Facteurs associés à la résilience des aidantes familiales d’un parent âgé en perte d’autonomie à domicile au Liban

Avec l’avancement en âge, les personnes âgées qui vivent à domicile ont besoin du soutien des membres de leur entourage, notamment d’un aidant familial dont le rôle n’est toutefois pas sans conséquence sur sa santé. Les écrits empiriques ont montré que certains aidants sont résilients, c’est-à-dire qu’ils s’adaptent à leur situation et continuent leur développement. Toutefois, aucune étude n’a été effectuée au Liban auprès des aidantes familiales pour expliquer la résilience dans ce contexte et, plus spécifiquement, pour déterminer les facteurs qui pourraient l’influencer. Cette étude à devis corrélationnel de prédiction avait pour but de vérifier certaines des relations postulées par un modèle empirique découlant des écrits, soit la contribution de facteurs personnels (stratégies de coping et auto-efficacité) et de facteurs contextuels (relations familiales, perception du soutien de l’entourage, et sens accordé au « prendre soin »),à la résilience des aidantes familiales libanaises qui prennent soin d’un proche âgé à domicile. L’étude a été effectuée au Liban auprès d’un échantillon de convenance composé de 140 aidantes familiales principales cohabitant à domicile avec un parent âgé de 65 ans et plus ayant une perte d’autonomie fonctionnelle ou cognitive. La collecte des données a été réalisée en arabe en utilisant un guide d’entrevue standardisé regroupant des instruments nord-américains traduits selon la méthode de traduction inversée parallèle, de même qu’une question ouverte portant sur la perception des aidantes de la résilience, soit des facteurs qui les aident à continuer à prendre soin de leur proche âgé tout en continuant à se développer. Une analyse de régression hiérarchique a permis de vérifier la contribution unique des facteurs personnels et contextuels à expliquer la résilience des aidantes familiales, en contrôlant pour l’âge et le niveau de scolarité des aidantes et pour le niveau de perte d’autonomie et la fréquence des comportements dysfonctionnels de leurs parents âgés. Une analyse de contenu a permis de décrire la perception des aidantes eu égard à la résilience. Les résultats ont montré que le modèle empirique, incluant les variables de contrôle explique 54% de la variance de la résilience et que quatre des facteurs considérés, soit les stratégies de coping centrées sur le problème, les stratégies de coping centrées sur les émotions, le sentiment d’auto-efficacité et le sens du « prendre soin » ont une contribution statistiquement significative à la résilience des aidantes. Parmi ces quatre facteurs, le sens du « prendre soin » et le sentiment d’auto-efficacité expliquent davantage de variance, soit 11% et 5% respectivement. L’analyse qualitative du discours des aidantes a montré qu’elles prennent soin de leur proche âgé surtout par souci de réciprocité, mais également parce qu’il s’agit d’un membre de la famille et par respect pour Dieu. Ce sont par ailleurs leurs croyances et la satisfaction liée au prendre soin qui les aident à continuer et à se développer. Cette étude offre une meilleure compréhension du concept de la résilience des aidantes familiales au Liban et de certains facteurs qui en sont des prédicteurs significatifs. Elle offre des pistes pour l’intervention infirmière dans le but de promouvoir la santé de la personne/famille en tant que partenaire de soins. Des recommandations pour la pratique, la formation et la recherche sont proposées.

Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.

Local WMR navigation with monocular data

This article presents recent WMR (wheeled mobile robot) navigation experiences using local perception knowledge provided by monocular and odometer systems. A local narrow perception horizon is used to plan safety trajectories towards the objective. Therefore, monocular data are proposed as a way to obtain real time local information by building two dimensional occupancy grids through a time integration of the frames. The path planning is accomplished by using attraction potential fields, while the trajectory tracking is performed by using model predictive control techniques. The results are faced to indoor situations by using the lab available platform consisting in a differential driven mobile robot

Prevalencia de alteraciones audiométricas y su relación con exposición a ruido ocupacional y extra ocupacional, en una cohorte de trabajadores de Santander, Colombia, Años 2014-2015

La hipoacusia neurosensorial inducida por ruido (HNIR) definida como la pérdida de la capacidad auditiva secundaria a la exposición ocupacional continua o intermitente a ruido en el lugar de trabajo, es la cuarta enfermedad ocupacional en prevalencia en Colombia. Objetivo: Determinar la prevalencia de alteraciones audiométricas y su relación con exposición a ruido ocupacional y extra ocupacional, en un grupo de trabajadores que asistieron a una IPS de la ciudad de Bucaramanga en el periodo comprendido entre agosto de 2014 y agosto de 2015. Diseño: Se realizó un estudio de corte transversal con 2725 registros de las historias clínicas de fonoaudiología realizadas a los trabajadores con audiometría tonal como parte de los exámenes ocupacionales, entre el 1 de agosto de 2014 al 31 de agosto de 2015, en una Institución Prestadora de Salud (IPS) ocupacional, en la ciudad de Bucaramanga, Santander. Resultados: El 17.2% de los trabajadores presentaron alteraciones audiométricas, de estos el 33,1%, cumplió con los criterios definidos en el estudio para ser calificados como casos probables de hipoacusia neurosensorial inducida por ruido, de estos el 87,8% fueron clasificados como leves, 10,8% como moderados y el 1,2% como moderado severo, no se registraron casos de HNIR severa o profunda. El 62,7% se clasificaron como no HNIR y el 4% correspondió a hipoacusias con afectación de frecuencias conversacionales. Conclusiones: Al aplicar un modelo de regresión logística para controlar las variables de confusión, no se encontró asociación con ninguna de las variables anteriormente descritas. A pesar de esto, existe suficiente evidencia de la relación entre algunas ocupaciones y la HNIR.

Computationally reliable approaches of contractive MPC for discrete-time systems

La tesis pretende explorar acercamientos computacionalmente confiables y eficientes de contractivo MPC para sistemas de tiempo discreto. Dos tipos de contractivo MPC han sido estudiados: MPC con coacción contractiva obligatoria y MPC con una secuencia contractiva de conjuntos controlables. Las técnicas basadas en optimización convexa y análisis de intervalos son aplicadas para tratar MPC contractivo lineal y no lineal, respectivamente. El análisis de intervalos clásicos es ampliado a zonotopes en la geometría para diseñar un conjunto invariante de control terminal para el modo dual de MPC. También es ampliado a intervalos modales para tener en cuenta la modalidad al calcula de conjuntos controlables robustos con una interpretación semántica clara. Los instrumentos de optimización convexa y análisis de intervalos han sido combinados para mejorar la eficacia de contractive MPC para varias clases de sistemas de tiempo discreto inciertos no lineales limitados. Finalmente, los dos tipos dirigidos de contractivo MPC han sido aplicados para controlar un Torneo de Fútbol de Copa Mundial de Micro Robot (MiroSot) y un Tanque-Reactor de Mezcla Continua (CSTR), respectivamente.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Bias from farmer self-selection in genetically modified crop productivity estimates: Evidence from Indian data

In the continuing debate over the impact of genetically modified (GM) crops on farmers of developing countries, it is important to accurately measure magnitudes such as farm-level yield gains from GM crop adoption. Yet most farm-level studies in the literature do not control for farmer self-selection, a potentially important source of bias in such estimates. We use farm-level panel data from Indian cotton farmers to investigate the yield effect of GM insect-resistant cotton. We explicitly take into account the fact that the choice of crop variety is an endogenous variable which might lead to bias from self-selection. A production function is estimated using a fixed-effects model to control for selection bias. Our results show that efficient farmers adopt Bacillus thuringiensis (Bt) cotton at a higher rate than their less efficient peers. This suggests that cross-sectional estimates of the yield effect of Bt cotton, which do not control for self-selection effects, are likely to be biased upwards. However, after controlling for selection bias, we still find that there is a significant positive yield effect from adoption of Bt cotton that more than offsets the additional cost of Bt seed.

Input constraints handling in an MPC/feedback linearization scheme

The combination of model predictive control based on linear models (MPC) with feedback linearization (FL) has attracted interest for a number of years, giving rise to MPC+FL control schemes. An important advantage of such schemes is that feedback linearizable plants can be controlled with a linear predictive controller with a fixed model. Handling input constraints within such schemes is difficult since simple bound contraints on the input become state dependent because of the nonlinear transformation introduced by feedback linearization. This paper introduces a technique for handling input constraints within a real time MPC/FL scheme, where the plant model employed is a class of dynamic neural networks. The technique is based on a simple affine transformation of the feasible area. A simulated case study is presented to illustrate the use and benefits of the technique.

An implicit pole-assignment self-tuning regulator

A new self-tuning implicit pole-assignment algorithm is presented which, through the use of a pole compression factor and different RLS model and control structures, overcomes stability and convergence problems encountered in previously available algorithms. Computational requirements of the technique are much reduced when compared to explicit pole-assignment schemes, whereas the inherent robustness of the strategy is retained.

The relative order and inverses of recurrent networks

Differential geometry is used to investigate the structure of neural-network-based control systems. The key aspect is relative order—an invariant property of dynamic systems. Finite relative order allows the specification of a minimal architecture for a recurrent network. Any system with finite relative order has a left inverse. It is shown that a recurrent network with finite relative order has a local inverse that is also a recurrent network with the same weights. The results have implications for the use of recurrent networks in the inverse-model-based control of nonlinear systems.

The real-time optimisation of DNO owned storage devices on the LV network for peak reduction

Energy storage is a potential alternative to conventional network reinforcementof the low voltage (LV) distribution network to ensure the grid’s infrastructure remainswithin its operating constraints. This paper presents a study on the control of such storagedevices, owned by distribution network operators. A deterministic model predictive control (MPC) controller and a stochastic receding horizon controller (SRHC) are presented, wherethe objective is to achieve the greatest peak reduction in demand, for a given storagedevice specification, taking into account the high level of uncertainty in the prediction of LV demand. The algorithms presented in this paper are compared to a standard set-pointcontroller and bench marked against a control algorithm with a perfect forecast. A specificcase study, using storage on the LV network, is presented, and the results of each algorithmare compared. A comprehensive analysis is then carried out simulating a large number of LV networks of varying numbers of households. The results show that the performance of each algorithm is dependent on the number of aggregated households. However, on a typical aggregation, the novel SRHC algorithm presented in this paper is shown to outperform each of the comparable storage control techniques.

Organizações sociais em Pernambuco: o desenho institucional do controle externo sobre os contratos de gestão

O objetivo desta pesquisa foi analisar o desenho institucional do controle externo sobre os contratos de gestão no âmbito do Tribunal de Contas do estado de Pernambuco quanto a sua aderência aos conteúdos da lei estadual que disciplina as Organizações Sociais e quanto a sua observância por parte dos atores envolvidos: Administração Pública, técnicos do tribunal de contas e membros do seu corpo julgador. Foram assumidas as seguintes premissas: que os novos arranjos de prestação de serviços públicos, por meio de parcerias com as Organizações Sociais, demandam por parte dos Tribunais de Contas desenhos institucionais de fiscalização específicos, que a pesar de variáveis devem primar por sua capacidade de revelar informações; que o processo de formatação destes desenhos institucionais deve ser dinâmico, permitindo-se que as contigências experimentadas na sua implementação possam contribuir no seu aperfeiçoamento; e que esses desenhos institucionais geram impacto no comportamento dos atores envolvidos. O estudo foi realizado por meio de pesquisa documental. A metodologia qualitativa de análise de conteúdo foi escolhida para análise dos dados. Os resultados da pesquisa permitiram concluir que o desenho institucional de controle dos contratos de gestão no âmbito do TCE-PE caracteriza-se por sua fragilidade como mecanismo de revelação de informação e, consequentemente, não contribui para a redução da assimetria de informação que se estabelece com a implementação dos contratos de gestão. Adicionalmente, compromete e limita o desempenho do Tribunal de Contas no controle destes ajustes. Verificou-se, também, uma a baixa observância do desenho institucional identificado, em que pese sua fragilidade, por parte dos atores envolvidos no controle dos contratos de gestão, implicando em uma baixa institucionalização deste desenho. Os resultados devem proporcionar uma rediscussão acerca dos mecanismos de controle dos contratos de gestão por parte do TCE-PE, que poderá resultar em um novo desenho institucional com vistas a conferir maior transparência às parcerias com as Organizações Sociais.

Controle preditivo robusto baseado em desigualdades matriciais lineares aplicado a um sistema de tanques acoplados

This work deals with an on-line control strategy based on Robust Model Predictive Control (RMPC) technique applied in a real coupled tanks system. This process consists of two coupled tanks and a pump to feed the liquid to the system. The control objective (regulator problem) is to keep the tanks levels in the considered operation point even in the presence of disturbance. The RMPC is a technique that allows explicit incorporation of the plant uncertainty in the problem formulation. The goal is to design, at each time step, a state-feedback control law that minimizes a 'worst-case' infinite horizon objective function, subject to constraint in the control. The existence of a feedback control law satisfying the input constraints is reduced to a convex optimization over linear matrix inequalities (LMIs) problem. It is shown in this work that for the plant uncertainty described by the polytope, the feasible receding horizon state feedback control design is robustly stabilizing. The software implementation of the RMPC is made using Scilab, and its communication with Coupled Tanks Systems is done through the OLE for Process Control (OPC) industrial protocol

Modelagem e controle adaptativo de uma planta didática de nível com instrumentação industrial

The control, automation and optimization areas help to improve the processes used by industry. They contribute to a fast production line, improving the products quality and reducing the manufacturing costs. Didatic plants are good tools for research in these areas, providing a direct contact with some industrial equipaments. Given these capabilities, the main goal of this work is to model and control a didactic plant, which is a level and flow process control system with an industrial instrumentation. With a model it is possible to build a simulator for the plant that allows studies about its behaviour, without any of the real processes operational costs, like experiments with controllers. They can be tested several times before its application in a real process. Among the several types of controllers, it was used adaptive controllers, mainly the Direct Self-Tuning Regulators (DSTR) with Integral Action and the Gain Scheduling (GS). The DSTR was based on Pole-Placement design and use the Recursive Least Square to calculate the controller parameters. The characteristics of an adaptive system was very worth to guarantee a good performance when the controller was applied to the plant

Controlador preditivo não linear aplicado ao controle de golfadas em processos de produção de petróleo

Slugging is a well-known slugging phenomenon in multiphase flow, which may cause problems such as vibration in pipeline and high liquid level in the separator. It can be classified according to the place of its occurrence. The most severe, known as slugging in the riser, occurs in the vertical pipe which feeds the platform. Also known as severe slugging, it is capable of causing severe pressure fluctuations in the flow of the process, excessive vibration, flooding in separator tanks, limited production, nonscheduled stop of production, among other negative aspects that motivated the production of this work . A feasible solution to deal with this problem would be to design an effective method for the removal or reduction of the system, a controller. According to the literature, a conventional PID controller did not produce good results due to the high degree of nonlinearity of the process, fueling the development of advanced control techniques. Among these, the model predictive controller (MPC), where the control action results from the solution of an optimization problem, it is robust, can incorporate physical and /or security constraints. The objective of this work is to apply a non-conventional non-linear model predictive control technique to severe slugging, where the amount of liquid mass in the riser is controlled by the production valve and, indirectly, the oscillation of flow and pressure is suppressed, while looking for environmental and economic benefits. The proposed strategy is based on the use of the model linear approximations and repeatedly solving of a quadratic optimization problem, providing solutions that improve at each iteration. In the event where the convergence of this algorithm is satisfied, the predicted values of the process variables are the same as to those obtained by the original nonlinear model, ensuring that the constraints are satisfied for them along the prediction horizon. A mathematical model recently published in the literature, capable of representing characteristics of severe slugging in a real oil well, is used both for simulation and for the project of the proposed controller, whose performance is compared to a linear MPC