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Education, which is as old as humanity, has existed in various personal forms in non-western societies, where an osmotic exchange of wisdom, values and life skills within families, tribes and communities was instrumental in the formation and continuation of diverse wisdom traditions all over the world. A personal system of education, called Gurukula (Sanskrit guru, teacher; kula, family) education, thrived in pre-colonial South Asia for centuries before it was replaced by colonial education. This article discusses the philosophy and science behind Gurukula education, where education was a personal process involving an osmotic exchange of essences between teacher and taught, giving wide scope for the celebration of the natural diversity and variability of human behaviour and personality leading to an education that provides equal opportunity in the real sense--as an equal fit between individuals and their environment(s), giving rise to a practical democracy.

Autism Spectrum Disorder (ASD) is a highly heterogeneous set of neurodevelopmental disorders with the global prevalence estimates of 2.20%, according to DSM5 criteria. With the advancements of technology and availability of huge amount of data, assistive tools for diagnosis of ASD are being developed using machine learning techniques. The present study examines the possibility of automating the Autism diagnostic tool using various machine learning techniques on a dataset of 701 samples that contains 10 fields from AQ-10-Adult and 10 from individual characteristics. It takes two scenarios into consideration. First one is ideal case, where there are no missing values in the test cases. In this case Artificial Neural Network (ANN), Support Vector Machine (SVM) and Random Forest (RF) classifiers are trained and tested on the pre-processed dataset. To reduce computational complexity Recursive Feature Elimination (RFE) based feature selection algorithm is applied. To deal with the real-world data, in the second case missing values are introduced in the test dataset for the fields' 'age', 'gender', 'jaundice', 'autism', 'used_app_before' and their three combinations. Support Vector Machine, Random Forest, Decision Tree and Logistic Regression based RFE algorithm is introduced to handle this scenario. ANN, SVM and RF classifier based learning models are trained with all the cases. Twelve classification models were generated with RFE, out of which best performing models specific to missing value were evaluated using test cases and suggested for ASD Diagnosis.

Due to aging infrastructure & hostile operating environment, the pipelines in oil industry also faces the all-time threat of corrosion & erosion affecting leakage in the pipelines, causing emergency plant shutdown triggering huge economical, infrastructure and sometimes human loss and also makes the refinery inoperable for least 7 days. The inspection, cleaning and monitoring techniques for critical components of oil pipelines is based on robotics. India ranks second in Asia for its overall oil reserve but to meet the rising demands of oil, India is the fifth largest importer of crude oil in the world. Cross country pipelines are a safest, economical and most eco-friendly method of transportation of crude oil imported to India, flowing from offshore to a petrochemical plant. These pipelines are the arteries and veins of the fuel industry, similar to natural one in these artificial arteries the crude oil is continuously flowing in deeply buried cross-country pipelines containing slug and wax which get deposited within the pipeline creating a blockage and the situation gets even worse due to the presence of corrosion & erosion because of their. In this research, we have focused on details of a pipe inspection gauge robot which can be used for cleaning and leakage detection of cross-country pipelines which runs with the current of crude oil within the buried pipelines. [ABSTRACT FROM AUTHOR]