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PurposeThis paper focusses on the roles and experiences of the human resource managers and safety, health and environment managers in promoting environmental sustainability in automobile industry.Design/methodology/approachThe exploration uses the data generated from ten in-depth interviews with human resource managers as well as safety, health and environment (SHE) managers from three automobile companies.FindingsThree main themes were derived from the inductive analysis in support of environmental sustainability. They comprised environment-oriented perspectives, green human resource management (HRM) practices and supportive mechanisms. These findings served as insights for the HR managers who played a supportive role in environment sustainability. This study also found that Green HRM practices within the automobile industry were confined to green training and development, green rewards, green employee involvement and green orientation.Research limitations/implicationsThe outcome of this study carries implications for managers and businesses in designing a more sophisticated framework for Green HRM practices for their companies so as to achieve a more progressive sustainable goal. For instance, HR managers who are environment-oriented can play a more active role in environmental sustainability. They can participate directly in developing policies by co-partnering with their SHE colleagues. They can encourage and motivate their employees to apply green practices both at work and in their homes. This integration would eventually create a cascading effect that could reduce the industry's negative impact on environment, thereby developing a more environmentally-friendly society.Practical implicationsThis study provided practical implications for both the HR and SHE managers in taking up responsibilities in environmental sustainability. This study also indicated the practical implications for the top management in the automobile industry, especially in the designing of the environmental sustainability framework.Originality/valueThis study contributes to the Green HRM area by understanding and comparing the roles of the HR managers and their counterpart, the SHE managers, in support of environmental sustainability. The comparison would provide a clearer picture on how the implementation of Green HRM can be implemented within the automobile industry.

An indoline derivative, 4-[(1-acetyl-2,3-dihydro-1H-indol-5-yl)sulfonylamino] benzamide (ADB), was synthesized and characterized using 1H NMR, 13C NMR, FT-IR, and LC-MS. Density functional theory (DFT) calculations revealed a small HOMO–LUMO energy gap, indicating potential biological activity. Mulliken charge, NPA, and NBO analyses confirmed the molecule’s polarity and stabilization through conjugation. Nonlinear optical (NLO) analysis showed that ADB exhibited superior NLO properties compared to urea. Molecular docking against 37 cancer-related proteins identified four high-affinity targets (PDB IDs: 3PXY, 1JXQ, 2IOK, 4AA4). Molecular dynamics (MD) simulations over 100 ns demonstrated that the 3PXY–ADB complex displayed the lowest RMSD variation and stable interactions. In silico ADMET profiling indicated favorable drug-likeness, pharmacokinetics, and an acceptable toxicity profile. Overall, these results highlight ADB as a novel indoline derivative with significant anticancer potential, meriting further in vitro and in vivo studies.

The quick convergence in the industrial infrastructure with computing and networking has proliferated the attacks of industry-based Cyber-Physical Systems (CPS). Protecting such massive, sophisticated, and varied industrial CPSs from cyber-attacks is difficult. This research proposed a novel Clustered Federated Learning (CFL) technique that identifies cyber-attacks against industrial CPSs. A CFL architecture enables many industrial CPS to create an extensive model for maintaining privacy. The contribution of the research is to identify the intrusion in the CPS using CFL that is highly effective in terms of accuracy, error rate, and MCC. CFL is a Federated Multi-Task Learning (FMTL) paradigm that groups the attacks into clusters with simultaneously trainable data distributions by taking advantage of geometric features of the Federated Learning (FL) loss surface. Extensive tests on an industrial CPS dataset show their proposed CFL technique's high efficacy in identifying cyber-attacks on industrial CPSs and its advantages over cutting-edge solutions. Experiments are conducted on gas pipelining system-based datasets. In this research study, the data resource is split into two main categories: training is 80%, and testing is 20%. The proposed CFL method is effective by attaining the highest accuracy of 99.3%, the minimal error rate of 86%, and the highest Mathews Coefficient Correlation (MCC) of 0.7994 for communication round 10.