Universidade Federal do Cearà / Desde o ano de 2000 que as cooperativas de crÃdito brasileiras tÃm experimentado um crescimento contÃnuo no nÃmero de novas unidades. De outro modo os bancos brasileiros tem diminuÃdo em quantidade pelo processo de aquisiÃÃo e concentraÃÃo. Este crescimento das cooperativas pode estar associado com um maior risco para os associados. Este trabalho investiga as causas de falÃncias das cooperativas de crÃdito dos profissionais de saÃde no Brasil. Para tanto busca fornecer um modelo de alerta precoce que informe aos gestores e supervisores do risco de insolvÃncia, fazendo uso de uma anÃlise de regressÃo logÃstica de Ãndices financeiros. Foi estimado um modelo de prediÃÃo de insolvÃncia que fosse parcimonioso e acurado. Este trabalho provà informaÃÃes adicionais a outros estudos brasileiros sobre falÃncia em cooperativas de crÃdito, de trÃs modos: à um estudo de abrangÃncia nacional, trata com cooperativa de crÃdito mÃtuo urbano, usa uma moderna tÃcnica estatÃstica com dados em painel, o que permite capturar as diferenÃas entre as cooperativas. O presente estudo tambÃm fornece uma maneira racional para a escolha do cut-off. Os resultados sugerem que provisÃo para emprÃstimo em atraso para total do ativo, Total de emprÃstimo para Total de ativo, Total de emprÃstimo para Total de depÃsitos e PatrimÃnio LÃquido Passivo total, sÃo os preditores mais significativos da insolvÃncias das cooperativas. De modo contrÃrio as Despesas Operacionais para Receitas Operacionais e Despesas Operacionais para ativo total nÃo indicam ser significativas em prever a insolvÃncia. / Since the year of 2000 Brazilians credit cooperatives has experienced a increasing growth in number of units. On the other hand Brazilians banks decreased their number, by the process of acquisition and concentration. This growth may imply increasing risk for the associates. This paper empirically investigates the causes of failures of credit cooperatives of heath professionals in Brazil. A goal of this paper is provide a early warning model that inform managers and supervisors of a risks of default, by using logistic regression analysis of financial ratios. It was estimate a default prediction model that was parsimonious and accurate. This work provided additional information over other Brazilian studies of credit cooperatives failure by three ways: it is a national wide study, deals with urban mutual credit cooperative, uses modern statistic technique panel data which can capture the differences across cooperatives. It also provided a reasonable for the choosing of cut-off. The results suggest that provision for bad debts over total assets, total loans over total assets, total loans over total deposits are the most significant predictors of credit cooperative failure. Operational expenses over operational incomes and operational expenses over total assets, contrary, do not seem to be significant indicators of failure

During recent years there has been a great focus on the climate changes within the media. More or less every day more newspaper articles are presented about the global warming issue and the effect on us human race. Climate models predict higher temperatures and more rain in the northern part of Europe. It is also predicted that the weather will become more extreme e.g. it will rain a lot during longer periods than has been the norm. If these predictions are correct, the amount of water that is going to be transported away in streams and rivers will increase and so also will the subsoil water level. The latter increases the risk for landslides in areas with fine grained soils. An early warning system that is able to alert people before a landslide take place would be of great interest. The purpose of this work is to develop a complete real-time displacement monitoring system based on observations from several GPS-receivers that could be used as an early warning system. Due to the complex correlation structure of the traditionally used double differences, an alternative method based on undifferenced observations is used. Theoretically this approach shows some advantages and simplifies the correlative structure of observables compared to the double differenced method. A complete model for the undifferenced approach is presented in this thesis including its software implementation. A displacement detection system includes not only the positioning algorithms, but also methods to detect if any displacement occurs. There are many methods available to discriminate displacements, which are used in the traditional control of manufacturing processes. Several of these methods are compared in this thesis, such as the Shewhart chart, different Weighted Moving Average (WMA) charts and the CUmulative SUMmation (CUSUM). Practical tests show that it is possible to detect an abrupt shift on sub centimetre level at the same epoch as the shift occurs. Smaller shifts are also detectable with the applied approach but with a slightly longer detection time. / QC 20100624

Översvämningar förekommer regelbundet på stora delar av jordklotet. Utgående från de klimatförändringar vi ser idag med, bland annat, kraftigare och intensivare nederbörd är det troligt att det blir fler och större översvämningar framöver. Områden som inte tidigare varit översvämmade kan komma att bli översvämmande.   Varje land eller område med regelbundna översvämningar har någon form av varningssystem. Översikter över olika typer av system för att varna för översvämningar är svåra att finna.   Syftet med denna uppsats är att: Undersöka viktiga principer för effektiva varningssystem för översvämningar som orsakas av kraftig nederbörd och/eller vårflöden. Skapa en systematisk och strukturerad sammanställning över nuvarande system för att varna för översvämningar orsakade av kraftig nederbörd och/eller vårflöden. Ge praktiska exempel på några svenska kommuners system för att varna för översvämningar.   Litteratur- och internetsökningar kring varningssystem för översvämningar samt intervjuer med ”översvämningsansvariga” i Ödeshög och Vetlanda kommuner utgör grunden för informationen i denna uppsats.   Exempel på olika typer av varningssystem för översvämningar presenteras. Saker att tänka på vid val av, och vid drift och underhåll av, automatiska varningssystem belyses.   En indelning (Grust, 2006) av varningssystem i manuella, enkla automatiska och sofistikerade automatiska utökas till en matris med i matrisens andra dimension lokala, avrinningsområdes, nationella, multinationella och globala varningssystem. De i uppsatsen beskrivna exemplen på varningssystem positioneras in i matrisen.   Varningssystem för översvämningar i två svenska kommuner, Ödeshög och Vetlanda, beskrivs och likheter och skillnader mellan kommunernas system presenteras. / Flooding occurs all over the world on a regular basis. Based on the climate change ongoing today with heavier and more intense precipitation we can expect more and larger floods than we have seen before. Areas which have not been flooded earlier may now become flooded.   Every country or area which is flooded on a regular basis has some kind of warning system. Overviews of various types of flood warning systems are difficult to find.   The purpose of this thesis is to: Investigate important principles of effective flood warning systems caused by heavy precipitation and/or spring flood. Create a structured and systematical overview of current flood warning system for floods caused by heavy precipitation and/or spring flood. Describe two local Swedish community´s flood warning systems.   Literature and Internet search covering flood warning systems combined with interviews on site at Ödeshög and Vetlanda forms the basis for the information in this thesis.   Examples of various types of flood warning systems are presented. Issues regarding the selection of, and the operation and maintenance of, automatic warning systems are described.   A grouping (Grust, 2006) of warning systems in manual, simple automatic and sophisticated automatic is expanded to a matrix with the second dimension of the matrix being local, drainage area, national, multinational and global warning systems. The examples of warning systems covered in this thesis are placed in the matrix.   The similarities and differences between the flood warning systems in two local Swedish communities Ödeshög and Vetlanda are described.

This paper analyzes the current operating situation of financial holding companies in Taiwan. After referring to the operation of financial early warning systems of various countries, the study chooses appropriate financial ratios to establish a financial early warning model for quantitative analysis, evaluate the management efficiency of financial holding companies, discriminate the correct classification rate of prediction probability and rating system, and seek an optimal early warning model as the basis for supervision and governance of financial holding companies. In 2008, the financial tsunami that swept over the global economy resulted in a disastrous loss to the financial industry. To cope with the impact of financial crisis, most countries in the world have developed their early warning models. In Taiwan, the CAMELS framework was adopted for the establishment of Taiwan¡¦s financial early warning system and a risk-oriented auditing system. With the financial liberalization, the government of Taiwan lifted the ban on the business operation of financial holding companies step by step in order to enhance the operating efficiency of financial holding companies and activate the financial market. However, the competitive ability of Taiwan¡¦s financial industry was not significantly improved. Instead, a series of problems with the financial sector erupted one after another. The reasons for such a condition were due to more risks faced by the financial holding companies after financial deregulation. Therefore, this study used 14 financial holding companies in Taiwan as of 2006 as subjects, and constructed a financial early warning system for the original samples by using the following two kinds of models. After factor analysis¡Athe remaining financial variables ¡Alike capital adequary ratio(C2) ¡Atotal debt/equit capital (C3) ¡A total deposit/equit capital(C4), ratio of non-performing loans(A2) the operational expense ratio(M3), efficiency ratio (M4), earnings before taxes/sales(E1) and so on, have more influence on the performances of the financial holding companies in Taiwan. As to the whole efficiency of the self-examination, CAMELS still has good prediction ability and can enable predicting ability increases after joining the risk parameters¡D Predictive sample enters two models and obtains¡Gthe predictive efficiency, type error and type error of Model Two work better than Model One¡Aso in predicting samples, think CAMELS still has good predicition ability and can enable predicting ability increases after joining the risk parameters.

Thesis--School of Advanced Airpower Studies, Maxwell Air Force Base, Ala., 1992-93. / Title from title screen (viewed Oct. 28, 2003). "28 June 1993." Includes bibliographical references.

Traditional communication channels like news channels are not able to provide spontaneous information about disasters unlike social networks namely, Twitter. The present research work proposes a framework by mining real-time disaster data from Twitter to predict the path a disaster like a tornado will take. The users of Twitter act as the sensors which provide useful information about the disaster by posting first-hand experience, warnings or location of a disaster. The steps involved in the framework are – data collection, data preprocessing, geo-locating the tweets, data filtering and extrapolation of the disaster curve for prediction of susceptible locations. The framework is validated by analyzing the past events. This framework has the potential to be developed into a full-fledged system to predict and warn people about disasters. The warnings can be sent to news channels or broadcasted for pro-active action.

Background: Dengue is a viral infectious disease that is transmitted by mosquitoes. The disease causes a significant health burden in tropical countries, and has been a public health burden in Singapore for several decades. Severe complications such as hemorrhage can develop and lead to fatal outcomes. Before tetravalent vaccine and drugs are available, vector control is the key component to control dengue transmission. Vector control activities need to be guided by surveillance of outbreak and implement timely action to suppress dengue transmission and limit the risk of further spread. This study aims to explore the feasibility of developing a dengue early warning system using temperature and rainfall as main predictors. The objectives were to 1) analyze the relationship between dengue cases and weather predictors, 2) identify the optimal lead time required for a dengue early warning, 3) develop forecasting models, and 4) translate forecasts to dengue risk indices. Methods: Poisson multivariate regression models were established to analyze relative risks of dengue corresponding to each unit change of weekly mean temperature and cumulative rainfall at lag of 1-20 weeks. Duration of vector control for localized outbreaks was analyzed to identify the time required by local authority to respond to an early warning. Then, dengue forecasting models were developed using Poisson multivariate regression. Autoregression, trend, and seasonality were considered in the models to account for risk factors other than temperature and rainfall. Model selection and validation were performed using various statistical methods. Forecast precision was analyzed using cross-validation, Receiver Operating Characteristics curve, and root mean square errors. Finally, forecasts were translated into stratified dengue risk indices in time series formats. Results: Findings showed weekly mean temperature and cumulative rainfall preceded higher relative risk of dengue by 9-16 weeks and that a forecast with at least 3 months would provide sufficient time for mitigation in Singapore. Results showed possibility of predicting dengue cases 1-16 weeks using temperature and rainfall; whereas, consideration of autoregression and trend further enhance forecast precision. Sensitivity analysis showed the forecasting models could detect outbreak and non-outbreak at above 90% with less than 20% false positive. Forecasts were translated into stratified dengue risk indices using color codes and indices ranging from 1-10 in calendar or time sequence formats. Simplified risk indices interpreted forecast according to annual alert and outbreak thresholds; thus, provided uniform interpretation. Significance: A prediction model was developed that forecasted a prognosis of dengue up to 16 weeks in advance with sufficient accuracy. Such a prognosis can be used as an early warning to enhance evidence-based decision making and effective use of public health resources as well as improved effectiveness of dengue surveillance and control. Simple and clear dengue risk indices improve communications to stakeholders.

Thesis (Ph. D.)--University of California, San Diego, 1999. / Vita. Includes bibliographical references (leaves 350-356).

Pregnancy-related complications affect approximately 15% of pregnancies and, if severe, can have long-term consequences. Timely recognition of physiological deterioration is known to reduce the prevalence and severity of complications. However pregnancy-associated changes in vital signs (blood pressure, heart rate, temperature, oxygen saturation, and respiratory rate) complicate the detection of abnormal physiology, and these changes are not well documented. This thesis describes the development of algorithms to ensure the collection of good-quality vital-sign data during the antenatal and postnatal stages of pregnancy, and the design of an evidence-based obstetric early warning score. Vital-sign information from 1,000 pregnant women during pregnancy, labour, and after delivery was collected during the 4P study using pulse oximetry, oscillometry for blood pressure measurement and a tympanic thermometer. Dynamic time warping was used to assess beat-by-beat quality in the photoplethysmograph (PPG) waveform obtained from the pulse oximeter. The resulting signal quality index enabled the exclusion of poor-quality sections and their associated measurements of heart rate and peripheral oxygen saturation. A robust measurement of respiratory rate was obtained by combining information from the PPG waveform, and accelerometer and gyroscope waveforms from a smartphone. After processing, frequency-based techniques, such as Fourier analysis and auto-regressive modelling, and time-domain peak detection were fused to estimate respiratory rate. When compared with the reference respiratory rate obtained from midwife measurement, the lowest mean absolute error of 1.16 breaths per minute was obtained from respiratory rate estimates from the y-axis of the gyroscope. Antenatal and postnatal reference ranges for each vital sign were developed with a standard polynomial multilevel (hierarchical) model using 10,000 vital sign measurements from 620 healthy women in the 4P study. Vital-sign trajectories confirmed known trends of blood pressure and heart rate changes during pregnancy, and provided new information about other vital-sign trends. Additional covariates were included to investigate the effect of parity and body mass index (BMI) on vital-sign trends. The outer centiles of the vital sign reference ranges were used to design an obstetric early warning score (C-ObsEWS) that took into account gestational age or time after delivery. The investigations in this thesis contribute additional knowledge of pregnancy-associated vital-sign changes, and lead to an initial proposal for an evidence-based obstetric early warning score specific to the stage of pregnancy.

Doctor of Philosophy / Department of Special Education, Counseling and Student Affairs / Fred O. Bradley / This study investigated the use of the MAP-Works[superscript]TM program that is designed to help retain first-year students by identifying the level of retention risk for each student early in their first semester and communicating this risk to key university faculty and staff. The participants for this study were all first semester freshman students enrolled during the academic years 2012 and 2013. This study sought to determine if the MAP-Works[superscript]TM program and resulting intervention were effective in predicting the retention of high-risk first semester freshman students to their second semester and second year. The data analysis for this study used quantitative data analysis methods. The first and second research questions asking which of the factors were significant in predicting retention were answered using independent samples t-tests. The third research question asking if the intervention was significant was answered using a 2x2 Chi-square test for independence. The fourth and final research question asked which of the factors contributed the most in predicting retention was answered using a direct (binary) logistic regression analysis. This study found for high-risk domestic students Cumulative GPA, Socio-Emotional, Test Anxiety, Peers, Homesickness: Distressed, Academic Integration, Social Integration and Environment were able to be associated significantly with retention from fall-to-spring semester. For international students GPA, Self-Efficacy and Self-Discipline were able to be associated significantly with retention. The study showed for fall-to-fall retention for domestic students that cumulative GPA, Socio-Emotional, Communication, Analytical, Social Integration and On-Campus Living Social were significant. The research found that the intervention conducted by their direct connects for high-risk domestic students was significant for fall-to-fall retention. The logistic regression analysis showed for domestic students that Cumulative GPA, Financial Means, Socio-Emotional, and ACT Composite score were significant for fall-to-fall retention. The strongest predictor of retention was Cumulative GPA followed by Socio-Emotional, Financial then ACT Composite score. The regression analysis for high-risk international students showed that Cumulative GPA, Gender, and Student Residence were significant for fall-to-fall retention. The strongest predictor of retention was cumulative GPA, Gender (Female) and Student Residence (Off Campus).