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A systematic correlation is established between the axial load-carrying capacity of the socketed piles and empirical methods. The axial load-carrying capabilities were calculated with the help of pile load tests for ten single piles in Navi Mumbai, India. Pile load tests were carried out to evaluate the skin friction and base resistance in overlying soil and rock. The test results of axially loaded bored piles socketed into the rock are mentioned in this paper. The pile socket involved rocks of various strengths such as gypsum, limestone, and basalt. In reality, axial capacity is calculated from skin friction of the pile, which occurs owing to a very small settlement in the rock socket. The unconfined compressive strength of the rock is collected from the laboratory test. The load-bearing capability of the socketed piles is determined using several empirical methods, which are usually evaluated by a back-assessment of the in-situ pile load test. However, the significant load-carrying capacity values derived from empirical correlations are found to be reasonably good compared with those estimated from pile load tests. [ABSTRACT FROM AUTHOR]

Early childhood education and care (ECEC) is a crucial intervention because of its long‐term impact on child development. However, information concerning the effectiveness of the ECEC in developing countries like India is sparsely available. This empirical study drew upon primary data collected from India's eastern state, West Bengal and investigated whether attending preschool contributed to children's cognitive and social skill development. The study found that attending preschool did not provide dividends in the form of cognitive and social skill accumulation. Furthermore, attending private preschool was associated with socioemotional development but no cognitive development. Moreover, parents' education seems to be a strong predictor for child's development. Given the findings of this paper, preschools in India, both in the public and private sectors, would need considerable quality improvement to deliver developmentally appropriate ECEC to children. It is particularly relevant in the context of the new National Education Policy in India, which emphasises the need to provide universal access to high‐quality ECCE to all young children across the country. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an important research domain in text analytics and natural language processing. Since the last few decades, it has become a fascinating and salient area for researchers to understand human sentiment. According to the 2011 census, the Assamese language is spoken by 15 million people. Despite being a scheduled language of the Indian Constitution, it is still a resource-constrained language. Though it is an official language and presents its script, less work on sentiment analysis is reported in the Assamese language. In a linguistically diverse country like India, it is essential to provide a system to help people understand the sentiments in their native languages. So, the multilingual society in India would not be able to fully leverage the benefits of AI without the state-of-the-art NLP systems for the regional languages. Assamese language become popular due to its wide applications. Assamese users in social media as well as other platforms also are increasing day by day. Automatic sentiment analysis systems become effective for individuals, government, political parties, and other organizations and also can stop the negativity from spreading without a language divide. This paper presents a study on textual sentiment analysis using different lexical features of the Assamese news domain using machine learning and deep learning techniques. In the experiments, the baseline models are developed and compared against the models with lexical features. The proposed model with AAV lexical features based on XGBoost classifier predicts the highest accuracy of 86.76% with TF-IDF approach. It is observed that the combination of the lexical features with the machine learning classifier can significantly help the sentiment prediction in a small dataset scenario over the individual lexical features. [ABSTRACT FROM AUTHOR]