565 research outputs found

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Continuous Estimation of Smoking Lapse Risk from Noisy Wrist Sensor Data Using Sparse and Positive-Only Labels

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    Estimating the imminent risk of adverse health behaviors provides opportunities for developing effective behavioral intervention mechanisms to prevent the occurrence of the target behavior. One of the key goals is to find opportune moments for intervention by passively detecting the rising risk of an imminent adverse behavior. Significant progress in mobile health research and the ability to continuously sense internal and external states of individual health and behavior has paved the way for detecting diverse risk factors from mobile sensor data. The next frontier in this research is to account for the combined effects of these risk factors to produce a composite risk score of adverse behaviors using wearable sensors convenient for daily use. Developing a machine learning-based model for assessing the risk of smoking lapse in the natural environment faces significant outstanding challenges requiring the development of novel and unique methodologies for each of them. The first challenge is coming up with an accurate representation of noisy and incomplete sensor data to encode the present and historical influence of behavioral cues, mental states, and the interactions of individuals with their ever-changing environment. The next noteworthy challenge is the absence of confirmed negative labels of low-risk states and adequate precise annotations of high-risk states. Finally, the model should work on convenient wearable devices to facilitate widespread adoption in research and practice. In this dissertation, we develop methods that account for the multi-faceted nature of smoking lapse behavior to train and evaluate a machine learning model capable of estimating composite risk scores in the natural environment. We first develop mRisk, which combines the effects of various mHealth biomarkers such as stress, physical activity, and location history in producing the risk of smoking lapse using sequential deep neural networks. We propose an event-based encoding of sensor data to reduce the effect of noises and then present an approach to efficiently model the historical influence of recent and past sensor-derived contexts on the likelihood of smoking lapse. To circumvent the lack of confirmed negative labels (i.e., annotated low-risk moments) and only a few positive labels (i.e., sensor-based detection of smoking lapse corroborated by self-reports), we propose a new loss function to accurately optimize the models. We build the mRisk models using biomarker (stress, physical activity) streams derived from chest-worn sensors. Adapting the models to work with less invasive and more convenient wrist-based sensors requires adapting the biomarker detection models to work with wrist-worn sensor data. To that end, we develop robust stress and activity inference methodologies from noisy wrist-sensor data. We first propose CQP, which quantifies wrist-sensor collected PPG data quality. Next, we show that integrating CQP within the inference pipeline improves accuracy-yield trade-offs associated with stress detection from wrist-worn PPG sensors in the natural environment. mRisk also requires sensor-based precise detection of smoking events and confirmation through self-reports to extract positive labels. Hence, we develop rSmoke, an orientation-invariant smoking detection model that is robust to the variations in sensor data resulting from orientation switches in the field. We train the proposed mRisk risk estimation models using the wrist-based inferences of lapse risk factors. To evaluate the utility of the risk models, we simulate the delivery of intelligent smoking interventions to at-risk participants as informed by the composite risk scores. Our results demonstrate the envisaged impact of machine learning-based models operating on wrist-worn wearable sensor data to output continuous smoking lapse risk scores. The novel methodologies we propose throughout this dissertation help instigate a new frontier in smoking research that can potentially improve the smoking abstinence rate in participants willing to quit

    Analysis and forecasting of asset quality, risk management and financial stability for the Greek banking system

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    The increase in non-performing loans (NPLs) during the financial crisis of 2008, which has been converted into a fiscal crisis, as well as the risk of a medium-term increase due to the COVID-19 pandemic has put into question the robustness of many banks and the financial stability of the whole sector. As far as the banking sector is concerned, the management of non-performing loans represents the most significant challenge as their stock reached unprecedented levels, with the deterioration in asset quality being widespread. Addressing the problem of non-performing loans with the assistance of credit risk modeling is important from both a micro and a macro-prudential perspective, since it would not only improve the financial soundness and the capital adequacy of the banking sector, but also free-up funds to be directed to other more productive sectors of the economy. This Thesis extends earlier research by employing a short-term monitoring system with the aim to forecast “failures” i.e. NPL creation. The creation of such a monitoring system allows the risk of a “failure” to change over time, measuring the likelihood of “failure” given the survival time and a set of explanatory variables. The application of Cox proportional hazards models and survival trees to forecast NPLs can be usefully employed in the Greek corporate sectors. The research aim of this thesis consists of two domains: The first aim is the investigation of the determinants that contribute to the NPLs formation. Two GAMLSS models are being tested, a linear GAMLSS model and a nonlinear semi-parametric GAMLSS model which includes smoothing functions that capture potential nonlinear relationships between the explanatory variables to model the parameters favorably. The explanatory variables of the models consist of credit risk variables, macroeconomic variables, bank-specific variables and supervisory and market variables, while the response variable is the non-performing loans. The second aim is to provide answers on whether proportional hazards Cox models and survival tree models can forecast NPLs of loans that are provided in specific corporate sectors in Greece by the use of the most granular data set of corporate borrowers. By evaluating a series of Cox models, a short-term monitoring system has been created with the aim to forecast “failures” i.e. NPL creation. The Cox proportional hazards regression models are incorporating time-to-event, involving a timeline, described by the survival function, indicating the probability that a loan becomes an NPL until time t. The time period counts from the origination of the loan until the “death” of the loan, i.e. its termination, incorporating an “in between” observation point. The event is when the loan is initially being “infected”, i.e. has become NPL. Regarding survival trees, the data set was divided into more subsets, which are easier to model separately and hence yield an improved overall performance. Such models are then beneficial to implement with different machine learning techniques. Predictors (or covariates) are defined as the sectors of the Greek economy and the model is fitted both for the whole sample and for the sample of early terminated loans. The Thesis is organized as follows: Chapter 1 - Introduction addresses the role of banks in financial intermediation, the evolution of credit risk and some issues regarding the Greek banking sector. Chapter 2 constitutes a literature review on research focused on improving the predictive performance of different credit risk assessment methods. Chapter 3 outlines the competitive conditions in the banking sector to demonstrate whether the increase in concentration had affected the competitive conditions in the Greek banking system. In Chapter 4, the funding and the liquidity conditions in the Greek banking sector are being addressed. Chapter 5 contains the selection of aggregate sample, results and analysis of GAMLSS models that have been used for determining NPLs. Chapter 6 provides an introduction to the granular database on Large Exposures, which is used for deriving the panel sample of corporate borrowers whereby models of forecasting and prediction are being employed. Chapter 7 contains the application of Cox models and decision trees, the estimation procedure, parameters, model fit, estimation results and empirical findings. Chapter 8 provides an evaluation and applicability of models as well as the implications for further research. Finally, a conclusion is provided by summarizing my contribution to the research community and my recommendations to the banking industr

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Trend assessment of changing climate patterns over the major agro-climatic zones of Sindh and Punjab

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    The agriculture sector, due to its significant dependence on climate patterns and water availability, is highly vulnerable to changing climate patterns. Pakistan is an agrarian economy with 30% of its land area under cultivation and 93% of its water resources being utilized for agricultural production. Therefore, the changing climate patterns may adversely affect the agriculture and water resources of the country. This study was conducted to assess the climate variations over the major agro-climatic zones of Sindh and Punjab, which serve as an important hub for the production of major food and cash crops in Pakistan. For this purpose, the climate data of 21 stations were analyzed using the Mann–Kendall test and Sen's slope estimator method for the period 1990–2022. The results obtained from the analysis revealed that, in Sindh, the mean annual temperature rose by ~0.1 to 1.4°C, with ~0.1 to 1.2°C in cotton-wheat Sindh and 0.8 to 1.4°C in rice-other Sindh during the study period. Similarly, in Punjab, the mean annual temperature increased by ~0.1 to 1.0°C, with 0.6 to 0.9°C in cotton-wheat Punjab and 0.2 to 0.6°C in rainfed Punjab. Seasonally, warming was found to be highest during the spring season. The precipitation analysis showed a rising annual precipitation trend in Sindh (+30 to +60 mm) and Punjab (+100 to 300 mm), while the monsoon precipitation increased by ~50 to 200 mm. For winter precipitation, an upward trend was found in mixed Punjab, while the remaining stations showed a declining pattern. Conclusively, the warming temperatures as found in the analysis may result in increased irrigation requirements, soil moisture desiccation, and wilting of crops, ultimately leading to low crop yield and threatening the livelihoods of local farmers. On the other hand, the increasing precipitation may favor national agriculture in terms of less freshwater withdrawals. However, it may also result in increased rainfall-induced floods inundating the crop fields and causing water logging and soil salinization. The study outcomes comprehensively highlighted the prevailing climate trends over the important agro-climatic zones of Pakistan, which may aid in devising an effective climate change adaptation and mitigation strategy to ensure the state of water and food security in the country

    Operational Research: methods and applications

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    This is the final version. Available on open access from Taylor & Francis via the DOI in this recordThroughout its history, Operational Research has evolved to include methods, models and algorithms that have been applied to a wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first summarises the up-to-date knowledge and provides an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion and used as a point of reference by a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes
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