The objective was to assess the efficacy of a one-year, peer-mediated interventional program consisting of yoga, meditation and play therapy maintained by student volunteers in a school in India. The population consisted of 69 students between the ages of 6 and 11 years, previously identified as having attention deficit hyperactivity disorder (ADHD). A program, known as Climb-Up, was initially embedded in the school twice weekly. Local high school student volunteers were then trained to continue to implement the program weekly over the period of one year. Improvements in ADHD symptoms and academic performance were assessed using Vanderbilt questionnaires completed by both parents and teachers. The performance impairment scores for ADHD students assessed by teachers improved by 6 weeks and were sustained through 12 months in 46 (85%) of the enrolled students. The improvements in their Vanderbilt scores assessed by parents were also seen in 92% (P < 0.0001, Wilcoxon). The Climb-Up program resulted in remarkable improvements in the students' school performances that were sustained throughout the year. These results show promise for a cost-effective program that could easily be implemented in any school.

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SARS-CoV-2, novel coronavirus initially detected in Wuhan, China in 2019, has been identified by World Health Organization as a cause of severe acute respiratory distress syndrome in children, adults, as well as the elderly. In light of rapid person to person transmission, CDC guidelines have advised to take precautions. As companies move to work-from-home, medicine has also moved significantly towards tele-medicine. We analyzed the efficacy of virtual assistants, including Apple Siri, Google Assistant, Windows Cortana, and Amazon Alexa in Coronavirus and their ability to diagnose and guide patients based on relevance of symptoms.

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Fault prediction in software is an important aspect to be considered in software development because it ensures reliability and the quality of a software product. A high-quality software product consists of a few numbers of faults and failures. Software fault prediction (SFP) is crucial for the software quality assurance process as it examines the vulnerability of software products towards failures. Fault detection is a significant aspect of cost estimation in the initial stage, and hence, a fault predictor model is required to lower the expenses used during the development and maintenance phase. SFP is applied to identify the faulty modules of the software in order to complement the development as well as the testing process. Software metric based fault prediction reflects several aspects of the software. Several Machine Learning (ML) techniques have been implemented to eliminate faulty and unnecessary data from faulty modules. This chapter gives a brief introduction to SFP and includes a bibliometric analysis. The objective of the bibliometric analysis is to analyze research trends of ML techniques that are used for predicting software faults. This chapter uses the VOSviewer software and Biblioshiny tool to visually analyze 1623 papers fetched from the Scopus database for the past twenty years. It explores the distribution of publications over the years, top-rated publishers, contributing authors, funding agencies, cited papers and citations per paper. The collaboration of countries and cooccurrence analysis as well as over the year’s trend of author keywords are also explored. This chapter can be beneficial for young researchers to locate attractive and relevant research insights within SFP.