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Prasad Babu, G.
- Evaluation of Three Tyne Wheel Hoe for Reducing Drudgery in Vegetable Crops
Abstract Views :189 |
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Authors
P. Aparna
1,
G. Prasad Babu
1
Affiliations
1 Krishi Vigyan Kendra, Banavasi (A.P.), IN
1 Krishi Vigyan Kendra, Banavasi (A.P.), IN
Source
International Journal of Agricultural Engineering, Vol 11, No 2 (2018), Pagination: 379-384Abstract
Women constitute a major task force in agricultural operations in India. Therefore, it becomes necessary to study the ergonomics of women operators involved in weeding. Weeding is a main drudgery prone activity mostly performed by farm women and to resolve this problem Krishi Vigyan Kendra, Banavasi conducted front line demonstrations on the performance of improved weeder that is three tyne wheel hoe in reducing drudgery among women engaged in weeding activity in vegetable crops. Twenty farm women were selected randomly for the study. The results showed that the overall discomfort rate of hand hoe and three tyne wheel hoe was 8.1 and 4.7 (Table 4) i.e. more than moderate and light discomfort, respectively. Musculo-skeletal problems were decreased with improved weeding tool induced moderate to light discomfort/pain in shoulders, hands and arms compare to traditional method. Moderate drudgery index score of 53 per cent was recorded compare to traditional practice 72 per cent recorded as maximum. In the recommended weeding practice i.e. with three tyne wheel hoe, the same amount of work could be done in almost half of the time and work efficiency was increased by 93.3 per cent than normal weeding. Improved technologies for weeding activity for farm women is recommended so they can increase their efficiency, reduce the drudgery with time saving while performing weeding activity.Keywords
Drudgery, Wheel Hoe, Weeding.
References
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- Energy Efficient Scheduling Algorithm for Cloud Computing Systems Based on Prediction Model
Abstract Views :171 |
PDF Views:0
Authors
Affiliations
1 University of Technology, Jaipur, IN
2 Department of C.S.E, University of Technology, Jaipur, IN
1 University of Technology, Jaipur, IN
2 Department of C.S.E, University of Technology, Jaipur, IN
Source
International Journal of Advanced Networking and Applications, Vol 10, No 5 (2019), Pagination: 4013-4018Abstract
Existing cloud resource scheduling approaches have mainly concentrated on enhancing the reducing power consumption and resource utilization by enhancing the legacy heuristic algorithms. Although, different resource-intensive applications running on cloud data centers in realistic scenarios have significant results on the power consumption and cloud application performance. Furthermore, occurring peak loads may lead to a scheduling error, which can significantly effects on the energy efficiency of scheduling algorithms. At peak loads may lead to scheduling errors because there is no prediction model to predict the coming resource utilization of a data center through the data collected by the monitoring model. Effective scheduling mechanism gives an optimal solutions for complex problems while providing the Quality-of-Service (QoS) and avoiding Service Level Agreement (SLA) violations. To enhance the resource scheduling mechanism in cloud environment, predicting future workload to the each virtual machine pool in different manners like number of physical machines, number of virtual machines, number of requests and resource utilization etc., is an essential step. According to the prediction results, resource scheduling can be done in the right time, while avoiding QoS dropping and SLA violations. To achieve efficient resource scheduling, proposed approach lease advantages of prediction models. The proposed algorithm consists of a prediction model which is based on iterative fractal model and a scheduler which is based on an improved heuristic algorithms. Proposed scheduler algorithm is responsible for scheduling of resources while reducing the energy consumption and giving the guaranteeing the QoS.Keywords
Cloud Computing, Energy Efficient, Prediction Model, Scheduling Algorithm, Virtual Machine.Full Text
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