Women participation in higher education of India: the enrolment scenario
The role of education in development of the economy is well established. Since independence, the contribution of investments made by the government in higher education has been substantial however post 1980s there has been a decline in the same. Access, equity, women participation and empowerment have been an issue of deliberations all around the world. Higher education provided to women would mean independence in decision making and economic independence. It would develop capabilities to discharge duties and responsibilities in the fields of social, economic, political and cultural arenas. The objective of the study is to highlight the trend of women enrolment in different faculties of higher education and understand its contribution towards women empowerment. This study is based on secondary data available from Census of India, Selected Educational Statistics, UGC annual reports and research articles. The limitations to the study are inadequacy of the data and variance in the data available from different sources. At the end, conclusions and suggestions are presented.
Women Engineers in India
This paper discusses the findings of two extensive studies on women engineers in India using data on enrolment in engineering colleges, out-turn job opportunities, career status and other factors. Responses of women engineers and employers on perceptions and barriers have also been analysed. Although there has been a significant increase in out-turn of women engineers, their prospects of employment and career advancement profiles remain matters of concern.
Research on Fracture Recognition in Well Logging Images: Adversarial Learning with Attention
Semantic recognition of fractures in well logging images is vital for engineers to implement oil and gas exploration. An essential approach to accomplishing the object is to adopt deep learning based on convolutional neural networks. However, due to the lack of annotated labels in well logging images, it is scarcely available to directly train the semantic segmentation network. In this paper, we explore a domain shift model attempting to achieve domain adaptation from one annotated dataset to our target well logging images. This model's core is to utilize adversarial learning, including generator and discriminator, which can prompt the model to generate fracture segmentation similar to the source domain in the target domain. For enhancing the domain adaptive model further, an attention module is introduced, which can suppress redundant noise in semantic segmentation results. We demonstrate that our proposed model performs well by extensive tests and ablation experiments in the ultrasonic well logging images.