morteza tayebi

This meta-analysis review aimed to investigate the effect of resistance training on functional performance and physiological factors in patients with ischemic stroke.

The search was conducted in PubMed, Web of Science, and Scopus using related keywords. All articles were reviewed, but 22 studies that met the inclusion criteria were included after screening.

Based on findings, resistance training had a significant effect on the Six-minute Walk Test (difference in means= -32.04, P= 0.028), the Berg Balance Scale (difference in means= -2.37, P= 0.018), handgrip strength test (Difference in means= -2.05, P= 0.001), and Gait Speed (difference in means= -0.089, P= 0.101) in patients with ischemic stroke. However, resistance training did not significantly affect the Up and Go Test (difference in means= -2.27, P= 0.192) in these patients. On the other hand, according to several physiological indicators in different subgroups, including body mass index, TC, HDL-C, and LDL-C, resistance training did not have a significant effect (difference in means= 0.02, P= 0.642) in patients with ischemic stroke.

Resistance training improves the performance of patients with ischemic stroke. This improvement occurs in both the upper and lower body, but resistance training did not improve physiological parameters in ischemic stroke patients.

Land surface temperature (LST) monitoring has been widely used as one of the most important environmental parameters by the high temporal resolution sensors such as the MODIS sensor (daily temporal resolution capability and spatial resolution of one kilometer). One of the main problems of these sensors is their low spatial resolution, which limits the performance of these sensors for applications such as fire detection in forest areas and the study of urban thermal islands. In contrast, high spatial resolution sensors such as the ASTER sensor (90 meter spatial resolution and 16-day temporal resolution at the land surface temperature product), they have low temporal resolution, which results in application such as rapid change monitoring. In fact, due to technical limitations, there is no sensor that has a high resolution in spatial and temporal dimensions. To solve this problem, low-cost and efficient spatial-temporal fusion methods have been developed. The most important methods for fusion spatial-temporal methods are enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Spatial and Temporal Data Fusion Approach (STDFA). This work uses the ESTARFM and STDFA algorithms and a new method (SWT-STDFA) based on the STDFA method and the two-dimensional stationary wavelet transformation to fuse LST data spatially and temporally. The LST products of ASTER and MODIS sensors were fused for a part of Tehran city and finally, a virtual image was obtained with a spatial resolution equal to that of the ASTER sensor and a temporal resolution equal to that of the MODIS sensor. Also, based on the existence of a classification map prepared on the basis of normalized vegetation difference index (NDVI) in STDFA and SWT-STDFA algorithms, the effect of using normalized Green Difference Vegetation Indices (GNDVI) and soil adjusted vegetation Index (SAVI) on the accuracy of the synthetic image of the output is discussed. The results of the research indicate the high accuracy of the proposed method with the root mean square error of 3.03 Kelvin, standard deviation of 2. 21 Kelvin, mean absolute difference 1.72 Kelvin and correlation coefficient of 0.92 between the image of the actual land surface temperature and the predicted synthetic image Compared to the other two methods. Also, the increase of vegetation’s indices GNDVI and SAVI in the classification of both STDFA and SWT-STDFA methods did not have much effect on the accuracy of the synthetic image of the output.

Although Al/SiC composite has attracted much attention of the researchers، no due attention has been paid so far to its physical properties particularly its dimensional stability. Therefore، the objective of this research is to investigate the thermal-physical properties of these composites and the presented models of these properties. Initially، the Al/4%Cu alloy was prepared by mechanical alloying، and then the aluminum composite was synthesized with different percentages of silicon carbide (with different particle sizes). To characterize the composite hardness and density tests were performed. Thermal test on the Al-4%Cu/SiC composites revealed that with increase of volume fraction of SiC particles up to 25%، thermal expansion coefficient of the composite has decreased linearly، which indicates the presence of ceramic particles with very low thermal expansion coefficient in the composite. Also، these results reveal that with increase of temperature up to 500°C، thermal expansion coefficient has increased up to 20. 6 for the composite with 15% of reinforcement particles، although this increase is not linear. In order to investigate the effect of the reinforcement particles size on thermal expansion coefficient of the Al-4%Cu/SiC composite thermal tests were performed which indicated that the reinforcement particles size has no conspicuous effect on thermal expansion coefficient of the composite but causes a thermal strain of 0. 8% in the composite to some extent.