2,926 research outputs found

    Impacts of Caste Based Reservation System on the Lives of Scheduled Caste Engineers in India: A Case Study

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    This case study in the qualitative inquiry investigated the social and economic conditions of scheduled caste engineers in India who utilized affirmative action known as caste-based reservation program, to complete their Bachelor of Engineering studies and proceeded onto an engineering career. I selected seven scheduled caste engineers (five males and two females) as participants in this study. Data collection methods consisted of in-depth interviews, documents, and personal reflexivity journals. Data analysis generated two major themes. The first theme summarized the participants’ socio-economic conditions before they received affirmative action while the second theme portrayed their lives afterward. The study revealed the existence of discrimination, overall poverty, social stigma, and lack of economic and social opportunities before affirmative action. Their economic conditions improved after affirmative action, although their social situation—marred with stigma and discrimination—either remained unchanged or worsened. Social identity theory (Tajfel & Turner, 1979), and critical race theory (Delgado & Stefancic, 2012) explained that the formation of social groups (lower versus upper castes) and the ordinariness of racism (normalization of the caste system) could lead to the participants’ unchanged social conditions. One major recommendation is for the government of India to continue affirmative action benefits to the scheduled castes with more focus on improving their social conditions. Future studies could investigate the impacts of caste-based reservation system on other professions such as teaching, medicine, and management

    Getting acquainted with gears and wheels – quantum mechanically

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    In this section of Resonance, we invite readers to pose questions likely to be raised in a classroom situation. We may suggest strategies for dealing with them, or invite responses, or both. “Classroom” is equally a forum for raising broader issues and sharing personal experiences and viewpoints on matters related to teaching and learning science. For a pair of wheels or gears with positive coupling, i.e., without slip or play, there are rules of engagement that have some interesting consequences when their dynamics is treated quantum mechanically. We will illustrate the principal ideas involved here with the help of an elementary, basically a textbook exercise whose solution, however, is not only interesting, but may also be re-interpreted rather creatively. Possible relevance of this simple exercise to the incredible, ever-shrinking world of the nano (1 nm = 10-9 m) is pointed out

    Software Defect Prediction using Deep Learning by Correlation Clustering of Testing Metrics

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    The software industry has made significant efforts in recent years to enhance software quality in businesses. The use of proactively defect prediction in the software will assist programmers and white box testing in detecting issues early, saving time and money. Conventional software defect prediction methods focus on traditional source code metrics such as code complexities, lines of code, and so on. These capabilities, unfortunately, are unable to retrieve the semantics of source code. In this paper, we have presented a novel Correlation Clustering fine-tuned CNN (CCFT-CNN) model based on testing Metrics. CCFT-CNN can predict the regions of source code that contain faults, errors, and bugs. Abstract Syntax Tree (AST) tokens are extracted as testing Metrics vectors from the source code. The correlation among AST testing Metrics is performed and clustered as a more relevant feature vector and fed into Convolutional Neural Network (CNN). Then, to enhance the accuracy of defect prediction, fine-tuning of the CNN model is performed by applying hyperparameters. The result analysis is performed on the PROMISE dataset that contains samples of open-source Java applications such as Camel Dataset, Jedit dataset, Poi dataset, Synapse dataset, Xerces dataset, and Xalan dataset. The result findings show that the CCFT- CNN model increases the average F-measure by 2% when compared to the baseline model

    Ethno-Medicinal Uses and Agro-Biodiversity of Barmana Region in Bilaspur District of Himachal Pradesh, Northwestern Himalaya

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    India is one of the richest countries in traditional knowledge, because of its ambient biodiversity, variety of habitats and rich ethnic divergence. Thus we have had well established local health tradition still relevant in indigenous healthcare system. The paper provides first hand information on the agro-biodiversity and ethno-medicinal uses of the area. In the present study 50 species belonging to 37 genera and 17 families i.e. Shrub (1 spp.), tree (1 spp.), herb (48 spp.) were recorded under the agro-biodiversity region of the area. The utilization pattern of the species indicated that leaves of 22 species, stem of 1 species and seeds of 23 species, whole part of 11 species, tubers and flowers of 4 species, fruits of 18 species, each are used. 6 species were Indian origins, while others were non-native to Indian Himalayan Region

    Molecular and Morphophysiological Analysis of Drought Stress in Plants

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    Drought is a major environmental stress factor that affects the growth and development of plants. Most of the physiological traits associated with drought tolerance are quantitative in nature. An important research strategy that has been widely used to deal with such complexity is to use molecular markers to identify quantitative trait loci (QTLs) in appropriate mapping populations. In response to drought brought about by soil water deficit, plants can exhibit either drought escape or drought resistance mechanisms, with resistance further classified into drought avoidance and drought tolerance. Drought escape is the ability of plants to complete the life cycle before severe stress arrives. Drought avoidance is the maintenance of high tissue water potential in spite of soil water deficit. Drought avoidance is consequence of improved water uptake under stress and the capacity of plant cells to hold acquired water that reduces water loss. Drought tolerance is the ability to withstand water deficit with low tissue water potential. Plant water status that includes leaf water potential, osmotic potential and relative water content (RWC) represents an easy measure of water deficit and provides best sensor for stress. Genomics‐assisted breeding (GAB) approaches, such as marker‐assisted selection (MAS), can greatly improve precision and efficiency of selection in crop breeding. Molecular markers can facilitate indirect selection for traits that are difficult or inconvenient to score directly, pyramiding genes from different sources and combining resistance to multiple stresses. Conventional breeding for developing drought‐tolerant crop varieties is time‐consuming and labor intensive due to the quantitative nature of drought tolerance and difficulties in selection for drought tolerance. The identification of genomic regions associated with drought tolerance would enable breeders to develop improved cultivars with increased drought tolerance using marker‐assisted selection (MAS). This requires integration of knowledge from plant physiology and biotechnology into plant breeding. The availability of a large number of molecular markers, dense genetic maps and markers associated with traits and transcriptomics resources have made it possible to integrate genomics technologies into chickpea improvement

    Dhanavajra Vajracarya (1932-1994): A Tribute

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