Delay tolerant networks (DTNs) are a class of systems that experience frequent and long-duration partitions. As in all distributed systems, DTN multicasting is a desirable feature for applications where some form of group communication is needed. The topological impairments experienced within a DTN pose unique challenges for designing effective DTN multicasting protocols. In this paper, we examine multicasting in DTNs. Unlike earlier work we assume no knowledge of node connectivity or mobility patterns.We propose the use of both single-copy and multicopy routing DTN routing algorithms. We also explore the use of gossiping and core nodes in DTNs to decrease the number of redundant messages while maintaining high message delivery ratios. We have performed extensive evaluations of our proposed methods. Our results show that with careful protocol parameter selection it is possible to achieve high delivery rates for various system scenarios.
Purpose This paper aims to examine technical efficiency and its determinants in Indian textile garments industry in post-agreement on textiles and clothing regime and evaluate the technical efficiency among micro, small and medium enterprises (MSMEs) firms. Design/methodology/approach This study uses unbalanced panel data for the period 2005–2010 to 2015–2016. The stochastic frontier function is used to estimate technical efficiency and its determinants. Findings The results show that the overall ecosystem of textile garments’ value chains could be improved to enhance the technical efficiency thereof. The result also reveals that small-scale firms have the highest technical efficiency scores, and medium-scale firms have the least technical efficiency score among all the categories of MSMEs. Research limitations/implications The textile garments industry needs to define its innovation strategies, as these strategies lead to different results that can be achieved only through the management of resources dedicated to the generation and implementation of innovations. Practical implications This study has shown that to offset India’s cost disadvantage in the international markets, there is a need to develop an ecosystem of textile manufacturing and value chains, eliminate the inverted duty structure (where inputs are taxed at a higher rate than the final product) and switch over from shuttle looms toward shuttle-less looms. This would unleash the potential of textile and garments industry and make it globally competitive and technically efficient. Further, there will be an alignment with the ease of doing business with an appropriate mix of policy, technology, institution, infrastructure, information and services. Originality/value Using frontier production function takes stochastic context into account for the dynamic character of technical efficiency and its components. Most of the past studies have assessed technical efficiency at the aggregate level using three-digit National Industrial Classification (NIC) or four-digit NIC code. An analysis at higher levels of aggregation masks the variation in technical efficiency. This study used five-digit NIC data to measure the firm-specific technical efficiency of the textile industry. According to the authors’ knowledge, this study is the first of its kind in the Indian textile industry using stochastic frontier approach and panel data. Further, it also looks at the contribution of different determinants in technical efficiency to the firms.
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
We measure the growth in the total factor productivity (TFP) of the Indian sugar industry from 2002-03 to 2017-18 using the stochastic frontier production approach. The TFP grew at -10% per annum on average during the study period. The growth was negative because the allocative change and scale effect declined. To arrest the negative growth, the technical change must be improved urgently, modern processing technology must be adopted on a large scale, and the pricing policy of inputs, especially production factors, must be rationalized.
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