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Articles 1411 - 1440 of 15469
Full-Text Articles in Entire DC Network
Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar
Ai Innovations In Rppg Systems For Driver Monitoring: Comprehensive Systematic Review And Future Prospects, Soha G. Ahmed, Katrien Verbert, Nazar Zaki, Ashraf Khalil, Hamad Aljassmi, Fady Alnajjar
All Works
Advanced technologies, notably camera-based systems using remote photoplethysmography (rPPG), are increasingly used in automotive safety to non-invasively monitor driver well-being and fatigue by measuring physiological metrics like heart and respiration rates. This review examines recent advancements in machine learning algorithms and signal processing for rPPG in driver monitoring. A literature search up to April 2, 2024, across major databases, identified 344 studies; 29 were analyzed in depth, focusing on: 1) rPPG signal extraction and heart rate estimation, where deep learning improved accuracy; 2) fatigue detection, showing benefits of multimodal data fusion; 3) mental state monitoring, with machine learning classifying cognitive …
Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali
Designing Ai To Foster Acceptance: Do Freedom To Choose And Social Proof Impact Ai Attitudes Among British And Arab Populations?, Sameha Alshakhsi, Mohamed Basel Almourad, Areej Babkir, Dena Al-Thani, Ala Yankouskaya, Christian Montag, Raian Ali
All Works
This study examines the impact of two key AI modalities–freedom of choice (FoC) and social proof (SP)–on public attitudes toward AI, focusing on cultural differences between UK and Arab participants. FoC refers to the option of selecting a non-AI, possibly human, alternative, while SP means knowing that others have used AI without issues. Four scenarios were designed, combining the presence or absence of these modalities. The context was a customer service chatbot for a telecommunications company, familiar to all participants. A total of 639 participants (316 British and 323 Arab) were introduced to the modalities and then the scenarios in …
Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure
Enhancing Insider Threat Detection Through A Hybrid Approach Using Different Artificial Intelligence Techniques., Jose Roberto Da Silva Dure
ICT
Insider threats pose significant challenges in cybersecurity due to their origin from individuals with legitimate access. Traditional defenses often fail to detect malicious behavior embedded within normal activities. This study proposes a hybrid artificial intelligence framework that integrates unsupervised anomaly detection, supervised and ensemble learning, and deep learning to enhance insider threat detection. Using the CERT 4.1 dataset, features encompassing temporal, behavioral, network, and psychometric aspects were engineered. Anomaly detection models informed supervised and ensemble classifiers, while a multi-input deep learning architecture captured sequential and contextual patterns. Performance evaluation using ROC-AUC, precision, recall, F1-score, and cost-sensitive analysis demonstrates that the …
Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti
Predicting Repeat Purchases In E-Commerce Using Interpretable Machine Learning, Zahid Bhatti
ICT
This project investigates the prediction of repeat purchase behaviour in e-commerce using machine learning, with a focus on balancing predictive accuracy and interpretability. Large volumes of transactional and behavioural data are analysed to identify customer-level features that drive loyalty and repeat purchases. Various supervised learning models, including Random Forests and Logistic Regression, are evaluated for predictive performance, while SHAP (SHapley Additive Explanations) is employed to provide both global and local interpretability. The study aims to generate actionable insights for customer relationship management and marketing strategy, demonstrating how advanced predictive models can support informed business decisions without sacrificing transparency.
Assemble The Ensemble: A Multi Model Approach For Customer Churn Prediction In The Gambling Industry., Paul Corcoran
Assemble The Ensemble: A Multi Model Approach For Customer Churn Prediction In The Gambling Industry., Paul Corcoran
ICT
Churn rates are remarkably high in the gambling industry, an extremely competitive landscape coupled with a severe lack of brand loyalty among its customer base makes churn prediction one of the main problems an operator will face. This paper explores the range of possible modelling solutions with a key emphasis on ensemble learning to improve on existing methods. During this exploration, a host of modelling techniques are formulated with a focus on scalability facilitated by Apache Spark distributed computing language. Thirteen variations of models, including single classifiers and ensemble families are evaluated as to their suitability in solving the problem. …
Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade
Development And Evaluation Of Machine Learning Models For Early Pediatric Sepsis Prediction, Ancita M. Andrade
Browse all Theses and Dissertations
Sepsis is a leading cause of pediatric mortality, claiming more lives in the United States annually than all childhood cancers combined. Early identification in Emergency Departments (EDs) remains challenging, as the current Phoenix criteria establishes an updated international consensus definition for sepsis, however is not designed for use as a screening tool. This study aimed to develop predictive models identifying pediatric patients at risk of sepsis within 24 hours of admission. Multiple tree-based and deep learning models were trained utilizing clinical and laboratory data from the initial four hours of presentation. Both the LightGBM and LSTM architectures demonstrated superior performance, …
Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula
Wearable Sensor Data Analysis For Machine Learning-Based Detection Of Posture And Autonomic Responses, Chaitanya Vardhini Anumula
Browse all Theses and Dissertations
This study investigates how Iyengar yoga postures influence autonomic nervous system (ANS) activity by analyzing multimodal physiological signals collected via wearable sensors. The physiological mechanisms underlying Iyengar yoga’s therapeutic effects remain under-explored at the granular, pose-level. Using data collected from 16 participants, this research evaluates whether machine learning models can distinguish between baseline, parasympathetic-dominant, and sympathetic-dominant states based on wrist-worn sensor data. The goals were to explore whether subtle postural variations elicit measurable autonomic responses and to identify which sensor features most effectively capture these changes. Participants performed a sequence of yoga poses while wearing synchronized sensors measuring electrodermal activity …
Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange
Personalized Physics Learning Through Ai: Insights From Problem Generation, Chatbot Dialogues, And Intelligent Tutoring Systems, Atharva Dange
Physics Dissertations - Archive
Artificial intelligence (AI) is poised to transform science education, yet questions remain on how best to integrate these technologies into teaching and learning. This dissertation investigates the use of AI-driven tools in university physics courses through three complementary studies. In the first study, a generative language model (ChatGPT) was used to create novel physics homework problems aligned with course objectives. Analysis showed that, after expert vetting, AI-generated questions can foster higher-order problem-solving and reduce student reliance on solution memorization, though careful instructor oversight is required to ensure accuracy. The second study embedded an AI chatbot as a learning aid in …
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel
Browse all Theses and Dissertations
This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes
Browse all Theses and Dissertations
The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …
Comparative Efficacy Of Hallucinogens In Treating Mood Disorders Through A Meta-Analysis Of Symptom Reduction, Dosage, And Duration, John Marco D.F. Muniz
Comparative Efficacy Of Hallucinogens In Treating Mood Disorders Through A Meta-Analysis Of Symptom Reduction, Dosage, And Duration, John Marco D.F. Muniz
Honors Undergraduate Theses
Background: Hallucinogens including psilocybin, lysergic acid diethylamide (LSD), ketamine, N,N-dimethyltryptamine (DMT) (as ayahuasca), have re-emerged as potential rapid-acting treatments for mood disorders. We conducted a meta-analysis of placebo-controlled trials evaluating their efficacy in depression and anxiety disorders. Methods: A systematic review identified 12 trials (Total ≈ 670) meeting inclusion criteria (randomized, placebo-controlled). Data on Cohen’s d and Hedges’ g effect sizes for depression- and anxiety-related outcomes were extracted. We computed pooled effect sizes (weighted by sample size and inverse variance), performed subgroup analyses by drug, diagnosis, follow-up duration, and outcome measure type, and assessed heterogeneity (I2, Q …
Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small
Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small
Honors Undergraduate Theses
Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a ’black box’. This thesis confronts the interpretability knowledge …
Generating Negotiations For Iago, Kylee R. Weener
Generating Negotiations For Iago, Kylee R. Weener
Honors Undergraduate Theses
Negotiation is a complex field that can benefit from introducing artificial intelligence (AI); doing so would benefit researchers as they try to deepen their understanding of human-human and human-agent negotiation. Investigating how large language models (LLMs) can generate negotiation dialogue with emotional context would bring agents closer to acting more human. This study explores how fine-tuning and prompt engineering can achieve this goal and the possibilities for an AI that fills these criteria to be included in the Interactive Arbitration Guide Online platform (IAGO). Doing so will make the negotiation interactions in IAGO feel more complex and natural, allowing researchers …
Partisan Divides In Environmental Spending Attitudes: A Two-Level Hierarchical Analysis, 1973-2022, Jordan Lipner
Partisan Divides In Environmental Spending Attitudes: A Two-Level Hierarchical Analysis, 1973-2022, Jordan Lipner
Honors Undergraduate Theses
Public attitudes toward environmental spending have become increasingly divided along party lines, with sharp shifts over the past five decades. This thesis updates and expands on Johnson and Schwadel’s 2019 study by applying a two-level hierarchical linear model to General Social Survey data updated to include data from 2015-2022, capturing how political affiliation, education, race, and economic context interact with broader political and economic contexts to shape environmental attitudes over time.
The results show that political affiliation remains the strongest and most reactive predictor of environmental spending attitudes. Republican respondents are significantly more likely to oppose environmental spending, especially under …
Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods, Prince Kofi Asare
Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods, Prince Kofi Asare
Theses and Dissertations
Unplanned hospital readmissions represent a significant challenge for healthcare systems, contributing to substantial financial burdens and highlighting gaps in patient care coordination. In the U.S., approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing billions annually. Social determinants of health, such as income, housing stability, and social support, account for up to 80% of health outcomes, yet their integration into predictive models remains underexplored. This study introduces a novel Bayesian framework for predicting 30-day readmission risk, combining Gaussian Process models with spike-and-slab priors and Bayesian Lasso regression with Laplace priors. Utilizing Markov Chain Monte Carlo methods, the approach …
Impact Of Color, Shape, And Typeface On Visual Attention: An Eye Tracking Study On Brand Logo, Suzayana Rosidah, Fransiskus Xaverius Ivan, Suatmi Murnani, Hafzatin Nurlatifa, Kristian Adi Nugraha, Sunu Wibirama
Impact Of Color, Shape, And Typeface On Visual Attention: An Eye Tracking Study On Brand Logo, Suzayana Rosidah, Fransiskus Xaverius Ivan, Suatmi Murnani, Hafzatin Nurlatifa, Kristian Adi Nugraha, Sunu Wibirama
ASEAN Journal on Science and Technology for Development
In numerous cases, companies undertake logo redesigns to enhance brand perception. However, little attention has been paid to the impact of each element of the redesigned logo on visual attention and brand perception. To address this research gap, we collected data from eye tracking and self-report questionnaires of 30 participants during exposure to the old and new logos of a prominent bookstore in Indonesia. The results of the questionnaires revealed a significant relationship between the responses concerning color, shape, typeface, and those pertaining to visual attention (p < 0.05). Most participants were able to grasp the value of creativity, flexibility, progress, change, and strength in the new logo shape. The results of eye tracking show that color was the most influential factor that attracted visual attention in old (F(1.5, 43.4) = 14.905, p < 0.05) and new logos (F(1.7, 50) = 34.757, p < 0.05). This study suggests that companies should selectively choose a color scheme of a logo that better attracts the attention of consumers. In addition, our finding is promising as a practical guide for similar research, as well as a case study on how logo redesign affects brand perception and visual attention.
Kadysnet: A Novel Approach To Dyslexia Prediction In Children: Combining Handwritten Text Recognition With A Hybrid Cnn-Bilstm-Ctc Model And Personalized Learning Strategies, Shailesh Prabhakar Patil, Ravindra Sadashivrao Apare, Ravindra Honaji Borhade, Parikshit Narendra Mahalle
Kadysnet: A Novel Approach To Dyslexia Prediction In Children: Combining Handwritten Text Recognition With A Hybrid Cnn-Bilstm-Ctc Model And Personalized Learning Strategies, Shailesh Prabhakar Patil, Ravindra Sadashivrao Apare, Ravindra Honaji Borhade, Parikshit Narendra Mahalle
ASEAN Journal on Science and Technology for Development
This study introduces a novel approach for early dyslexia detection in children through automated handwriting analysis, integrating a hybrid CNN-BiLSTM-CTC architecture with personalized learning strategies. Our method combines a custom CNN-BiLSTM-CTC model with tailored educational interventions to support dyslexic learners. We analyzed children's handwritten text images in English, collected from specially designed tests involving word rewriting, sentence reconstruction, and paragraph composition, particularly challenging tasks for dyslexic individuals. Notably, our CNN-BiLSTM-CTC model achieved the best result with an accuracy of 97.67%, outperforming other architectures. Compared to pre-trained models like EfficientNetB7, DenseNet121, and MobileNetV2, our custom CNN-BiLSTM-CTC model demonstrated superior performance. Key …
Leveraging Strategic Information Systems For Competitive Advantage: A Case Study Of Veolia As A Multinational Enterprise, Sohail Nawaz Sabir
Leveraging Strategic Information Systems For Competitive Advantage: A Case Study Of Veolia As A Multinational Enterprise, Sohail Nawaz Sabir
ASEAN Journal on Science and Technology for Development
Instead of examining several components that link planning and strategy implementation, researchers have focused on the alignment of IT and business, as well as the Strategic Information Systems Planning (SISP) process. The goal of this study is to examine several approaches that can close the gap between IT project planning and execution. Therefore, the primary objective of the proposed study is to examine SISP implementation strategies that can be theorized in the context of Veolia, a global multinational corporation (MNC). The primary aspects influencing the SISP for this study are the frequency of planning, the function of IT/business managers, assessment …
Macro-Ergonomic And Organizational Behavior Mapping For Organic Waste Management Using Hofstede Culture In Semarang, Indonesia : Pca & Sem Analysis Using Smart Pls, Ratih Setyaningrum, Sahana Pramukti, Ravindran Balasubramani
Macro-Ergonomic And Organizational Behavior Mapping For Organic Waste Management Using Hofstede Culture In Semarang, Indonesia : Pca & Sem Analysis Using Smart Pls, Ratih Setyaningrum, Sahana Pramukti, Ravindran Balasubramani
ASEAN Journal on Science and Technology for Development
Waste management has a very close and complex relationship with community behavior and culture . This relationship is two-way: community culture influences how waste is managed, and conversely, waste management practices can shape or change culture. This study to assess the organizational behavior related to organic waste management. Employing a quantitative methodology, data were gathered using the Hofstede Values Survey Module 13 (VSM 13) - closed questionnaire, with 109 Semarang city Community participants responding via a Google Form. The collected data were analyzed utilizing Hofstede's prescribed formula. The next stage is PCA (Principal Component Analysis) and SEM (Structural Equation Modeling) …
Analysis Of Early Interventions To Retain Underrepresented Students In Computer Science, Michael Conti
Analysis Of Early Interventions To Retain Underrepresented Students In Computer Science, Michael Conti
Open Access Dissertations
Computer science, like many STEM disciplines, faces persistent challenges in recruiting and retaining women and individuals from racially and ethnically minoritized backgrounds. This study examines whether targeted interventions can produce sustained improvements in academic performance and sense of belonging among these underrepresented groups. By analyzing longitudinal data, this research aims to evaluate the effectiveness of these interventions in promoting equity and persistence in computer science education.
Sealing The Deal: A Case Study Of A Private, Southeastern, Regional College’S Student Onboarding Practices, Alicia R. Gaston, Kristen Meyer, Tyler Ogden, Jennifer Perkinson, Casey Yocum Michaels
Sealing The Deal: A Case Study Of A Private, Southeastern, Regional College’S Student Onboarding Practices, Alicia R. Gaston, Kristen Meyer, Tyler Ogden, Jennifer Perkinson, Casey Yocum Michaels
Doctor of Education Capstones
Each year, higher education institutions offer support to new, incoming students through an onboarding process that requires dedication and collaboration across multiple departments. Offering streamlined guidance and clear communication throughout the onboarding process is essential to ensure incoming students understand action steps without becoming overwhelmed with new terminology, processes, and environments. This explanatory case study is set to understand the current communication practices and technology use across onboarding departments at Brightside College – also referred to as Brightside or BC (pseudonym). The research team aimed to understand Brightside's onboarding staff's perspective of current processes and practices. With emphasis on the …
Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur
Crime Modeling Using An Integrated Cnn–Lstm Architecture With Embedded Self-Excitation, Pawandeep Kaur
Theses and Dissertations (Comprehensive)
It is often assumed that natural phenomena occur randomly over time. However, careful analysis reveals that these events typically form some series or sequences and exhibit distinctive temporal patterns. These patterns are not exclusive to nature. They also appear in human activities, often studied under the concept of bursty human dynamics. The statistical methods analyzing bursty human dynamics not only capture overall trends or seasonality but also explore how past events influence future ones. It makes the analysis more realistic and the results more closely aligned with reality. Bursty human dynamics can be studied at two levels: the individual level …
Precision Haptics For Rehabilitation: Quantifying Directional Bias And Force Threshold Effects On Motor Learning, Conor J. Nolan
Precision Haptics For Rehabilitation: Quantifying Directional Bias And Force Threshold Effects On Motor Learning, Conor J. Nolan
UNF Graduate Theses and Dissertations
Hand rehabilitation represents a critical challenge in modern physical therapy, with significant implications for patients' quality of life and functional independence. Despite technological advancements across healthcare, traditional hand assessment methods often rely on subjective measures or lack task-specific biomechanical assessment capabilities. This thesis investigates the effects of haptic force feedback, handedness, and rotation direction on circle-tracing task completion using a within-subjects factorial design with 20 university participants examining varying resistance levels (0.0N, 0.5N, 1.2N) across different movement configurations using a 3D Systems Touch X haptic device with 0.023mm precision. Results demonstrate that moderate haptic force (0.5N) significantly enhanced spatial accuracy …
Frustrated Quantum Magnetism: The Interplay Of Isotropic And Anisotropic Interactions With Application To Α-Rucl3, Evan M. Wilson
Frustrated Quantum Magnetism: The Interplay Of Isotropic And Anisotropic Interactions With Application To Α-Rucl3, Evan M. Wilson
UNF Graduate Theses and Dissertations
We investigate how anisotropic spin interactions, including Dzyaloshinskii–Moriya and Kitaev terms, manifest across quantum spin systems ranging from a single S = 1/2 dimer to molecular spin clusters and layered magnetic materials. Beginning with an exact analysis of the spin dimer, we demonstrate how singlet–triplet mixing induced by Dzyaloshinskii–Moriya interaction directly influences both thermodynamic observables and inelastic neutron scattering spectra. These microscopic fingerprints are then extended to trimer, tetramer, and tetrahedron geometries, where field-induced phase transitions and heat capacity anomalies reveal the interplay between isotropic Heisenberg and anisotropic Kitaev exchanges. In the frustrated zigzag honeycomb lattice, we show that a …
Caregiving Burdens Of Task Time And Task Difficulty Among Paid And Unpaid Caregivers Of Persons Living With Dementia, Matthew Lee Smith, Jodi L. Southerland, Malinee Neelamegam, Gang Han, Shinduk Lee, Chung Lin Kew, Juanita Dawne R. Bacsu, Elyse Couch, Steffi M. Kim, Monique J. Brown Ph.D., Mph, Ayse Malatyali, Lucas Wilson, Zahra Rahemi, Jeremy Holloway, Marcia G. Ory
Caregiving Burdens Of Task Time And Task Difficulty Among Paid And Unpaid Caregivers Of Persons Living With Dementia, Matthew Lee Smith, Jodi L. Southerland, Malinee Neelamegam, Gang Han, Shinduk Lee, Chung Lin Kew, Juanita Dawne R. Bacsu, Elyse Couch, Steffi M. Kim, Monique J. Brown Ph.D., Mph, Ayse Malatyali, Lucas Wilson, Zahra Rahemi, Jeremy Holloway, Marcia G. Ory
Faculty Publications
Background: Demands of caregivers of persons living with dementia (PLWD) are often influenced by the context of their caregiving situation. This study examines common and unique factors associated with caregiving burden in terms of task time and task difficulty among paid and unpaid caregivers of PLWD. Methods: Cross-sectional baseline survey data were analyzed from 107 paid and unpaid caregivers of PLWD participating in a larger NIH-funded study assessing the feasibility of using a novel in-situ sensor system. Oberst Caregiving Burden Scale constructs of task time and task difficulty served as dependent variables. Two least squares regression models were fitted, controlling …
Investing In The Development Of The Next Generation Of Mch Leaders, Karen A. Mcdonnell, Jamal Percy, Lisa Anders, Monique J. Brown Ph.D., Mph, Alice R. Richman, Julianna Deardorff, Monica S. Ruiz, Jihong Liu Sc.D., Kelli Russell, Audrey Snyder, Cassondra Marshall
Investing In The Development Of The Next Generation Of Mch Leaders, Karen A. Mcdonnell, Jamal Percy, Lisa Anders, Monique J. Brown Ph.D., Mph, Alice R. Richman, Julianna Deardorff, Monica S. Ruiz, Jihong Liu Sc.D., Kelli Russell, Audrey Snyder, Cassondra Marshall
Faculty Publications
The public health landscape is constantly evolving to address the strengths and needs of the community. Training for the public health workforce is leading the way, establishing an ecosystem approach that integrates individuals within social, political, and environmental contexts to promote health equity within a framework of social justice. One area of public health that is innovatively preparing the next generation of leaders is maternal and child health (MCH). In the United States, key indicators of health disparities within MCH remain stagnant, highlighting the need for training programs that develop future MCH professionals from diverse backgrounds. These professionals will deliver …
Healthcare Providers’ Perspective On Hiv Testing And Hypothetical Mhealth-Connected Linkage To Care Among Men Who Have Sex With Men (Msm) In South Carolina, Tony Brown, Prince Nii Ossah Addo, Monique J. Brown Ph.D., Mph, Xiaoming Li, Oluwafemi Adeagbo
Healthcare Providers’ Perspective On Hiv Testing And Hypothetical Mhealth-Connected Linkage To Care Among Men Who Have Sex With Men (Msm) In South Carolina, Tony Brown, Prince Nii Ossah Addo, Monique J. Brown Ph.D., Mph, Xiaoming Li, Oluwafemi Adeagbo
Faculty Publications
Background: HIV continues to be an important public health concern in South Carolina (SC). However, an examination of providers’ willingness to use mHealth technologies to address ongoing barriers to HIV care and prevention strategies, particularly among men who have sex with men (MSM) is currently lacking in SC. We therefore explored HIV care providers’ perceptions of HIV testing and treatment uptake among MSM, and providers’ willingness to use mHealth technology to address barriers to HIV testing and treatment in SC. Methods: Between August and December 2021, we conducted semistructured virtual interviews with 10 HIV care providers recruited purposively based on …
Evidence For Crustal Brines And Deep Fluid Infiltration In An Oceanic Transform Fault, Christine Chesley, Katherine Enright
Evidence For Crustal Brines And Deep Fluid Infiltration In An Oceanic Transform Fault, Christine Chesley, Katherine Enright
All Student Scholarship
Although oceanic transform faults (OTFs) are ubiquitous plate boundaries, the geological processes occurring along these systems remain underexplored. The Gofar OTF of the East Pacific Rise has gained attention due to its predictable, yet enigmatic, earthquake cycle. Here, we present results from the first ever controlled-source electromagnetic survey of an OTF, which sampled Gofar. We find that the fault is characterized by a subvertical conductor, which extends into the lower crust and thus implies deep fluid penetration. We also image subhorizontal crustal conductors distributed asymmetrically about the fault. We interpret these subhorizontal anomalies as crustal brines, and we suggest that …
Exploring The Red Sea Crisis’S Supply Chain Disruption Impacts On The Shipping Industry, Po-Hsing Tseng, Nick Pilcher
Exploring The Red Sea Crisis’S Supply Chain Disruption Impacts On The Shipping Industry, Po-Hsing Tseng, Nick Pilcher
Journal of Marine Science and Technology–Taiwan
Since October 7, 2023, the Houthi militia in Yemen has continuously attacked Israel and its nearby ships in the Red Sea. This crisis has impacted on global ship operations, freight rates, and shipping networks, since this conflict seriously affects the international trade between Europe and Asia and brings many unpredictable butterfly effects (e.g. supply chain disruption, and high transportation costs in shipping and air cargo). This papero explores its impact on the supply chain and suggests potential solutions;, and to consider perceptions on who should be responsible for the crisis, and who will actually shoulder the responsibility for the crisis. …
Apply Cluster Analysis To Improve Interpretation Of Ert Images, Yin-Chun Hung, Yu-Xiang Zhao
Apply Cluster Analysis To Improve Interpretation Of Ert Images, Yin-Chun Hung, Yu-Xiang Zhao
Journal of Marine Science and Technology–Taiwan
The completeness and accuracy of collecting and investigating geotechnical and marine engineering geological conditions are key to the success or failure of engineering development. Traditional marine engineering and geotechnical surveys often require the assistance of geophysical methods to fully present the geological conditions. Electrical Resistivity Tomography (ERT) is an advanced geophysical method that produces accurate cross-sections of underground resistivity. Although 2D ERT has been widely used in geotechnical engineering and marine engineering, many uncertainties remain hidden behind the vivid and colorful resistivity profiles. To apply 2D ERT profiles effectively in geotechnical and marine geological surveys and monitoring, it is essential …