926 resultados para Muscular Load
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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In this paper was evaluated, using the software ANSYS, the stiffness (El) of the log-concrete composite beams, of section T, with connectors formed by bonded-in steel rods, type CA-50, disposed in X, with application of cyclical load. The stiffness of the system was evaluated through the simulation of bending tests, considered 1/2 beam, with cyclical shipment varying among 40 % and 5 % of the strength of the connection with the load relationship R=0,125, for a total of 10 load cycles applied. The numeric results show a good agreement with experimental tests.
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A multi-agent framework for spatial electric load forecasting, especially suited to simulate the different dynamics involved on distribution systems, is presented. The service zone is divided into several sub-zones, each subzone is considered as an independent agent identified with a corresponding load level, and their relationships with the neighbor zones are represented as development probabilities. With this setting, different kind of agents can be developed to simulate the growth pattern of the loads in distribution systems. This paper presents two different kinds of agents to simulate different situations, presenting some promissory results.
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In 21 normal adult male subjects, the muscular activity of the levator scapulae and rhomboideus major muscles was studied electromyographically during the movements of the shoulder and arm. Two single coaxial needle electrodes were used for registering the action potentials. Concerning shoulder movement, it was shown that the levator scapulae was active in elevation and rhomboideus major was active in retraction. Both muscles were inactive during protrusion, in most events. Concerning free movements of the arm, both muscles were active in abduction, elevation, adduction, flexion and circumduction, but inactive in extension. During the same movements, performed with a load, we observed greater intensity in the activity of these muscles in comparison to their activity during free movements.
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The use of anthropometric measurements of triceps (TSF) and subscapular skinfolds (SSF) and mid-upper arm muscle circumference (MAMC) was examined as far as the diagnosis of energy-protein malnutrition (EPM) is concerned. The study was undertaken in five groups of patients (n = 231): arterial hypertension (AH, n = 63), chronic obstructive pulmonary disease (COPD, n = 17), hemodialyzed chronic renal failure (CRF, n = 19), critically ill patients with an acute event (CA, n = 42) and critically ill patients with chronic diseases (CCD, n = 90). The results were compared to those obtained in a group of healthy individuals (control group, n = 102). The control group and the group of patients were allocated in subgroups according to sex and age (less than 50 and more than 50 years). It was expected that significant differences would be found for the anthropometric values between the control subgroups and the COPD, the CRF and the CCD subgroups of patients. For the skinfold thicknesses (TSF and SSF), significant differences were found between CRF, CCD subgroups and the control subgroups under fifty years of age; however, the differences were not significant when the subgroups over fifty were analyzed. Concerning the MAMC, significant differences were found: 1 degree) between the CRF subgroups (males and females) and the control subgroups under fifty years of age; 2 degrees) between the CCD male subgroups (younger and older subgroups) and the respective control subgroups and 3 degrees) between the COPD and the control subgroups.(ABSTRACT TRUNCATED AT 250 WORDS)
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This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. The quadrature axis parameters are obtained with a rejection under an arbitrary reference, reducing the present difficulties.
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The objective of this work is the development of a methodology for electric load forecasting based on a neural network. Here, it is used Backpropagation algorithm with an adaptive process based on fuzzy logic. This methodology results in fast training, when compared to the conventional formulation of Backpropagation algorithm. Results are presented using data from a Brazilian Electric Company and the performance is very good for the proposal objective.
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This work shows a computational methodology for the determination of synchronous machines parameters using load rejection test data. By machine modeling one can obtain the quadrature parameters through a load rejection under an arbitrary reference, reducing the present difficulties. The proposed method is applied to a real machine.
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Purpose: To identify the electromyographic fatigue threshold in the erector spinae muscle. Methods: Eight 19 to 24-year-old male volunteers participated in this study, in which surface electrodes were used, as well as a biological signals acquisition module (Lynx) with a sampling frequency of 1000Hz, a 1000 times gain, a 20Hz high pass filter and a 500Hz low pass filter. The test consisted of repeated isometric contractions of the erector spinae muscle in a 45° hip flexion posture, with 30%, 40%, 50% and 60% of the maximum voluntary isometric contraction. Results: A positive correlation of the RMS (root mean square) value as a function of time was found for most of the subjects with 40% (N = 6), 50% (N = 7) and 60% (N = 8) loads of the maximum voluntary isometric contraction. Conclusions: It was concluded, from this study, that the proposed protocol provides evidence, through the electromyographic signal, of the development of fatigue in the erector spinae muscle with loads of 40%, 50% and 60% of the maximum voluntary isometric contraction. The protocol also allows the electromyographic fatigue threshold and its probable applicability in the diagnosis of this phenomenon during repetitive activities to be determined.
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This work presents a procedure for electric load forecasting based on adaptive multilayer feedforward neural networks trained by the Backpropagation algorithm. The neural network architecture is formulated by two parameters, the scaling and translation of the postsynaptic functions at each node, and the use of the gradient-descendent method for the adjustment in an iterative way. Besides, the neural network also uses an adaptive process based on fuzzy logic to adjust the network training rate. This methodology provides an efficient modification of the neural network that results in faster convergence and more precise results, in comparison to the conventional formulation Backpropagation algorithm. The adapting of the training rate is effectuated using the information of the global error and global error variation. After finishing the training, the neural network is capable to forecast the electric load of 24 hours ahead. To illustrate the proposed methodology it is used data from a Brazilian Electric Company. © 2003 IEEE.
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The objective of this study was to analyze the relationship of maximal aerobic power and the muscular strength (maximal isotonic strength and vertical jump explosive power) with the running economy (RE) in endurance athletes. Twenty-six male runners (27.9 ± 6.4 years; 62.7 ± 4.3 kg; 168.6 ± 6.1 cm; 6.6 ± 3.1% of body fat) performed in different days the following tests: a) incremental test to determine the maximal oxygen uptake (V̇O2max) and the intensity corresponding to the V̇O2max (IV̇O2max); b) constant-velocity treadmill run to determine RE; c) 1-RM test in the leg press and; d) maximal vertical jump test (VJ). V̇O2max (63.8 ± 8.3 ml/kg/min) was significantly correlated (r = 0.63; p < 0.05) with RE (48.0 ± 6.6 ml/kg/min). However, the IV̇O2max (18.7 ± 1.1 km/h), the maximal isotonic strength (230.3 ± 41.2 kg) and the VJ (30.8 ± 3.8 cm) were not significantly correlated with RE. One concludes that the maximal aerobic power can explain in part the inter-individual RE variability in endurance athletes. However, maximal isotonic strength and explosive strength seem not to be associated with RE values observed in this group of athletes.
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A 1000-kgf resistive strain-gauge load cell has been developed for quality testing of rocket propellant grain. A 7075-T6 aluminum alloy has been used for the elastic column, in which 8 uniaxial, 120-Ω strain gauges have been bonded and connected to form a full Wheatstone bridge to detect the strain. The chosen geometry makes the transducer insensitive to moments and, also, to the temperature. Experimental tests using a universal testing machine to imposed compression force to the load cell have demonstrated that its behavior is linear, with sensitivity of 2.90 μV/kgf ± 0.34%, and negligible hysteresis. The designed force transducer response to a dynamic test has been comparable to that of a commercial load cell. © 2005 IEEE.
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Due to a shortage of textbooks with specific data on muscular activity concerning physical conditioning and sports, we analysed electromyographically the muscles pectoralis major and deltoideus anterior, bilaterally, in inclined flying exercises, during the concentric and eccentric phases, with external loads of 25, 50, 75 and 100% of the maximum load. The electromyographic analysis was performed in eleven male volunteers with MEDITRACE-200 surface electrodes connected to a six-channel biologic signal acquisition module coupled to a PC/AT computer. The electromyographic signals were processed and the obtained effective values were normalized through maximum voluntary isometric contraction. Statistically, the results showed that all the muscles studied presented significant differences between the concentric and the eccentric phases, with higher electromyographic activity during the concentric phase. By analysing the different loads for each muscle in both phases, significant electromyographic activity was observed for all muscles. When the effect of each load on each muscle during the concentric phase was analysed, it was noticed that the muscles on the left were more active than those on the right side, while in the eccentric phase the muscles had different behavior.
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With the considerable increase of the losses in electric utilities of developing countries, such as Brazil, there is an investigation for loss calculation methodologies, considering both technical (inherent of the system) and non-technical (usually associated to the electricity theft) losses. In general, all distribution networks know the load factor, obtained by measuring parameters directly from the network. However, the loss factor, important for the energy loss cost calculation, can only be obtained in a laborious way. Consequently, several formulas have been developed for obtaining the loss factor. Generally, it is used the expression that relates both factors, through the use of a coefficient k. Last reviews introduce a range of factor k within 0.04 - 0.30. In this work, an analysis with real life load curves is presented, determining new values for the coefficient k in a Brazilian electric utility. © 2006 IEEE.
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The aim of this study was to determine the time to restore the biceps brachii (BB) electromyographic (EMG) activity after the biceps curl (BC) exercise, at different intensities. Ten males performed initially maximal voluntary isometric contractions (MVC) of the elbow flexors, followed by one isometric submaximal contraction at 50% MVC (reference contraction). After this, four bouts of the BC at 25%, 30%, 35%, and 40% 1 RM during 1 minute (randomly assigned, with 10 minutes rest between them) were performed. During the rest intervals at preestablished moments (15 seconds, 1, 3, 5, and 10 min), isometric 50% MVC were performed. The EMG variables (root mean square [RMS], zero crossings [ZC], median frequency, [MF] and peak power [PP]) at rest were compared with reference values. Immediately after the exercise, RMS and PP increased, while ZC and MF decreased, indicating fatigue. After 1 minute most of the variables were similar to the reference. Different load levels did not affect the EMG recovery. In conclusion, the EMG variables recovered after 1 minute rest, indicating the optimal muscular condition for subsequent bouts. Copyright © Taylor & Francis Group, LLC.