Data Science Commons^™

Prevalence And Relevant Factors Of Nocturia And Its Impact On Sleep Quality In Chinese University Students, Pengchao Xu, Qifeng Dou, Cuiping Song, Huiqing Zhang, Israel Franco, Konstantinos Kamperis, Søren Rittig, Jianguo Wen

Faculty, Staff and Student Publications

The purpose of this study was to investigate the prevalence and relevant factors of nocturia and its impact on sleep quality in university students in Mainland China. A large-scale survey was conducted on 14,000 university students from 3 universities in Henan province, China by using an anonymous questionnaire. The questionnaire collected the information from the past six months. The relationships between the prevalence of nocturia and its relevant factors were evaluated. A total of 13,874 questionnaires were collected and 13,104 qualified for statistical analysis. A total of 659 students suffered from clinically relevant nocturia (CRN) (4.56% in male and 5.34% …

Gis-Based Dashboard For Data Analysis And Visualization At The Water Hub In Franschhoek, South Africa, Liliana Rosa Simon

Geography and the Environment: Graduate Student Capstones

This capstone project analyzes the land use change in the Franschhoek Valley, South Africa, and implements this information into an ArcGIS Experience Builder Dashboard. The development of an ArcGIS Experience Builder is utilized as a tool for improved spatial data communication at The Water Hub. An increase in urban development in the informal settlement upstream of the Stiebeuel River in the catchment basin north of the Water Hub research institute is found. The capstone uses geographic information systems, remote sensing, and ArcGIS Experience Builder to integrate spatial data into the Water Hub’s standard operating procedures. It highlights the importance of …

Information Technology In The Christian Sphere, Kaylen Frazee

NEXUS: The Liberty Journal of Interdisciplinary Studies

No abstract provided.

The Institutional Challenges Of A Quantified Self Study: An Attempt To Ascertain How Data Collected From A Mobile Device Can Be An Indicator Of Personal Mental Health Over Time, Julian Lazaras

University Honors Theses

The adoption of an application of new technology always comes with a bias, this is never more true for the case of human behavioral analytics within higher education. While movements such as the quantified self movement make strides to reinterpret the realm of data analytics, psychology, and computer science, there are inevitably limitations to the adoption and application of such approaches within the standard realm of research. Herein is presented a case where an effort to evaluate the prospect of use of mobile phone data as secondary indicators of personal mental health through the lens of data analysis was put …

Eye Diseases Prediction And Classification Using Deep Learning Techniques., Ahmed M. Fahmy

Artificial Intelligence

In human anatomy, the eye is a major sense organ and loss of vision would have an enormous impact on quality of life. In addition, it may also be possible that the eye is showing signs of severe health problems. Eye diseases detection is mainly the problem here. Detecting eye diseases using different advanced AI- based techniques to analyze and predict the type of eye disease from retinal eye images and improve the performance and the accuracy of the proposed system. This proposed system can help ophthalmologists to save their time in manual examination. This paper aims to develop Deep …

A Machine Learning Framework For Predicting Fabrication Hours For Industrial Steel Structure Projects, Dalia Ibrahim

Theses and Dissertations

Construction projects are considered high risk projects especially due to their required large capital making them require extreme attention in estimation as overestimating a project will lead to losing bids and underestimating them will lead to incurring more costs than budgeted resulting in losses. However, estimators are often faced with very tight timelines to finish their estimates leading them to primarily rely on their experience disregarding some crucial factors resulting in inaccurate estimates. In the steel structures industry, the steel fabrication phase accounts for 30 to 40% of the overall project cost; in addition, the steel industry is labor driven; …

The Budget Proposal As A Constructive Collections Engagement Tool And Practice, Evan Rusch, Pat Lienemann, Heidi J. Southworth, Nat Gustafson-Sundell

Library Services Publications

At Minnesota State University Mankato (MNSU), our story might sound familiar. After more than a decade of flat or decreased budgets, we have cancelled hundreds of journal subscriptions and numerous journal packages. We do occasionally add journals, but only by cancelling others. We often say we are “managing library decline,” and our primary objective is to cut cleanly and accurately, so that we can continue to support accreditation and an evolving curriculum. Over the years, we have developed various tools to guide our cuts and we have demonstrated these at NASIG and elsewhere. These tools have also served for collection …

Ai's Ethical Frontier

DePaul Magazine

Artificial intelligence (AI) is affecting every aspect of the university and society. Experts from across DePaul share their insights on artificial intelligence's advantages and pitfalls. Learn about DePaul's new Artificial Intelligence Institute and research projects that use AI for societal benefit.

Investigating The Instagram Comments Of Professional Soccer Players: The Impact Of Social Media On Athletic Performance, Samuel Carlson Winchester

Quantitative Social Science Undergraduate Senior Theses

Since the rise of social media platforms like Facebook, Instagram, and Twitter, many celebrities have spoken out about the influence social media has on their mental health. Among the most vocal have been professional athletes, who have highlighted the hateful comments and direct messages they receive from fans on social media platforms. In some cases, athletes have even chosen to step away from social media to avoid the toxicity of their timeline. Accordingly, researchers have studied the impact social media has on professional athletes’ mental health, in some cases focusing on how social media can negatively impact professional athletes’ athletic …

Semirecumbent Positioning During Anesthesia Recovery And Postoperative Hypoxemia: A Randomized Clinical Trial, Yan Wu, Xihui Tang, Yuqing Xu, Qingsong Chen, Si Zeng, Liwei Wang, Su Liu

Faculty, Staff and Student Publications

IMPORTANCE: The efficacy of a semirecumbent position (SRP) in reducing postoperative hypoxemia during anesthesia emergence is unclear despite its widespread use.

OBJECTIVE: To determine the differences in postoperative hypoxemia between patients in an SRP and a supine position.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was performed at a tertiary hospital in China between March 20, 2021, and May 10, 2022. Patients scheduled to undergo laparoscopic upper abdominal surgery under general anesthesia were enrolled. Study recruitment and follow-up are complete.

INTERVENTIONS: Patients were randomized to 1 of the following positions at the end of the operation until leaving the …

Design And Implementation Of A Vision-Based Deep-Learning Protocol For Kinematic Feature Extraction With Application To Stroke Rehabilitation, Juan Diego Luna Inga

Master's Theses

Stroke is a leading cause of long-term disability, affecting thousands of individuals annually and significantly impairing their mobility, independence, and quality of life. Traditional methods for assessing motor impairments are often costly and invasive, creating substantial barriers to effective rehabilitation. This thesis explores the use of DeepLabCut (DLC), a deep-learning-based pose estimation tool, to extract clinically meaningful kinematic features from video data of stroke survivors with upper-extremity (UE) impairments.

To conduct this investigation, a specialized protocol was developed to tailor DLC for analyzing movements characteristic of UE impairments in stroke survivors. This protocol was validated through comparative analysis using peak …

Accessible Real-Time Eye-Gaze Tracking For Neurocognitive Health Assessments, A Multimodal Web-Based Approach, Daniel C. Tisdale

Master's Theses

We introduce a novel integration of real-time, predictive eye-gaze tracking models into a multimodal dialogue system tailored for remote health assessments. This system is designed to be highly accessible requiring only a conventional webcam for video input along with minimal cursor interaction and utilizes engaging gaze-based tasks that can be performed directly in a web browser. We have crafted dynamic subsystems that capture high-quality data efficiently and maintain quality through instances of user attrition and incomplete calls. Additionally, these subsystems are designed with the foresight to allow for future re-analysis using improved predictive models, as well as enable the creation …

Interpretable Learning In Multivariate Big Data Analysis For Network Monitoring, José Camacho, Katarzyna Wasielewska, Rasmus Bro, David Kotz

Dartmouth Scholarship

There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows …

Semantic Structuring Of Digital Documents: Knowledge Graph Generation And Evaluation, Erik E. Luu

Master's Theses

In the era of total digitization of documents, navigating vast and heterogeneous data landscapes presents significant challenges for effective information retrieval, both for humans and digital agents. Traditional methods of knowledge organization often struggle to keep pace with evolving user demands, resulting in suboptimal outcomes such as information overload and disorganized data. This thesis presents a case study on a pipeline that leverages principles from cognitive science, graph theory, and semantic computing to generate semantically organized knowledge graphs. By evaluating a combination of different models, methodologies, and algorithms, the pipeline aims to enhance the organization and retrieval of digital documents. …

Modeling The Effect Of Population Size On Banking Transaction Channels In Nigeria: Grey Box Vs Support Vector Regression, Desmond Bartholomew, Ngozi P. Olewuezi, Chrysogonus C. Nwaigwe, Felix C. Akanno

CBN Journal of Applied Statistics (JAS)

This study investigates the effect of Nigeria’s population on four selected banking transaction channels. The Nigerian projected population (2022-2027) was used as an input variable for forecasting future volumes of transactions for each channel. The results show that the Support Vector Regression (SVR) model best fits the ATM, Online, and USSD channels of transaction while the Grey-box was better for POS. The forecast results show that ATM, online, and USSD channels had their highest volume of transactions in 2023, while for POS, the highest volume was recorded in 2027. Further results indicate that online and POS transactions would dominate payment …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Contrastive Filtering And Dual-Objective Supervised Learning For Novel Class Discovery In Document-Level Relation Extraction, Nicholas Hansen

Master's Theses

Relation extraction (RE) is a task within natural language processing focused on the classification of relationships between entities in a given text. Primary applications of RE can be seen in various contexts such as knowledge graph construction and question answering systems. Traditional approaches to RE tend towards the prediction of relationships between exactly two entity mentions in small text snippets. However, with the introduction of datasets such as DocRED, research in this niche has progressed into examining RE at the document-level. Document-level relation extraction (DocRE) disrupts conventional approaches as it inherently introduces the possibility of multiple mentions of each unique …

Impact Of Operational Ladar Occlusions On Point Cloud Instance Segmentation, Andrew D. Gibson

Theses and Dissertations

Data exploitation techniques are the enabler for technological advancements in military ISR applications of ladar ISR. By identifying instances of military objects in observed scenes, point cloud deep learning models can unlock new standards of real-time information delivery to warfighters. Although current deep learning training datasets do not include real-world collection occlusions consistent with military applications, this research characterizes SPT model performance by adding occlusions to the DALESObjects dataset via artificial flyby simulations.We find that a baseline model trained on unoccluded data suffers performance degradation on both semantic and instance segmentation tasks when evaluated on occluded data, but that the …

Associations Between Longer Leukocyte Telomere Length And Increased Lung Cancer Risk Among Never Smokers In Urban China, Jianxin Shi, Kevin Wang, Richard Cawthon, Qiuyin Cai, Gong Yang, Mohammad L Rahman, Bu-Tian Ji, Yutang Gao, Wei Zheng, Nathaniel Rothman, Qing Lan

Faculty, Staff and Student Publications

BACKGROUND: The complex relationship between measured leukocyte telomere length (LTL), genetically predicted LTL (gTL), and carcinogenesis is exemplified by lung cancer. We previously reported associations between longer pre-diagnostic LTL, gTL, and increased lung cancer risk among European and East Asian populations. However, we had limited statistical power to examine the associations among never smokers by gender and histology.

METHODS: To investigate further, we conducted nested case-control analyses on an expanded sample of never smokers from the prospective Shanghai Women's Health Studies (798 cases and 792 controls) and Shanghai Men's Health Studies (161 cases and 162 controls). We broke the case-control …

Enhancing Foot Care Education And Support Strategies In Adults With Type 2 Diabetes, Constance Johnson

Faculty, Staff and Student Publications

BACKGROUND: People with diabetes are susceptible to serious and disabling foot complications, which increase their morbidity and mortality rates. Examining the perspectives of people with diabetes on their foot care routines could help elucidate their beliefs and offer practical ways to prevent foot problems.

PURPOSE: We explored the perspectives of adults with diabetes on their foot care practices to identify and enhance foot care education and support strategies.

METHODOLOGY: Using the Zoom platform, 29 adults with diabetes completed a 3-month telehealth educational program, during which interviews were conducted. This article reports the results of thematic content analysis of the qualitative …