Digital Commons Network^™

การทำความเย็นส่วนบุคคลโดยใช้วัสดุเปลี่ยนสถานะสำหรับบุคลากรที่สวมใส่ชุดป้องกันการติดเชื้อส่วนบุคคล, พรพุทธ วิสุทธิวัชรกุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

ชุดป้องกันส่วนบุคคล (PPE) เป็นอุปกรณ์สำคัญที่ช่วยลดความเสี่ยงในการติดเชื้อของบุคลากรทางการแพทย์ อย่างไรก็ตาม การสวม PPE เป็นเวลานาน โดยเฉพาะในสภาพอากาศร้อนและชื้นอย่างประเทศไทย มักนำไปสู่การกักเก็บความร้อนในร่างกาย การระบายความร้อนลดลง และเกิดความไม่สบาย ความเหนื่อยล้า รวมถึงภาวะเครียดจากความร้อน งานวิจัยนี้จึงได้พัฒนาแบบจำลองชีวความร้อนอย่างง่าย (simplified bioheat model) โดยอ้างอิงโครงสร้างแบบจำลองแบบ passive ของ Fiala และเสริมด้วยการตอบสนองทางสรีรวิทยาแบบ active ที่จำเป็น เพื่อใช้ทำนายอุณหภูมิของร่างกายและประเมินความสบายทางความร้อนเมื่อสวม PPE ร่วมกับการระบายความร้อนด้วยวัสดุเปลี่ยนสถานะ (Phase Change Material: PCM) ผลการตรวจสอบความถูกต้องเทียบกับแบบจำลองอ้างอิงของ Fiala แสดงให้เห็นว่าแบบจำลองที่พัฒนาขึ้นมีความแม่นยำดี โดยมีความคลาดเคลื่อนของอุณหภูมิอยู่ภายใน ±0.2°C นอกจากนี้ผลการจำลองยังพบว่า PCM สามารถลดความเครียดจากความร้อนและเพิ่มความสบายทางความร้อนอย่างมีนัยสำคัญในช่วง 60 นาทีแรกของการทำงาน แม้ว่าประสิทธิภาพจะลดลงเมื่อ PCM หลอมละลายเต็มที่ก็ตาม ข้อจำกัดของงานวิจัยนี้ คือ แบบจำลองตั้งอยู่บนสมมติฐานของสภาวะอากาศร้อน (เริ่มต้นที่ 30°C) ทำให้ยังไม่สามารถแทนสภาวะอากาศเย็นได้อย่างสมบูรณ์ อีกทั้งไม่ได้พิจารณาการตอบสนองทางสรีรวิทยาระยะยาว เช่น ภาวะขาดน้ำ ความเหนื่อยล้า หรือความเครียดเชิงกลจากน้ำหนัก PCM ที่เพิ่มขึ้น ถึงแม้จะมีข้อจำกัดดังกล่าว แบบจำลองนี้ยังคงสามารถทำนายอุณหภูมิร่างกายและค่าความรู้สึกทางความร้อนแบบพลวัต (DTS) ได้อย่างมีประสิทธิภาพก่อนการใช้งานจริง จึงสามารถนำไปใช้เป็นเครื่องมือช่วยวางแผนระยะเวลาการทำงานที่ปลอดภัย และช่วยกำหนดปริมาณ รวมถึงตำแหน่งการติดตั้ง PCM ที่เหมาะสม ผลลัพธ์จากงานวิจัยนี้สามารถเป็นฐานข้อมูลสำคัญสำหรับการออกแบบชุด PPE ที่ปลอดภัย และช่วยลดภาระความร้อนสำหรับบุคลากรทางการแพทย์ในสภาพแวดล้อมร้อนชื้นได้ดียิ่งขึ้น

การผลิตละอองลอยที่มีลูกหลานเรดอนเกาะติดสำหรับการประเมินปริมาณรังสียังผลที่ปอด, รุ่งโรจน์ สกุลเนรมิตร์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อพัฒนาระบบและกระบวนการผลิตละอองลอยกัมมันตรังสีที่มีนิวไคลด์ลูกหลานเรดอนเกาะติดภายใต้สภาวะควบคุม เพื่อจำลองลักษณะทางกายภาพของอนุภาคในสิ่งแวดล้อมจริง และประเมินปริมาณรังสียังผลที่ปอดได้รับ โดยมุ่งเน้นการศึกษาในช่วงขนาดอนุภาค 10 ถึง 150 นาโนเมตร ซึ่งเป็นช่วงขนาดวิกฤตที่มีโอกาสตกสะสมในถุงลมปอดและส่งผลกระทบทางชีวภาพสูงสุด การศึกษาดำเนินการโดยใช้สารละลายตั้งต้น 3 ชนิด ได้แก่ โซเดียมคลอไรด์, กลูโคส และโพแทสเซียมเปอร์แมงกาเนต ที่ระดับ ความเข้มข้นแปรผันตั้งแต่ 1,000 ถึง 10,000 ppm ร่วมกับการศึกษาอิทธิพลของปัจจัยสิ่งแวดล้อม ได้แก่ อุณหภูมิ (25 และ 35 องศาเซลเซียส) และความชื้นสัมพัทธ์...

ปัญญาประดิษฐ์ในบทบาทนักวิเคราะห์ข้อมูล : กรอบแนวคิดสำหรับการวิเคราะห์เชิงข้อมูลแบบอัตโนมัติด้วยแบบจำลองภาษาขนาดใหญ่และตัวแทนปัญญาประดิษฐ์, วิชญาดา เล้าสุบินประเสริฐ

Chulalongkorn University Theses and Dissertations (Chula ETD)

การวิเคราะห์เชิงข้อมูลที่ดำเนินการโดยมนุษย์มีความท้าทาย เนื่องจากต้องใช้เวลา ทักษะเฉพาะทาง และทรัพยากรจำนวนมาก งานวิจัยนี้มีจุดมุ่งหมายเพื่อค้นหาวิธีในการใช้ปัญญาประดิษฐ์เชิงสร้างสรรค์เพื่อทำให้กระบวนการวิเคราะห์ข้อมูลเป็นแบบอัตโนมัติ โดยปฏิบัติตามวิธี 6 ขั้นตอน ได้แก่ ถาม เตรียม ประมวลผล วิเคราะห์ แบ่งปัน และดำเนินการ โดยไม่มีมนุษย์เข้ามาแทรกแซงตลอดกระบวนการวิเคราะห์ การดำเนินการเริ่มตั้งแต่ผู้ใช้ป้อนชุดข้อมูล วัตถุประสงค์ที่ต้องการ บริบทของข้อมูล และสมมติฐานที่มีอยู่ก่อน จากนั้นระบบจะสร้างคำสั่ง และดำเนินงานต่าง ๆ โดยอัตโนมัติผ่านตัวแทนปัญญาประดิษฐ์ที่ออกแบบเฉพาะทาง โดยตัวแทนเหล่านี้มีบทบาทในการวางแผนและกำหนดการดำเนินงาน โดยอาศัยแบบจำลองภาษาขนาดใหญ่ในการสร้างแนวคิดและใช้เหตุผลเพื่อกำหนดแนวทางการวางแผนและการดำเนินการ ผลการทดลองจาก 5 ชุดข้อมูลในสาขาที่แตกต่างกัน ได้แก่ การศึกษา สุขภาพ ธุรกิจ สิ่งแวดล้อม และเศรษฐกิจ แสดงให้เห็นว่า ผลการประเมินตามเกณฑ์คะแนนการวิเคราะห์เชิงข้อมูลเฉลี่ย 8.1 – 9.4 จากคะแนนเต็ม 10 โดยมีความสอดคล้องระหว่างผลการประเมินโดยแบบจำลองภาษาขนาดใหญ่และมนุษย์เฉลี่ย 0.94 – 0.97 มีเวลาในการดำเนินงานเฉลี่ย 1.8 - 6.6 นาที มีข้อผิดพลาดเฉลี่ย 0.2 - 1.8 ครั้ง และมีความสามารถในการทำงานต่าง ๆ เช่น ประมวลผลโค้ด คำนวณสถิติหรือสร้างแบบจำลองการเรียนรู้ของเครื่อง และแสดงผลภาพได้ งานวิจัยนี้ชี้ให้เห็นถึงศักยภาพของแบบจำลองภาษาขนาดใหญ่ในการทำหน้าที่เป็นนักวิเคราะห์ข้อมูลเสมือน และสามารถต่อยอดระบบวิเคราะห์เชิงข้อมูลแบบอัตโนมัติในสาขาต่าง ๆ ได้ในอนาคต

Mapping Biomedical Ontology Terms To Ids: Effect Of Domain Prevalence On Prediction Accuracy, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

This study evaluates the ability of large language models (LLMs) to map biomedical ontology terms to their corresponding ontology IDs across the Human Phenotype Ontology (HPO), Gene Ontology (GO), and UniProtKB terminologies. Using counts of ontology IDs in the PubMed Central (PMC) dataset as a surrogate for their prevalence in the biomedical literature, we examined the relationship between ontology ID prevalence and mapping accuracy. Results indicate that ontology ID prevalence strongly predicts accurate mapping of HPO terms to HPO IDs, GO terms to GO IDs, and protein names to UniProtKB accession numbers. Higher prevalence of ontology IDs in the biomedical …

Improved Optimal Tracking Of Uncertain Nonlinear Discrete-Time Systems Using Experience Replay, Maxwell Geiger, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper addresses the infinite horizon optimal tracking control problem for partially uncertain control-affine nonlinear discrete-time (DT) systems, where the control input dynamics are known. Multi-layer critic and actor neural networks (MNNs) are utilized for online estimation of the infinite horizon value function and optimal control input. The NN weights are tuned online using a direct temporal difference error (TDE)-driven learning approach, which modifies the singular values of the gradient with respect to the NN weights to accelerate their convergence. The critic NN uses a novel experience replay technique to improve sample efficiency without introducing biased TDEs and guarantee the …

Dual Parameter Fss-Based Sensing For Structural Health Monitoring Applications, Swathi Muthyala Ramesh, Doyle T. Motes, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

frequency selective surfaces (FSSs) are periodic arrays of conductive elements or apertures that reflect and/or transmit incident electromagnetic energy. Their response depends on parameters, such as element shape, unit cell dimensions, dielectric properties, and the local environment, making them suitable for structural health monitoring (SHM) applications. This article presents a dual-parameter FSS-based sensor design capable of measuring small-scale uni-directional longitudinal strain (0%–0.5%) and temperature (23 ◦C–223 ◦C). The sensor integrates two-unit cells: 1) a patch-based cell on a thin substrate for strain sensing, offering enhanced strain transfer and superior sensitivity (~16–18 MHz/0.1%) and 2) a loop-based cell with a temperature-sensitive …

Phase-Variation Microwave Resonator For Highly Sensitive Dynamic Sensing, Chen Zhu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

High-precision dynamic sensing is critical in fields, such as industrial automation, structural health monitoring, and environmental sensing, where real-time responses to minuscule changes can prevent system failures or optimize performance. In this work, we introduce and demonstrate a phase-variation coaxial cable resonator (CCR) as a highly sensitive sensor for dynamic sensing applications. As a proof of concept, a prototype device based on a custom-designed CCR is thoroughly investigated for dynamic displacement measurements, as displacement is a fundamental quantity essential to numerous applications. The sensor consists of two components: a static CCR device and a movable conducting plate. As the conducting …

An Extensive Analysis Of Match-Tracking Methods For Artmap, Niklas M. Melton, Leonardo Enzo Brito Da Silva, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper identifies and studies five match-tracking (MT) methods in the adaptive resonance theory (ART) literature and conducts a detailed comparative analysis of these in ARTMAP applications. We focus on model performance for each MT method with respect to time and space efficiency as well as classification accuracy. Experimental results indicate that one MT variant, used in ARTMAP applications for the first time in this work, provides significant improvements in computational efficiency: depending on the ARTMAP variant, it was able to achieve up to one order of magnitude reduction in both time and space requirements, albeit with a compromise in …

Advancing Temperature Monitoring Of The Bottom Anode In A Direct Current Electric Arc Furnace Operations With Distributed Optical Fiber Sensors., Ogbole Collins Inalegwu, Rony Kumer Saha, Yeshwanth Reddy Mekala, Farhan Mumtaz, Nicholas Dionise, Zane Voss, Jeffrey D. Smith, Ronald J. O'Malley, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The bottom anode in the Direct Current Electric Arc Furnace (DC EAF) is critical for completing the electrical circuit necessary for sustaining the arc within the furnace. For pin-type bottom anodes, monitoring of the temperature of select pins instrumented with thermocouples is performed to track bottom wear in the EAF and inform the operator when the furnace should be removed from service. This work presents the results from a plant trial using distributed temperature monitoring of bottom anode pins in a 165-ton DC EAF over a two-month service period utilizing two optical fiber sensing techniques: fiber Bragg grating (FBG) and …

Enhanced Continual Reinforcement Learning-Based Output Feedback Control Of Heterogeneous Quadrotors Formation, Ehsan Soleimani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a unified framework for the safe and optimal control of heterogeneous quadrotor unmanned aerial vehicles (QUAVs) in formation, enabling multitask missions without requiring precise system dynamics. To address partial state observability, a multilayer neural network (MNN) observer is designed to estimate unmeasured states. Reinforcement learning (RL) is employed for optimal control utilizing an MNN ensuring adaptability. Barrier Lyapunov Functions (BLFs) are integrated into the RL framework to enforce safety by maintaining QUAVs within predefined constraints. An enhanced continual learning (ECL) method is proposed to improve the adaptability of MNNs. This method enables effective multitask learning while mitigating …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Miniaturized Wearable Biosensors For Continuous Health Monitoring Fabricated Using The Femtosecond Laser-Induced Graphene Surface And Encapsulated Traces And Electrodes, Homayoon Soleimani Dinani, Tatianna Reinbolt, Bohong Zhang, Ganggang Zhao, Rex E. Gerald, Zheng Yan, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Wearable sensors are increasingly being used as biosensors for health monitoring. Current wearable devices are large, heavy, invasive, skin irritants, or not continuous. Miniaturization was chosen to address these issues, using a femtosecond laser-conversion technique to fabricate miniaturized laser-induced graphene (LIG) sensor arrays on and encapsulated within a polyimide substrate. The femtosecond laser-converted conductive traces can have a size of 20 to 2 μm compared to the traditionally larger CO2 laser dimensions of around 300 to 100 μm. This marks a 93-98% decrease in trace size when using a femtosecond laser. This miniaturization allows for the ability to process temperature, …

Preparing The Built Environment In Grenada For Future Sea Level Rise And Extreme Precipitation Events, Rocco D. Boyd, Meagan P. Duncan, Alivia D. Markham, Adelle K. Novak, Blaizen B. Bloom, Ashawne Edwards, Jai S. Lewis, Hans-Peter Plag

CCPO Publications

[Introduction] Communities across the globe are being affected by climate change in numerous ways. However, those most often affected, especially by sea level rise, are those small island nations that are pressed for space already; this includes the island of Grenada. Grenada is located in the southern portion of the Caribbean and is bordered by the Caribbean Sea (West) and the Atlantic Ocean (East). The planning and development authority (PDA) has been identified as the main stakeholder involved in this case study, but recommendations will be made to all parties involved to best accomplish our mission.

Key stakeholders were initially …

Traffic Safety In Tacoma: A Dual Study On Community Perceptions Of Vision Zero And The Application Of The Safe System Framework, Janeroza W. Matyenyi

UNF Graduate Theses and Dissertations

The city of Tacoma adopted Vision Zero in 2020 and is planning to achieve zero traffic deaths and severe injuries by 2035. As part of its outreach plan to engage the local community, the city of Tacoma’s Vision Zero team surveyed the critical issues affecting traffic safety as perceived by road users. One of the Vision Zero strategies is to implement safer speeds across streets by reducing speed limits that are too high. This study aims to analyze the factors influencing the perceptions of speeding and high-speed limits as safety concerns, analyze narratives from respondents on issues affecting traffic safety, …

Connecting Bicyclists And Transit: A Multimodal Routing Tool With Bicycle Facilities Scoring, Raphael Y. Mrema

UNF Graduate Theses and Dissertations

This research develops a comprehensive, data-driven framework for assessing multimodal bicycle accessibility using open-source technologies and real-time routing data. Traditional active transportation studies often depend on proprietary GIS tools and static network datasets, which limit scalability, reproducibility, and integration with live mobility systems. In contrast, this study introduces a Python-based approach that leverages GeoPandas, the Google Maps Directions API, and General Transit Feed Specification (GTFS) data to dynamically evaluate infrastructure quality, operational stress, and multimodal connectivity. The framework was applied to Duval County, Florida, to examine how roadway design, facility type, and transit availability jointly influence bicycle network performance and …

Balanced Benchmarking Of Zero-Shot And Rag Approaches For Biomedical Term Normalization, Thanh Son Do, Daniel B. Hier, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

Normalization of medical concepts to an ontology is a key aspect of the natural language processing of biomedical text. It enables the mapping of medical expressions to standardized ontology terms and their identifiers, thereby enhancing the interoperability and computability of medical concepts. Although large language models (LLMs) can identify and standardize medical terms, they may struggle to accurately map ontology terms to their corresponding ontology identifiers. These challenges arise from the stochastic nature of LLMs, their limited exposure to uncommon ontology identifiers during training, and their lack of an integrated lookup mechanism. We generated test sets of synthetic terms to …

Multifunctional Nanoscale Pigments: Emerging Risks And Circular Strategies For A Sustainable Future, Ajay Vikram Singh, Preeti Bhardwaj, Vimal Kishore, Sunil Choudhary, Akihiko Hirose, Neha Gupta, Madleen Busse, Swarn Lata Singh, Christopher J. Osgood

Biological Sciences Faculty Publications

The substantial penetration of nanoscale pigments into a range of sectors has changed the dynamics of industries such as medical, material science, and many more. Nonetheless, their persistence in the environment and probable adverse impacts on health require that an assessment of such risks be formulated considering the One Health perspective. This viewpoint considers the crossing of boundaries of progress in the nanotechnology of nanoscale pigments with environmental, animal, and human health and emphasizes the significance of collaborative activity. Traditional perspectives explain the distribution of pigment history, while the nanotechnology of today's accessibility poses problems regarding utilization, toxicities, and interactions …

Predictive Modeling For Optimal Gel Treatment Design In Brownfields Using Ensemble Machine Learning And Data Upsampling Via Generative Ai, Munqith Aldhaheri, Baojun Bai, Mingzhen Wei

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Efficiently designed gel treatments play a vital role in extending the lifespan of brownfields through rejuvenating oil production. Recently, a three-mode mathematical methodology named the VCR approach has been proposed for designing effective treatments. To optimize this approach, it is crucial to determine the appropriate design mode systematically rather than relying solely on the intuitive judgments of field operators. This study introduces an advanced methodology for predicting the optimal design type of gel treatments using 12 reservoir and production variables. The methodology integrates ensemble machine-learning (EML) models with historical data from 65 field projects across 11 countries (1985-2020). The Random …

Navigation In Underground Mine Environments: A Simulation Framework For Quadruped Robots, Yixiang Gao, Kwame Awuah-Offei

Mining Engineering Faculty Research & Creative Works

Quadruped robots have shown significant potential for navigating complex and hazardous environments, such as underground mines, where traditional wheeled or tracked systems have limitations. However, their development and deployment are hindered by the disparity between controlled laboratory testing and real-world conditions and the lack of tools (e.g., simulation testbeds) that expedite the required development and testing. This work develops a simulation testbed for expediting and advancing navigation algorithms, perception systems, and control strategies for quadruped robots in subterranean and hazardous environments. By utilizing high-fidelity 3D maps, ranging from intricate cave systems to real-world sites like the Edgar Mine, simulation environment …

Investigation Of Radiation Absorption Behavior Of Banana Leaves And Onion: A Cost-Effective Way Of Radiation Protection, Arpita Datta, Alpana Goel, Shreya Singh

International Journal of Nuclear Security

Shielding of nuclear radiation is an important component of radiation safety aiming to reduce the exposure of ionizing radiation to radiation workers. In the present work, beta and gamma radiation absorption properties of various environmentally friendly natural materials such as onion, banana leaf, and banana stem were investigated with a view to understand their radiation shielding capability. Pure beta source ⁹⁰Sr–⁹⁰Y and gamma sources such as ²⁴¹Am, ¹³⁷Cs and ⁶⁰Co were used to study the attenuation of beta and gamma radiation using these natural materials. Intensity of the emitted radiation was measured by placing …

Development Of An Ecg-Based Deep Learning Model For Pediatric Congenital Heart Disease (Chd) Diagnosis, Annbar Mekouar

Selected Full-Text Master Theses 2021-

Congenital heart disease (CHD) stands as the leading congenital anomaly which affects pediatric populations throughout the world. The effectiveness of treatment depends on both early diagnosis and accurate identification but echocardiography requires manual interpretation which proves time-consuming and inconsistent especially when examining pediatric patients with their distinct cardiac systems. The research aims to create a deep learning-based diagnostic framework which uses ECG data to identify coronary artery disease subtypes in pediatric patients. The model uses high-quality datasets from Dr. Ignacio Lugones to extract R-R intervals and QRS durations through convolutional neural networks (CNNs). The system addresses pediatric-specific challenges while enhancing …

Analysis Of Tar And Oil Derived From Pyrolysis And Copyrolysis Of Waste Plastics And Biomass, Anitha Shankaralinge Gowda, Dimitrios Karadimas, Jeffrey Seay

Chemical and Materials Engineering Faculty Publications

Pyrolysis has been proposed as a potential technology for managing the growing volume of plastic waste generated worldwide. Co-pyrolysis of plastic waste with biomass is a promising technology for generating fuel and chemical products. However, this process generates tar as a waste product. The chemical properties of this tar have yet to be thoroughly analyzed. This study presents the results of gas chromatography–mass spectrometry (GC–MS), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) of oil and tar obtained from the pyrolysis of pure plastics including high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene (PE), polystyrene (PS), and plastic-biomass mixtures. GC–MS analysis …

Clinical Correlation Of Sars-Cov-2 Wastewater Passive Sampling In Long-Term Care Facilities And Wastewater Treatment Plants, William Strike, Alexus Lori Rockward, Blazan Mijatovic, Ann Noble, Cullen Olsson, Soroosh Torabi, Mohammad Dehghan Banadaki, Reuben Adatorwovor, James W. Keck, Scott M. Berry

Biomedical Engineering Faculty Publications

Wastewater-based epidemiology (WBE) is a promising tool for improving health outcomes through early detection and cost-effective pathogen surveillance. Long-term care facilities (LTCFs) serve and employ vulnerable populations that may particularly benefit from the use of WBE, but financial and technical costs associated with standard sampling methods limit the feasibility of WBE in the LTCF setting. In this work, we used passive sampling to simplify the wastewater analysis process and compared its performance to the standard composite sampling method. Moore swabs and automatic composite samplers were used concurrently to sample wastewater from two LTCFs, and samples were analyzed for SARS-CoV-2 concentration. …

Affordable Miniaturized Speckle Contrast Diffuse Correlation Tomography Device For Depth-Sensitive Mapping Of Cerebral Blood Flow In Rodents, Fatemeh Hamedi, Faezeh Akbari, Mehrana Mohtasebi, Chong Huang, Li Chen, Lei Chen, Guoqiang Yu

Biomedical Engineering Faculty Publications

Significance: Continuous and longitudinal monitoring of cerebral blood flow (CBF) is critical for understanding brain pathophysiology and guiding interventions. Although rodents are the primary models in neuroscience, existing imaging modalities often fail to provide the optimal combination of low cost, high spatiotemporal resolution, wide head coverage, and sufficient penetration depth for small-animal brain imaging.

Aim: Leveraging a clinical speckle contrast diffuse correlation tomography (scDCT) system, we aimed to develop an affordable, user-friendly, fast, and miniaturized scDCT (mini-scDCT) device tailored for depth-sensitive CBF imaging in small rodents.

Approach: The mini-scDCT replaces bulky and costly optoelectronic components with compact, low-cost alternatives while …

Implementing Wastewater-Based Epidemiology For Long-Read Metagenomic Sequencing Of Antimicrobial Resistance In Kampala, Uganda, William Strike, Temitope O. C. Faleye, Brian Lubega, Alexus Lori Rockward, Soroosh Torabi

Biomedical Engineering Faculty Publications

Antimicrobial resistance (AMR) is an emerging global threat that is expanding in many areas of the world. Wastewater-based epidemiology (WBE) is uniquely suited for use in areas of the world where clinical surveillance is limited or logistically slow to identify emerging threats, such as in Sub-Saharan Africa (SSA). Wastewater was analyzed from three urban areas of Kampala, including a local HIV research clinic and two informal settlements. Wastewater extraction was performed using a low-cost, magnetic bead-based protocol that minimizes consumable plastic consumption followed by sequencing on the Oxford Nanopore Technology MinION platform. The majority of the analysis was performed using …

Enhancing Risk And Crisis Communication With Computational Methods: A Systematic Literature Review, Madison H. Munro, Ross J. Gore, Christopher J. Lynch, Yvette D. Hastings, Ann Marie Reinhold

VMASC Publications

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational …

Faithful Narratives From Complex Conceptual Models: Should Modelers Or Large Language Models Simplify Causal Maps, Tyler J. Gandee, Philippe J. Giabbanelli

VMASC Publications

(1) Background: Comprehensive conceptual models can result in complex artifacts, consisting of many concepts that interact through multiple mechanisms. This complexity can be acceptable and even expected when generating rich models, for instance to support ensuing analyses that find central concepts or decompose models into parts that can be managed by different actors. However, complexity can become a barrier when the conceptual model is used directly by individuals. A ‘transparent’ model can support learning among stakeholders (e.g., in group model building) and it can motivate the adoption of specific interventions (i.e., using a model as evidence base). Although advances in …

Sex-Dependent Changes In Risk-Taking Predisposition Of Rats Following Space Radiation Exposure, Elliot Smits, Faith E. Reid, Ella N. Tamgue, Paola Alvarado Arriaga, Charles Nguyen, Richard A. Britten

Department Radiation Oncology & Biophysics Faculty Publications

The Artemis missions will establish a sustainable human presence on the Moon, serving as a crucial steppingstone for future Mars exploration. Astronauts on these ambitious missions will have to successfully complete complex tasks, which will frequently involve rapid and effective decision making under unfamiliar or high-pressure conditions. Exposure to low doses of space radiation (SR) can impair key executive functions critical to decision making. This study examined the effects of exposure to 10 cGy of Galactic Cosmic Ray simulated radiation (GCRsim) on decision-making performance in male and female rats with a naturally low predisposition for risk-taking (RTP) prior to exposure. …

A Two-Hit Model Of Executive Dysfunction: Simulated Galactic Cosmic Radiation Primes Latent Deficits Revealed By Sleep Fragmentation, Richard A. Britten, Ella N. Tamgue, Paola Arriaga Alvarado, Arriyam S. Fesshaye, Larry D. Sanford

Department Radiation Oncology & Biophysics Faculty Publications

Future Artemis-class missions to Mars will expose astronauts to prolonged space radiation (SR), sleep disruption, and operational demands requiring greater autonomy, placing decision making and executive function at heightened risk. Both SR and sleep fragmentation (SF) independently impair cognition, yet their combined effects remain poorly understood. Using the Associative Recognition Memory and Interference (ARMIT) task, we assessed cognitive performance in male rats exposed to 10 cGy of Galactic Cosmic Ray simulation (GCRsim), SF, or both. Under well-rested conditions, GCRsim-exposed rats exhibited overt deficits in the C.1.2 stage, performing at chance when reinforcement contingencies shifted, consistent with impaired cognitive flexibility. In …

The Study Of Knee And Ankle Sagittal Plane Angles For On-Court Versus Off-Court Cutting, Alexi Rebecca Hempe

Dissertations and Theses

Lower extremity injuries are common among basketball athletes. Majority of these injuries are non-contact and therefore preventable. While previous research has explored high-risk movements associated with lower extremity injuries, limited research has explored the influence of settings for these movement patterns, particularly on a basketball court. This study aims to examine the knee and ankle joint angles in the sagittal plane of a 90-degree cut for on-court versus off court. Thirteen (13) subjects, four males (19.5 ± 1.0 years of age; 198.8 ± 4.8 cm of height; 198.8 ± 13.8 lbs. of weight) and nine females (19.8 ± 1.5 years …