Teachers Perception About Epilepsy.

To identify in a town of Brazil the knowledge, attitude and perception of epilepsy in teachers of elementary schools and to compare these before and after a training exercise. Teachers of nine public schools of Barão Geraldo, Campinas, Brazil completed a questionnaire. Two researchers had meetings with teachers, presenting the Global Campaign Epilepsy out of the shadows, when the questionnaire was first completed by all attendees. Twenty teachers of these schools were motivated to attend a training course entitled Epilepsy and Health as part of their continuous education programme. Two years later the same questionnaire was again completed (post-test) by these 20 teachers. 100 teachers originally completed the questionnaire (97 women, mean age 42 years, 64 married). Forty-three percent of teachers said that they had enough knowledge regarding epilepsy and 20% said that they had poor knowledge about the condition. Regarding the IQ of children with epilepsy, 45% of teachers believed that they had average IQ, 18% above average, six percent under average and 29% did not know. Teachers believed that children with epilepsy have a higher possibility of acquiring mental disease in the future (51%); that epilepsy is a disease (68%); that epilepsy is contagious (1%); epilepsy is treatable (90%). After the course, the teachers beliefs seem to have improved. This work with elementary school teachers identified difficulties related to epilepsy which, if addressed, may help promote better quality of life of people with epilepsy in the community and help to decrease stigma attached to the condition. Better informed teachers are likely to have a more positive attitude and this will be passed to others. Educational campaigns about epilepsy amongst teachers should be encouraged as this may improve the management of epilepsy, by helping to develop a well informed and tolerant community.65 Suppl 128-3

Optimal control based on a linear quadratic controller with feedforward action for solar furnace system

[Resumen] La energía solar es una fuente de energ´ıa renovable prometedora que se puede utilizar para experimentos de resistencia de materiales mediante el uso de hornos solares. Sin embargo, la operación manual de este tipo de sistemas requiere de operadores muy entrenados y capacitados. Este artículo analiza la aplicación de controladores óptimos basados en seguimiento cuadr´atico lineal con acción de control por adelanto (LQT-FF, por sus siglas en inglés, Linear Quadratic Tracking-FeedForward) para el control de hornos solares utilizados para pruebas de estrés térmico de materiales. El controlador LQT-FF propuesto se basa en estudios anteriores que proporcionan una solución analítica que utiliza un modelo lineal del horno solar, lo que reduce el costo computacional del algoritmo de control óptimo. La principal contribución del trabajo radica en la formulación de este modelo en forma incremental, añadiendo un integrador artificial a los estados originales, eliminando así el error de seguimiento para diferentes puntos de operación del sistema no lineal. El controlador propuesto ha sido implementado y validado sobre el modelo de horno solar SF60 de la Plataforma Solar de Almería. Los resultados obtenidos permiten establecer que las contribuciones del artículo avanzan el estado del arte de los controladores óptimos propuestos hasta ahora en la literatura, presentando una ley de control óptima con rechazo de perturbaciones formulada con una forma incremental de las entradas para eliminar el error de seguimiento de referencia. Además, la solución se resuelve de manera eficiente y presenta un esfuerzo computacional menor, lo que es fundamental para una implementación práctica real.[Abstract] Solar energy is a promising renewable energy source that can be used for material resistance experiments in solar furnace systems. However, the manual operation of such facilities requires highly experienced operators. Therefore, this paper discusses the application of optimal controllers based on Linear Quadratic Tracking with Feedforward action (LQT-FF) to control solar furnaces used for thermal stress testing of materials. Herein, the proposed LQT-FF controller builds on previous studies by providing an analytical solution that uses a linearized model of the solar furnace, reduces the computational cost of the optimal control algorithm, and a model formulation in the incremental form, including an artificial integrator to the original states, which eliminates the tracking error for different operating points of the nonlinear system. The proposed controller has been implemented and validated on the SF60 solar furnace model of the Plataforma Solar de Almer´ıa, southern Spain. The paper’s contributions advance the state of the art of optimal controllers proposed so far in the literature, presenting an optimal control law with disturbance rejection formulated with an incremental form of the inputs to eliminate the reference tracking error. Moreover, the solution is efficiently solved and presents a minor computational effort, promising for actual implementation.Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 201143/2019 − 4Portugal. Fundação para a Ciência e a Tecnologia; UIDB/50021/2020Ministerio de Ciencia e Innovación; PID2021-122560OB-I0

Effect of post-harvest pulsed light treatment on the respiration rate of grapes: modelling and validation

Introduction. The quality and shelf-life of packed fresh produce is strictly related to the dynamic of the gas composition, namely O2, CO2, and ethylene, in the head space of the packages. Therefore, the challenge is to control the head space composition and, hence the respiration rate of fresh produce during storage. However, several factors can affect the dynamic of the head space gas composition including harvesting time, presence of injury due to handling, microbial infection level, type of sanitization technique, and storage conditions (e.g., temperature, humidity), among others. In this framework, numerical simulation could be applied to predict the dynamic of the head space composition, as well as to select optimal conditions to be adopted during post-harvest treatment, storage, and handling of fresh produce. The aim of this paper was to develop and validate a mathematical model describing the effects of both Pulsed Light (PL) treatment and film permeability on the dynamic of the concentration of O2 and CO2 in the head space of packages during the passive modified atmosphere packaging of fruit. Materials and Methods. A 2D numerical model describing the mass transport of O2, CO2 in the packages as a function of both diffusivity and film permeability was developed. Simulations were performed on three different films with high (MRX), medium (PPCX) and low (PSF530) permeability. The computation of both O2 and CO2 mass transport equations was performed using an implicit finite difference method (Crank Nicolson) solved with Matlab® (v.R2012b). For the validation of the model, experimental data on the respiration rate of table grape were collected. Samples of grapes were exposed to PL treatments at fluences from 1 to 12 J/cm2 before being packed in passive modified atmosphere packaging, and then stored 10°C for up to 10 days. Results. Results demonstrated that the model set up is able to predict the dynamic of the head space gas composition of either untreated and PL treated grape during storage in packages with films of different permeability. The concentration of O2 increased with storage time, while that of CO2 decreased accordingly. Changes in the head space composition were, besides the storage time, dependent on the film permeability and PL fluence applied. Conclusions. The developed model can represent a valuable tool to predict the concentration of O2 and CO2 in the head space of packages during the passive modified atmosphere packaging of fruit. Further work is necessary in order improve the capability of the model to predict the dynamics of the gas composition in more complex systems, where, for example, the influence of the ethylene production is also taken into accout

Simplified modeling approaches of a solarpowered absorption machine focusing on model-based controllers development

[Resumen] Las máquinas de absorción, a pesar de ser soluciones prometedoras para los sistemas de refrigeración de edificios, tienen un principio de funcionamiento no trivial, y los modelos estudiados suelen ser complejos, con un alto costo computacional y requieren la medición de muchas variables o condiciones específicas de operación. Por ello, con el objetivo de desarrollar modelos adecuados para el control automático, este trabajo propone la formulación de modelos simplificados que puedan representar adecuadamente el comportamiento de una máquina de absorción. En concreto, se estudia un modelo de parámetros concentrados basado en un balance energético, un modelo Auto-Regresivo con variables exógenas (ARX), y un modelo no lineal ARX (NARX) para proporcionar una solución simple y eficiente para estimar la temperatura de salida de los diferentes elementos de una máquina de absorción: generador, evaporador y condensador. El sistema estudiado es la instalación solar térmica del edificio CIESOL, situada en la Universidad de Almería, España. Los resultados han demostrado que todos los modelos propuestos estiman satisfactoriamente las salidas de la máquina de absorción, siendo el NARX el que ha conseguido los mejores resultados en términos de índices de error. Sin embargo, el modelo de primeros principios tiene el mejor compromiso entre rendimiento de error y coste computacional, presentando un tiempo de cómputo casi 100 veces menor al resto durante la validación.[Abstract] Absorption machines, despite being promising solutions for building cooling systems, have a non-trivial operating principle, and the studied models are usually complex, with a high computational cost and require the measurement of many variables or specific conditions. of operation. Therefore, aiming to develop suitable models for automatic control purposes, this work proposes the formulation of simplified models that can adequately portray the evolution of an absorption machine. A lumped parameters model based on energy balance, an AutoRegressive with exogenous input (ARX), and a Nonlinear ARX (NARX) are investigated to provide a simplistic and efficient solution to estimate the absorption machine outlet temperature for the generator,evaporator, and condenser sections. The study case system is the CIESOL thermal solar facility, located at the University of Almería, Spain. The results have demonstrated that all the proposed models satisfactorily estimate the absorption chiller outputs, in which the NARX has achieved the best results of the error indices. In contrast, the first principles model has the best compromise between error performance and computational cost, presenting almost 100 times less processing time during the validation experiment.Los autores agradecen el soporte económico del Consejo Nacional de Desarrollo Científico y Tecnológico (CNPq, Brasil) que ha financiado parte de este trabajo bajo la beca con código 201143/2019 - 4. Además, este trabajo ha sido desarrollado en el marco del proyecto “Microrredes para el autoabastecimiento solar de entornos productivos aislados (Microprod-Solar)” con código PCI2019-103378, financiado por la Agencia estatal de Investigación del Gobierno de España y por el Programa Iberoamericano de Ciencia y tecnología para el desarrollo (CYTED). Juan D. Gil agradece el apoyo económico del programa Personal Investigador Doctor de la Junta de Andalucía 2021, número de beca POSTDOC 21 00854.Conselho Nacional de Desenvolvimento Científico e Tecnológico; 201143/2019 - 4Agencia Estatal de Investigación; PCI2019-103378Junta de Andalucía: POSTDOC 21 0085

A stabilizing predictive controller with feedforward action: preliminary results

[Resumen] Uno de los aspectos esenciales en el diseño de un sistema de control consiste en garantizar su estabilidad en bucle cerrado. Este artículo presenta un controlador predictivo basado en modelo de horizonte infinito con compensación por adelanto y garantía de estabilidad en bucle cerrado para sistemas estables. La estabilidad del controlador se garantiza mediante los conceptos de solución recursiva y disminución asintótica de la función de coste. La técnica desarrollada se prueba en simulación usando como sistema de referencia un campo de captadores solares de placa plana. Además, se presenta una comparación con el mismo enfoque de control pero sin incluir la compensación por adelanto. Los resultados obtenidos demuestran que la estrategia propuesta mejora el desempeño del controlador rechazando correctamente las perturbaciones y manteniendo las características de estabilidad nominal, lo que asegura la convergencia en bucle cerrado tanto para los problemas de seguimiento de referencia como de rechazo de perturbaciones.[Abstract] This paper presents a nominal stabilizing predictive controller with a feedforward action for stable systems. An infinite horizon model predictive controller is developed to guarantee closed-loop stability and compensating disturbances in a single layer controller. The controller stability is assured using the recursive solution and the asymptotical decreasing of the cost function concepts. The infinite horizon predictive controller with feedforward action is implemented in a simulation scenario of a solar collector field in which it is compared to the same control approach without the disturbance compensations. It is demonstrated that the novel strategy can improve the control performance by correctly rejecting the disturbances and keeping the nominal stability features, leading to the convergence of the closed-loop system both for the reference tracking and measured disturbances scenarios.Brasil. Conselho Nacional de Desenvolvimento Científico e Tecnológico; 201143/2019−4Agencia Estatal de Investigación; PCI2019-10337

Food Recognition and Food Waste Estimation Using Convolutional Neural Network

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation