Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn

Meta-heuristic improvements applied for steel sheet incremental cold shaping

In previous studies, a wrapper feature selection method for decision support in steel sheet incremental cold shaping process (SSICS) was proposed. The problem included both regression and classification, while the learned models were neural networks and support vector machines, respectively. SSICS is the type of problem for which the number of features is similar to the number of instances in the data set, this represents many of real world decision support problems found in the industry. This study focuses on several questions and improvements that were left open, suggesting proposals for each of them. More specifically, this study evaluates the relevance of the different cross validation methods in the learned models, but also proposes several improvements such as allowing the number of chosen features as well as some of the parameters of the neural networks to evolve, accordingly. Well-known data sets have been use in this experimentation and an in-depth analysis of the experiment results is included. 5 × 2 CV has been found the more interesting cross validation method for this kind of problems. In addition, the adaptation of the number of features and, consequently, the model parameters really improves the performance of the approach. The different enhancements have been applied to the real world problem, an several conclusions have been drawn from the results obtained

Mediterráneamente también en invierno. Plan de comunicación de desestacionalización de Estrella Damm

Curs 2021-2022Este plan de comunicación viene precedido por la necesidad detectada de desestacionalizar un producto como es la cerveza, que claramente tiene un consumo mucho superior en los meses de verano, y sobre todo en los canales HORECA. Este trabajo está enfocado concretamente a la marca Estrella Damm y su concepto de comunicación actual Mediterráneamente. La finalidad de analizar este eje comunicativo es extraer la máxima información y poder obtener unas líneas de comunicación generales e insights que llevan conectando con los consumidores tantos años para posteriormente poder elaborar el teaser o campaña de desestacionalización que conecte con las necesidades y expectativas del público. El trabajo tiene una finalidad clara de investigación, y va a contar con un extenso análisis de la marca, el mercado de las cervezas, las redes sociales, canales comunicativos usados y un análisis de la competencia en todos sus aspectos. Una vez analizadas todas las variantes que rodean a la marca y sacando las conclusiones pertinentes, se va a establecer un target o core target en el que se va a centrar nuestro plan de desestacionalización y el posterior teaser. La finalidad clara de este trabajo, y aplicado a nuestros estudios de Publicidad y Relaciones Públicas, es encontrar un eje comunicativo que siga el Mediterráneamente que actualmente está comunicando la marca pero desestacionalizado, que se pueda comunicar en cualquier momento del año. Este proyecto termina con la presentación de un plan de acciones para la implementación del nuevo concepto comunicativo de la marca

Social inequalities in the use of physiotherapy in women diagnosed with breast cancer in Barcelona : DAMA cohort

This study aimed to analyze social inequalities in the use and access of physiotherapy service and its clinical and socio-economic determinants in women diagnosed with breast cancer in the hospital network of Barcelona. Data from 2235 women belonging to the mixed (prospective and retrospective) DAMA Cohort were analyzed, including demographic, socio-economic, clinical, and breast cancer treatment outcomes. To determine the influence of such variables on access to physiotherapy, different Poisson regression models with robust variance (obtaining Prevalence Ratios and confidence intervals) were estimated. Although when experiencing different chronic and acute symptoms, only between 20 and 35% of women visited physiotherapist. Two out of 3 women reported to have received insufficient information about medical care and rehabilitation. Age of women, job occupation, education level, having a mutual or private insurance, as well as outcomes related to breast cancer, appear to be factors influencing the access to physiotherapy. Social and economic inequalities exist on the access to physiotherapy by women diagnosed with breast cancer, which is generally low, and may clearly impact on their functional recovery. Promoting strategies to reduce social bias, as well as improve communication and patient information regarding physiotherapy may be of interest for a better health care in breast cancer diagnosed women

Meta-heuristic improvements applied for steel sheet incremental cold shaping

In previous studies, a wrapper feature selection method for decision support in steel sheet incremental cold shaping process (SSICS) was proposed. The problem included both regression and classification, while the learned models were neural networks and support vector machines, respectively. SSICS is the type of problem for which the number of features is similar to the number of instances in the data set, this represents many of real world decision support problems found in the industry. This study focuses on several questions and improvements that were left open, suggesting proposals for each of them. More specifically, this study evaluates the relevance of the different cross validation methods in the learned models, but also proposes several improvements such as allowing the number of chosen features as well as some of the parameters of the neural networks to evolve, accordingly. Well-known data sets have been use in this experimentation and an in-depth analysis of the experiment results is included. 5 × 2 CV has been found the more interesting cross validation method for this kind of problems. In addition, the adaptation of the number of features and, consequently, the model parameters really improves the performance of the approach. The different enhancements have been applied to the real world problem, an several conclusions have been drawn from the results obtained