors

Civil and Environmental Engineering Faculty Publications and Presentations

Tidal rivers are defined as the tide‐influenced, salinity‐free inland reaches of estuaries. Understanding the occurrence of peak water levels (PWLs) is critical for flood risk management, yet the timing and magnitude of PWLs in tidal rivers have been little studied. We address this gap by investigating PWLs during two catastrophic floods (1954 and 2020) in the tidal Yangtze River, which reveals that PWLs are higher during perigean spring tides under intermediate discharges following peak flow. Three factors modulate the PWLs in tidal rivers: river‐enhanced tidal damping, flow‐elevation hysteresis that raises the falling flow limb of the hydrograph, and low‐frequency subharmonics. …

Engineering Evaluation Of Post-Stroke Gait Using A Wearable G-Walk Sensor Under Different Ankle-Foot Orthosis Conditions: A Longitudinal Case Study, Mohamed Sattar Jabbar, Ammar Issam Kubba

AUIQ Technical Engineering Science

The gait profile of post-stroke patients is typically one of reduced speed, asymmetry and decreased functional mobility. Ankle–foot orthoses (AFOs) are commonly prescribed to enhance gait stability and promote safer ambulation; however, within-person longitudinal gait changes across multiple orthotic conditions have not yet been quantitatively characterized using wearable motion analysis systems. The objective of this study was to perform a longitudinal assessment of spatiotemporal gait parameters through a wearable G-Walk inertial measurement unit (IMU) with three walking conditions: without orthosis, Conventional custom-made ankle foot orthosis AFO and Prefabricated posterior leaf spring AFO. Two functional tasks, normal walking and Timed Up …

Efficient Performance Recovery Of Pruned Llms For End Devices, Paul Vardhan Bethapudi

Master’s Dissertations

Large Language Models have shown strong performance across reasoning, language understanding, and generation tasks, but their computational and memory requirements make deployment on low resource devices difficult. This dissertation studies efficient performance recovery of pruned LLMs, focusing on whether a pruned model can regain useful task performance through parameter-efficient fine-tuning while preserving the benefits of compression. The work uses Llama-3.2-3B-Instruct as the dense reference model and applies activationaware Wanda pruning at multiple sparsity levels(20%, 30% and 50%). The pruned models are evaluated on language modeling and reasoning tasks using WikiText-2 perplexity and GSM8K (Grade School Math 8000 problems) accuracy. Adaptation …

Structure–Optical Property Relationships In Amm′Q3 Chalcogenides, Ayat Tassanov, Huiju Lee, Daniel W. Spainhour, Yi Xia, James M. Hodges

Mechanical and Materials Engineering Faculty Publications and Presentations

Ten ACuHfQ₃ (A = Na, K, Rb, Cs; Q = S, Se, Te) chalcogenides were prepared via high-temperature solid-state methods and structurally characterized using single-crystal X-ray diffraction. The type-I AMM′Q₃ structures are defined by the arrangement of HfQ₆ octahedra (O) and CuQ₄ tetrahedra (T) in their layers, which adopt either OTOT (Cmcm, Pnma) or OOTT (Pnma, P2₁/m) connectivity. UV–vis absorption and photoluminescence spectra indicate optical band gaps ranging from 1.2 to 1.7 eV and reveal that OTOT and OOTT variants have direct and indirect band gaps, …

Transport And Interfacial Dynamics Of Electrochemical Formation Processes In Li-Based Batteries, Penghao Zhang

McKelvey School of Engineering Graduate Student Theses & Dissertations

Next-generation high-capacity batteries often rely on electrochemical formation processes rather than conventional intercalation alone. Such processes include lithium metal deposition and Li–O2 discharge-product formation, in which new phases form at the electrochemical interface during operation. As a result, the measured response reflects both electrochemical driving forces and the evolving product morphology. This coupling makes it difficult to interpret electrochemical data, microscopic observations, or model results in isolation. This dissertation examines this problem through three main studies and one preliminary extension on Li–O2 batteries and lithium metal deposition, each addressing a different factor that affects how electrochemical product formation is measured, …

Improved Method Of Evaluating Gas-Phase Air Cleaners Considering Adsorption And Desorption Mechanisms To And From Internal Test Chamber Surfaces, Casey Douglas Coffland

Dissertations and Theses

Portable air cleaners are playing an increasingly important role in managing indoor air quality in response to pressing issues like wildfire smoke events. Smoke is a complex mixture of gases and particulate matter; while established test methods exist for particulate matter, the dynamic behavior of gaseous pollutants is not well accounted for in current test methods. This is because prevailing air cleaner testing methods typically treat background losses as irreversible, whereas gases can engage in partitioning to and from test chamber surfaces. Ignoring these sorption interactions causes inaccurate estimation of an air cleaner's impact.

A new approach has been developed …

Visionarium, June 2026, Full Issue

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

No abstract provided.

Toward A Metric For Disciplinary Learning In The Age Of Ai: The Unblooms™ Metacognitive Awareness Scale And Discernment Rate In Ai-Mediated Learning, Tina R. Austin

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Telling students to “reflect on their own thinking” has become insufficient in AI-mediated learning environments. When students are rewarded for polished outputs, cognitive offloading to AI tools becomes rational, and traditional snapshot assessments (single-moment evaluations of task completion) produce false signals about whether durable learning has occurred. This problem is sharpened by the broader shift in AI learning tools toward Socratic tutors, study modes, Khan Academy's Khanmigo, and agentic systems that can shape the learner’s process over time. Lodge and Loble (2026) distinguish beneficial cognitive offloading, which frees working memory for intrinsic learning, from detrimental outsourcing, which bypasses the cognitive …

Metacognition As Disciplinary Infrastructure In Ai-Mediated Learning, Tina R. Austin, Jason Gulya, Nick Potkalitsky

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

Metacognition is often presented as a response to generative AI’s disruption of teaching and learning, yet the term has become too generalized to guide practice. In AI-mediated environments, asking students merely to “reflect on your thinking” is insufficient. Because AI tools can redistribute cognitive labor, their educational value depends on how students use them and whether that use supports disciplinary forms of reasoning. This essay argues that metacognition must be understood as disciplinary infrastructure: students cannot effectively monitor their thinking without understanding the epistemological and ontological demands of the field in which they are working. Classrooms therefore become sites where …

Law Or Flaw: A Double – Blind Study Comparing Student Comprehension Of Real And Ai Generated Legal Case Briefs, Grant Shostak, Nick Wintz, Melissa A. Petkovsek

International Journal of Emerging and Disruptive Innovation in Education : VISIONARIUM

In recent years, the U.S. legal system has seen an increase of legal filings using fictitious court cases or legal propositions generated by artificial intelligence (AI) (Stokel-Walker, 2026). Rules of professional responsibility require that lawyers review filings in which AI was used to ensure their accuracy (Missouri Bar, Office of Legal Ethics Counsel, 2024, Opinion No. 2024‑11) Despite this mandate, filings with fictitious cases and incorrect statements of law are being filed. Besides posing a threat to the parties to an action, such filings may set an unwarranted precedent for future cases. They also tie up court resources searching for …

Label-Flip Attack Detection Via Trust-Weighted Aggregation In Federated Learning For Underground Mine Security, Md Sazedur Rahman, Sanjay Madria, Samuel Frimpong

Computer Science Faculty Research & Creative Works

Underground mining operations are increasingly dependent on autonomous vehicles, robotic drilling systems, and intelligent inspection platforms operating in confined, GPS-denied tunnel environments. These systems rely on distributed perception models to interpret navigation cues, hazard warnings, and environmental signals in real time. While centralized deep learning can enhance model performance, transferring raw operational data across mining sites introduces serious confidentiality and security risks. Federated Learning (FL) offers a privacy-preserving alternative by enabling collaborative model training without sharing local datasets. However, deploying FL in underground mining introduces several critical challenges: (i) Training labels may be modified either maliciously by compromised clients or …

A Storm-Ai Primer With An Operationally Representative Example, Shiu-Kai Chin, Matthew Clark, Garrett Katz, Jae C. Oh, William Young

Electrical Engineering and Computer Science - All Scholarship

A STORM-AI Primer with an Operationally Representative Example presents the Systems-Theoretic Technical and Operational Risk Management with Artificial Intelligence (STORM-AI) methodology, a transdisciplinary framework for establishing the trustworthiness of mission systems that may incorporate AI components. The core value proposition of STORM-AI is mission assurance—ensuring that mission systems behave with the predictability and proportionality required by senior leadership and demanded by international humanitarian law. STORM-AI achieves this by faithfully preserving commander's intent from high-level mission descriptions down to hardware-level behavioral specifications through a structured sequence of four formally grounded models: a Mission Model, a Protection (Loss Control) Model, a Security …

Toward Passwordless Document Encryption, Isaac Henry Teuscher

Theses and Dissertations

Document files with sensitive information are used across nearly every industry. In recent years, cyberattacks targeting cloud-based file transfer applications have resulted in millions of sensitive documents being exposed. Although document encryption methods exist, they are often limited in usability, security, or deployability. Password-based encryption, the most widely available option, remains vulnerable to brute-force attacks, unauthorized sharing, and human error. We address these gaps in three phases. First, we present a structured comparative framework adapted from the usability-deployability-security model to evaluate nine document encryption methods across 15 design properties, identifying the benefits and limitations of current approaches. Second, we design …