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Home-based resistance training performed at either fast or slow speeds improves power output in older adults
No full textPURPOSE: We investigated the effect of an unsupervised, body mass- home-based resistance training program in older adults performed at either a fast or slow contractile speed on changes to muscle-power, -volume, -architecture, and fatigue resistance of the knee extensors.METHODS: Thirty-two male older adults (age 65-88 years) were separated into 1) fast-speed exercise (Fast-group), 2) slow-speed exercise (Slow-group), and 3) no exercise (Control-group) groups. Participants in the exercise groups performed 30-45 repetitions of knee-extension and sit-to-stand exercises 3 times a week for 8 weeks with different exercise speed between the groups. Before and after the intervention period, the following variables were measured: Isotonic power, isometric strength, twitch contractile properties, muscle-activity, -architecture, and -quality, neuromuscular fatigue resistance of the knee extensors, and thigh muscle volume.RESULTS: Peak power was increased in both the Fast-group (+24 %, P < 0.01, d = 0.65) and Slow-group (+12 %, P < 0.05, d = 0.33) but not in the Control-group. Training increased pennation angle of the vastus lateralis in both the Fast-group (+8 %, P < 0.01, d = 0.42) and Slow-group (+8 %, P < 0.01, d = 0.42), while only the Fast-group showed increase in pennation angle of the rectus femoris (+12 %, P < 0.01, d = 0.64) and thigh muscle volume (+16 %, P < 0.01, d = 0.52). There was no time × group interaction effect for the other neuromuscular measures.CONCLUSIONS: Unsupervised, body mass- and home-based resistance training performed at either fast or slow speeds can improve muscle power in older adults, while fast-speed exercise may be preferable over slow-speed owing to the relatively greater improvement of muscle-power, -volume, -architecture, and better time efficiency.</p
Declining to be church? Rediscovering God’s time and action through paying careful attention to a numerically declining church
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Monetary-fiscal policies design and financial shocks in currency unions
Get PDFThis paper analyzes the design of monetary and fiscal policies in a currency union by focusing on the capacity to react to symmetric and asymmetric financial shocks. The model is constructed in order to mimic the institutional design adopted for the policy making in the EMU. The paper shows how a currency union set-up like the one adopted by the EMU can easily cope with symmetric financial shocks. However, it shows how in the face of asymmetric shocks more space for fiscal interventions is crucial, especially in more peripheral member countries
Automated classification of remote sensing satellite images using deep learning based vision transformer
Get PDFAutomatic classification of remote sensing images using machine learning techniques is challenging due to the complex features of the images. The images are characterized by features such as multi-resolution, heterogeneous appearance and multi-spectral channels. Deep learning methods have achieved promising results in the analysis of remote sensing satellite images in the recent past. However, deep learning methods based on convolutional neural networks (CNN) experience difficulties in the analysis of intrinsic objects from satellite images. These techniques have not achieved optimum performance in the analysis of remote sensing satellite images due to their complex features, such as coarse resolution, cloud masking, varied sizes of embedded objects and appearance. The receptive fields in convolutional operations are not able to establish long-range dependencies and lack global contextual connectivity for effective feature extraction. To address this problem, we propose an improved deep learning-based vision transformer model for the efficient analysis of remote sensing images. The proposed model incorporates a multi-head local self-attention mechanism with patch shifting procedure to provide both local and global context for effective extraction of multi-scale and multi-resolution spatial features of remote sensing images. The proposed model is also enhanced by fine-tuning the hyper-parameters by introducing dropout modules and a decay linear learning rate scheduler. This approach leverages local self-attention for learning and extraction of the complex features in satellite images. Four distinct remote sensing image datasets, namely RSSCN, EuroSat, UC Merced (UCM) and SIRI-WHU, were subjected to experiments and analysis. The results show some improvement in the proposed vision transformer on the CNN-based methods
