Digital Commons Network^™

Assessing Fault-Induced Floor Water Inrushes Based On Improved Water Inrush Coefficients, Jiao Bin, Tan Wenfeng, Li Hongsheng, Chen Juntao, Gao Hongbing, Zhao Zizhang, Wu Fangcan

Coal Geology & Exploration

Background As coal mining in mines constantly expands to greater depths, water hazards in the Ordovician limestone aquifers of coal seam floors pose a serious threat to the mining of coal seams in lower formations. Fault reactivation represents a primary factor inducing water inrushes. However, the original water inrush coefficient (T = p/M) suffers from certain limitations when used to assess the water inrushes in areas with faults. Methods Four models of floor water inrushes induced by non-penetrating faults (i.e., concealed hydraulically conductive faults, concealed isolated faults, and exposed faults) and penetrating faults were constructed, and …

Numerical Simulations And Dispersion Analysis Of 3d In-Seam Waves Based On Finite Difference Method On Curvilinear Grids, Liu Dan, Ren Zhiming

Coal Geology & Exploration

Background With the comprehensive advancement in intelligent coal mine construction, creating high-precision, transparent geological models of coal seams and their surrounding strata has emerged as a prerequisite for intelligent coal mining. The accurate identification of concealed geological anomalies, such as folds and faults, within coal seams represents a key challenge in safe coal mining. In-seam seismic exploration has become an important approach to detecting underground concealed structures owing to its advantages, including a considerable detection distance along coal seams, high precision, pronounced frequency dispersion effects, and facilitating the identification of waveform signatures. However, conventional methods for seam wave simulation are …

A Machine Learning Approach For Water Quality Assessment In The Lower Rio Grande Valley Watershed, Saika Nowshin Nowrin, Chu-Lin Cheng, Jungseok Ho, Jinwoo An, Fatemeh Nazari

Civil Engineering Faculty Publications

Water quality analysis plays an essential role in maintaining the health and sustainability of river ecosystems, especially in semi-arid regions like the Arroyo Colorado Watershed in South Texas. Since the river is a vital source of water supply for local communities, agriculture, and wildlife, it faces significant challenges and pollution from land use changes, climate variation, and agricultural runoff. Continuous monitoring and assessment of water quality parameters and their temporal variability are essential to ensure the drinking water supply and aquatic ecosystem health. However, comprehensive laboratory-based water quality investigations are often constrained by higher costs, logistical complexity, and limited manpower. …

Whole-Life Carbon–Cost–Circularity Optimization For Ai-Era Data Centers: A Robust Multi-Objective Framework Under Carbon Pricing Uncertainty, Arezou Shafaghat, Da Hu, Ali Keyvanfar

Faculty Articles

The explosive growth of artificial intelligence workloads has triggered unprecedented data center construction, yet environmental assessment remains focused on operational energy efficiency. This paper introduces DC-WLC3O, a framework that simultaneously optimizes three objectives across the full data center lifecycle: whole-life cost, whole-life carbon, and material circularity index. The framework integrates cradle-to-cradle lifecycle assessment with dynamic material flow analysis, employing a four-layer analytical stack: Monte Carlo simulation with Latin Hypercube Sampling and Iman–Conover rank correlation; NSGA-III tri-objective Pareto optimization; Sobol’ variance-based global sensitivity analysis; and Many-Objective Robust Decision Making (MORDM). The framework evaluates 192 design configurations for a 30 MW hyperscale …

Sustainable Consumer Behavior Modeling: A Complex-Systems Approach To Neuromarketing, Preethi Nanjundan, Nupoor Sanjay Bhute, Ragini Topre, Lijo Thomas

Northeast Journal of Complex Systems (NEJCS)

This study examines the application of complex-systems modeling to neuromarketing data for gaining deeper insights into the mechanisms underlying sustainable consumer behavior. It investigates how neural and biometric responses, interpreted through a systems-based perspective, can uncover dynamic interactions, feedback mechanisms, and emergent behavioral patterns influencing sustainable purchase decisions. The research explores the impact of sustainability-oriented marketing stimuli on long-term behavioral intentions by emphasizing the interconnected roles of cognitive processing, emotional engagement, implicit associations, and collective consumer dynamics. Through simulation-based modeling and structural analysis, the study demonstrates how subconscious neural responses and affective mechanisms mediate the relationship between marketing interventions, consumer …

Vertical Alignment As A Systemic Strategy For Sustaining Workforce-Aligned K–12 Engineering Initiatives: A Collective Case Study Of District-Wide Microelectronics Integration, Greg J. Strimel, Victoria Constantine, Deana Lucas, Emily Haluschak, Yubin Lee, Morgan Hynes, Selcen Guzey, Tamara J. Moore

School of Engineering Technology Faculty Publications

Background

In response to the 2022 CHIPS and Science Act, a K–12 workforce development initiative was launched to embed microelectronics education across US school districts. To promote sustainability, the initiative employed a district-wide vertical alignment strategy that progressively integrates microelectronics content and engineering-related career experiences from kindergarten through high school. While vertical alignment is often used for curriculum planning, its broader potential as a systems-level strategy remains underexplored.

Purpose

This study investigated how vertical alignment functions within a K–12 engineering workforce development initiative, focusing on enablers/barriers toward expanding and sustaining integration efforts. The research questions addressed stakeholder experiences with the …

Radiation Effects On The Amd Versal Running An Automatic Modulation Classification Model, Allan D. Howe

Theses and Dissertations

With the recent increase of interest into artificial intelligence (AI), graphics processing units (GPUs) have been used as the main hardware architecture for AI computation. Because of their high power consumption and large size, it is necessary to consider other architectures to perform AI computing in outer-space where size, weight, power, and cost (SWaP-C) are tightly constrained. One such architecture is the Advanced Micro Devices (AMD) Versal Adaptive Compute Acceleration Platform (ACAP), which can achieve acceptable AI throughput at a lower power and size compared to a GPU. As the Versal ACAP gets used in space applications, it is necessary …

Comparative Analysis Of Strain-To-Velocity Conversion Methods For Active-Source Das Data And Collocated Nodal Stations, Prajwal Panthi, Brady R. Cox

Civil and Environmental Engineering Student Research

Distributed Acoustic Sensing (DAS) provides dense spatial measurements of the dynamic strain along fiber optic cables, offering high-resolution wave sensing for ground motion monitoring and subsurface imaging applications. However, DAS records the axial strain or strain rate, whereas traditional seismic and engineering ground motion equipment and derived metrics are based on particle displacement, velocity, or acceleration, necessitating reliable strain-to-velocity conversion methods. This study evaluates three widely used conversion approaches: the fk-rescaling, curvelet-based conversion, and slant-stack methods. These approaches are applied to a unique high-energy, near-field, active-source dataset collected at the Birds Landing Site in Sherman Island, California. The dataset includes …

Developing A Natural Language Interface For Knowledge Graphs, Ruth Assefa, Sarah Mendoza, Luke Voinov, Oyku Serap Ogut, Nurcan Yuruk

SMU Journal of Undergraduate Research

This paper proposes to solve the challenge of making databases more user-friendly by interfacing them with OpenAI's ChatGPT-3.5 model. We implemented this solution to assist researchers in easily finding others with similar research interests. Our study involves 184 researchers from 14 departments at Southern Methodist University (SMU). We collected researchers' areas of expertise and biographies and stored them in a Neo4j graph database. We used OpenAI's embedding models to create vector representations of the collected data, allowing for accurate similarity assessments via Neo4j's built-in algorithms. By integrating this system with LangChain, we enabled natural language queries. The results demonstrated high …

Equivocation Analysis Across Continuous And Discrete Memoryless Channels, Md Munibun Billah

Theses and Dissertations

Physical-layer security uses the noise already present in the channel to keep a message secret without making any assumption about the eavesdropper's computing power. In Wyner's wiretap channel model, coset coding uses randomness to map the message to many codewords and protects against information leakage from an active eavesdropper. The secrecy provided by such codes is measured by their equivocation, which is the eavesdropper's remaining uncertainty about the message after she observes her channel. So computing equivocation is an important aspect of analyzing the wiretap channel model. Many channels of practical interest have no closed-form expression for equivocation, and exact …

Experimental Evaluation Of Membranes In The Recovery Of Hydrogen From Serpentinization, Rafael Dos Santos

LSU Master's Theses

Natural hydrogen generated through serpentinization reactions has emerged as a promising low-carbon energy resource. However, serpentinization-derived gas streams commonly contain methane (CH₄) and carbon dioxide (CO₂), requiring purification before hydrogen can be utilized in industrial applications. Membrane-based gas separation technologies offer a potentially cost-effective solution for hydrogen recovery; however, to the best of the author's knowledge, no previous studies have specifically investigated their application to serpentinization-derived gas streams.

This work evaluates the performance of two commercial polymeric membrane modules (Evonik and Generon) and one palladium-copper (Pd-Cu) membrane module (Okaya) for hydrogen recovery from gas compositions representative of serpentinization environments. An …

Global Time-Resolved Measurements Of Inlet/Isolator Unstart Induced By Mass Injection, Andrew N. Bustard, Benjamin L. Bemis, Aaron Marques, Matthew J. Zahr, Thomas J. Juliano

Publications

The inner surface pressure of an axisymmetric inlet/isolator model was measured using anodized-aluminum pressure-sensitive paint (PSP) viewing through cast acrylic. Temperaturesensitive paint was utilized to correct for the PSP’s temperature sensitivity. The model was tested under Mach 5.7 flow at 𝑹𝒆 = 7.1 ×106 /m under conventional noise conditions. Transverse jet injection with jet-to-inlet mass-flow ratios up to 0.8 was used to induce unstart in the inlet/isolator. Background-oriented schlieren visualization of the inlet shocks was collected simultaneously with the PSP to determine when the inlet unstarted. Computational fluid dynamics results were used to determine off-wall flow structures and quantify approach …

Dual-Waste Integration For Geotechnical Improvement: Silica Fume And Eggshell Ash In Kaolin Soil Stabilization, Ng Jun Shen, Muzamir Hasan, Siti Sarah Eliyun, Md. Ikramul Hoque, Mohamad Afiq Jazrin Mohamad Sidek

Journal of Sustainable Construction Materials and Technologies

This study reveals the potential substitution of silica fume and eggshell ash in the technique of soil stabilization, varying across distinct proportions in the modification of geotechnical parameters. The untreated kaolinitic soil which has low soil bearing capacity in nature is frequently reflected in its mechanical instability, for instance, soil settlement and extreme compressibility. By associating the industrial and agricultural material, it is potentially to resolve the above-mentioned issues with explicit designs. A number of laboratory approaches have been implemented in the assessment of basic geotechnical properties, including grain size distribution, Atterberg limits, specific gravity, proctor properties, and shear strength …

Understanding Transgender And Gender-Nonconforming Students’ Experiences In Precollege Engineering Education: A Latent Profile Analysis, Jialing Wu, Medha Dalal, Rachel Figard, Adam R. Carberry

Journal of Pre-College Engineering Education Research (J-PEER)

Promoting gender equity and inclusion in STEM education remains a central priority, yet empirical research inclusive of transgender and gendernonconforming (TGNC) students is limited, particularly in precollege engineering contexts. This study leverages social cognitive career theory (SCCT) to identify latent profiles of students’ engineering-related beliefs while examining how TGNC students are represented across these profiles relative to their cisgender and gender nonreporting peers. The study draws on four years (2019–2023) of repeated cross-sectional survey data from a national precollege engineering initiative involving 75 high schools across 20 US states. Seven SCCT-related constructs were measured and used as part of latent …

Policy Reflections On Promoting Strategic Oriented Basic Research In China: Insights From The U. S. Department Of Energy’S Basic Research Funding System, You Yu, Yun Liu, Wenneng Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

Strategy-oriented basic research is a key pathway to driving major original breakthroughs at the frontiers of science, serving national strategic needs, and securing a leading position in global science and technology. It is also a vital pillar for achieving high-level self-reliance and self-strengthening in science and technology and enhancing the capacity for original innovation. Faced with the urgent need to build a science and technology powerhouse and achieve breakthroughs in key core technologies, China has gradually improved its organizational framework for advancing strategy-oriented basic research and has established a solid foundation for policy support and resource allocation. Nevertheless, when measured …

Structural Design And Finite Element Analysis Of A 54 Ton Freight Wagon Bolster Test Rig Under Vertical Center Plate Load, Shandy Dwi Prayoga, I Dewa Gede Ary Subagia, I Ketut Adi Atmika, Abdul Rohman Farid, Yunior Lanang Satrio

Journal of Mechanical Engineering Science and Technology (JMEST)

Bolster is one of the bogie's components that receives direct loading from the car body and freight movement. One of the problems that occurs in the bolster is permanent deflection during vertical and transversal static loading. Therefore, a static loading test is required to evaluate the structural strength of the bolster. During the static loading test, a rig is required to hold the bolster so that the bolster is in a fixed position. The rig owned by PT. INKA (Persero) showed a plastic deformation at the vertical center plate load (VCP 5.22P). Therefore, a modified design of the rig is …

Provocable Forgiveness In Noisy Brand--Consumer Systems:An Agent-Based Study Of Repeated Interaction, Tahere Ahmadiyan, Hamidreza Navidi, Behbod Keshavarzi

Northeast Journal of Complex Systems (NEJCS)

Repeated brand--consumer exchange is often treated as a managerial problem of loyalty, recovery, and trust. It can also be read as a small complex system: many local decisions about cooperation, retaliation, and forgiveness accumulate into market-level selection. This study uses that perspective to examine which relational rules survive when communication is imperfect. Eight canonical Iterated Prisoner's Dilemma strategies are translated into marketing archetypes and evaluated through round-robin tournaments, a six-level noise sweep, proportional-fitness ecological dynamics, and finite-population Moran invasion tests. The tournament leaderboard is calculated without same-strategy self-play, so that reported payoffs reflect inter-archetype competition rather than homogeneous self-coordination. At …

Privacy-Preserving Intrusion Detection For The Internet Of Medical Things Using Ensemble And Federated Learning, Theyab Alsolami

Electronic Theses and Dissertations 2020 - Present

The rapid proliferation of the Internet of Medical Things (IoMT) has transformed healthcare by enabling continuous monitoring, intelligent diagnostics, and data-driven clinical decision-making. However, this increased connectivity has significantly expanded the attack surface of healthcare systems, exposing sensitive patient data and critical medical devices to cyber threats such as intrusion and data exfiltration attacks. Ensuring both strong security and strict privacy preservation in IoMT environments remains a fundamental and unresolved challenge.

This dissertation investigates the design and evaluation of robust and privacy-preserving intrusion detection systems (IDS) for IoMT networks using advanced machine learning techniques. The research first examines the effectiveness …

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 …

Enhancement Of A Copolyester’S Impact Resistance Via Inorganic Reinforcement, David Felipe Gonzalez

Electronic Theses and Dissertations 2020 - Present

Thermoplastic copolyester elastomers (TPEEs) are widely used in engineering applications because of their flexibility, toughness, and chemical resistance. However, prolonged exposure to marine environments can reduce their mechanical performance through moisture-induced degradation. This research investigated the use of titanium dioxide (TiO2) nanoparticles and APTES-functionalized TiO2 nanoparticles to improve the mechanical, thermal, and environmental durability of Hytrel 5556. Nanocomposites containing 1 wt.% and 2 wt.% TiO2 were fabricated through melt blending and compression molding. Mechanical properties were evaluated through tensile, compression, flexural, and impact testing, while thermal behavior was characterized using differential scanning calorimetry (DSC). Nanoparticle dispersion was examined using scanning …

A Reduced-Order Framework For Stochastic Criticality Estimation In Granular Energetic Materials, Philip T. Melton

LSU Master's Theses

Granular energetic materials (EMs) exhibit stochastic shock initiation because pore collapse, frictional dissipation, and localized thermal activation depend on microstructural descriptors that vary between nominally identical samples. Fully resolved mesoscale and atomistic calculations can represent these mechanisms, but their computational cost limits direct ensemble evaluation of microstructure-conditioned criticality thresholds. This thesis introduces a reduced-order framework for stochastic criticality estimation in granular EMs by coupling five explicitly defined model components: first, a one-dimensional steady compaction-shock model that maps initial solid volume fraction and shock pressure to a bulk mass-specific dissipated-work budget; second, an SEM/synthetic-image segmentation workflow that extracts pore area, perimeter, …

Critical Assessment On Challenges And Advances In Coal Pillar Strength Estimation Techniques: A Focus On Geological Discontinuities, Abhishek Kumar Singh, Sahendra Ram

Journal of Sustainable Mining

Accurate estimation of coal pillar strength is essential for ensuring safety and operational efficiency in underground mining. Current assessment methods often face limitations in addressing time-dependent failure mechanisms, geological discontinuities, and dynamic loading conditions. This paper identifies future research directions aimed at enhancing the reliability of pillar strength evaluations. One important focus is the development of advanced numerical models that account for time-dependent behaviours such as creep and fatigue. Incorporating multi-scale modelling techniques, which connect micro-scale material responses to macro-scale structural performance, may lead to more precise predictions. The integration of real-time monitoring systems measuring stress, deformation, and environmental factors …

Efficient Edge Implementation Of Midasnet For Real-Time Depth Estimation In Indoor Robotics, Muhammed Yasi̇n Adiyaman, İsmai̇l Fai̇k Başkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Real-time depth estimation is crucial in many vision-related tasks, including autonomous driving, 3D reconstruction, robotics, and simultaneous localization and mapping. In recent years, many methods have been proposed to solve depth maps from images by utilizing different modality setups like monocular vision, binocular vision, or sensor fusion. However, for real-time deployment on edge devices, complex methods are not suitable due to latency constraints and limited computation capacity. For edge implementation, models should be simple, minimal in size, and hardware-friendly. Considering these factors, we implemented MiDaSNet, which works on the simplest setup of monocular vision and utilizes hardware-friendly convolutional neural network-based …

Evolutionary Neural Architecture Search: A Survey, Ferda Nur Özçeli̇k, Mehmet Önder Efe

Turkish Journal of Electrical Engineering and Computer Sciences

Deep Neural Networks (DNNs) have achieved remarkable success across diverse machine learning applications, yet designing effective architectures remains a laborious, expert-driven process. Neural Architecture Search (NAS) was introduced to automate this process, with Evolutionary NAS (ENAS) emerging as one of the most effective and widely adopted NAS paradigms. This survey provides a comprehensive and systematic review of 164 ENAS studies published between 2020 and 2024, categorized according to the specific evolutionary algorithm employed as the search strategy. Unlike prior surveys—which either treat evolutionary methods at a high level or focus on general NAS pipelines—this study is, to the best of …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Dual-Stream Bilstm Framework With Histogram-Based Shape Features For Household Load Forecasting, Chang Xu, Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a dual-stream BiLSTM framework for household load forecasting that integrates time-series dynamics with histogram-based daily shape features. Unlike existing models relying on weather or external data, the proposed method extracts intrinsic load-shape information directly from normalized daily curves. A multihead attention module fuses temporal and shape representations, enabling adaptive weighting of informative dimensions. Experiments on three real-world datasets show consistent improvements over the baseline BiLSTM, with up to 30.12%, 24.27%, and 19.03% reductions in MAE, RMSE, and SMAPE, respectively. The results highlight the framework’s robustness and efficiency for fine-grained load forecasting without external inputs.

Robust Variable-Gain Backstepping Control For Nonlinear Systems With Real-Time Application To Induction Motor, Fadi Alyoussef, İbrahi̇m Kaya, Ahmad Akrad, Rabia Sehab, Cristina Morel

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel variable-gain mechanism with a minimal number of tuning parameters to enhance the performance of conventional backstepping controllers for nonlinear systems while avoiding singularity and peaking phenomena. The proposed approach is simple, computationally efficient, and well suited for real-time implementation without imposing a significant computational burden. Its effectiveness is validated through real-time experiments conducted using a dSPACE DS1104 controller board and a 7.5-kW induction motor (IM). Simulation results demonstrate that the proposed controller outperforms the conventional backstepping controller. Robustness analyses under variations in stator resistance, load inertia, and viscous friction coefficient reveal substantial reductions in the …

Enhancement Of Nested Hexagonal Fractal Antenna Performance For Multiband Wireless Applications, Abdelbasset Azzouz, Rachid Bouhmidi, Mohammed Chetioui, Redouane Berber, Ahmed Jamal Abdullah Al-Gburi

Turkish Journal of Electrical Engineering and Computer Sciences

This work focuses on developing a compact multiband antenna to meet the growing demand for versatile and efficient radiating structures in modern wireless communication systems. A hexagonal fractal antenna is proposed and analyzed for applications such as mobile communications, WLAN, industrial, scientific and medical (ISM) bands, Wi-Fi, satellite links, radar systems, and military communications. By iteratively modifying the antenna geometry with larger hexagonal elements, the design enhances multiband behavior and improves key performance parameters including gain, S11, voltage standing wave ratio (VSWR), and radiation characteristics. The antenna is modeled using high-frequency structure simulator (HFSS)® and fabricated on a low-cost 0.8 …