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The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The author identifies the traits of consumer resilience in emerging markets, classifies these major traits into five categories and analyses the influence relationships among them with distinctive focus on the psychological and personal resilience aspects.

The influence relations among the traits of consumer resilience from an expert perspective were identified with typical focus on electronic supply chains, and later the same was analysed through an intelligent influence modelling method, the grey causal modelling (GCM).

The major traits were analysed using the GCM, where the cause–consequence relations were observed for various objectives and the situational effects are noted. By constructing a magnitude plot and further a causal magnitude table, the important influence traits of consumer resilience for the considered case were observed and the same were auxiliary validated using an interpretive structural modelling (ISM) based approach.

As perceived from the results, it is evident that social support and recommendations from customers emerge as the principal influence traits of consumer resilience from an expert perspective, considering the case. The study can be further extended empirically to validate the findings.

Altogether, the author can recommend for practitioners that the influence of family, society, friends, peers as well as ratings from the customers can determine the level of consumer resilience. Hence, practitioners of customer relationship management can focus on improving the product and brand awareness among customers, so that more customers may recommend for typical products.

Consumer resilience depend on several factors, where the author has identified 25 major traits of the same and classified them into five major categories, including individual psychological factors, individual attitudes, individual socio demographic factors, micro environmental factors and macro environmental factors and the influence relations among them were studied from an expert perspective.

The researchers aim to investigate the role of psychological capital (PsyCap) in facilitating intrinsic motivation and goal-commitment among employees at the workplace, affecting outcome variables, namely, in-role and extra-role job performance.

Data were collected from 640 employees working in the information technology sector of India. Covariance-based structural equation modeling (CB-SEM) was used to test the hypothesized relationships.

Analysis revealed a significant positive impact of PsyCap on the two behavioral facets of job performance. Intrinsic motivation and goal-commitment were found mediating the influence of PsyCap on the two facets of job performance.

The information technology sector is characterised by continuous change. It requires voluntary prosocial behavior from employees, where the employees are expected to display multifaceted job performance behaviors, where they go beyond their job duties to cater for the dynamics of the IT sector. The present study provides means by which intrinsic motivated and goal-committed behavior are facilitated for both the in-role and extra-role job performance.

The present study is among the few preliminary studies that have provided evidence that intrinsic motivation and goal-commitment are the two variables which aid PsyCap in predicting both the prescribed and voluntary job performance behaviors.

The study aims to highlight the barriers faced by the entrepreneurs toward achieving sustainability in business and innovation cultivation by offering solutions for academicians, practitioners and policymakers. The study uses the resource-based view (RBV) theory to discuss how an organization’s resources and capabilities influence the competitive ambience and barriers faced by entrepreneurs.

The present research uses grey-causal modelling (GSC) to analyse the barriers against successful entrepreneurship.

The research focuses on the usefulness of dynamic capabilities, managing and cooperating resources in the entrepreneurship setting. The paper highlights the importance of resource gathering and nurturing as a method to combat scarcity. This research further identifies that financial limitations, regulatory obstacles, challenges to sourcing qualified labour, poor infrastructure and technology, limited mentorship opportunities, lack of scalability, low initial cost barriers in product development and risk-averse attitudes are the major factors hindering entrepreneurs from obtaining sustainable business and innovation.

The contribution of this research to the literature is that it assesses RBV theory within the realm of entrepreneurship, providing a different perspective on resources and capabilities as well as the challenges faced by entrepreneurs. The systematic approach to the analysis and prioritization of various barriers is innovative, and it adds knowledge in this area.

The study aims to work on two objectives, first to provide a theoretical foundation along with ongoing trend in the field of green human resource management (GHRM) and employee green creativity (EGC) in the hospitality and tourism industry. Second, the study also proposes a conceptual research model to understand the dynamics of the relationship between GHRM and EGC for future studies.

The study uses multi-method approach, systematic literature review and bibliometric analysis is performed on published work on GHRM and EGC. This study initially shortlisted 52 publications from multiple databases, which was refined to 11 Scopus-indexed papers, which discussed both GHRM and EGC attributes in a hospitality context. VOSviewer and advance excel software's are being used to perform the analysis.

The systematic literature review concluded that very limited studies have been conducted on GHRM and EGC in the hospitality context and it has recently gained prominence during the covid pandemic. While bibliometric analysis also identified h-index authors with their co-authorship network, citations and keywords matrix and the changing trends in the domain of GHRM and EGC in hospitality and tourism industry. The analysis also highlights the individual and organisational factors influencing the relation between GHRM and EGC.

This study is the first to conduct a systematic literature review and bibliometric analysis on GHRM and EGC in hospitality and tourism sector. Moreover the paper also provides researchers with an in-depth summary of the available literature and a blue print for future studies on GHRM and EGC.

The choice of crops to produce at a location depends to a large degree on the climate. As the climate changes and food demand evolves, farmers may need to produce a different mix of crops. This study assesses how much cropland may be subject to such upheavals at the global scale, and then focuses on China as a case study to examine how spatial heterogeneity informs different contexts for adaptation within a country.

A global agricultural economic model is linked to a cropland allocation algorithm to generate maps of cropland distribution under historical and future conditions. The mix of crops at each location is examined to determine whether it is likely to experience a major shift.

Two-thirds of rainfed cropland and half of irrigated cropland are likely to experience substantial upheaval of some kind.

This analysis helps establish a global context for the local changes that producers might face under future climate and socioeconomic changes. The scale of the challenge means that the agricultural sector needs to prepare for these widespread and diverse upheavals.

Through the “Going Digital Initiative,” the Ghanaian government has introduced policies that aim at improving the information and communication technology (ICT) infrastructure of the country. These ICT policies have benefited numerous sectors of the Ghanaian economy. In logistics management, ICT has impacted drone medical delivery in the healthcare and maritime sectors. However, the importance of ICT is not realized in the motorcycle goods transport (MGT) industry, regardless of its popularity and high economic dependency. Second, all research on motorcycles is focused on diverse social concerns, and no study has attempted to analyze ICT implementation for MGT operations. This is a significant gap in logistics management. Hence, the study aimed to investigate the impact of ICT on Ghana's MGT industry empirically.

The study adopts a two-phase data collection approach to collect the data. The authors use partial least square structural equation modeling to analyze the study's measurement and structural assessment model.

ICT positively impacts MGT and the drivers considered. The drivers positively influence MGT. The study further analyzes novel results on the relationships between the drivers and their mediating roles in enhancing MGT performance.

The study's originality is the extension of ICT adoption and usage in MGT. The lack of literature on the importance of ICT for MGT services makes this study the primary source of literature, and the relationships investigated are unique as the research area is unexplored.

The patient-side manipulator (PSM) achieves high torque capability by combining harmonic servo system with high reduction ratio and low torque motor. However, high reduction ratio can increase inertia and decrease compliance of the manipulator. To enhance the backdrivability of the minimally invasive surgical robot, this paper aims to propose a resistance torque compensation algorithm.

A resistance torque compensation algorithm based on dynamics and Luenberger observer is proposed. The dynamics are established, considering joint flexibility and an improved Stribeck friction model. The dynamic parameters are experimentally identified by using the least squares method. With the advantages of clear structure, simple implementation and fast solution speed, the Luenberger observer is selected to estimate the unmeasured dynamic information of PSM and realize the resistance torque compensation.

For low-speed surgical robots, the centrifugal force term in the dynamic model can be simplified to reduce computational complexity. Joint flexibility and an improved Stribeck friction model can be considered to improve the accuracy of the dynamic model. Experiment results show that parameter identification and estimated results of the Luenberger observer are accurate. The backdrivability of the PSM is enhanced in ease and smoothness.

This algorithm provides potential application prospects for surgical robots to maintain high torque while remaining compliant. Meanwhile, the enhanced backdrivability of the manipulator helps to improve the safety of the preoperative manual adjustment.

Due to the environmental issues caused by petroleum lubricants used in lubrication, the concept of creating various bio-lubricants requires research globally. Thus, this study aims to develop, characterize and test the base ficus carica oil (fig oil) for its ethylene vinyl acetate copolymer (EVA) and sodium dodecylbenzene sulfonate (SDBS) content.

The sample characterization was done using the Fourier transmission infrared spectrum, whereas the morphologies of the EVA, SDBS particles and lubricated surfaces were carried out under scanning electron microscope equipment. To ensure the homogeneity of the solution (base oil and additives), the formulations were subjected to the sonication process. The anti-friction and anti-wear properties of EVA and SDBS particles as lubricant additives were investigated using a ball on a flat high-frequency reciprocating rig tribo-tester.

According to the findings, the base oil’s anti-friction and anti-wear capabilities can be greatly enhanced by the additions. revealed that the best results were obtained when 1.2% EVA + 2% SDBS was applied for the examination of wear (597.8 µm) and friction coefficient (0.106). Commercial references were used, nevertheless, and the results were excellent. This is because the particles in the contact area during lubrication have strong solubility and quickly penetrate the contact zone. The lubricating mechanisms were explained by a tribological model of the EVA + SDBS and SDBS particles.

The coefficient of friction and wear reduction caused by the use of the additives will certainly enhance system performance and protect the machine components from excessive wear that could cause damage or failure.

The originality and uniqueness of this work are officially affirmed by the authors. The authors’ autonomous and original contribution to the development of sustainable lubrication is represented in this work. To the best of the authors’ knowledge, no other study has been published or made publicly available that duplicates the precise scope and goals of our research, and this conclusion is based on a thorough literature assessment.