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Harini, S.
- Effect of Low Frequency Vibratory Stimulation on Biceps Brachii Spasticity in Subjects with Hemiplegia
Authors
1 College of Physiotherapy, Sri Venkateswara Institute of Medical Sciences (SVIMS), Tirupati, Andhra Pradesh, IN
Source
Indian Journal of Physiotherapy & Occupational Therapy-An International Journal, Vol 7, No 4 (2013), Pagination: 192-196Abstract
Objective of the Study:• To evaluate the effect of low frequency vibratory stimulation on biceps brachii spasticity in subjects with hemiplegia through Modified Ashworth Scale (MAS).
• To evaluate the effect of low frequency vibratory stimulation on biceps brachii spasticity in subjects with hemiplegia through Passive elbow extension Range of Motion (PROM).
Materialsandmethod: 30 hemiplegic subjects were divided into two groups, experimental group received Vibration treatment in addition to the daily sessions of conventional physiotherapy which includes weight bearing exercises of upper limb, stretching of biceps brachii muscle and cryotherapy,where as control group received only daily sessions of conventional physiotherapy treatment for a duration of 30-45 minutes, six days per week for a total of 4 weeks.
Results: After a 4 week treatment period, the subjects in the experimental group compared with the subjects in the control group had shown a statistically significant improvement with outcome measures at 0.05 level.
Conclusion: Vibratory stimulation along with conventional physiotherapy was found much effective in reducing biceps brachii spasticity in the subjects with hemiplegia
Keywords
ROM, Vibratory Motor Stimulation(VMS), Spasticity, VibrationReferences
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- Trends in Working Capital Management and Evidences of Zero Working Capital:An Empirical Investigation in SAIL and TSL
Authors
1 Department of Commerce, Vellalar College for Women, Erode, IN
2 Vellalar College for Women, Erode, IN
Source
HuSS: International Journal of Research in Humanities and Social Sciences, Vol 3, No 1 (2016), Pagination: 36-44Abstract
The manufacturing companies subjected to operating cycle aim at shorter conversion cycle, so that working capital requirements are managed spontaneously without tapping external sources. It is in this context the concept of zero working capital is gaining momentum. Hence an attempt has been made in the present study to observe between the sample companies-SAIL and TSL the trends in liquidity management; trends in percentage share of individual components in the total current assets; and whether the strategy of zero working capital has been adopted and further its impact on profitability. The study covers a period of ten years from 2005-06 to 2014-15, of which the first five years (2005-06 to 2009-10) were taken as phase I and the second five years (2010-11 to 2014-15) were taken as Phase II and the data of these two sets stand independent. For the purpose of analysis relevant accounting ratios, descriptive statistics, correlation and regression coefficients were computed. Null hypotheses were set and ‘t’ and ‘F’ tests were applied to draw conclusion. The study has divulged that unlike SAIL, TSL was adopting zero working capital consistently throughout the study period and has impacted significantly on the variability of its Return on Capital Employed. This, however, could be continued without straining the relationship with creditors.Keywords
Cash Conversion Cycle, Zero Working Capital, Zero Working Capital Ratio.References
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- Artificial Intelligence in Google Maps
Authors
Source
Artificial Intelligent Systems and Machine Learning, Vol 13, No 1 (2021), Pagination: 13-15Abstract
Artificial Intelligence (A.I.) is a multidisciplinary field whose goal is to automate activities that presently require human intelligence. (AI) enables machines to extract, integrate, exchange, and analyze large number of datasets to answer complex problems in a timely manner. The massive amounts of data acquired and processed by corporations such as Google, Facebook, Amazon, and Apple have provided accelerated advances in a variety of industries and created new opportunities driven by machine intelligence insights. This phenomenon has also driven a demand for new Machine Learning (ML) techniques that improve the accuracy of AI predictions and decision-making abilities. One aspect of modern ML and AI that often gets obfuscated by the sheen of the machines showing any ability to perform humanlike decision-making or identify things unforeseen by humans, regardless of domain specificity, is the underlying fact that the AI/ML algorithms depend on the data derived from their decision-making models and that drives their decisional output. The preprocessing of the data is fundamental to the success of the artificial intelligence.