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To propose a newer perspective for an often-tabooed knowledge hiding by highlighting the socio-psychological experiences in knowledge sharing.

An in-depth interviewing process was adopted to study the influencers of knowledge flow, taking 42 educators of the online teaching platforms.

Unrelatedness, supervision, and engagement propelled knowledge sharing-conducive ambiance, contrary to the conclusions of the earlier studies.

Emerged themes established a connection between knowledge sharing, and the feel-good factor, promoting future researchers to extend it to higher psychological order approaches like Guanxi, Mianzi, and Ikigai.

The purpose of this paper is to empirically exhibit the moderating effect of learner interaction (LI) on motivation to learn (MTL), mobile learning (ML) and online learning climate (OLC), so as to bring in enhanced rigour to the virtual knowledge dissemination during the times of crisis.

A total of 784 valid responses were considered for the confirmatory factor analysis to test the proposed hypotheses.

The study found that MTL and ML contributed to improved OLC and high LI moderated the positive relationship between MTL, ML and OLC. LI also directly contributed to an improved OLC.

Measures need to be designed to crowbar motivation to ensure heightened interaction of learners, to gear up the ML reach soaring heights achieving a dynamic OLC. Acclimatization of the OLC will be the visionary solution to tackle learning disruption during today’s pandemic times and also many other challenges to come in near-far future.

The current study established the moderating role of LI in influencing OLC, and also motivating facilitator’s for designing upgraded content, and thereby fuelling the intention to learn.

One of the main reasons for increase in mortality rate in woman is breast cancer. Accurate early detection of breast cancer seems to be the only solution for diagnosis. In the field of breast cancer research, many new computer-aided diagnosis systems have been developed to reduce the diagnostic test false positives because of the subtle appearance of breast cancer tissues. The purpose of this study is to develop the diagnosis technique for breast cancer using LCFS and TreeHiCARe classifier model.

The proposed diagnosis methodology initiates with the pre-processing procedure. Subsequently, feature extraction is performed. In feature extraction, the image features which preserve the characteristics of the breast tissues are extracted. Consequently, feature selection is performed by the proposed least-mean-square (LMS)-Cuckoo search feature selection (LCFS) algorithm. The feature selection from the vast range of the features extracted from the images is performed with the help of the optimal cut point provided by the LCS algorithm. Then, the image transaction database table is developed using the keywords of the training images and feature vectors. The transaction resembles the itemset and the association rules are generated from the transaction representation based on a priori algorithm with high conviction ratio and lift. After association rule generation, the proposed TreeHiCARe classifier model emanates in the diagnosis methodology. In TreeHICARe classifier, a new feature index is developed for the selection of a central feature for the decision tree centered on which the classification of images into normal or abnormal is performed.

The performance of the proposed method is validated over existing works using accuracy, sensitivity and specificity measures. The experimentation of proposed method on Mammographic Image Analysis Society database resulted in classification of normal and abnormal cancerous mammogram images with an accuracy of 0.8289, sensitivity of 0.9333 and specificity of 0.7273.

This paper proposes a new approach for the breast cancer diagnosis system by using mammogram images. The proposed method uses two new algorithms: LCFS and TreeHiCARe. LCFS is used to select optimal feature split points, and TreeHiCARe is the decision tree classifier model based on association rule agreements.

This study aims to investigate the influence of consumer value on women’s attitudes towards their intention to adopt menstrual cups. Drawing on consumer value theory, the research seeks to provide an empirical investigation of value-attitude-behaviour, an extant exploration of value concept in behaviour.

Data for this study was collected through survey questionnaires administered to 304 Indian women and analysed using partial least squares structural equation modelling.

The study’s finding indicate that emotional values (EVs) significantly predict women’s attitudes towards their intention to use menstrual cups. In addition, the desire for knowledge, price and quality considerations and awareness of the environmental impact were also found to influence women’s attitudes towards their behavioural intention to adopt menstrual cups.

This study contributes valuable empirical evidence supporting the importance of consumer values in shaping attitudes and behavioural intentions towards menstrual cups in a social marketing context. By understanding the role of EVs, social marketers can design more effective campaigns to encourage the adoption of menstrual cups. Promoting the sustainable aspects of menstrual cups, such as reduced environmental wastage, can further facilitate behaviour change among women.

This study demonstrates the value-attitude-behaviour framework, which has been a subject of very little research in the context of menstrual cup adoption in a developing country.

This research investigates the barriers hindering women from pursuing entrepreneurship as a career choice, particularly in the Indian context.

The study employs rigorous data analysis techniques, including Confirmatory Factor Analysis and Multiple Regression Analysis, on a sample of 590 MBA students, comprising both male and female participants.

The findings reveal that social and psychological factors significantly influence women’s decisions regarding entrepreneurship. Social factors such as social stigma and cultural norms, along with psychological factors like societal expectations, emerge as major barriers.

The findings have implications for policymakers, practitioners, and academics in designing interventions to address social and psychological barriers effectively. Recommendations include promoting cultural sensitivity and fostering confidence among women entrepreneurs.

This study contributes to the existing literature by quantifying the specific barriers faced by women entrepreneurs in India. It offers insights into advancing gender equity and inclusive economic development through targeted policies and programs.

Menstruation is a part of the female reproductive cycle that begins with adolescence. Menstruation is a natural change; it relates to several malpractices and misconceptions that may contribute to adverse health outcomes.

The authors have searched relevant papers using Google Scholar and PubMed to write this mini review.

During menstruation, poor hygiene maintenance can cause serious illness, which includes the urinary tract and reproductive tract infection. Menstruation management is a hygienic system, and it is essential for females because poor hygiene maintenance during menstruation can cause some infections and numerous sexually transmitted diseases. There are a few nanotechnology-based products that have come into the market to offer some relief to females during their periods.

This mini review will help researchers to design innovative female hygiene products that can relieve the discomfort caused to women during their reproductive age.

We propose cloud IoT based LMS (Livestock Management System) with three features. i) Animal healthcare monitoring and recording using IoT sensors via wearable collar, ii) Animal livestock identification using UID for animals (smart tag) and owners (smart card), iii) QR code reading, processing and display of the details in mobile via wireless technologies.

The developed animal monitoring device is used to detect animal physiological parameters such as body temperature; physical gestures like sitting, standing, eating and heartbeat, environmental parameters such as air temperature and relative humidity. Also, e-animal husbandry information network management system is the comprehensive web-based animal husbandry software designed for better interaction between veterinary hospital, veterinary doctor, owner, farmer and animal husbandry management.

Animal monitoring device mounted on the neck sense the values and predict the health status of the animal by using cloud IoT analytics platform. The accuracy of the system is 90 per cent and it can be well placed in the livestock environment.

This research is carried out in livestock cows located in Tirunelveli district. The practical difficulty was in placing sensors on the animal. The digital feed from the farmers and the veterinary hospital is input in the animal husbandry management software.

The developed system can be implemented for monitoring the health status of the animal from anywhere using mobile applications. Also, the digitized animal information helps the government to take the right decisions on policies and fund allocation.

The implemented system can be easily scaled up to large environments by using wireless communication and animal husbandry data will be available immediately. UID scheme for animals can uniquely identify the animal and its details.

The proposed work implements novel livestock monitoring and analytics system along with Aadhar (Unique ID) for animal. The proposed UID scheme is innovative and unique.