Applications Of Independent And Identically Distributed (Iid) Random Processes In Polarimetry And Climatology, Dan Kestner

Dissertations, Master's Theses and Master's Reports

The unifying theme of this thesis is the characterization of “perfect randomness,” i.e., independent and identically distributed (IID) stochastic processes as these are applied in physical science. Two specific and mathematically distinct applications are chosen: (i) Radar and optical polarimetry; (ii) Analysis of time series in meteorology. In (i), IID process of a special kind, namely, with a distribution defined by symmetry, is used to link its multivariate Gaussian density to uniformity on the Poincaré sphere. This “statistical ellipsometry” approach is then used to relate polarimetric mismatches or imbalances to ellipsometric variables and suitably chosen cross-correlation measures. In (ii), recently …

Stigma And Efficacy Beliefs Regarding Opioid Use Disorder Treatment And Naloxone In Communities Participating In The Healing Communities Study Intervention, Nicky Lewis, Barry Eggleston, Redonna K. Chandler, Dawn Goddard-Eckrich, Jamie E. Luster, Dacia Beard, Emma Rodgers, Rouba A. Chahine, Philip M. Westgate, Shoshana N. Benjamin, Janae Holloway, Thomas Clarke, R. Craig Lefebvre, Michael D. Stein, Donald W. Helme, Jennifer Reynolds, Sharon L. Walsh, Darcy Freedman, Nabila El-Bassel, Kara Stephens, Anita Silwal, Michelle R. Lofwall, Janet E. Childerhose, Hilary L. Surratt, Brooke N. Crockett, Amy L. Farmer, James L. David, Laura Fanucchi, Judy Harness, Ben Wilburn, Kelli Bursey, Kristin Mattson, Sarah Mann, Rebecca D. Jackson, Aimee Shadwick, Katherine Calver, Deborah Chassler, Jennifer Kimball, Nancy Regan, Jeffrey H. Samet, Rachel Sword-Cruz, Michael D. Slater

Biostatistics Faculty Publications

Background The HEALing Communities Study (HCS) included health campaigns as part of a community-engaged intervention to reduce opioid-related overdose deaths in 67 highly impacted communities across Kentucky, Massachusetts, New York, and Ohio. Five campaigns were developed with community input to provide information on opioid use disorder (OUD) and overdose prevention, reduce stigma, and build demand for evidence-based practices (EBPs). An evaluation examined the recognition of campaign messages about naloxone and whether stigma and efficacy beliefs regarding OUD treatment and naloxone changed in HCS intervention communities.

Methods Data were collected through surveys offered on Facebook/Instagram to members of communities participating in …

A Journey From Univariate To Multivariate Functional Time Series: A Comprehensive Review, Hossein Haghbin, Mehdi Maadooliat

Mathematical and Statistical Science Faculty Research and Publications

Functional time series (FTS) analysis has emerged as a potent framework for modeling and forecasting time-dependent data with functional attributes. In this comprehensive review, we navigate through the intricate landscape of FTS methodologies, meticulously surveying the core principles of univariate FTS and delving into the nuances of multivariate FTS. The journey commences with an exploration of the foundational aspects of univariate FTS analysis. We delve into representation, estimation, and modeling, spotlighting the effectiveness of various parametric and nonparametric models at our disposal. The stage then transitions to multivariate FTS analysis, where we confront the intricacies posed by high-dimensional data. We …

Maximizing The Number Of H-Colorings Of Graphs With A Fixed Minimum Degree, John Engbers

Mathematical and Statistical Science Faculty Research and Publications

For graphs G and H, an H-coloring of G is an adjacency-preserving map from the vertex set of G to the vertex set of H.

Incorporating Effects-Based Approaches Into Environmental Impact Assessment To Improve Post-Development Monitoring, Carolyn J M Brown

Theses and Dissertations (Comprehensive)

Over the last 50 years, improvements in design of industrial facilities have significantly reduced environmental impacts. But impacts still occur and monitoring programs are the main mechanism to inform when modification/implementation of mitigation is needed. Informed decisions require adequate baseline (pre-development) data to predict impacts based on the development’s design and to understand when the post-development environment has changed. An adaptive monitoring plan provides an effective way to evaluate monitoring results and allow for proactive responses to environmental change before impacts become difficult or challenging to reverse. Unfortunately, baseline data gathered during an environmental impact assessment (EIA) is often inadequate …

Development Of Probabilistic Dynamic Model Building And Bayesian Machine Learning Approaches, Samuel Oladayo Adeyemo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

Development of Probabilistic Dynamic Model Building and Bayesian Machine Learning Approaches

Samuel Adeyemo

The recent years have seen a tremendous increase in the use of artificial intelligence (AI) and machine learning (ML) for the development of data-driven mathematical models needed for performing real-time optimization, model-based control, performance optimization, dynamic data reconciliation, and process performance monitoring. However, the development of data-driven models is faced with some challenges including lack of model interpretability, sensitivity of algorithm to noise in training data, limited extrapolation capabilities and violation of conservation laws. Drawing motivation from these existing gaps, this work aims to develop robust …

Advanced Techniques In Time Series Forecasting: From Deterministic Models To Deep Learning, Xue Bai

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation discusses three instances of temporal prediction, applied to population dynamics and deep learning.

In population modeling, dynamic processes are frequently represented by systems of differential equations, allowing for the analysis of various phenomena. The first application explores modeling cloned hematopoiesis in chronic myeloid leukemia (CML) via a nonlinear system of differential equations. By tracking the evolution of different cell compartments, including cycling and quiescent stem cells, progenitor cells, differentiated cells, and terminally differentiated cells, the model captures the transition from normal hematopoiesis to the chronic and accelerated-acute phases of CML. Three distinct non-zero steady states are identified, representing …

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 …

Genome-Wide Search Algorithms For Identifying Dynamic Gene Co-Expression Via Bayesian Variable Selection, Wenda Zhang, Zichen Ma, Lianming Wang, Daping Fan, Yen Yi Ho

Faculty Publications

A wealth of gene expression data generated by high-throughput techniques provides exciting opportunities for studying gene-gene interactions systematically. Gene-gene interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change flexibly under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in gene-gene interactions. However, due to the massive number of possible gene combinations that need to be considered in a typical genomic dataset, intensive computation is a common challenge for exploring gene-gene interactions. On the other hand, oftentimes only a small proportion of gene …

Cabozantinib Plus Atezolizumab In Previously Untreated Advanced Hepatocellular Carcinoma And Previously Treated Gastric Cancer And Gastroesophageal Junction Adenocarcinoma: Results From Two Expansion Cohorts Of A Multicentre, Open-Label, Phase 1b Trial (Cosmic-021)., Daneng Li, Yohann Loriot, Adam Burgoyne, James Cleary, Armando Santoro, Daniel Lin, Santiago Ponce Aix, Ignacio Garrido-Laguna, Ramu Sudhagoni, Xiang Guo, Svetlana Andrianova, Scott Paulson

Kimmel Cancer Center Faculty Papers

BACKGROUND: Cabozantinib is approved for previously treated advanced hepatocellular carcinoma (aHCC) and has been investigated in gastric cancer (GC) and gastroesophageal junction adenocarcinoma (GEJ). Atezolizumab plus bevacizumab is approved for unresectable or metastatic HCC untreated with prior systemic therapy. We evaluated efficacy and safety of cabozantinib plus atezolizumab in aHCC previously untreated with systemic anticancer therapy or previously treated GC/GEJ.

METHODS: COSMIC-021 (ClinicalTrials.gov, NCT03170960) is an open-label, phase 1b study in solid tumours with a dose-escalation stage followed by tumour-specific expansion cohorts, including aHCC (cohort 14) and GC/GEJ (cohort 15). Eligible patients were aged ≥18 years with measurable locally advanced, …

Cancer Survivorship And Subjective Cognitive Decline In The Us: Results From A Nationally Representative Sample, Monique J. Brown, Ally Hucek, Jingkai Wei, Loretta Anderson, Muzi Na

Faculty Publications

Cancer may be associated with objective cognitive decline. However, studies examining the link between cancer and subjective cognitive decline (SCD), which occurs earlier during cognitive trajectories, are lacking. In addition, research assessing risk factors for SCD among cancer survivors is scant. Therefore, this study aimed to examine the association between cancer and SCD in the past year, and assess the association between age of diagnosis, cancer treatment, and SCD among cancer survivors. Data were obtained from the 2021 Behavioral Risk Factor Surveillance System (N=15,447). Crude and adjusted logistic regression models, controlling for sociodemographic characteristics, were used to determine the association …

Market Making In Limit Order Books With Latency And Running Inventory Control, Chang Liu

Arts & Sciences Graduate Student Theses and Dissertations

In this thesis, we delve into the intricate optimization challenges of market making, the concurrent provision of buy and sell prices in financial assets. The focus is particularly on the complexities inherent in high-frequency trading scenarios, addressing optimal market making in the presence of latency and incorporating a running inventory penalty. The initial exploration involves the formulation of a stochastic control model that aptly captures the actions of an electronic market maker navigating a trading environment influenced by latency. The main objective of the market maker lies in the maximization of expected terminal wealth. To systematically address and resolve this …

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 …

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 …

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, …

Prt543, A Protein Arginine Methyltransferase 5 Inhibitor, In Patients With Advanced Adenoid Cystic Carcinoma: An Open-Label, Phase I Dose-Expansion Study, Renata Ferrarotto, Paul Swiecicki, Dan Zandberg, Robert Baiocchi, Robert Wesolowski, Cristina Rodriguez, Meredith Mckean, Hyunseok Kang, Varun Monga, Rajneesh Nath, Neil Palmisiano, Naveen Babbar, William Sun, Glenn Hanna

Department of Medical Oncology Faculty Papers

OBJECTIVES: Currently, no systemic treatments are approved for patients with recurrent and/or metastatic (R/M) adenoid cystic carcinoma (ACC). PRT543, a protein arginine methyltransferase 5 inhibitor that downregulates NOTCH1 and MYB signalling in tumours, is a potential candidate for R/M ACC treatment. We report the safety, tolerability and preliminary efficacy of PRT543 in a dose-expansion cohort of patients with R/M ACC.

MATERIALS AND METHODS: This phase I multicentre, open-label, sequential-cohort, dose-escalation and dose-expansion study (NCT03886831) enrolled patients with advanced solid tumours and select haematologic malignancies. Dose-escalation study design and results were reported previously. In the dose expansion, patients with R/M ACC …

Stereotactic Mr-Guided On-Table Adaptive Radiation Therapy (Smart) For Borderline Resectable And Locally Advanced Pancreatic Cancer: A Multi-Center, Open-Label Phase 2 Study, Michael Chuong, Percy Lee, Daniel Low, Joshua Kim, Kathryn Mittauer, Michael Bassetti, Carri Glide-Hurst, Ann Raldow, Yingli Yang, Lorraine Portelance, Kyle Padgett, Bassem Zaki, Rongxiao Zhang, Hyun Kim, Lauren Henke, Alex Price, Joseph Mancias, Christopher Williams, John Ng, Ryan Pennell, M Raphael Pfeffer, Daphne Levin, Adam Mueller, Karen Mooney, Patrick Kelly, Amish Shah, Luca Boldrini, Lorenzo Placidi, Martin Fuss, Parag Jitendra Parikh

Department of Radiation Oncology Faculty Papers

BACKGROUND AND PURPOSE: Radiation dose escalation may improve local control (LC) and overall survival (OS) in select pancreatic ductal adenocarcinoma (PDAC) patients. We prospectively evaluated the safety and efficacy of ablative stereotactic magnetic resonance (MR)-guided adaptive radiation therapy (SMART) for borderline resectable (BRPC) and locally advanced pancreas cancer (LAPC). The primary endpoint of acute grade ≥ 3 gastrointestinal (GI) toxicity definitely related to SMART was previously published with median follow-up (FU) 8.8 months from SMART. We now present more mature outcomes including OS and late toxicity.

MATERIALS AND METHODS: This prospective, multi-center, single-arm open-label phase 2 trial (NCT03621644) enrolled 136 …

Bitcoin Price Modeling Using Machine Learning Algorithms, Azadeh Golduzian

Mathematics & Statistics ETDs

Forecasting in financial markets with various technologies is critical nowadays. The Bitcoin cryptocurrency has grown in popularity in recent years, and as a result, many people all around the world have attempted to forecast its price. To improve forecast accuracy, we must employ multiple types of data and diverse approaches. This thesis combines textual and financial data and employs statistical methods and machine learning to forecast Bitcoin's price as precisely as possible. We show the performance of each strategy using Bitcoin data at the end of each chapter.

Interpretable Word-Level Sentiment Analysis With Attention-Based Multiple Instance Classification Models, Chenyu Yang

Statistical Science Theses and Dissertations

In this study, our main objective is to tackle the black-box nature of popular machine learning models in sentiment analysis and enhance model interpretability. We aim to gain more insight into the decision-making process of sentiment analysis models, which is often obscure in those complex models. To achieve this goal, we introduce two word-level sentiment analysis models.

The first model is called the attention-based multiple instance classification (AMIC) model. It combines the transparent model structure of multiple instance classification and the self-attention mechanism in deep learning to incorporate the contextual information from documents. As demonstrated by a wine review dataset …