Gender inequality in science is common to most nations. To a large extent, this inequality is a product of the socio-cultural environment in which science is conducted. The professional environment and the socio-cultural context are inextricably linked together in the practice of science. This Note analyses the perceptions of women academic scientists in India regarding the work and social environment, and the nature of problems faced by them. A triangulation of questionnaire and interview methods was employed to develop a holistic picture. The findings show that women academic scientists are influenced by the prevailing sociocultural system. Thus, ‘patrifocal’ ideology prevails at the workplace and in the family. Women face gender-related difficulties at work, and also shoulder a dual burden resulting in stress. These problems have significant consequences for the career of women academic scientists.

This paper examines the social milieu of women academic scientists, parental influence in decision making in regard to the career of their daughters, parents' expectations, importance of marriage and the criteria involved therein. The support of parents and spouse are vital for the success of women scientists. Nevertheless, the 'dual burden' has an impact on professional work, and the consequent redefinition of 'success' is clearly a product of patrifocal social structures and ideology..

Analyses of the work environment in any professional organization in terms of western conceptual categories remain incomplete, in the case of a developing country, without an understanding of the social context in which the organization is placed.This article analyses the problems faced by women academic scientists in the work environment at four institutes reputed for excellence in teaching and research in science and technology in India. 'Patrifocality' in Indian society forms an essential backdrop to an understanding of this subject. The article examines the rule-related aspects referred to as the 'formal environment', and the 'informal' interaction in the work situation. The findings reveal that social stereotypes infiltrate the workplace and that there are latent aspects in the work environment that place women academic scientists at a disadvantage. These disadvantages are a function of a 'patrifocal' structure of Indian society, a general 'lack of critical mass' of women scientists and a lack of 'universalism' in science.

This paper presents a quick learning mechanism for intelligent fault diagnosis of rotating machines operating under changeable working conditions. Since real case machines in industries run under different operating conditions, the deep learning model trained for a laboratory case machine fails to perform well for the fault diagnosis using recorded data from real case machines. It poses the need of training a new diagnostic model for the fault diagnosis of the real case machine under every new working condition. Therefore, there is a need for a mechanism that can quickly transform the existing diagnostic model for machines operating under different conditions. we propose a quick learning method with Net2Net transformation followed by a fine-tuning method to cancel/minimize the maximum mean discrepancy of the new data to the previous one. This transformation enables us to create a new network with any architecture almost ready to be used for the new dataset. The effectiveness of the proposed fault diagnosis method has been demonstrated on the CWRU dataset, IMS bearing dataset, and Paderborn university dataset. We have shown that the diagnostic model trained for CWRU data at zero load can be used to quickly train another diagnostic model for the CWRU data at different loads and also for the IMS dataset. Using the dataset provided by Paderborn university, it has been validated that the diagnostic model trained on artificially damaged fault dataset can be used for quickly training another model for real damage dataset.Impact Statement-Industrial rotating machines require continuous monitoring and analysis of the experimentation data. Since operating conditions of real case machines differ from the laboratory case machines. Therefore, the diagnostic model trained using recorded signals from laboratory case machines fails to perform well on the real case machines under the changeable conditions. Training of the deep learning-based diagnostic model for every new working condition of the real case machines is uneconomical and time-consuming. The quick learning mechanism proposed in this paper solves this problem by transforming the existing diagnostic model from laboratory case machines to real case machines running under any load conditions. This method enables us to quickly transform the deep learning model trained for a machine under one condition to another condition or for one machine to another. Therefore, the proposed methodology can be very useful for the realtime monitoring of the industrial machines operating under changeable operating conditions.