UTSA Runner Research Press (Univ. of Texas at San Antonio)
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Remote Visualization and Optimization of Fluid Dynamics Using Mixed Reality
Get PDFThis study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines.Mechanical Engineerin
Honey Compounds Exhibit Antibacterial Effects Against Aggregatibacter actinomycetemcomitans JP2
Get PDF<b>Background:</b>&nbsp;<i>Aggregatibacter actinomycetemcomitans</i> JP2 genotype is a virulent pathogen linked to severe periodontitis and systemic diseases. Honey and royal jelly (RJ) have demonstrated bioactive properties against this microorganism. This study aims to assess the bioactive properties of honeys and RJ against this key periodontal pathogen and to preliminarily identify key compounds with antibacterial potential. <b>Methods:</b> The antibacterial activity of honeys and commercial products (manuka, L-Mesitran<sup>&reg;</sup> as a medical-grade honey-based formulation (MGHF), and Honix<sup>&reg;</sup> RJ) against <i>A. actinomycetemcomitans</i> JP2 was evaluated using the agar well diffusion method and microdilution assays. Extensive physicochemical characterization (e.g., hydrogen peroxide level, total phenolic content, and total flavonoid content) was conducted to correlate the bioactive compounds with the antimicrobial activity. <b>Results:</b> All tested samples exhibited varying antibacterial potency, with inhibition zones ranging from 21 to 37 mm. The MICs ranged from 40.7 to 104.3 mg/mL. MGHF, RJ, and multifloral honeys showed the lowest MICs. The pH of six out of eight samples could not induce enamel decalcification while the pH of three samples may not influence cementum demineralization. Vitamin C, zinc, magnesium, and potassium were present in measurable quantities, and were not associated with significant antibacterial activity. MGHF showed the highest hydrogen peroxide activity and TFC values. TFC and H<sub>2</sub>O<sub>2</sub> content were statistically correlated with lower MIC values. <b>Conclusions:</b> Honey and RJ showed antibacterial activity against <i>A. actinomycetemcomitans</i> JP2, partly attributed to their content of hydrogen peroxide and flavonoids. Clinical trials are needed to confirm the potential role of honey, RJ, and their bioactive compounds in managing periodontitis.Biology, Health, and Environmen
EVALUATING DATA PRIVACY, SECURITY, AND TRUST IN THE ADOPTION OF WEARABLE SENSING DEVICES FOR CONSTRUCTION SAFETY AND HEALTH MANAGEMENT
Get PDFDespite the numerous benefits of wearable sensing devices (WSDs) as reported in existing studies, their adoption and implementation in the construction industry have been slower than that of other industries such as healthcare, logistics, sports and fitness, etc. This slow uptake has been attributed to construction workers’ and industry professionals’ lack of trust rooted in privacy and security concerns. A deeper understanding of the factors associated with these concerns is expected to enable users and stakeholders to gain better insights into their relationships and how they influence the future of WSDs in the construction industry. This study proposes a conceptual model for assessing the impact of data privacy, security, and trust on the adoption and implementation of WSDs for safety and health management in construction. First, the constructs and factors associated with privacy, security, and trust are identified, defined, and characterized. Second, applicable theories and models for adoption and implementation with respect to privacy, security, and trust in WSDs are reviewed. Thereafter, a conceptual framework that could be used to evaluate key stakeholders’ perceptions of the impact of data privacy, security, and trust in adopting WSDs is developed. It is expected that the insights generated from the application of the developed model will help foster the effective adoption and implementation of WSDs among construction stakeholders for the prediction and prevention of injuries, illnesses, and fatalities in the construction industry.Construction Science and Managemen
Optimizing Industrial Robotic Arm Energy Consumption Using Digital Twin Technology
Get PDFThis study develops a digital-twin-based framework for optimizing the energy efficiency of industrial robotic operations through experimental modeling, simulation, and coordination analysis. Using the SCORBOT-ER9 educational robot as a testbed, energy consumption was empirically measured under varying speed, dwell time, payload, and distance parameters through a full factorial Design of Experiments. Statistical analysis identified speed and dwell time as the dominant factors affecting energy usage, while payload and distance had negligible influence within the tested range. A quadratic regression model explaining 96.9% of the variance was derived and embedded into a validated digital shadow built in Siemens Plant Simulation, which replicated the physical robot’s performance with a mean energy prediction error of 4.4% and a cycle-time deviation below 1%. Optimization studies showed that energy consumption can be minimized without compromising throughput by adjusting speed-dwell combinations and coordinating operations between multiple robots. In a dual-robot configuration, staggering cycle start times reduced peak energy demand by 2.4%, confirming the potential of synchronization to balance power loads.Mechanical Engineerin
Underlying Mechanisms of Age-Related Loss of Glycosaminoglycans (GAGs) in Bone Matrix and Its Effect on Bone Fragility
Get PDFAge-related fragility fractures are a significant public health issue, with Bone Mineral Density (BMD) explaining only half of the associated fracture risk. A key factor in this decline is the loss of Glycosaminoglycans (GAGs), which are crucial for bone toughness. This study hypothesized that the age-related decline in bulk fracture toughness is driven by a shift toward a higher proportion of old, low-GAG tissue, and that this GAG loss is causally linked to the accumulation of Advanced Glycation End Products (AGEs) via both cell-mediated and cell-free mechanisms. The investigation used a newly developed Raman Spectroscopy method to quantify local GAG content in human cadaver bone from 45 donors, comparing the compositional properties of new osteons and old interstitial tissue. Bulk bone properties were assessed using three-point bending fracture toughness tests, histomorphometry, and biochemical assays. In parallel, in vitro cell-mediated experiments utilized MLO-A5 osteoblasts and MLO-Y4 osteocytes treated with the AGEs Pentosidine and Carboxymethyl Lysine to determine the mechanism of GAG loss. The role of Matrix Metalloproteinases (MMPs) was tested using the inhibitor Illomastat. Furthermore, cadaveric bone samples were treated with AGEs and sugars to investigate cell-free loss of GAGs. The results confirmed that local tissue properties, including GAG content, were primarily dependent on Tissue Age (Old vs. New), not donor chronological age. The study established that the age-related shift toward a greater volume of inherently low-GAG old tissue caused an overall loss of bulk GAG content, which was significantly correlated with the loss of bulk fracture toughness. Crucially, cell-mediated experiments demonstrated that AGEs cause a dose-dependent reduction in GAGs and alter Biglycan turnover. This GAG loss was found to be an MMP-mediated process, as Illomastat abrogated the AGE-induced GAG/BGN changes. Glycations to cadaveric tissue also caused GAG loss, though at supraphysiologic sugar levels. In conclusion, the dissertation confirms that age-related GAG loss stems from local tissue decline and a shift in tissue proportion, suggesting that the AGE/MMP/GAG axis is a core driver of microstructural aging and loss of bone quality.Biomedical Engineerin
Optimization of a Mixed Fleet of Aerial Drones for Medical Supplies: A Case Study of Blood Delivery Logistics
Get PDFAerial drones have emerged as an innovative solution for faster transportation of time-sensitive items (e.g., emergency medical supplies), potentially reducing the transmission of contagious diseases and enhancing healthcare availability through contactless autonomous delivery. We study fleet sizing and efficient scheduling of a mixed fleet of drones for delivering time-sensitive medical items having distinct release and due times to minimize the required fleet size and fleet composition, the required number of additional batteries, and the total energy consumption. We continuously track the remaining battery energy of drones to determine the optimal timing for battery replacement, rather than replacing the battery at each node. Using actual drone flight test data, we employed a machine learning (ML) method to estimate the energy consumption of different drone types during flight segments for different operating parameters. We present a novel mixed-integer programming model to efficiently formulate the problem that integrates the estimated energy consumption functions from ML. We propose a new greedy heuristic (GH) algorithm and a customized genetic algorithm (GA) for solving large-scale instances of this problem faster. Results demonstrate that the GH algorithm is substantially faster than the accelerated CPLEX and the GA, while sacrificing the solution quality by a small amount. Results based on an actual blood sample delivery case study from Pendleton, Oregon, United States, show that using a mixed fleet of drones reduces the total cost and total energy consumption up to 18.18% and 28.7%, respectively, compared to using a homogeneous fleet.Mechanical Engineerin
A Game-Theoretic and AI Approach to Secure and Intelligent Hospital at Home Monitoring
Get PDFIn Hospital-at-Home settings, there is a growing demand for cost-effective, personalized, and continuous care solutions. Continuous care solutions incorporate wearable sensors and remote monitoring devices to provide comprehensive care. These tools facilitate the seamless collection and transmission of patient data in real time. Additionally, providing interconnected and in-depth information on an individual's health status, as well as their level of acuity. As healthcare continues to shift towards decentralized in-home models. There is a critical need to ensure confidentiality, integrity, assurance, and privacy of transmitted medical information. This paper proposes a three-tier federated learning architecture designed to detect both physiological anomalies and monitor for unusual patterns in data aggregation that may suggest a cyber intrusion. The proposed architecture offers a scalable, privacy-preserving, and intelligent solution for proactive health monitoring in home care environments, helping to prevent clinician alarm fatigue. By combining the analytical and computational rigor of game theory and AI, a transition towards more proactive and informed decision-making in healthcare systems can be achieved.Electrical and Computer Engineerin
Deepfakes’ Cognitive, Emotional, and Behavioral Impact: A Systematic Review and Meta-Analysis of Individual Responses
Get PDFThis meta-analysis examines 24 experimental studies on deepfake effects on credibility, emotions, and sharing intention, comprising 20,685 participants from 10 countries. Moderator effects of media literacy, control type, video topic, literacy type, gender, age, and country on individual responses were also analyzed. Effect sizes indicated deepfakes’ impact on elevated emotions. Media literacy moderated the effects of deepfake exposure on diminished credibility and sharing intention. The moderator effect of no literacy on emotions was positive. The results suggest that critical media consumers with media literacy, depending on the topic and type, can mitigate the adverse effects of deepfakes.Communicatio
Testing the Limits: Exploring Teacher Perspectives on State-Mandated Assessments and Wellbeing in Texas Title I Schools
Get PDFThis qualitative study examines how current Title I elementary teachers in Texas perceive the relationship between standardized testing mandates (e.g., STAAR 2.0) and their wellbeing. Using narrative inquiry with three current classroom teachers, I developed individual narratives and a composite account that center educators’ lived experiences and sense-making. Analyses integrate the Job Demands–Resources (JD–R) model with Self-Determination Theory (SDT) to connect structural pressures (demands and resources) with teachers’ felt experiences of autonomy, competence, and relatedness. Findings reveal a dependable pattern: benchmark cycles, pacing audits, and walk-through scorecards depleted energy, narrowed autonomy, and strained relationships; wellbeing stabilized when resources aligned with the specific psychological need being threatened. For instance, bounded choice and protected planning time restored autonomy; timely, actionable feedback and bilingual instructional materials enhanced competence; and collegial coverage and family partnerships deepened relatedness. A practical design rule for Title I leaders emerges—identify the dominant demand, diagnose the SDT need it threatens, and invest in the specific JD–R resource that maps to that need. The study contributes a teacher-informed mechanism for understanding wellbeing under accountability pressures and offers actionable guidance for sustaining educators in historically under-resourced settings.Educational Leadership and Policy Studie
Drug Discovery and Repurposing for Coccidioides: A Systematic Review
Get PDF<i>Coccidioides immitis</i> and <i>C. posadasii</i> are the causative agents of coccidioidomycosis (CM) or Valley Fever, endemic to the alkaline deserts of North and South America. Clinical treatment of CM is predominantly limited to the triazole and polyene drug classes. There are limited therapeutic options for the treatment of CM, most commonly requiring prolonged courses of therapy with established antifungal agents such as azoles and Amphotericin B, which often lead to toxicity and drug resistance. Clearly, there is a need to develop novel and better antifungal drugs against CM. This review examines both repurposed and recently discovered compounds in various stages of development for the treatment of CM