Statistical Methodology Commons^™

Multiscale Modelling Of Brain Networks And The Analysis Of Dynamic Processes In Neurodegenerative Disorders, Hina Shaheen

Theses and Dissertations (Comprehensive)

The complex nature of the human brain, with its intricate organic structure and multiscale spatio-temporal characteristics ranging from synapses to the entire brain, presents a major obstacle in brain modelling. Capturing this complexity poses a significant challenge for researchers. The complex interplay of coupled multiphysics and biochemical activities within this intricate system shapes the brain's capacity, functioning within a structure-function relationship that necessitates a specific mathematical framework. Advanced mathematical modelling approaches that incorporate the coupling of brain networks and the analysis of dynamic processes are essential for advancing therapeutic strategies aimed at treating neurodegenerative diseases (NDDs), which afflict millions of …

Measuring The Performance Of Sdgs In Provincial Level Using Regional Sustainable Development Index, Nurafiza Thamrin, Ika Yuni Wulansari, Puguh Bodro Irawan

Journal of Environmental Science and Sustainable Development

Measuring the national and sub-national progress in achieving such globally adopted development agendas as Sustainable Development Goals (SDGs) is particularly challenging due to data availability and compatibility of indicators to measure SDGs, especially in Indonesia. This paper attempts to measure the performance of sustainable development at the regional level in Indonesia by newly constructing a multidimensional composite index called the Regional Sustainable Development Index (RSDI). RSDI comprises four dimensions, covering comprehensive economic, social, environmental, and governance indicators. By applying factor analysis, the paper assesses the uncertainty of RSDI and the sensitivity of its composing indicators, then further investigates the relationship …

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 …

Challenges And Countermeasures For Treatment And Remediation Of Contaminated Mega-Sites In China, Xiaoyong Liao, Yixuan Hou, You Li, Tianyi Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The treatment and remediation of pollution at contaminated mega-sites poses a significant challenge in the environmental science both domestically and internationally. Contaminated mega-sites are characterized by their widespread impact, multiple types of pollutants, and significant ecological and environmental threats. The environmental behavior cognition and efficient remediation at contaminated mega-sites face enormous challenges, among which key technological issues such as the formation mechanism of soil and groundwater pollution, accurate identification of pollution sources, and intelligent decision-making optimization urgently need to be solved. In China, contaminated mega-sites are concentrated in economically developed regions such as Beijing-Tianjin-Hebei, the Yangtze River Economic Belt, and …

Spatial Agglomeration And Environmental Effects Of Heavy Polluting Industries In China: Characteristics And Enlightenment, Hongyang Chen, Jianhui Yu, Wenzhong Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Heavy polluting industries are the important sources of industrial pollutant. Understanding the spatial agglomeration characteristics, influencing factors, agglomeration mechanism and environmental effects of China’s heavy polluting industries can help identify potential pollution risk areas to cope with increasingly severe environmental pollution problems. Based on the industrial economic data from 1999 to 2021, the spatial distribution and agglomeration characteristics of heavy polluting industries are characterized. It is found that: (1) Shandong, Jiangsu, Zhejiang, and Guangdong are the regions with high output value of the development of heavy polluting industries in the past 20 years, while Xinjiang, Inner Mongolia, Shanxi, Shaanxi, Henan, …

Atmospheric 14Co2 Observation: A Novel Method To Evaluate Carbon Emissions, Zhenchuan Niu, Peng Wang, Shugang Wu, Weijian Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

As an important carbon emitter, China faces the stress of carbon peaking and carbon neutrality goals and international carbon reduction duty. The accurate data of carbon emissions are important to evaluate the carbon peaking and carbon neutrality goals and fulfill the international duty of carbon reduction. The Intergovernmental Panel on Climate Change (IPCC) report recommends the combination of top-down atmospheric CO₂ observation with atmospheric inversion to verify the bottom-up inventory of carbon emissions, and the atmospheric ¹⁴CO₂ observation can make the verification more accurate. Radiocarbon (14C) is the most precise tracer of fossil fuel CO₂ and …

Ohio Recovery Housing: Resident Risk And Outcomes Assessment, Elyjiah Potter, Bivin Sadler

SMU Data Science Review

Addiction and substance abuse disorder is a significant problem in the United States. Over the past two decades, the United States has faced a boom in substance abuse, which has resulted in an increase in death and disruption of families across the nation. The State of Ohio has been particularly hard hit by the crisis, with overdose rates nearly doubling the national average. Established in the mid 1970’s Sober Living Housing is an alcohol and substance use recovery model emphasizing personal responsibility, sober living, and community support. This model has been adopted by the Ohio Recovery Housing organization, which seeks …

Investigating The Effects Of A Southward Flow In The Southeastern Florida Shelf Using Robotic Instruments, Alfredo Quezada

All HCAS Student Capstones, Theses, and Dissertations

We deployed a Slocum G3 glider fitted with an acoustic Doppler current profiler (ADCP), a Conductivity-Temperature-Depth sensor (CTD), optics sensor channels, and a propeller on the Southeastern Florida shelf. The ADCP and CTD provide continuous measurements of Northern and Eastern current velocity components, salinity, temperature, and density, throughout the water column in a high-current environment. The optics sensor channels are able to provide measurements of chlorophyll concentrations, colored dissolved organic matter (CDOM), and backscatter particle counts. Additionally, for one of the glider deployments, we deployed a Wirewalker wave-powered profiling platform system also fitted with an ADCP and a CTD in …

Is The Declining Birthrate Really An Issue For The Economy?, Harsh Ramesh Pednekar, Theodore Lee, Darrion Chin

Introduction to Research Methods RSCH 202

This study aims to explore the complex implications of declining birth rates on the economy, focusing on GDP per capita as a crucial metric, and aims to uncover both potential opportunities and challenges stemming from this demographic transformation using regression analysis. Using a quantitative methodology and secondary data from OECD.stat, World Population Review, and World Bank, the study explores the relationship between declining birth rates and economic impacts. GDP per capita serves as an essential dependent variable, and it accounts for control variables such as labour force participation, literacy, and education levels, child dependence ratio, and physical capital. Past studies …

The Private Pilot Check Ride: Applying The Spacing Effect Theory To Predict Time To Proficiency For The Practical Test, Michael Scott Harwin

Theses and Dissertations

This study examined the relationship between a set of targeted factors and the total flight time students needed to become ready to take the private pilot check ride. The study was grounded in Ebbinghaus’s (1885/1913/2013) forgetting curve theory and spacing effect, and Ausubel’s (1963) theory of meaningful learning. The research factors included (a) training time to proficiency, which represented the number of training days needed to become check-ride ready; (b) flight training program (Part 61 vs. Part 141); (c) organization offering the training program (2- or 4-year college/university vs. FBO); (d) scheduling policy (mandated vs. student-driven); and demographical variables, which …

Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako

Doctoral Dissertations

This dissertation is in the field of Nonparametric Derivative Estimation using
Penalized Splines. It is conducted in two parts. In the first part, we study the L2
convergence rates of estimating derivatives of mean regression functions using penalized splines. In 1982, Stone provided the optimal rates of convergence for estimating derivatives of mean regression functions using nonparametric methods. Using these rates, Zhou et. al. in their 2000 paper showed that the MSE of derivative estimators based on regression splines approach zero at the optimal rate of convergence. Also, in 2019, Xiao showed that, under some general conditions, penalized spline estimators …

Parameter Estimation For Normally Distributed Grouped Data And Clustering Single-Cell Rna Sequencing Data Via The Expectation-Maximization Algorithm, Zahra Aghahosseinalishirazi

Electronic Thesis and Dissertation Repository

The Expectation-Maximization (EM) algorithm is an iterative algorithm for finding the maximum likelihood estimates in problems involving missing data or latent variables. The EM algorithm can be applied to problems consisting of evidently incomplete data or missingness situations, such as truncated distributions, censored or grouped observations, and also to problems in which the missingness of the data is not natural or evident, such as mixed-effects models, mixture models, log-linear models, and latent variables. In Chapter 2 of this thesis, we apply the EM algorithm to grouped data, a problem in which incomplete data are evident. Nowadays, data confidentiality is of …

Applying Structural Equation Modeling To Better Understand The Relationship Between Stressors, Social Support And Wellbeing In The Lives Of Spouse Dementia Caregivers, Craig Holden

Dissertations, Theses, and Capstone Projects

Applying Structural Equation Modeling to Better Understand the Relationship Between Stressors, Social Support and Wellbeing in the Lives of Spouse Dementia Caregivers considers the utility of Pearlin et al.’s (1990) stress process model in understanding the needs of spouse caregivers. Data were drawn from eight biennial waves of the University of Michigan Health and Retirement Study (HRS) and analyzed using structural equation modeling. The final study sample comprised 774 spouses, average age 73, who were categorized based on Alzheimer’s Disease and Related Dementia (ADRD) and non-ADRD caregiver status. Results showed that for the study sample as a whole, social support …

Prediction Of Factors For Patients With Hypertension And Dyslipidemia Using Multilayer Feedforward Neural Networks And Ordered Logistic Regression Analysis: A Robust Hybrid Methodology, Wan Muhamad Amir W Ahmad, Mohamad Nasarudin Bin Adnan, Norhayati Yusop, Hazik Bin Shahzad, Farah Muna Mohamad Ghazali, Nor Azlida Aleng, Nor Farid Mohd Noor

Makara Journal of Health Research

Background: Hypertension is characterized by abnormally high arterial blood pressure and is a public health problem with a high prevalence of 20%–30% worldwide. This research combined multiple logistic regression (MLR) and multilayer feedforward neural networks to construct and validate a model for evaluating the factors linked with hypertension in patients with dyslipidemia.

Methods: A total of 1000 data entries from Hospital Universiti Sains Malaysia and advanced computational statistical modeling methodologies were used to evaluate seven traits associated with hypertension. R-Studio software was utilized. Each sample's statistics were calculated using a hybrid model that included bootstrapping.

Results: Variable …

Using Geographic Information To Explore Player-Specific Movement And Its Effects On Play Success In The Nfl, Hayley Horn, Eric Laigaie, Alexander Lopez, Shravan Reddy

SMU Data Science Review

American Football is a billion-dollar industry in the United States. The analytical aspect of the sport is an ever-growing domain, with open-source competitions like the NFL Big Data Bowl accelerating this growth. With the amount of player movement during each play, tracking data can prove valuable in many areas of football analytics. While concussion detection, catch recognition, and completion percentage prediction are all existing use cases for this data, player-specific movement attributes, such as speed and agility, may be helpful in predicting play success. This research calculates player-specific speed and agility attributes from tracking data and supplements them with descriptive …

A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman

Electronic Theses and Dissertations

This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …

A Framework For Statistical Modeling Of Wind Speed And Wind Direction, Eva Murphy

All Dissertations

Atmospheric near surface wind speed and wind direction play an important role in many applications, ranging from air quality modeling, building design, wind turbine placement to climate change research. It is therefore crucial to accurately estimate the joint probability distribution of wind speed and direction. This dissertation aims to provide a modeling framework for studying the variation of wind speed and wind direction. To this end, three projects are conducted to address some of the key issues for modeling wind vectors.\\

First, a conditional decomposition approach is developed to model the joint distribution of wind speed and direction. Specifically, the …

A Multivariate Investigation Of The Motivational, Academic, And Well-Being Characteristics Of First-Generation And Continuing-Generation College Students, Christopher L. Thomas, Staci Zolkoski

Journal of Research Initiatives

Prior research has noted differences in motivational, academic, and well-being factors between first-generation and continuing-education students. However, past investigations have primarily overlooked the interactive influence of protective and risk factors when comparing the characteristics of first-generation and continuing-education students. Thus, the current study adopted a multivariate approach to gain a more nuanced understanding of the influence of generational status on students' self-regulated learning capabilities, academic anxiety, sense of belonging, academic barriers, mental health concerns, and satisfaction with life. University students (N = 432, 67.46% Caucasian, 87.55% female, _Age = 28.10 ± 9.46) completed the Cognitive Test Anxiety Scale-2^{nd …}

Addressing The Impact Of Time-Dependent Social Groupings On Animal Survival And Recapture Rates In Mark-Recapture Studies, Alexandru M. Draghici

Electronic Thesis and Dissertation Repository

Mark-recapture (MR) models typically assume that individuals under study have independent survival and recapture outcomes. One such model of interest is known as the Cormack-Jolly-Seber (CJS) model. In this dissertation, we conduct three major research projects focused on studying the impact of violating the independence assumption in MR models along with presenting extensions which relax the independence assumption. In the first project, we conduct a simulation study to address the impact of failing to account for pair-bonded animals having correlated recapture and survival fates on the CJS model. We examined the impact of correlation on the likelihood ratio test (LRT), …

Functional Generalized Linear Mixed Models, Harmony Luce

Dissertations

With the advancements in data collection technologies, researchers in various fields such as epidemiology, chemometrics, and environmental science face the challenges of obtaining useful information from more detailed, complex, and intricately-structured data. Since the existing methods often are not suitable for such data, new statistical methods are developed to accommodate the complicated data structures.

As a part of such efforts, this dissertation proposes Functional Generalized Linear Mixed Model (FGLMM), which extends classical generalized linear mixed models to include functional covariates. Functional Data Analysis (FDA) is a rapidly developing area of statistics for data which can be naturally viewed as smooth …