Passive activity enhances residual control ability in patients with complete spinal cord injury

Patients with complete spinal cord injury retain the potential for volitional muscle activity in muscles located below the spinal injury level.However,because of prolonged inactivity,initial attempts to activate these muscles may not effectively engage any of the remaining neurons in the descending pathway.A previous study unexpectedly found that a brief clinical round of passive activity significantly increased volitional muscle activation,as measured by surface electromyography.In this study,we further explored the effect of passive activity on surface electromyographic signals during volitional control tasks among individuals with complete spinal cord injury.Eleven patients with chronic complete thoracic spinal cord injury were recruited.Surface electromyography data from eight major leg muscles were acquired and compared before and after the passive activity protocol.The results indicated that the passive activity led to an increased number of activated volitional muscles and an increased frequency of activation.Although the cumulative root mean square of surface electromyography amplitude for volitional control of movement showed a slight increase after passive activity,the difference was not statistically significant.These findings suggest that brief passive activity may enhance the ability to initiate volitional muscle activity during surface electromyography tasks and underscore the potential of passive activity for improving residual motor control among patients with motor complete spinal cord injury.

Experimental Analysis of Methods Used to Solve Linear Regression Models

Predicting the value of one or more variables using the values of other variables is a very important process in the various engineering experiments that include large data that are difficult to obtain using different measurement processes.Regression is one of the most important types of supervised machine learning,in which labeled data is used to build a prediction model,regression can be classified into three different categories:linear,polynomial,and logistic.In this research paper,different methods will be implemented to solve the linear regression problem,where there is a linear relationship between the target and the predicted output.Various methods for linear regression will be analyzed using the calculated Mean Square Error(MSE)between the target values and the predicted outputs.A huge set of regression samples will be used to construct the training dataset with selected sizes.A detailed comparison will be performed between three methods,including least-square fit;Feed-Forward Artificial Neural Network(FFANN),and Cascade Feed-Forward Artificial Neural Network(CFFANN),and recommendations will be raised.The proposed method has been tested in this research on random data samples,and the results were compared with the results of the most common method,which is the linear multiple regression method.It should be noted here that the procedures for building and testing the neural network will remain constant even if another sample of data is used.

High-Speed EMU TCMS Design and LCC Technology Research

This paper introduces the high-speed electrical multiple unit （EMO） life cycle, including the design, manufacturing, testing, and maintenance stages. It also presents the train control and monitoring system （TCMS） software development platform, the TCMS testing and verification bench, the EMU driving simulation platform, and the EMU remote data transmittal and maintenance platform. All these platforms and benches combined together make up the EMU life cycle cost （LCC） system. Each platform facilitates EMU LCC management and is an important part of the system.

Effect of Compound Rare Earth on Shape Memory Effect of Fe-Mn-Si-Ni-C Alloys

Effect of compound rare earth (RE) on shape memory effect (SME) of Fe-Mn-Si-Ni-C shape memory alloy was studied by bent measurement,thermal cycle training, SEM and XRD etc. The results show that metallurgic microstructure is refined and SME improved evidently with the addition of compound RE. The alloy appears little two-way shape memory effect. The former training and addition of compound RE are two effective ways to restrain martensitic stability. XRD analysis also indicates that ε→γ reversible transition ratio increases by training greatly help to improve SME of the alloy.

Effectiveness on post-stroke hemiplegia in patients:electroacupuncture plus cycling vs electroacupuncture alone

OBJECTIVE:To evaluate the effectiveness of cycling in combination with electroacupuncture in treatment of poststroke hemiplegia patients at National Hospital of Acupuncture,Vietnam.METHODS:The study was designed as a single-centre,outcome-assessor-blinded parallel randomised controlled trial with 120 post-stroke hemiplegia patients randomly assigned into two groups:electroacupuncture plus cycling(CT group) and electroacupuncture(AT group).Patients were assessed before and after the treatment(using muscle grading,modified Rankin,Barthel,Orgorozo scores and electromyography).Statistical Man–Whitney U test,and Fisher’s exact tests were used to compare between CT and AT groups.RESULTS:The results reported statistically significant improvement in motor function in patients suffering from hemiplegia following ischemic stroke in both CT and AT groups.Patients in CT group experienced a greater improvement compared to those in AT group including better muscle contraction(increased frequency and amplitude of electromyography and increased muscle grading scale);increased recovery(Orgogozo scale),increased independency(Barthel scale) and decreased disability(Modified Rankin scale)(P < 0.01).CONCLUSIONS:Combination with cycling training significantly improves the recovery of post-stroke patients treated with electroacupuncture.