Effect of Combined Low-frequency Repetitive Transcranial Magnetic Stimulation and Virtual Reality Training on Upper Limb Function in Subacute Stroke:a Double-blind Randomized Controlled Trail

The effect of combined low-frequency repetitive transcranial magnetic stimulation（LF r TMS） and virtual reality（VR） training in patients after stroke was assessed. In a double-blind randomized controlled trial, 112 patients with hemiplegia after stroke were randomly divided into two groups： experimental and control. In experimental group, the patients received LF r TMS and VR training treatment, and those in control group received sham r TMS and VR training treatment. Participants in both groups received therapy of 6 days per week for 4 weeks. The primary endpoint including the upper limb motor function test of Fugl-meyer assessment（U-FMA） and wolf motor function test（WMFT）, and the secondary endpoint including modified Barthel index（MBI） and 36-item Short Form Health Survey Questionnaire（SF-36） were assessed before and 4 weeks after treatment. Totally, 108 subjects completed the study（55 in experimental group and 53 in control group respectively）. After 4-week treatment, the U-FMA scores [mean difference of 13.2, 95% confidence interval（CI） 3.6 to 22.7, P〈0.01], WMFT scores（mean difference of 2.9, 95% CI 2.7 to 12.3, P〈0.01）, and MBI scores（mean difference 16.1, 95% CI 3.8 to 9.4, P〈0.05） were significantly increased in the experimental group as compared with the control group. The results suggested the combined use of LF r TMS with VR training could effectively improve the upper limb function, the living activity, and the quality of life in patients with hemiplegia following subacute stroke, which may provide a better rehabilitation treatment for subacute stroke.

The Design of Intelligent Corporate E-commerce System

The paper shows how to design and implement an inte ll igent e-commerce system for medium or small enterprises. The corporate intranet is connected with Internet, which constitutes the hardware of the system. The s ystem adopts modern voice-identification technology for user authentication to enable secure access to key databases and thus enhance the security of system da tabases. The software system consists of four modules: 1. Corporate website and advertisement information system, intending for updatin g online advertisement, collecting and analyzing market research information. 2. Employee technology training and assessment system, including a variety of co urses containing technology, technique and management and so on. Login the training system, employees are able to study courses of technology or technique or others. The training system adopts modern multimedia E-Learning te chnology and in fact it is an innovative education method, which includes self- study, tutorship and lecture. It is able to help employees enjoy their learning. Employees can take simulative exams, and managers can test employees onlin e periodically. 3. Corporate decision-making management system, calculating and analyzing the o peration, predicting future development, facilitating communication between mana gers and employees. It provides valuable foundation for decisions, which enables managers to make strategic decisions and scientific management. 4. E-commerce online transaction system, which facilitates material purchase an d product sale. By using intelligent e-commerce system, corporations will enhance the employee’ s skills and management level, reduce production cost, promote product sale , and thus strengthen corporation’s market competitiveness effectively.

No-reference image quality assessment based on nonsubsample shearlet transform and natural scene statistics

A novel no-reference(NR) image quality assessment(IQA) method is proposed for assessing image quality across multifarious distortion categories. The new method transforms distorted images into the shearlet domain using a non-subsample shearlet transform(NSST), and designs the image quality feature vector to describe images utilizing natural scenes statistical features: coefficient distribution, energy distribution and structural correlation(SC) across orientations and scales. The final image quality is achieved from distortion classification and regression models trained by a support vector machine(SVM). The experimental results on the LIVE2 IQA database indicate that the method can assess image quality effectively, and the extracted features are susceptive to the category and severity of distortion. Furthermore, our proposed method is database independent and has a higher correlation rate and lower root mean squared error(RMSE) with human perception than other high performance NR IQA methods.