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INTRODUCTION: Although current recommendations suggest the use of specific formulas in enteral nutrition in people with diabetes, there is little evidence of their long-term effectiveness in glycemic control. The main objective of this study is to evaluate the long-term efficacy (24 weeks) of a specific high-protein hypercaloric enteral nutrition formula for people with diabetes in glycemic control and in their improvement in nutritional status. METHODOLOGY: This was a multicenter, prospective, observational, real-life study of patients with long-term enteral nutrition prescription through gastrostomy or nasogastric tube who received a high protein hypercaloric formula specific for diabetes. Once the participant's informed consent was obtained and the inclusion and exclusion criteria were verified, data relating to glycemic control, inflammation parameters, biochemical data, nutritional status and gastrointestinal tolerance at 0, 12 and 24 weeks were collected. RESULTS: 112 patients were recruited, 44.6% women, age 75.0 (12.0) years and a mean time of evolution of diabetes of 18.1 (9.5) years. The percentage of patients with malnutrition according to VGS decreased throughout the treatment from 78.6% to 29.9% (p?0.001). Glycemic and HbA1c levels were significantly reduced at 12 and 24 weeks (Blood glucose 155.9-139.0-133.9?mg/dl, p?

Deep learning has become increasingly popular over the years, having proved their efficiency in input-output functions for distinct types of data. However, their mathematical complexity and the training optimizations can be challenging. Moreover, access to dedicated hardware and the setup of a working environment is most of the times a barrier to its usage. This dissertation describes the design and implementation of a web platform to be used by students and advanced researchers. This platform aims to abstract the processes behind this technology and simplify its usage. It is a multi-user distributed platform that offers services, software and hardware, through a common web browser interface. The solution allows easy addition of new computational nodes, the upload of datasets, the visual design of models and datasets, the monitoring of hardware and training sessions, and also supports validation and test procedures. A utilização de deep learning tem vindo a aumentar em diferentes áreas aplicacionais devido à sua eficiência em processos de previsão e à capacidade de se adaptar a vários tipos de dados. No entanto, a sua complexidade matemática e as otimizações dos processos de treino são desafiantes. Mais ainda, o acesso a hardware dedicado e a configuração correcta de um ambiente de trabalho são muitas vezes barreiras à sua utilização. Esta dissertação propõe uma arquitetura e descreve a implementação de uma plataforma web de serviços de deep learning para utilização de estudantes e investigadores. Esta plataforma permite abstrair os processos complexos que suportam esta tecnologia, simplificando e democratizando o seu uso. Trata-se de uma solução distribuída e multiutilizador que oferece serviços, desde hardware a software, através de um navegador web comum. A solução permite a adição fácil de unidades computacionais de processamento, a inserção de dados de estudo, o desenho dos modelos com ferramentas visuais, a monitorização de sessões de treino, teste e validação destes modelos. Mestrado em Engenharia de Computadores e Telemática

Introduction: Although current recommendations suggest the use of specific formulas in enteral nutrition in people with diabetes, there is little evidence of their long-term effectiveness in glycemic control. The main objective of this study is to evaluate the long-term efficacy (24 weeks) of a specific high-protein hypercaloric enteral nutrition formula for people with diabetes in glycemic control and in their improvement in nutritional status. Methodology: This was a multicenter, prospective, observational, real-life study of patients with long-term enteral nutrition prescription through gastrostomy or nasogastric tube who received a high protein hypercaloric formula specific for diabetes. Once the participant's informed consent was obtained and the inclusion and exclusion criteria were verified, data relating to glycemic control, inflammation parameters, biochemical data, nutritional status and gastrointestinal tolerance at 0, 12 and 24 weeks were collected. Results: 112 patients were recruited, 44.6% women, age 75.0 (12.0) years and a mean time of evolution of diabetes of 18.1 (9.5) years. The percentage of patients with malnutrition according to VGS decreased throughout the treatment from 78.6% to 29.9% (P

Resumen Introducción: la malnutrición hospitalaria tiene una elevada prevalencia y comporta un incremento del coste sanitario. La hospitalización a domicilio (HaD) permite el control en el domicilio de un episodio hospitalario agudo estable clínicamente por un equipo sanitario especializado. Objetivo: conocer el estado nutricional de los pacientes que ingresen en HaD con una misma aplicación informática (app) para poder instaurar medidas nutricionales precoces en el domicilio y mejorar la evolución posthospitalaria de los pacientes. Material y metodología: estudio multicéntrico prospectivo y descriptivo del estado nutricional en cuatro unidades de HaD, durante un periodo de dos meses consecutivos durante el año 2016. Se recogieron las variables: unidad de HaD, edad, sexo, motivo de ingreso, patología asociada, procedencia, diagnóstico, valoración social, soporte nutricional previo, talla, peso, pérdida de peso, tiempo de la pérdida de peso, proteínas totales, albúmina, linfocitos, colesterol, índice de masa corporal (IMC), estado nutricional según el IMC, riesgo nutricional, tipo y grado de desnutrición. Se realizó la valoración nutricional con la app HEN-Persan y se analizaron los resultados estadísticamente con el programa informático SPSS 21.0. Resultados: no existen diferencias significativas entre las cuatro unidades. Globalmente, el 36% de pacientes ingresados en HaD presentaban un estado nutricional normal. El 87% presentaba algún tipo de malnutrición, predominando la desnutrición de tipo mixta (63%). Según el grado, fue leve (36%), moderada (27%) y grave (35%). Conclusiones: la utilización de una app permite tener una valoración nutricional inmediata, de manera fácil, segura y fiable en HaD, para poder introducir medidas nutricionales precoces y mejorar la evolución posthospitalaria de los pacientes. Abstract Introduction: the prevalence of hospital malnutrition is high and involves an increase in health care costs. Home hospitalization (HH) allows better clinically stable patient control after an acute illness by a highly specialized health care team. Objective: to know the nutritional condition of home hospitalization patients using a computer application that allows the implementation of early nutritional measures at home and improves post-hospital control of these patients. Material and methods: prospective multicenter study of the nutritional condition of patients in four different home hospitalization centers during a period of two consecutive months in 2016. Variables were collected: home hospitalization, age, gender, reason for admission, associated morbidity, origin, diagnosis, social assessment, previous nutritional support, height, weight, weight loss, time of weight loss, total proteins, albumin, lymphocytes, cholesterol, body mass index (BMI), nutritional condition, type and degree of malnutrition. Nutritional condition was assessed using the application HEN-Persan and results were statistically analyzed using the SPSS 21.0 software. Results: no significant differences were found between the four centers. In home hospitalization patients, 36% presented a normal nutritional screening and 87% presented some degree of malnutrition, while combined malnutrition prevailed (63%). Depending on the nutritional degree, 36% of patients had mild malnutrition, 27% presented moderate malnutrition and 35% had severe malnutrition. Conclusions: a computer application allows for an immediate, secure and reliable nutritional assessment in home hospitalization that helps introduce early nutritional measures and improve post-hospital control of patients.

Service companies, for the most part, do not have physical inventories that allow them to cushion demand variability. The high logistics costs of each process of the company are the reflection of these differences in the forecast. For this reason, having a successful demand forecast will generate a competitive advantage in companies that take these processes with interest. In the present work it is possible to estimate the amount of tourist packages that will be sold in the next three months using ANN (artificial neural networks) that present a decrease in the error of the current situation of 9.89% which, together with a forecast management system of adequate demand, it was thus possible to reduce the logistics costs of the services company by up to 33%.

[EN] Knowledge management and engineering management are closely related. However, the latter has focused primarily in the codification and systemization of knowledge. This article proposes a schema based on organizational learning and cultural values that enhance the latter and aims at explaining how knowledge can be actioned and deployed successfully in an organization. The construct was field tested among the technical staff of a large perfumes firm. This article parts from the influential concept of actionable knowledge introduced by Chris Argyris and his school and follows with subsequently-developed paradigms, such as the knowledge maturity model, organizational competencies, and Actional Intelligence. From a practical point of view, it proposes concrete recommendations for the utilization of knowledge management by engineering managers and fills the cultural and organizational gap of traditional knowledge engineering approaches. Additionally, it aims at contributing to a better understanding of knowledge management systemization and practice. It fills a particular research gap in the state of the art of knowledge management practice, while it suggests a more reasonable and comprehensible focus of knowledge management and a more precise and dynamic perspective to engineering management and knowledge engineering.

the prevalence of hospital malnutrition is high and involves an increase in health care costs. Home hospitalization (HH) allows better clinically stable patient control after an acute illness by a highly specialized health care team.to know the nutritional condition of home hospitalization patients using a computer application that allows the implementation of early nutritional measures at home and improves post-hospital control of these patients.prospective multicenter study of the nutritional condition of patients in four different home hospitalization centers during a period of two consecutive months in 2016. Variables were collected: home hospitalization, age, gender, reason for admission, associated morbidity, origin, diagnosis, social assessment, previous nutritional support, height, weight, weight loss, time of weight loss, total proteins, albumin, lymphocytes, cholesterol, body mass index (BMI), nutritional condition, type and degree of malnutrition. Nutritional condition was assessed using the application HEN-Persan and results were statistically analyzed using the SPSS 21.0 software.no significant differences were found between the four centers. In home hospitalization patients, 36% presented a normal nutritional screening and 87% presented some degree of malnutrition, while combined malnutrition prevailed (63%). Depending on the nutritional degree, 36% of patients had mild malnutrition, 27% presented moderate malnutrition and 35% had severe malnutrition.a computer application allows for an immediate, secure and reliable nutritional assessment in home hospitalization that helps introduce early nutritional measures and improve post-hospital control of patients.INTRODUCCIÓN: la malnutrición hospitalaria tiene una elevada prevalencia y comporta un incremento del coste sanitario. La hospitalización a domicilio (HaD) permite el control en el domicilio de un episodio hospitalario agudo estable clínicamente por un equipo sanitario especializado.Objetivo: conocer el estado nutricional de los pacientes que ingresen en HaD con una misma aplicación informática (app) para poder instaurar medidas nutricionales precoces en el domicilio y mejorar la evolución posthospitalaria de los pacientes. MATERIAL Y METODOLOGÍA: estudio multicéntrico prospectivo y descriptivo del estado nutricional en cuatro unidades de HaD, durante un periodo de dos meses consecutivos durante el año 2016. Se recogieron las variables: unidad de HaD, edad, sexo, motivo de ingreso, patología asociada, procedencia, diagnóstico, valoración social, soporte nutricional previo, talla, peso, pérdida de peso, tiempo de la pérdida de peso, proteínas totales, albúmina, linfocitos, colesterol, índice de masa corporal (IMC), estado nutricional según el IMC, riesgo nutricional, tipo y grado de desnutrición. Se realizó la valoración nutricional con la app HEN-Persan y se analizaron los resultados estadísticamente con el programa informático SPSS 21.0. RESULTADOS: no existen diferencias significativas entre las cuatro unidades. Globalmente, el 36% de pacientes ingresados en HaD presentaban un estado nutricional normal. El 87% presentaba algún tipo de malnutrición, predominando la desnutrición de tipo mixta (63%). Según el grado,fue leve (36%), moderada (27%) y grave (35%). CONCLUSIONES: la utilización de una app permite tener una valoración nutricional inmediata, de manera fácil, segura y fiable en HaD, para poder introducir medidas nutricionales precoces y mejorar la evolución posthospitalaria de los pacientes.

Introducción: la malnutrición hospitalaria tiene una elevada prevalencia y comporta un incremento del coste sanitario. La hospitalización a domicilio (HaD) permite el control en el domicilio de un episodio hospitalario agudo estable clínicamente por un equipo sanitario especializado. Objetivo: conocer el estado nutricional de los pacientes que ingresen en HaD con una misma aplicación informática (app) para poder instaurar medidas nutricionales precoces en el domicilio y mejorar la evolución posthospitalaria de los pacientes. Material y metodología: estudio multicéntrico prospectivo y descriptivo del estado nutricional en cuatro unidades de HaD, durante un periodo de dos meses consecutivos durante el año 2016. Se recogieron las variables: unidad de HaD, edad, sexo, motivo de ingreso, patología asociada, procedencia, diagnóstico, valoración social, soporte nutricional previo, talla, peso, pérdida de peso, tiempo de la pérdida de peso, proteínas totales, albúmina, linfocitos, colesterol, índice de masa corporal (IMC), estado nutricional según el IMC, riesgo nutricional, tipo y grado de desnutrición. Se realizó la valoración nutricional con la app HEN-Persan y se analizaron los resultados estadísticamente con el programa informático SPSS 21.0. Resultados: no existen diferencias significativas entre las cuatro unidades. Globalmente, el 36% de pacientes ingresados en HaD presentaban un estado nutricional normal. El 87% presentaba algún tipo de malnutrición, predominando la desnutrición de tipo mixta (63%). Según el grado, fue leve (36%), moderada (27%) y grave (35%). Conclusiones: la utilización de una app permite tener una valoración nutricional inmediata, de manera fácil, segura y fiable en HaD, para poder introducir medidas nutricionales precoces y mejorar la evolución posthospitalaria de los pacientes. Introduction: the prevalence of hospital malnutrition is high and involves an increase in health care costs. Home hospitalization (HH) allows better clinically stable patient control after an acute illness by a highly specialized health care team. Objective: to know the nutritional condition of home hospitalization patients using a computer application that allows the implementation of early nutritional measures at home and improves post-hospital control of these patients. Material and methods: prospective multicenter study of the nutritional condition of patients in four different home hospitalization centers during a period of two consecutive months in 2016. Variables were collected: home hospitalization, age, gender, reason for admission, associated morbidity, origin, diagnosis, social assessment, previous nutritional support, height, weight, weight loss, time of weight loss, total proteins, albumin, lymphocytes, cholesterol, body mass index (BMI), nutritional condition, type and degree of malnutrition. Nutritional condition was assessed using the application HEN-Persan and results were statistically analyzed using the SPSS 21.0 software. Results: no significant differences were found between the four centers. In home hospitalization patients, 36% presented a normal nutritional screening and 87% presented some degree of malnutrition, while combined malnutrition prevailed (63%). Depending on the nutritional degree, 36% of patients had mild malnutrition, 27% presented moderate malnutrition and 35% had severe malnutrition. Conclusions: a computer application allows for an immediate, secure and reliable nutritional assessment in home hospitalization that helps introduce early nutritional measures and improve post-hospital control of patients.