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A workplace bullying dynamic involving multiple individuals targeting victims can lead to the victim losing emotional bonds or affect-based trust with their colleagues, resulting in employee silence. The literature has largely ignored this negative aspect of social dynamics. This study aims to examine the relationship between workplace bullying and employee silence behaviors and determine whether affect-based trust mediates this relationship and whether climate for conflict management moderates the mediated relationship.

Hypotheses are tested using surveys and scenario-based experiments among faculty members in Indian Universities. There were 597 participants in the survey and 166 in the scenario-based experiment.

Results revealed that workplace bullying correlated positively with silence behaviors, and affect-based trust mediated the bullying-silence relationship. The hypothesized moderated mediation condition was partially supported as moderated the mediating pathway, i.e. indirect effects of workplace bullying on defensive silence and ineffectual silence via affect-based trust were weaker for employees with high climate for conflict management. However, the study failed to support the moderation of climate for conflict management in the relationship between workplace bullying and affect-based trust and workplace bullying and relational silence. The results of this moderated effect of climate for conflict management were similar in both studies.

This study is one of the few attempts to examine employee silence in response to workplace bullying in academia. Additionally, the study revealed a critical area of trust depletion associated with bullying and the importance of employee perceptions of fairness toward their institutions’ dispute resolution processes.

High incidence of workplace bullying in Indian organizations is well-acknowledged, but less is known about the games that bullies play to create a favorable atmosphere for bullying in academic institutions. This study aims to reveal the “safe game” tactics that the bullies use to chase targets like a predator chases his prey.

The study explores various manifestations of academic bullying by analyzing the victimization experiences of women faculty in academic institutions. The data were collected through in-depth, semi-structured interviews and informal discussions to explore the victimization episodes in detail.

The analysis indicates a set of common manifestations framed by bullies in academia to create a favorable environment for bullying. These manifestations often play out in a sequence. Initially, the targets are overloaded with work to portray the victims as incapable and less competent. Then, the bullies lodge many formal complaints with the help of their supporters. Finally, they create an environment of silence by threatening them for their responses against bullying.

This paper is supported by previous research in this area and progresses by exploring the experiences of the victims in academics to find a common sequence in the mistreatment they suffer. The study concludes by showing unexplored areas in research on workplace bullying in the academic sector and provides a foundation for further research.

Workplace bullying generates various emotions, including shame in the target; these emotions can induce employee silence. However, the role of shame in the relationship between workplace bullying and employee silence, and the individual differences in how victims experience shame and silence, has not yet been explored. The present study aims to fill this gap in the literature, using the effect of shame as a mediator and core self-evaluation (CSE) as a moderator.

Two thousand faculty members working in different colleges in India were invited to participate in the online survey. The participants were invited to fill in the questionnaire only if they had experienced shame by bullying during the preceding two weeks. Three hundred and twenty faculty members responded to the survey.

The results showed that shame mediates the relationship between workplace bullying and diffident silence. In addition, CSE moderates the relationship between shame and diffident silence but not the relationship between workplace bullying and shame. That is, diffident silence induced by shame was noted to be weaker for employees with high CSE. Importantly, the study could not find any individual difference in experiencing shame by bullying.

Improved CSE can effectively influence diffident silence through shame, helping the management to recognize workplace bullying.

It is a unique attempt to address diffident silence among Indian academicians, and study the role of targets’ shame and CSE while adopting silence on workplace bullying.

Post COVID-19 crisis, healthcare has become a priority for every government. Furthermore, the pandemic has also made us realise why do we need an affordable healthcare delivery service at the grassroots level. As a result, the Government of India has come out with the Ayushman Sahakar scheme. This scheme has been launched by the Union Government with an aim to assist the cooperatives in the creation of healthcare infrastructure in this country. It is pertinent to note that the cooperatives in the last few years have transformed rural areas and have pushed them out of poverty. As a result, it will be interesting to see the impact cooperatives will have in the field of healthcare.

The authors in this work have discussed the history of healthcare cooperatives in India. After this, the authors have analysed the government schemes and legal provisions which regulate the functioning of healthcare cooperatives in this country. In the next part, the authors studied the Ayushman Sahakar scheme. The authors have discussed the features of the scheme and the impact it has generated in the field of healthcare. Lastly, the author has discussed the challenges which healthcare cooperatives face in this country and how we can overcome those challenges.

The purpose of this study is to forecast the mechanical properties of ternary blended concrete (TBC) modified with oyster shell powder (OSP) and shea nutshell ash (SNA) using deep neural network (DNN) models.

DNN models with three hidden layers, each layer containing 5–30 nodes, were used to predict the target variables (compressive strength [CS], flexural strength [FS] and split tensile strength [STS]) for the eight input variables of concrete classes 25 and 30 MPa. The concrete samples were cured for 3–120 days. Levenberg−Marquardt's backpropagation learning technique trained the networks, and the model's precision was confirmed using the experimental data set.

The DNN model with a 25-node structure yielded a strong relation for training, validating and testing the input and output variables with the lowest mean squared error (MSE) and the highest correlation coefficient (R) values of 0.0099 and 99.91% for CS and 0.010 and 98.42% for FS compared to other architectures. However, the DNN model with a 20-node architecture yielded a strong correlation for STS, with the lowest MSE and the highest R values of 0.013 and 97.26%. Strong relationships were found between the developed models and raw experimental data sets, with R² values of 99.58%, 97.85% and 97.58% for CS, FS and STS, respectively.

To the best of the authors’ knowledge, this novel research establishes the prospects of replacing SNA and OSP with Portland limestone cement (PLC) to produce TBC. In addition, predicting the CS, FS and STS of TBC modified with OSP and SNA using DNN models is original, optimizing the time, cost and quality of concrete.

Accurate predictions of the steady-state corrosion phase and service life to achieve specific safety limits are crucial for assessing the service of reinforced concrete (RC) structures. Forecasting the service life (SL) of structures is imperative for devising maintenance and repair strategy plans. The optimization of maintenance strategies serves to prolong asset life, mitigate asset failures, minimize repair costs and enhance health and safety standards for society.

The well-known empirical conventional (traditional) approaches and machine learning (ML)-based SL prediction models were presented and compared. A comprehensive parametric study was conducted on existing models, considering real-world conditions as reported in the literature. The analysis of traditional and ML models underscored their respective limitations.

Empirical models have been developed by considering simplified assumptions and relying on factors such as corrosion rate, steel reinforcement diameter and concrete cover depth, utilizing fundamental mathematical formulas. The growth of ML in the structural domain has been identified and highlighted. The ML can capture complex relationships between input and output variables. The performance of ML in corrosion and service life evaluation has been satisfactory. The limitations of ML techniques are discussed, and its open challenges are identified, along with insights into the future direction to develop more accurate and reliable models.

To enhance the traditional modeling of service life, key areas for future research have been highlighted. These include addressing the heterogeneous properties of concrete, the permeability of concrete and incorporating the interaction between temperature and bond-slip effect, which has been overlooked in existing models. Though the performance of the ML model in service life assessment is satisfactory, models overlooked some parameters, such as the material characterization and chemical composition of individual parameters, which play a significant role. As a recommendation, further research should take these factors into account as input parameters and strive to develop models with superior predictive capabilities.

Recent deployment has revealed that ML algorithms can grasp complex relationships among key factors impacting deterioration and offer precise evaluations of remaining SL without relying on traditional models. Incorporation of more comprehensive and diverse data sources toward potential future directions in the RC structural domain can provide valuable insights to decision-makers, guiding their efforts toward the creation of even more resilient, reliable, cost-efficient and eco-friendly RC structures.