Medicine and Health Sciences Commons^™

3d-Printable Open Source Hardware Developed For Sustainable Technology, Dawei Liu

Electronic Thesis and Dissertation Repository

As open-source technology and additive manufacturing evolve, their advantages become increasingly evident, offering solutions to global challenges. This thesis presents the development of two open-source 3D-printable hardware tools to accelerate this trend: a melt flow index (MFI) tool and tourniquet tester. The MFI tool is introduced as a low-cost method for measuring the MFI of thermal-sensitive material, particularly assessing their suitability for recycling thermoplastics for 3D printing. The tourniquet tester provides a low-cost instrument for measuring the pressure of tourniquets to assess efficacy. This device offers a cost-effective solution to ensure the safety and functionality of these critical emergency tools …

Ultrashort Pulsed Laser Treatment Is Effective At Sterilizing Metal Surfaces For Planetary Protection, Kaleb Mcquillan

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

To prevent forward contamination from microbes aboard spacecraft intended for exploration of solar system bodies there is a need for effective sterilization methods. However, current techniques are both time-consuming and expensive. For example, dry heat sterilization requires removal from the assembly site and several days of treatment. Furthermore, some components such as optics and electronics are not compatible with current sterilization techniques. In this thesis, a novel femtosecond laser surface processing technique for the rapid sterilization of spacecraft hardware is reported. Femtosecond lasers produce extremely high photon fluxes (10²⁹ photons/s*cm², ~0.03 J/cm²) in extremely short …

"Blockchain-Enhanced Electronic Health Records: A Secure And Immutable Approach To Ehr Management", Mostafa Abdelwahed Eltabakh, Mohamed E. Nasr, Emad Abd-Elrahman, Roayat Ismail Abdelfatah

Journal of Engineering Research

Traditional Electronic Health Record (EHR) systems face security risks, data misuse, and audit trail concerns. This paper proposes leveraging blockchain technology to improve EHRs, emphasizing Blockchain's potential to enhance data security, accessibility, and privacy through its encrypted and interconnected data block system, thereby addressing the core challenges of conventional EHR practices. This paper introduces a blockchain-based framework designed to consolidate and secure patient data within a singular record owned by the patient. Developed on the Ethereum network utilizing Ganache, this system employs programming languages and tools such as Solidity and web3.js. It leverages the blockchain platform for storing patient data …

Heat And Mass Transfer Characteristics During Vacuum Drying Of Wood, Mohamed Salah Elmetwaly, Lotfy Hassan Rabie Saker, Mohamed Sameh Salem

Journal of Engineering Research

The properties affecting the characteristics of heat and mass transfer during vacuum drying of wood are studied in this paper. The experimental work is carried out in 0.0365 m³ test rig vacuum dryer. The drying chamber dimensions are 0.5 m long and 0.305 m diameter carbon steel cylinder. This drying chamber is internally coated with epoxy paint, also this chamber is detachable closing caps at both ends meaning welded at one end and bolted at the other end to facilitate loading and unloading of the specimen. Two stainless steel heat exchanger plates with dimensions 0.3 m length, 0.15 m …

Multi-Classification Model For Brain Tumor Early Prediction Based On Deep Learning Techniques, Abdelrahman T. Elgohr, Mohamed S. Elhadidy, Mahmoud Elazab Dr, Raneem Ahmed Hegazii, Moataz M. El Sherbiny

Journal of Engineering Research

Brain tumor early prediction is a critical task in medical imaging, as early detection and classification of tumors can significantly improve patient outcomes and treatment planning. In this study, we propose multi-classification models based on deep learning techniques for early prediction of brain tumors using magnetic resonance imaging (MRI) scans. Specifically, we investigate the effectiveness of Convolutional Neural Networks (CNN) in the You Only Look Once (YOLO) approach for an accurate classification of brain tumors into multiple classes based on their morphological characteristics. The proposed model is designed to extract spatial features from MRI images, capturing local patterns and structures …

Evaluating Visual Dependence In Postural Stability Using Smartphone And Stroboscopic Glasses, Brent A. Harper, Michael Shiraishi, Rahul Soangra

Physical Therapy Faculty Articles and Research

This study explores the efficacy of integrating stroboscopic glasses with smartphone-based applications to evaluate postural control, offering a cost-effective alternative to traditional forceplate technology. Athletes, particularly those with visual and visuo-oculomotor enhancements due to sports, often suffer from injuries that necessitate reliance on visual inputs for balance—conditions that can be simulated and studied using visual perturbation methods such as stroboscopic glasses. These glasses intermittently occlude vision, mimicking visual impairments that are crucial in assessing dependency on visual information for postural stability. Participants performed these tasks under three visual conditions: full vision, partial vision occlusion via stroboscopic glasses, and no vision …

Robust Ai-Driven Segmentation Of Glioblastoma T1c And Flair Mri Series And The Low Variability Of The Mrimath© Smart Manual Contouring Platform, Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Carla Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, Houman Sotoudeh

Henry M. Rowan College of Engineering Departmental Research

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive …

Co-Designing Situated Displays For Family Co-Regulation With Adhd Children, Lucas M. Silva, Franceli L. Cibrian, Clarisse Bonang, Arpita Bhattacharya, Aehong Min, Elissa M. Monteiro, Jesus A. Beltran, Sabrina E. B. Schuck, Kimberley D. Lakes, Gillian R. Hayes, Daniel A. Epstein

Engineering Faculty Articles and Research

Family informatics often uses shared data dashboards to promote awareness of each other’s health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members …

The Contribution Of Micrornas To Rybp Silencing In Glioblastoma Multiforme, Alex B. Lee

Graduate Theses

Glioblastoma multiforme (GBM) is a highly aggressive and invasive tumor of the central nervous system (CNS). Survival rates are abysmal, with only 7.2% of patients alive 5-years after diagnosis. Because of this, understanding epigenetic alterations that give GBM tumors their aggressive phenotypes is critical for the development of more targeted and effective therapies. These alterations frequently affect a group of proteins called the Polycomb group proteins, which play important oncogenic and tumor suppressive roles in cancer. One Polycomb protein, the RING1- and YY1-binding protein (RYBP), is downregulated in a majority of GBM patients, suggesting a strong tumor suppressive property. In …

Selective Transfection Of A Transferrin Receptor-Expressing Cell Line With Dna-Lipid Nanoparticles And Synthesis Of Parasite-Derived Glycans As Biomarkers For Leishmaniasis, Irodiel Vinales Lozano

Open Access Theses & Dissertations

Despite notable progress in lipid nanoparticle (LNP)-mediated gene delivery, achieving selective transfection of specific cell types, such as cancer cells, remains a significant hurdle, hindering the advancement of innovative gene therapies. In this study, we engineered an LNP formulation encapsulating plasmid DNA (pDNA) encoding the monomeric Green Lantern (mGL) fluorescent reporter protein. The DT7 peptide ligand targeting human transferrin receptor 1 (hTfR1) was also conjugated to the LNP surface for targeted delivery to hTfR1-expressing cells. Optimization of LNP composition yielded favorable particle diameter, ζ-potential, yield, and pDNA encapsulation efficiency. Evaluation of transfection selectivity using a panel of two engineered cell …

Development And Feasibility Studies Of Ai-Powered Socially Assistive Robotics To Promote Wellbeing Of Persons With Alzheimer’S Disease And Related Dementias, Fengpei Yuan

Doctoral Dissertations

The number of persons living with Alzheimer's Disease and Related Dementias (PLWDs) has been keeping growing. In 2024, it is estimated that there will be approximately 6.7 million individuals living with Alzheimer's Dementia. This number will increase to about 14 million in 2060. Due to the damage in neurons, the capabilities of memory, thinking, and language will decline as the disease progress. As a result, persons with dementia will gradually withdraw from their social activities and become more dependent on others during their activities of daily living. Making it worse, our society is not ready for the increasing requirements of …

Bcl2 Mediated Targeted Drug Delivery For The Treatment Of Kidney Fibrosis And Stomach Cancer, Humayra Afrin

Open Access Theses & Dissertations

Apoptosis, the programmed death of cells, is primarily regulated by a delicate balance between pro-apoptotic and anti-apoptotic signals. The Bcl-2 (B-cell lymphoma 2) family of proteins acts as anti-apoptotic agents, promoting cell survival. Dysregulation of these proteins is a common occurrence in conditions such as cancer and fibrosis, where overexpression of anti-apoptotic members can foster tumor cell survival and fibroblast activation. In this study, our aim was to explore the therapeutic potential of Bcl-2 inhibitors, both as a small molecule (specifically Navitoclax (Navi)), inhibitor and as Bcl-2 siRNA, for targeted treatment. Intravenous administration of Navi often leads to thrombocytopenia, necessitating …

Advancing Brain Tumor Segmentation With Spectral–Spatial Graph Neural Networks, Sina Mohammadi, Mohamed Allali

Engineering Faculty Articles and Research

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and …

Modeling The Human Learning Process Using An Industrial Steam Boiler Analogy To Design A Psychophysiological-Based Hypermedia Adaptive Automation System, Liliana María Villavicencio López

USF Tampa Graduate Theses and Dissertations

This dissertation aims to address the existing gap in the integration of various dimensions within the student learning system, encompassing cognitive, emotional, and physical variables. The primary objective is to construct a Personalized Learning Adaptive Automation model using Electroencephalography (EEG) technology.

To provide deeper insight into the intricate nature of the Human Learning Process, this study introduces a novel analogy with an Industrial Steam Boiler. This analogy serves as a distinctive contribution to research in the field.

The research methodology involved the collection of brainwaves data from engineering students while they undertook educational tasks of varying levels of difficulty, categorized …

Towards A Wearable Device For Measuring Impedance Plethysmography Of The Radial Artery, Pritom Chowdhury

Dartmouth College Master’s Theses

Recent advancements in bioimpedance technology have demonstrated significant promise in the application of cardiac health monitoring. This research explores the design and development of a forearm-based wearable bioimpedance device for non-invasive measurement of heart rate and respiratory rate at an accuracy level comparable to medical-grade monitors. It utilizes a tetrapolar electrode configuration to analyze bioimpedance changes in the radial artery due to blood flow.

An ongoing aspect of this work involves the preliminary development of an embedded framework intended to integrate signal generation, acquisition, and processing within the device to achieve compact and efficient system design, anticipated to contribute to …

Market Analysis And Bidding Strategy Of Hybrid Renewable Energy Systems Considering Emissions, Fatma Elzahraa, Mohamed Elnemr, Samir Dawoud

Journal of Engineering Research

The competition in the electricity markets makes it difficult to choose a suitable strategy for maximizing profit while reducing harmful emissions. To have an adequate energy price for consumers while minimizing the harmful emissions to the atmosphere and maximizing profits of all participants in the electricity market needs an aggressive bidding strategy. Developing these bidding strategies with the integration of renewable energy (RE) in the electricity market became important. This research studies various bidding strategies for maximizing profits in the deregulated energy market since participants are keen on developing bidding strategies considering emissions. These bidding strategies will consider the integration …

Non-Invasive Monitoring Device For Early Detection Of Breast Cancer Related Lymphedema, Amy Prendergast

Honors Theses and Capstones

Breast Cancer Related Lymphedema (BCRL) is a common co-morbidity in cancer survivors following neoadjuvant therapies such as chemotherapy, radiation, and/or surgery. It is brought about by the disruption in the lymphatic system (think lymph node biopsy) that leads to a buildup of lymphatic fluid in the arm. Current diagnostic strategies for this condition are merely retroactive, and fairly limited in the parameters that are examined to ensure patient well-being long term. We hypothesize that with an approach that mimics bioimpedance spectroscopy analysis, we will be able to provide a clinical support tool that would better determine early stages of lymphedema …

Detection Of Tooth Position By Yolov4 And Various Dental Problems Based On Cnn With Bitewing Radiograph, Kuo Chen Li, Yi-Cheng Mao, Mu-Feng Lin, Yi-Qian Li, Chiung-An Chen, Tsung-Yi Chen, Patricia Angela R. Abu

Department of Information Systems & Computer Science Faculty Publications

Periodontitis is a high prevalence dental disease caused by bacterial infection of the bone that surrounds the tooth. Early detection and precision treatment can prevent more severe symptoms such as tooth loss. Traditionally, periodontal disease is identified and labeled manually by dental professionals. The task requires expertise and extensive experience, and it is highly repetitive and time-consuming. The aim of this study is to explore the application of AI in the field of dental medicine. With the inherent learning capabilities, AI exhibits remarkable proficiency in processing extensive datasets and effectively managing repetitive tasks. This is particularly advantageous in professions demanding …

Tailored Micromagnet Sorting Gate For Simultaneous Multiple Cell Screening In Portable Magnetophoretic Cell-On-Chip Platforms, Jonghwan Yoon, Yumin Kang, Hyeonseol Kim, Abbas Ali, Keonmok Kim, Sri Ramulu Torati, Mi-Young Im, Changyeop Jeon, Byeonghwa Lim, Cheolgi Kim

Bioelectronics Publications

Conventional magnetophoresis techniques for manipulating biocarriers and cells predominantly rely on large-scale electromagnetic systems, which is a major obstacle to the development of portable and miniaturized cell-on-chip platforms. Herein, a novel magnetic engineering approach by tailoring a nanoscale notch on a disk micromagnet using two-step optical and thermal lithography is developed. Versatile manipulations are demonstrated, such as separation and trapping, of carriers and cells by mediating changes in the magnetic domain structure and discontinuous movement of magnetic energy wells around the circumferential edge of the micromagnet caused by a locally fabricated nano-notch in a low magnetic field system. The motion …

Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna

Electrical & Computer Engineering Faculty Publications

Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …

Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim

Electrical & Computer Engineering Faculty Publications

Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …

Energy Harvesting Face Mask Using A Thermoelectric Generator For Powering Wearable Health Monitoring Sensors, Ugur Erturun, Cansu Yalim, James E. West

Engineering Management & Systems Engineering Faculty Publications

A wearable energy harvester (EH) incorporating a face mask with a thermoelectric generator is demonstrated. The function of this device is to generate electrical power from the heat produced by the human body, particularly breath, with the specific aim of powering wearable sensor applications. A prototype was built using a commercially available N95 face mask, a thermoelectric generator, and a heatsink. The performance of this EH device was assessed using experimental and numerical methodologies. The experimentally tested power output of the prototype was found to be ≈100 µW, with a corresponding power density of ≈30 µW/cm3, for a temperature difference …

Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

Trumpet Directivity From A Rotating Semicircular Array, Samuel D. Bellows, Joseph E. Avila, Timothy W. Leishman

Directivity

The directivity function of a played musical instrument describes the angular dependence of its acoustic radiation and diffraction about the instrument, musician, and musician’s chair. Directivity influences sound in rehearsal, performance, and recording environments and signals in audio systems. Because high-resolution, spherically comprehensive measurements of played musical instruments have been unavailable in the past, the authors have undertaken research to produce and share such data for studies of musical instruments, simulations of acoustical environments, optimizations of microphone placements, and other applications. The authors acquired the data from repeated chromatic scales produced by a trumpet played at mezzo-forte in an anechoic …

Reducing Brain Kynurenic Acid Synthesis Precludes Kynurenine-Induced Sleep Disturbances, Katherine M. Rentschler, Snezana Milosavljevic, Annalisa M. Baratta, Courtney J. Wright, Maria V. Piroli, Zachary Tentor, Homayoun Valafar, Christian O'Reilly, Ana Pocivavsek

Publications

Patients with neurocognitive disorders often battle sleep disturbances. Kynurenic acid is a tryptophan metabolite of the kynurenine pathway implicated in the pathology of these illnesses. Modest increases in kynurenic acid, an antagonist at glutamatergic and cholinergic receptors, result in cognitive impairments and sleep dysfunction. We explored the hypothesis that inhibition of the kynurenic acid synthesising enzyme, kynurenine aminotransferase II, may alleviate sleep disturbances. At the start of the light phase, adult male and female Wistar rats received systemic injections of either: (i) vehicle; (ii) kynurenine (100 mg kg−1; i.p.); (iii) the kynurenine aminotransferase II inhibitor, PF-04859989 (30 mg kg−1; s.c.); …

Vertical Federated Learning Using Autoencoders With Applications In Electrocardiograms, Wesley William Chorney

Theses and Dissertations

Federated learning is a framework in machine learning that allows for training a model while maintaining data privacy. Moreover, it allows clients with their own data to collaborate in order to build a stronger, shared model. Federated learning is of particular interest to healthcare data, since it is of the utmost importance to respect patient privacy while still building useful diagnostic tools. However, healthcare data can be complicated — data format might differ across providers, leading to unexpected inputs and incompatibility between different providers. For example, electrocardiograms might differ in sampling rate or number of leads used, meaning that a …

Auto-Segmentation In Pancreatic And Liver Radiation Therapy, Cenji Yu

Dissertations & Theses (Open Access)

Background

Gastrointestinal cancers exhibit a high mortality rate compared to other cancer types. Among these, pancreatic cancer ranks as the fourth leading cause of cancer-related deaths worldwide. The five-year survival rate remains alarmingly low at a mere 9%. Hepatocellular carcinoma (HCC), another aggressive form of cancer, is rapidly becoming the primary cause of cancer-related deaths in the United States. The treatment of both liver cancer and pancreatic cancer heavily relies on a multidisciplinary approach. Innovative treatment strategies involving dose-escalated regimens, such as stereotactic body radiation therapy (SBRT), are emerging as an important pillar of the management of liver and pancreatic …

Survey Of How Irregularities In The Electrical System Of The Human Heart Link To Different Heart Arrhythmias, Jazmine Boloor

UNLV Theses, Dissertations, Professional Papers, and Capstones

This thesis explores the circuital pathway that a signal takes within the heart throughout a full heartbeat and provides a timing analysis. It then surveys the different pathways that could arise in the electrical system of the heart, and the associated heart diseases linked to the location of each pathway. Common heart arrhythmias such as atrioventricular nodal reentrant tachycardia, atrioventricular reentrant tachycardia, atrial flutter, and atrial fibrillation are surveyed, and their pathways are analyzed. Electrocardiograms are presented to aid in understanding of the timing analysis associated with irregularity in heart function. Finally, this thesis explores an avenue of the heartbeat …