A eficácia entre duas intervenções na Síndrome do Canal Cárpico

A Síndrome do Canal Cárpico (SCC) é a neuropatia compressiva mais comum do membro superior, causada pela compressão direta sobre o nervo mediano no interior do canal cárpico.Os resultados deste estudo mostram em cada um dos grupos, após a intervenção, uma melhoria estatisticamente significativa da sintomatologia no G-AFN (p=0,02) e no GTRN/ EAA (p=0,004) e uma melhoria estatisticamente significativa do estado funcional no G-AFN (p=0,022). Verificamos também em cada um dos grupos, após a intervenção, uma melhoria estatisticamente significativa na “Força de preensão” (p=0,005), na “Pinça polegar/dedo indicador” (p=0,021), na “Pinça polegar/dedo médio” (p=0,026) e “Pinça polegar/dedo anular” (p=0,026) no G-AFN, e uma melhoria estatisticamente significativa na “Pinça polegar/indicador” (p=0,016), na “Pinça polegar/dedo médio” (p=0,035), na “Pinça polegar/dedo anular” (p=0,010), na “Pinça trípode” (p=0,005) e na “Pinça lateral” (p=0,051) no G-TRN/EAA. Após a intervenção, não verificamos diferenças estatisticamente significativas nos valores das escalas de gravidade de sintomas (p=0,853) e de estado funcional (p=0,148) entre os grupos, mas diferenças estatisticamente significativas nos valores dos testes neurofisiológicos (p=0,047) e força de preensão da mão (p=0,005). Do estudo, concluímos que a utilização da intervenção articular/fascial/neural (AFN) e a intervenção com tala de repouso noturna e exercícios de auto alongamento (TRN/EAA), beneficia os indivíduos com SCC não severa, como nos casos incipientes, ligeiros ou moderados. Os indivíduos com esta condição clínica apresentam sintomatologia caraterística de dor, parestesia, especialmente noturna e disfunção muscular da mão. Tais manifestações originam perda funcional com implicações nas áreas de desempenho ocupacional, nomeadamente, nas atividades da vida diária, produtivas e de lazer. O tratamento conservador na SCC não severa, como nos casos incipientes, ligeiros e moderados, apesar de controverso, é recomendado. O tema suscita o nosso interesse, razão pela qual nos propomos realizar um estudo experimental em indivíduos com o diagnóstico clínico de SCC não severa e aplicar num grupo a intervenção articular, fascial e neural (AFN) e noutro grupo a intervenção com tala de repouso noturna e exercícios de auto alongamento (TRN/EAA). O estudo tem como principais objetivos, por um lado, verificar o impacto das intervenções em cada um dos grupos e, por outro lado, comparar o seu impacto entre os grupos, no que respeita à gravidade de sintomas, ao estado funcional, à força de preensão da mão e força de pinças finas. Fomos também comparar os resultados dos testes neurofisiológicos (Velocidade de Condução Motora) antes e depois da intervenção AFN e da intervenção com TRN/EAA, e averiguar o seu impacto nos valores da latência motora distal e da velocidade de condução sensitiva, entre os grupos. Identificamos também quais as variáveis sócio demográficas e as que caraterizam a patologia que estão relacionadas com o problema em estudo e com os valores obtidos com as escalas do Boston Carpal Tunnel Questionnaire (BCTQ), no grupo articular, fascial e neural (G-AFN) e no grupo com tala de repouso noturna e exercícios de auto alongamento (G-TRN/EAA). Para a concretização do estudo, recorremos a uma amostra de 23 indivíduos de ambos os sexos do Hospital Curry Cabral, Empresa Pública Empresarial -Centro Hospitalar de Lisboa Central (HCC, EPE -CHLC).

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Scheduling distributed energy resources in an isolated grid - an artificial neural network approach

Power Systems (PS), have been affected by substantial penetration of Distributed Generation (DG) and the operation in competitive environments. The future PS will have to deal with large-scale integration of DG and other distributed energy resources (DER), such as storage means, and provide to market agents the means to ensure a flexible and secure operation. Virtual power players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. This paper proposes an artificial neural network (ANN) based methodology to support VPP resource schedule. The trained network is able to achieve good schedule results requiring modest computational means. A real data test case is presented.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.

Metalearner based on Dynamic Neural Network for Strategic Bidding in electricity Markets

The restructuring of electricity markets, conducted to increase the competition in this sector, and decrease the electricity prices, brought with it an enormous increase in the complexity of the considered mechanisms. The electricity market became a complex and unpredictable environment, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. Software tools became, therefore, essential to provide simulation and decision support capabilities, in order to potentiate the involved players’ actions. This paper presents the development of a metalearner, applied to the decision support of electricity markets’ negotiation entities. The proposed metalearner executes a dynamic artificial neural network to create its own output, taking advantage on several learning algorithms implemented in ALBidS, an adaptive learning system that provides decision support to electricity markets’ players. The proposed metalearner considers different weights for each strategy, depending on its individual quality of performance. The results of the proposed method are studied and analyzed in scenarios based on real electricity markets’ data, using MASCEM - a multi-agent electricity market simulator that simulates market players’ operation in the market.

Neural control of an autonomous robot

This article aims to apply the concepts associated with artificial neural networks (ANN) in the control of an autonomous robot system that is intended to be used in competitions of robots. The robot was tested in several arbitrary paths in order to verify its effectiveness. The results show that the robot performed the tasks with success. Moreover, in the case of arbitrary paths the ANN control outperforms other methodologies, such as fuzzy logic control (FLC).

Immediate effects of hip mobilization with movement in patients with hip osteoarthritis: A randomised controlled trial

Background Mobilization with movement (MWM) has been shown to reduce pain, increase range of motion (ROM) and physical function in a range of different musculoskeletal disorders. Despite this evidence, there is a lack of studies evaluating the effects of MWM for hip osteoarthritis (OA). Objectives To determine the immediate effects of MWM on pain, ROM and functional performance in patients with hip OA. Design Randomized controlled trial with immediate follow-up. Method Forty consenting patients (mean age 78 ± 6 years; 54% female) satisfied the eligibility criteria. All participants completed the study. Two forms of MWM techniques (n = 20) or a simulated MWM (sham) (n = 20) were applied. Primary outcomes: pain recorded by numerical rating scale (NRS). Secondary outcomes: hip flexion and internal rotation ROM, and physical performance (timed up and go, sit to stand, and 40 m self placed walk test) were assessed before and after the intervention. Results For the MWM group, pain decreased by 2 points on the NRS, hip flexion increased by 12.2°, internal rotation by 4.4°, and functional tests were also improved with clinically relevant effects following the MWM. There were no significant changes in the sham group for any outcome variable. Conclusions Pain, hip flexion ROM and physical performance immediately improved after the application of MWM in elderly patients suffering hip OA. The observed immediate changes were of clinical relevance. Future studies are required to determine the long-term effects of this intervention.