Outside Caste? The Enclosure of Caste and Claims to Castelessness in India and the United Kingdom

Caste has always generated political and scholarly controversy but the forms that this takes today newly combine anti-caste activism with counter-claims about the irrelevance or non-existence of caste, or claims to castelessness. Such claims to castelessness are in turn viewed as a new disguise for caste power and privilege, as well as being an aspiration for people subject to caste-based discrimination. This article looks at elite claims to ‘enclose’ caste within religion (specifically Hinduism) and the (Indian) nation so as to restrict the field of social policy with regard to caste, to exempt caste (as a basis of discrimination) from the law, and limit the social politics of caste. It does so taking the comparative cases of caste and caste-based discrimination among non-Hindus, and outside India — the exclusion of Christian and Muslim Dalits (members of castes subordinated as ‘untouchable’) from provisions and protections as Scheduled Castes in India, and responses to the introduction of caste into anti-discrimination law in the UK. While Hindu organisations in the UK reject ‘caste’ as a colonial and racist term, deploying postcolonial scholarship to deny caste discrimination, Dalit organisations (representing its potential victims) turn to scholarly discourse on caste, race or human rights. These are epistemological disputes about categories of description and how ‘the social’ is made available for public debate, and especially for law. Such disputes engage with anthropology whose analytical terms animate and change the social world that is their subject

Movies and TV Influence Tobacco Use in India: Findings from a National Survey

Background: Exposure to mass media may impact the use of tobacco, a major source of illness and death in India. The objective is to test the association of self-reported tobacco smoking and chewing with frequency of use of four types of mass media: newspapers, radio, television, and movies. Methodology/Principal Findings: We analyzed data from a sex-stratified nationally-representative cross-sectional survey of 123,768 women and 74,068 men in India. All models controlled for wealth, education, caste, occupation, urbanicity, religion, marital status, and age. In fully-adjusted models, monthly cinema attendance is associated with increased smoking among women (relative risk [RR]: 1·55; 95% confidence interval [CI]: 1·04–2·31) and men (RR: 1·17; 95% CI: 1·12–1·23) and increased tobacco chewing among men (RR: 1·15; 95% CI: 1·11–1·20). Daily television and radio use is associated with higher likelihood of tobacco chewing among men and women, while daily newspaper use is related to lower likelihood of tobacco chewing among women. Conclusion/Significance: In India, exposure to visual mass media may contribute to increased tobacco consumption in men and women, while newspaper use may suppress the use of tobacco chewing in women. Future studies should investigate the role that different types of media content and media play in influencing other health behaviors

Genetic Programming Method of Evolving the Robotic Soccer Player Strategies with Ant Intelligence

This paper presents the evolved soccer player strategies with ant-intelligence through genetic programming. To evolve the code for players we used the Evolutionary Computation tool (ECJ simulatorEvolutionary Compuation in Java). We tested the evolved player strategies with already existing teams in soccerbots of teambots. This paper presents brief information regarding learning methods and ant behaviors. Experimental results depicts the performance of the evolved player strategies

Management of dentoalveolar injuries in children: A case report

Children aged 6-15 years old experience more injuries to their teeth and the injuries sustained are more serious as evidenced by a higher percentage of luxations, avulsions, fractures and dislocations. The mandible is the most frequently fractured facial bone and mandibular alveolar injuries have been reported to range between 8.1-50.6%. Those with mandibular or midface fractures have a higher incidence of associated chest, extremity, abdomen and cervical spine injuries. The growing patient with facial injuries presents the clinician with a series of thought-provoking circumstances. Dentoalveolar and mandibular injuries are especially important to understand because of the potential complications related to tooth eruption, alveolar development, occlusion and facial growth. However, the principles involved in the treatment for children need to be modified by certain anatomical, physiological and psychological factors specifically related to childhood. This case report documents the trauma, management and follow-up care of an 11-year-old boy who sustained undisplaced infraorbital, nasal fractures and mandibular dentoalveolar fracture along with other associated injuries of the extremities

Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning

3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election.

PurposeThe purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level.MethodsTweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents.ResultsOf 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week.ConclusionSurveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level

Online negative sentiment towards Mexicans and Hispanics and impact on mental well-being: A time-series analysis of social media data during the 2016 United States presidential election.

PurposeThe purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level.MethodsTweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents.ResultsOf 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week.ConclusionSurveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level