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.

Prediction of glucose excursions under uncertain parameters and food intake in intensive insulin therapy for type 1 diabetes mellitus

Considering the difficulty in the insulin dosage selection and the problem of hyper- and hypoglycaemia episodes in type 1 diabetes, dosage-aid systems appear as tremendously helpful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as the large intra-patient variability and food intake. This work addresses the prediction of glycaemia for a given insulin therapy face to parametric and input uncertainty, by means of modal interval analysis. As result, a band containing all possible glucose excursions suffered by the patient for the given uncertainty is obtained. From it, a safer prediction of possible hyper- and hypoglycaemia episodes can be calculated

Probabilistic models to assist maintenance of multiple instruments

The paper discusses maintenance challenges of organisations with a huge number of devices and proposes the use of probabilistic models to assist monitoring and maintenance planning. The proposal assumes connectivity of instruments to report relevant features for monitoring. Also, the existence of enough historical registers with diagnosed breakdowns is required to make probabilistic models reliable and useful for predictive maintenance strategies based on them. Regular Markov models based on estimated failure and repair rates are proposed to calculate the availability of the instruments and Dynamic Bayesian Networks are proposed to model cause-effect relationships to trigger predictive maintenance services based on the influence between observed features and previously documented diagnostics

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.

Strategic management of non-point source pollution from sewage sludge

In the UK, the recycling of sewage sludge to land is expected to double by 2006 but the security of this route is threatened by environmental concerns and health scares. Strategic investment is needed to ensure sustainable and secure sludge recycling outlets. At present, the security of this landbank for sludge recycling is determined by legislation relating to nutrient rather than potentially toxic elements (PTEs) applications to land - especially the environmental risk linked to soil phosphorus (P) saturation. We believe that not all land has an equal risk of contributing nutrients derived from applications to land to receiving waters. We are currently investigating whether it is possible to minimise nutrient loss by applying sludge to land outside Critical Source Areas (CSAs) regardless of soil P Index status. Research is underway to develop a predictive and spatially-sensitive, semi-distributed model of critical thresholds for sludge application that goes beyond traditional 'end-of-pipe" or "edge-of-field" modelling, to include hydrological flow paths and delivery mechanisms to receiving waters from non-point sources at the catchment scale.

Sensitivity analysis of a catchment-scale nitrogen model

There are now considerable expectations that semi-distributed models are useful tools for supporting catchment water quality management. However, insufficient attention has been given to evaluating the uncertainties inherent to this type of model, especially those associated with the spatial disaggregation of the catchment. The Integrated Nitrogen in Catchments model (INCA) is subjected to an extensive regionalised sensitivity analysis in application to the River Kennet, part of the groundwater-dominated upper Thames catchment, UK The main results are: (1) model output was generally insensitive to land-phase parameters, very sensitive to groundwater parameters, including initial conditions, and significantly sensitive to in-river parameters; (2) INCA was able to produce good fits simultaneously to the available flow, nitrate and ammonium in-river data sets; (3) representing parameters as heterogeneous over the catchment (206 calibrated parameters) rather than homogeneous (24 calibrated parameters) produced a significant improvement in fit to nitrate but no significant improvement to flow and caused a deterioration in ammonium performance; (4) the analysis indicated that calibrating the flow-related parameters first, then calibrating the remaining parameters (as opposed to calibrating all parameters together) was not a sensible strategy in this case; (5) even the parameters to which the model output was most sensitive suffered from high uncertainty due to spatial inconsistencies in the estimated optimum values, parameter equifinality and the sampling error associated with the calibration method; (6) soil and groundwater nutrient and flow data are needed to reduce. uncertainty in initial conditions, residence times and nitrogen transformation parameters, and long-term historic data are needed so that key responses to changes in land-use management can be assimilated. The results indicate the general, difficulty of reconciling the questions which catchment nutrient models are expected to answer with typically limited data sets and limited knowledge about suitable model structures. The results demonstrate the importance of analysing semi-distributed model uncertainties prior to model application, and illustrate the value and limitations of using Monte Carlo-based methods for doing so. (c) 2005 Elsevier B.V. All rights reserved.

Weighted input predictive controller

A discrete-time algorithm is presented which is based on a predictive control scheme in the form of dynamic matrix control. A set of control inputs are calculated and made available at each time instant, the actual input applied being a weighted summation of the inputs within the set. The algorithm is directly applicable in a self-tuning format and is therefore suitable for slowly time-varying systems in a noisy environment.

Adaptive control: an insight

In this article, an overview is given of some of the more common approaches taken in applying adaptive control. Gain scheduling, model reference control and self-tuning control are all discussed and in each case suggestions are given for further reading.

Real-time application of a constrained predictive controller based on dynamic neural networks with feedback linearization

This paper describes an experimental application of constrained predictive control and feedback linearisation based on dynamic neural networks. It also verifies experimentally a method for handling input constraints, which are transformed by the feedback linearisation mappings. A performance comparison with a PID controller is also provided. The experimental system consists of a laboratory based single link manipulator arm, which is controlled in real time using MATLAB/SIMULINK together with data acquisition equipment.

Testing projected wild bee distributions in agricultural habitats: predictive power depends on species traits and habitat type

Species distribution models (SDM) are increasingly used to understand the factors that regulate variation in biodiversity patterns and to help plan conservation strategies. However, these models are rarely validated with independently collected data and it is unclear whether SDM performance is maintained across distinct habitats and for species with different functional traits. Highly mobile species, such as bees, can be particularly challenging to model. Here, we use independent sets of occurrence data collected systematically in several agricultural habitats to test how the predictive performance of SDMs for wild bee species depends on species traits, habitat type, and sampling technique. We used a species distribution modeling approach parametrized for the Netherlands, with presence records from 1990 to 2010 for 193 Dutch wild bees. For each species, we built a Maxent model based on 13 climate and landscape variables. We tested the predictive performance of the SDMs with independent datasets collected from orchards and arable fields across the Netherlands from 2010 to 2013, using transect surveys or pan traps. Model predictive performance depended on species traits and habitat type. Occurrence of bee species specialized in habitat and diet was better predicted than generalist bees. Predictions of habitat suitability were also more precise for habitats that are temporally more stable (orchards) than for habitats that suffer regular alterations (arable), particularly for small, solitary bees. As a conservation tool, SDMs are best suited to modeling rarer, specialist species than more generalist and will work best in long-term stable habitats. The variability of complex, short-term habitats is difficult to capture in such models and historical land use generally has low thematic resolution. To improve SDMs’ usefulness, models require explanatory variables and collection data that include detailed landscape characteristics, for example, variability of crops and flower availability. Additionally, testing SDMs with field surveys should involve multiple collection techniques.

Análise e desenvolvimento de controladores preditivos multivariáveis baseados em multi-modelos bilineares

This work addresses issues related to analysis and development of multivariable predictive controllers based on bilinear multi-models. Linear Generalized Predictive Control (GPC) monovariable and multivariable is shown, and highlighted its properties, key features and applications in industry. Bilinear GPC, the basis for the development of this thesis, is presented by the time-step quasilinearization approach. Some results are presented using this controller in order to show its best performance when compared to linear GPC, since the bilinear models represent better the dynamics of certain processes. Time-step quasilinearization, due to the fact that it is an approximation, causes a prediction error, which limits the performance of this controller when prediction horizon increases. Due to its prediction error, Bilinear GPC with iterative compensation is shown in order to minimize this error, seeking a better performance than the classic Bilinear GPC. Results of iterative compensation algorithm are shown. The use of multi-model is discussed in this thesis, in order to correct the deficiency of controllers based on single model, when they are applied in cases with large operation ranges. Methods of measuring the distance between models, also called metrics, are the main contribution of this thesis. Several application results in simulated distillation columns, which are close enough to actual behaviour of them, are made, and the results have shown satisfactory

Determinação dos parâmetros do controlador preditivo generalizado com função de pesos variante no tempo através de algoritmos genéticos

The predictive control technique has gotten, on the last years, greater number of adepts in reason of the easiness of adjustment of its parameters, of the exceeding of its concepts for multi-input/multi-output (MIMO) systems, of nonlinear models of processes could be linearised around a operating point, so can clearly be used in the controller, and mainly, as being the only methodology that can take into consideration, during the project of the controller, the limitations of the control signals and output of the process. The time varying weighting generalized predictive control (TGPC), studied in this work, is one more an alternative to the several existing predictive controls, characterizing itself as an modification of the generalized predictive control (GPC), where it is used a reference model, calculated in accordance with parameters of project previously established by the designer, and the application of a new function criterion, that when minimized offers the best parameters to the controller. It is used technique of the genetic algorithms to minimize of the function criterion proposed and searches to demonstrate the robustness of the TGPC through the application of performance, stability and robustness criterions. To compare achieves results of the TGPC controller, the GCP and proportional, integral and derivative (PID) controllers are used, where whole the techniques applied to stable, unstable and of non-minimum phase plants. The simulated examples become fulfilled with the use of MATLAB tool. It is verified that, the alterations implemented in TGPC, allow the evidence of the efficiency of this algorithm

Controle preditivo bilinear aplicado a um motor de indução

The present work is based on the applied bilinear predictive control applied to an induction motor. As in particular case of the technique based on predictive control in nonlinem systems, these have desperted great interest, a time that present the advantage of being simpler than the non linear in general and most representative one than the linear one. One of the methods, adopted here, uses the linear model "quasi linear for step of time" based in Generalized Predictive Control. The modeling of the induction motor is made by the Vectorial control with orientation given for the indirect rotor. The system is formed by an induction motor of 3 cv with rotor in squirregate, set in motion for a group of benches of tests developed for this work, presented resulted for a variation of +5% in the value of set-point and for a variation of +10% and -10% in the value of the applied nominal load to the motor. The results prove a good efficiency of the predictive bilinear controllers, then compared with the linear cases

Estudo e implementação de um controlador preditivo generalizado bilinear compensado adaptativo

The present work presents the study and implementation of an adaptive bilinear compensated generalized predictive controller. This work uses conventional techniques of predictive control and includes techniques of adaptive control for better results. In order to solve control problems frequently found in the chemical industry, bilinear models are considered to represent the dynamics of the studied systems. Bilinear models are simpler than general nonlinear model, however it can to represent the intrinsic not-linearities of industrial processes. The linearization of the model, by the approach to time step quasilinear , is used to allow the application of the equations of the generalized predictive controller (GPC). Such linearization, however, generates an error of prediction, which is minimized through a compensation term. The term in study is implemented in an adaptive form, due to the nonlinear relationship between the input signal and the prediction error.Simulation results show the efficiency of adaptive predictive bilinear controller in comparison with the conventional.

Identificação e comparação entre controle preditivo com modelo não linear e PI sintonizados com PSO em sistema de separação gravitacional de águia-óleo

The separation methods are reduced applications as a result of the operational costs, the low output and the long time to separate the uids. But, these treatment methods are important because of the need for extraction of unwanted contaminants in the oil production. The water and the concentration of oil in water should be minimal (around 40 to 20 ppm) in order to take it to the sea. Because of the need of primary treatment, the objective of this project is to study and implement algorithms for identification of polynomial NARX (Nonlinear Auto-Regressive with Exogenous Input) models in closed loop, implement a structural identification, and compare strategies using PI control and updated on-line NARX predictive models on a combination of three-phase separator in series with three hydro cyclones batteries. The main goal of this project is to: obtain an optimized process of phase separation that will regulate the system, even in the presence of oil gushes; Show that it is possible to get optimized tunings for controllers analyzing the mesh as a whole, and evaluate and compare the strategies of PI and predictive control applied to the process. To accomplish these goals a simulator was used to represent the three phase separator and hydro cyclones. Algorithms were developed for system identification (NARX) using RLS(Recursive Least Square), along with methods for structure models detection. Predictive Control Algorithms were also implemented with NARX model updated on-line, and optimization algorithms using PSO (Particle Swarm Optimization). This project ends with a comparison of results obtained from the use of PI and predictive controllers (both with optimal state through the algorithm of cloud particles) in the simulated system. Thus, concluding that the performed optimizations make the system less sensitive to external perturbations and when optimized, the two controllers show similar results with the assessment of predictive control somewhat less sensitive to disturbances