1,160 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Undergraduate Catalog of Studies, 2023-2024

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    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Undergraduate Catalog of Studies, 2022-2023

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    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

    Disability-free life expectancy of Italian older adults: trends, inequalities, and applications

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    Italy's ageing population may pose challenges to the sustainability of the country's socioeconomic and healthcare systems. This depends on the (un)healthy ageing process. The disability status of mid-to-older adults is a crucial determinant of individuals' autonomy and participation in society. Disability-free life expectancy (DFLE) is an important metric for assessing the health and disability risks of the population in a summary indicator, neat of the age structure. Demographic changes also affect intergenerational relationships and in Italy, where grandparents play a significant role in caregiving, it is crucial to study their health evolution. This thesis aims to, first, detect the long-term trend of DFLE in Italy and to analyse the drivers of its change in terms of disability-specific mortality and dynamics of disability onset and recovery. Second, to shed light on gender, socioeconomic, and territorial inequalities in DFLE (and their intersections) and the factors driving these inequalities in terms of differences in mortality and disability risks. Third, to analyse the trend of the length of life to live as grandparents free from disability and understand how it is influenced by age-specific survival and grandparenthood-disability prevalence evolution. The thesis applies different demographic and statistical methods to different cross-sectional and longitudinal data and provides DFLE estimates, trends and applications for mid-to-older Italian men and women. The findings show that while DFLE at mid-to-older ages has increased, it has not always progressed as favourably as life expectancy. The greatest contribution to DFLE changes is the changes in the transition in and out of disability. There are notable differences in DFLE at older ages within the country, between genders and educational groups. Women have a life expectancy advantage, but their health disadvantage counterbalances it. The disadvantage in DFLE accumulates over education and region of residence, resulting in higher educated living in northern regions having more than double DFLE than lower educated living in southern regions. Health differences are also the major contributors to educational differences in DFLE. Italian grandmothers and grandfathers are gaining years of coexistence-life-time with their grandchildren in good functional health. Women can expect to live more years as disability-free grandmothers than men, but their share of disability-free grandmothers years over total years as grandmothers is lower than that for men. The increase in disability-free grandparenthood years is primarily led by improved survival and health conditions and, for men, by the postponement of grandparenthood to older ages

    Statistical Modeling: Regression, Survival Analysis, and Time Series Analysis

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    Statistical Modeling provides an introduction to regression, survival analysis, and time series analysis for students who have completed calculus-based courses in probability and mathematical statistics. The book uses the R language to fit statistical models, conduct Monte Carlo simulation experiments and generate graphics. Over 300 exercises at the end of the chapters makes this an appropriate text for a class in statistical modeling. Part 1: RegressionChapter 1: Simple Linear Regression Chapter 2: Inference in Simple Linear Regression Chapter 3: Topics in RegressionPart II: Survival Analysis Chapter 4: Probability Models in Survival AnalysisChapter 5: Statistical Methods in Survival Analysis Chapter 6: Topics in Survival Analysis Part III: Time Series Analysis Chapter 7: Basic Methods in Time Series AnalysisChapter 8: Modeling in Time Series Analysis Chapter 9: Topics in Time Series Analysi

    Big data analytics in cardiovascular sciences

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    Introduction It has been challenging for researchers to access granular electronic health record (EHR) data at scale in England. The National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) enables the sharing of routine EHR data across NHS hospitals for research. One emerging prospect is to use big data to traverse the translational spectrum. As an example of an early discovery phase study, I assessed the effect of invasive versus non-invasive management on the survival of patients aged 80 years or older with non-ST elevation myocardial infarction (NSTEMI) (SENIOR-NSTEMI Study). As an example of a later implementation phase study, I determined the relationship between the full spectrum of troponin level and mortality in patients in whom troponin testing was performed for clinical purposes (TROP-RISK Study). Methods Five NHS Trusts contributed data: Imperial, University College London, Oxford, King’s and Guy’s and St Thomas’. Microsoft SQL was used to develop a dataset of 257,948 consecutive patients who had a troponin measured between 2010 and 2017. Phenotypically detailed data were extracted, including patient demographics, blood tests, procedural data, and survival status. All studies conducted were retrospective cohort studies. For the SENIOR-NSTEMI Study, eligible patients were 80 years or older who were diagnosed with NSTEMI. Mortality hazard ratios were estimated comparing invasive with non-invasive management. For the TROP-RISK Study, the relation between peak troponin level and all-cause mortality was modelled using multivariable adjusted restricted cubic spline Cox regression analyses. Results For the SENIOR-NSTEMI Study, 2672 patients with NSTEMI were included who had a median age of 85 (interquartile range (IQR) 82-89) years of whom 59.8% received non-invasive management. During a median follow-up of 2.7 (IQR 1.0-4.5) years, the adjusted cumulative five-year mortality was 40% in the invasive and 63% in the non-invasive group (hazard ratio 0.52, 95% confidence interval 0.43-0.62). For the TROP-RISK Study, during a median follow-up of 1198 days (IQR 514-1866 days), 55,850 (21.7%) deaths occurred. There was an unexpected inverted U-shaped relation between troponin level and mortality in acute coronary syndrome (ACS) patients (n=120,049). The paradoxical decline in mortality at very high troponin levels may be driven in part by the changing case mix as troponin levels increase; a higher proportion of patients with very high troponin levels received invasive management. Conclusion Routinely collected EHR data can be aggregated across multiple sites to create highly granular datasets for research which can be used to answer research questions that cross the translational spectrum. The SENIOR-NSTEMI Study demonstrates a survival advantage of invasive compared with non-invasive management of NSTEMI patients aged 80 years or older, who were underrepresented in previous trials. In the TROP-RISK Study, the inverted U-shaped relationship between troponin level and mortality in ACS patients demonstrates that assembling sufficiently large datasets can cast light on patterns of disease that are impossible to adequately define in single centre studies.Open Acces

    Annual Report 2022 - Institute of Resource Ecology

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    The Institute of Resource Ecology (IRE) is one of the ten institutes of the Helmholtz-Zentrum Dresden – Rossendorf (HZDR). Our research activities are mainly integrated into the program “Nuclear Waste Management, Safety and Ra-diation Research (NUSAFE)” of the Helmholtz Association (HGF) and focus on the topics “Safety of Nuclear Waste Disposal” and “Safety Research for Nuclear Reactors”. The program NUSAFE, and therefore all work which is done at IRE, belong to the research field “Energy” of the HGF

    Breaking together: a freedom-loving response to collapse

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    The collapse of modern societies has already begun. That is the conclusion of two years of research by the interdisciplinary team behind the book 'Breaking Together'. How did it come to this? Because monetary systems caused us to harm each other and nature to such an extent it broke the foundations of our societies. So what can we do? This book describes people allowing the full pain of our predicament to liberate them into living more courageously and creatively. They demonstrate we can be breaking together, not apart, in this era of collapse. Professor Jem Bendell argues that reclaiming our freedoms is essential to soften the fall and regenerate the natural world. Escaping the efforts of panicking elites, we can advance an ecolibertarian agenda for both politics and practical action in a broken world. Endorsing the text, the founder of Schumacher College, Satish Kumar, remarked: “this is a prophetic book.
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