Women mentoring programs are holistic approaches to empower women to achieve their individual objectives but also contribute to the broader movement of gender equality and representation in diverse fields. The present paper critically reviews the available literature on mentorship programs for women in STEM fields. It also lists the benefits and challenges of these programs. This paper proposes a structured mentoring framework that integrates algorithmic, heuristic, and hybrid strategies tailored to support women in STEM fields. While conceptual, the model provides a foundation for scalable mentoring initiatives and highlights areas for future empirical validation.

Women who return to careers after a career break face unique challenges and untapped opportunities in the industry. Career breaks, often due to family responsibilities, childcare, eldercare, or personal reasons, create significant barriers to re-entry into the workforce. However, with increasing awareness, diversity and inclusion initiatives, and government policies, industries and academia are beginning to create pathways for women engineers to return. The engineering sector must evolve to accommodate returning women, leveraging their experience and skills. With structured mentorship, upskilling, returnship programs, and inclusive hiring policies, returning women can rebuild their careers and contribute significantly to STEM innovation, research, and leadership. This paper analyses the education initiatives to reskill and upskill the returning women by two international training programs - Returning Mothers Conference of IEEE and SAR100 program of WePOWER network of the World Bank and consolidates the outcomes which can evolve to benefit both industries and women.

Higher Education is an area where the rapid advancements in technology have created a disparity between what educational institutions teach and what industries require. More than the technology skills that are taught, the learning skills are required for the students to grab the essence of the technology and apply it. The accreditation organisations across the globe have included the mentorship for the students as a part of the assessment of effective teaching learning process but the assessment of any mentorship training for the teachers or any assessment metrics to evaluate the training has not been included in any of their framework. This paper provides a framework for the teacher’s mentorship programme and a methodology to evaluate the programme by assessing it in the informal learning atmosphere of a talent show. The outcome shows that there is a positive improvement in the approach of the mentors to the students as a result of this effort.

The persistent disconnect between academic curricula and industry requirements remains a critical challenge in higher education, particularly in fast-evolving fields such as artificial intelligence (AI), semiconductor technology, and cyber security. This paper proposes faculty industry internships as a transformative mechanism to align academic training with industry needs. By immersing faculty in industry environments, these programs empower educators to acquire practical skills, integrate real-world insights into teaching, and mentor students effectively. Case studies from Indian institutions like IISc Bengaluru and C-DAC demonstrate the success of Faculty Development Programs (FDPs) in fostering industry-relevant competencies, while empirical data from a mixed-methods study involving 150 faculty members and 30 industry experts highlight improved curriculum alignment and student employability. Challenges such as faculty resistance and funding constraints are addressed through hybrid models and policy reforms like the National Education Policy (NEP) 2020. The paper concludes with actionable strategies for institutions and policymakers to prioritize faculty-industry collaboration, ensuring the sustainability and relevance of technical education in India.

Speech enhancement is an interesting research area that aims at improving the quality and intelligibility of the speech that is affected by the additive noises, such as airport noise, train noise, restaurant noise, and so on. The presence of these background noises degrades the comfort of listening of the end user. This article proposes a speech enhancement method that uses a novel feature extraction which removes the noise spectrum from the noisy speech signal using a novel fractional delta-AMS (amplitude modulation spectrogram) feature extraction and the D-matrix feature extraction method. The fractional delta-AMS feature extraction strategy is the modification of the delta-AMS with the fractional calculus that increases the sharpness of the feature extraction. The extracted features from the frames are used to determine the optimal mask of all the frames of the noisy speech signal and the mask is employed for training the deep belief neural networks (DBN). The two metrics root mean square error (RMSE) and perceptual evaluation of speech quality (PESQ) are used to evaluate the method. The proposed method yields a better value of PESQ at all level of noise and RMSE decreases with increased noise level.