Functional Data Analysis (FDA) has become a very important eld in recent years due to its wide range of applications. However, there are several real-life applications in which hybrid functional data appear, i.e., data with functional and static covariates. The classi cation of such hybrid functional data is a challenging problem that can be handled with the Support Vector Machine (SVM). Moreover, the selection of the most informative features may yield to drastic improvements in the classi cation rates. In this paper, an embedded feature selection approach for SVM classi cation is proposed, in which the isotropic Gaussian kernel is modi ed by associating a bandwidth to each feature. The bandwidths are jointly optimized with the SVM parameters, yielding an alternating optimization approach. The e ectiveness of our methodology was tested on benchmark data sets. Indeed, the proposed method achieved the best average performance when compared to 17 other feature selection and SVM classi cation approaches. A comprehensive sensitivity analysis of the parameters related to our proposal was also included, con rming its robustness. ; Spanish Government MTM2015-65915-R Junta de Andalucia P11-FQM-7603 P18-FR-2369 FQM329 German Research Foundation (DFG) VI PPITUS (Universidad de Sevilla) EU ERDF funds FBBVA-COSECLA ANID, FONDECYT project 1200221 Complex Engineering Systems Institute (ANID, PIA) FB0816
Over the last two decades, governments have increased their investment in information technology to improve the use of public resources, using public electronic procurement systems to obtain better prices, better solutions and to show transparency in the procurement process. Public procurement of software development projects is specific acquisitions having specific technical, methodological, and management constraints that make transparency an elusive target. This article proposes a maturity model as a tool to measure tendering transparency when government agencies procure software development. We have used a procedural model to support the design of maturity models along four dimensions: Institutionalization, Software procurement process, Communication, and Accountability. We have defined a five-step model, and we have tested it with real government buyers. The model is supported by an appraisal tool that helps to guide the next steps in the transparency of software acquisitions. ; Project Transparency in Public Electronic Procedures, from the Universidad de La Frontera Universidad de Los Andes Idea2019001 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA-BASAL AFB180003 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 1200221 11200007 ; Versión publicada - versión final del editor
Background: Preliminary country-specific reports suggest that the COVID-19 pandemic has a negative impact on the mental health of the healthcare workforce. In this paper, we summarize the protocol of the COVID-19 HEalth caRe wOrkErS (HEROES) study, an ongoing, global initiative, aimed to describe and track longitudinal trajectories of mental health symptoms and disorders among health care workers at different phases of the pandemic across a wide range of countries in Latin America, Europe, Africa, Middle-East, and Asia. Methods: Participants from various settings, including primary care clinics, hospitals, nursing homes, and mental health facilities, are being enrolled. In 26 countries, we are using a similar study design with harmonized measures to capture data on COVID-19 related exposures and variables of interest during two years of follow-up. Exposures include potential stressors related to working in healthcare during the COVID-19 pandemic, as well as sociodemographic and clinical factors. Primary outcomes of interest include mental health variables such as psychological distress, depressive symptoms, and posttraumatic stress disorders. Other domains of interest include potentially mediating or moderating influences such as workplace conditions, trust in the government, and the country's income level. Results: As of August 2021, ~ 34,000 health workers have been recruited. A general characterization of the recruited samples by sociodemographic and workplace variables is presented. Most participating countries have identified several health facilities where they can identify denominators and attain acceptable response rates. Of the 26 countries, 22 are collecting data and 2 plan to start shortly. Conclusions: This is one of the most extensive global studies on the mental health of healthcare workers during the COVID-19 pandemic, including a variety of countries with diverse economic realities and different levels of severity of pandemic and management. Moreover, unlike most previous studies, we included workers (clinical and non-clinical staff) in a wide range of settings. ; Background: Preliminary country-specific reports suggest that the COVID-19 pandemic has a negative impact on the mental health of the healthcare workforce. In this paper, we summarize the protocol of the COVID-19 HEalth caRe wOrkErS (HEROES) study, an ongoing, global initiative, aimed to describe and track longitudinal trajectories of mental health symptoms and disorders among health care workers at different phases of the pandemic across a wide range of countries in Latin America, Europe, Africa, Middle-East, and Asia. Methods: Participants from various settings, including primary care clinics, hospitals, nursing homes, and mental health facilities, are being enrolled. In 26 countries, we are using a similar study design with harmonized measures to capture data on COVID-19 related exposures and variables of interest during two years of follow-up. Exposures include potential stressors related to working in healthcare during the COVID-19 pandemic, as well as sociodemographic and clinical factors. Primary outcomes of interest include mental health variables such as psychological distress, depressive symptoms, and posttraumatic stress disorders. Other domains of interest include potentially mediating or moderating influences such as workplace conditions, trust in the government, and the country's income level. Results: As of August 2021, ~ 34,000 health workers have been recruited. A general characterization of the recruited samples by sociodemographic and workplace variables is presented. Most participating countries have identified several health facilities where they can identify denominators and attain acceptable response rates. Of the 26 countries, 22 are collecting data and 2 plan to start shortly. Conclusions: This is one of the most extensive global studies on the mental health of healthcare workers during the COVID-19 pandemic, including a variety of countries with diverse economic realities and different levels of severity of pandemic and management. Moreover, unlike most previous studies, we included workers (clinical and non-clinical staff) in a wide range of settings.
