A note on Kendall’s Tau coefficient for gap times in presence of right censoring

In several clinical and epidemiology studies, data from events that occur successively in time in the same individual, are frequently reported. Among these, the most common are recurrent events where each subject may experience a number of failures over the course of follow-up. Examples include repeated hospitalization of patients, recurrences of tumor, recurrent infections, among others. In this work, the interest is to study the correlation between successive recurrent events, gap times, in the presence of right censoring. To measure the association between two gap times we use the Kendall’s τ correlation coefficient, by incorporating suitable bivariate estimators of the joint distribution function of the gap times and of the marginal distribution function of the second gap time, into the integrals that define the probability of concordant pairs and the probability of discordant pairs. Two of the estimators of the joint distribution function of the gap times considered in this work are already known, but we consider also estimators with Kaplan-Meier weights defined by using decision trees and random forests methodology. We conclude that all the estimators perform better in a scenario of negative association. When the association is moderately negative, the performance of the estimator with smoothed weights using random forests is superior. In the case of strong positive association, the best estimator is the presmoothed nonparametric but, in the case of moderate positive association, this estimator has identical performance as the estimator with presmoothed weights using random forests.This work was supported by Portuguese funds through the CMAT - Research Centre of Mathematics of University of Minho - within projects UIDB/00013/2020 and UIDP/00013/2020. Authors thank to referees for their careful reading and for their constructive suggestions

Doutores palhaços em ambiente hospitalar: O uso do riso como instrumento terapêutico

Trabalho apresentado no 31º SEURS - Seminário de Extensão Universitária da Região Sul, realizado em Florianópolis, SC, no período de 04 a 07 de agosto de 2013 - Universidade Federal de Santa Catarina.O Programa de Extensão Acadêmica “Palhaços-Doutores em Ambiente Hospitalar- O Uso do Riso como Instrumento Terapêutico” tem como objetivo manter um grupo de palhaços atuante dentro do ambiente hospitalar. O intuito é melhorar o bem estar do paciente internado e da comunidade em geral. Contribuir para a humanização das instituições parceiras, bem como dos acadêmicos envolvidos. Capacitar e desenvolver o indivíduo para o cumprimento do trabalho voluntário e enriquecer a comunidade acadêmica tanto pela melhoria da formação, quanto pela visão da sociedade frente à integração da universidade com um novo programa social. O público alvo são os pacientes e familiares atendidos por hospitais parceiros. A metodologia do programa consiste em visitas semanais aos pacientes por voluntários caracterizados de doutores-palhaços capacitados na arte do riso, levando atividades cômicas e lúdicas aos internados e ao público em geral, visando à melhoria da qualidade de vida durante a hospitalização. Espera-se melhorar o quadro clínico do paciente, permitir que sua permanência no hospital tenha um impacto menos negativo sobre esse e seus responsáveis. O programa encontra-se em fase de implementação, tendo realizado cinco meses de intervenção, atingindo cerca de 7840 pacientes. Os números estimados concretizam de maneira positiva o trabalho dos doutores palhaços na internação do paciente

An overview of kriging and cokriging predictors for functional random fields

This article presents an overview of methodologies for spatial prediction of functional data, focusing on both stationary and non-stationary conditions. A significant aspect of the functional random fields analysis is evaluating stationarity to characterize the stability of statistical properties across the spatial domain. The article explores methodologies from the literature, providing insights into the challenges and advancements in functional geostatistics. This work is relevant from theoreti cal and practical perspectives, offering an integrated view of methodologies tailored to the specific stationarity conditions of the functional processes under study. The practical implications of our work span across fields like environmental monitoring, geosciences, and biomedical research. This overview encourages advancements in functional geostatistics, paving the way for the development of innovative techniques for analyzing and predicting spatially correlated functional data. It lays the groundwork for future research, enhancing our understanding of spatial statistics and its applications.This research was partially supported by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science, Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.)

Portuguese sentiment analysis applied to a reality show using Twitter and NLP in real time

The motivation for this study was to measure the impact that Twitter publications have on voting and the choosing of winners. To this end, an experimental study was carried out based on a set of data collected from tweets (published on Twitter) related to the reality show “Big Brother - A Revolução”, broadcast on a television station in Portugal, TVI. The procedure adopted for conducting the experiment consisted of creating a completely self-contained service, built from scratch for this project, and the correspondent implementation, in order to allow the collection, storage, cleaning, pre-processing and analysis of as many tweets as possible, as long as they are associated with the program. A tool to analyze the polarity (positive, negative or neutral) of the sentiment was implemented and applied to the phrase (or phrases) contained in the tweet and stored in a database. Then, running in the database, the tweets were divided according to how they referred to one or more competitors. Throughout the time that the reality show existed, the results of this experiment were public presented in daily/weekly summaries and posted on Twitter through a “Twitter bot”.FCT-CEECIND/04331/201

Statistical procedures applied to floods in the Douro river basin

The aim was to study flood event triggers. To this end, flood occurrence data were collected and stored, as well as hydro-meteorological variables for the Douro River basin. The Douro River and its tributaries have very steep longitudinal profiles in some sections, and consequently sudden rises in water levels are observed after heavy precipitation. The data treatment and analysis begins with a univariate study of the different variables. Several statistical procedures are used, in order to understand the possible relationship of each of the observed factors with the occurrence of floods, either individually or globally. This is done using Fisher's exact tests, chi-square tests, logistic regression models, and random forests explaining the flood phenomenon, adjusted on the basis of available data. In the logistic regression model, there is a need to use the categorized predictors because their empirical distributions exhibit very sharp positive skewness, with many outliers. In this model, the important predictors are monthly-accumulated precipitation (mm) and monthly surface discharge (dam3). The model has a specificity of over 90% but sensitivity of only 33.3%, which is not surprising given the complexity of the phenomenon under analysis. The discriminatory ability of the logistic regression model, measured by the area under the ROC curve, AUC, is 76.8% and is therefore acceptable. The random forest algorithm is used with the uncategorized variables, since it does not depend on their distributions. With the same predictors, specificity higher than 99% and a sensitivity of only 60% is obtained with this procedure, indicating an excellent performance taking into account the complexity of the phenomenon and the fact that only two predictors are being used

RODA - A Service-Oriented Repository to Preserve Authentic Digital Objects

4th International Conference on Open RepositoriesThis presentation was part of the session : Fedora User Group PresentationsDate: 2009-05-20 03:30 PM – 05:00 PMIn mid 2006, the Portuguese National Archives (Directorate-General of the Portuguese Archives) launched a project called RODA (Repository of Authentic Digital Objects) aiming at identifying and bringing together all the necessary technology, human resources and political support to carry out long-term preservation of digital materials being produced by the Portuguese public administration. As part of the original goals of RODA was the development of a digital repository capable of ingesting, managing and providing access to the various types of digital objects produced by national public institutions. The development of such repository should be supported by open-source technologies and, as much as possible, be based on existing standards. Since RODA is nearly finished, this communication aims at describing its main results.European Union; POAP; Ministry of Culture; Portuguese Republi

Effect of macro polypropylene fiber and basalt fiber on impact resistance of basalt fiber‐reinforced polymer‐reinforced concrete

First published: 07 April 2020In this paper, the effect of macro non-metallic fibers (i.e. polypropylene fibers and basalt fibers) on the impact response of basalt FRP-reinforced concrete discs is experimentally investigated using a self-developed drop-weight impact test device. The plain concrete and conventional steel reinforced concrete samples are explored as references. The impact resistance and failure behaviors are analyzed. Statistical analyses for first-crack strength and failure strength are performed. The composite effect of basalt FRP bars and macro non-metallic fibers on the impact energy at failure is also compared. The results indicate that the behaviors under impact load, i.e. failure strength, crack number, the indent diameter and penetration depth of the shriveled area, are greatly improved by adding of macro non-metallic fibers, in particular macro polypropylene fibers. Additionally, the incorporation of these fibers into the basalt FRP-reinforced concrete transforms the brittle failure mode into a well ductile failure mode. Two-parameter Weibull models are fitted by graphical methods and used to characterize the first crack strength and failure strength distributions. Reliability functions for first crack strength and for failure strength are estimated and failure strength can be predicted from first-crack strength by using a linear regress model. The hybrid use of basalt FRP bars and macro non-metallic fibers demonstrates a positive synergetic effect on the impact energy at failure.The authors gratefully acknowledge the National NaturalScience Foundation of China (Grant: 51578109), the Por-tuguese Funds through FCT, within the Project UID/MAT/ 00013/2013

An overview of forecast analysis with ARIMA Models during the COVID-19 Pandemic: methodology and case study in Brazil

This comprehensive overview focuses on the issues presented by the pandemic due to COVID-19, understanding its spread and the wide-ranging effects of government-imposed restric tions. The overview examines the utility of autoregressive integrated moving average (ARIMA) models, which are often overlooked in pandemic forecasting due to perceived limitations in han dling complex and dynamic scenarios. Our work applies ARIMA models to a case study using data from Recife, the capital of Pernambuco, Brazil, collected between March and September 2020. The research provides insights into the implications and adaptability of predictive methods in the context of a global pandemic. The findings highlight the ARIMA models’ strength in generating accurate short-term forecasts, crucial for an immediate response to slow down the disease’s rapid spread. Accurate and timely predictions serve as the basis for evidence-based public health strategies and interventions, greatly assisting in pandemic management. Our model selection involves an automated process optimizing parameters by using autocorrelation and partial autocorrelation plots, as well as various precise measures. The performance of the chosen ARIMA model is confirmed when comparing its forecasts with real data reported after the forecast period. The study successfully forecasts both confirmed and recovered COVID-19 cases across the preventive plan phases in Recife. However, limitations in the model’s performance are observed as forecasts extend into the future. By the end of the study period, the model’s error substantially increased, and it failed to detect the stabilization and deceleration of cases. The research highlights challenges associated with COVID-19 data in Brazil, such as under-reporting and data recording delays. Despite these limitations, the study emphasizes the potential of ARIMA models for short-term pandemic forecasting while emphasizing the need for further research to enhance long-term predictions.This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through the grant 303192/2022-4 (R.O.), and Comissão de Aperfeiçoamento de Pessoal do Nível Superior (CAPES), from the Brazilian government; by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT—Research Centre of Mathematics of University of Minho—within projects UIDB/00013/2020 and UIDP/00013/2020 (C.C.)