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This study aims to evaluate and compare the quality of work life (QWL) of nurses, in pre- and post-COVID-19 pandemic situations.

The study adopts a descriptive research design. Data were collected during the pre- and post-pandemic periods. The target sampling unit of the study comprises nurses working in Bangalore city, Karnataka, India. The minimum sample size was determined (Bartlett et al., 2001) as 385. The scale validation is carried out. The factors for the present study were explored using exploratory factor analysis and confirmed by confirmatory factor analysis. Model fitness (proposed measurement model) is ensured by using fit indices. The linear regression method was used to measure the level of QWL of nurses.

The present study noted that key factors that affects the QWL of nursing staff are work condition; work environment; work-life balance; compensation and reward; career development; job satisfaction and security; organization culture; relationship among co-workers and stress. Further, it is noticed that QWL of nurses pre-COVID-19 pandemic is 87.2%, while post-COVID-19 pandemic, it is 67%.

Present study can be extended to address the same research question by considering sampling unit such as therapist, technicians and sanitarians who have equally undergone tremendous pressure during pandemic.

The study outcome provides references for organizations engaged in health services to understand the extreme job conditions posed by pandemic. The constructive inspiration (physio-social and organizational support) reinforces the nurses to continue in their professions by decreasing negative impact.

The research paper extends the contributions of Hwang (2002), Nikeghbal et al. (2021), Howie–Esquivel et al. (2022) and Rania et al. (2023) and add to the existing body of the QWL literature. The outcome of the research records the prevailing conditions of pandemic and its effect on changes in work environment with specific reference to health-care sector.

This study aims to explore the mediating role of self-efficacy and the relationship between job satisfaction and employee commitment.

The study is designed based on social cognitive theory. To collect the data, survey method is used in the present study. Questionnaires were distributed to the 75 randomly selected textile industry registered and located in Bengaluru city, India. Employees of 71 organizations participated in the survey. Out of 700 employees who received the email survey, 452 valid responses were considered for the data analysis. To examine the defined research hypothesis, a structural equation model is used.

The mediating analysis explored that the direct effect is 0.700, the indirect effect is 0.1730 and the total effect is 0.8731; it indicated that self-efficacy mediates the relationship between job satisfaction and employee commitment. Research also reveals that there is a positive relationship between self-efficacy, employee commitment and job satisfaction of employees working in the textile industry. The proposed measurement model statistics are as follows: CMIN = 2.322; df = 49; GFI = 0.958; AGFI = 0.934; NFI = 0.943; RFI = 0.923; IFI = 0.967; TLI = 0.955; CFI = 0.966. All these indices were nearer to unity.

The research findings provide insights to the management, practitioners and employers about the status of job satisfaction, self-efficacy and commitment of employees in textile organizations which will help make the strategies to increase the overall performance of the organization by enhancing the levels of job satisfaction, self-efficacy and commitment of textile industry employees.

To the best of the authors’ knowledge, this is the first study that tests the relationship among self-efficacy, job satisfaction and the mediating effect of self-efficacy of employees in Indian textile industries.

This paper aims to establish the microstructures and the process-structure relationships in duplex stainless steel powders consolidated by selective laser melting (SLM).

A priori data on energy density levels most appropriate to consolidation of duplex stainless steel powders through SLM served as the basis to converge on the laser settings. Experimental designs with varying laser power and scan speeds and test pieces generated allowed metallographic evaluations based on optical and scanning electron microscopy and electro backscatter diffraction analyses.

Duplex stainless steel powders are established for processing by SLM. However, the dynamic point heat source and associated transient thermal fields affect the microstructures to be predominantly ferritic, with grains elongated in the build direction. Austenite precipitated either at the grain boundaries or as Widmanstätten laths, whereas the crystallographic orientations and the grain growth are affected around the cavities. Considerable CrN precipitation is also evidenced.

Duplex stainless steels are relatively new candidates to be brought into the additive manufacturing realm. Considering the poor machinability and other difficulties, the overarching result indicating suitability of duplex powders by SLM is of considerable value to the industry. More significantly, the metallographic evaluation and results of the current research allowed further understanding of the material consolidation aspects and pave ways for fine tuning and establishment of the process-structure-property relationships for this important process-material combination.

A new cutting tool is always well-defined and sharp at the onset of the metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of the service period of the cutting tool while machining. It is significant to provide a corresponding real-time varying damping to control this chatter, which directly influences accuracy and quality of productivity. This paper aims to review the literature related to the application of smart fluid to control vibration in metal cutting and also focused on the challenges involved in the implementation of active control system during machining process.

Smart dampers, which are used as semi-active and active dampers in metal cutting, were reviewed and the research studies carried out in the field of the magnetorheological (MR) damper were concentrated. In smart materials, MR fluids possess some disadvantages because of their sedimentation of iron particles, leakage and slow response time. To overcome these drawbacks, new MR materials such as MR foam, MR elastomers, MR gels and MR plastomers have been recommended and suggested. This review intents to throw light into available literature which exclusively deals with controlling chatter in metal cutting with the help of MR damping methods.

Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration. In the past, many researchers have attempted to implement MR damper in metal cutting to control vibration and were successful. Various methods with the help of MR fluid are illustrated.

A new cutting tool is always well-defined and sharp at the onset of metal cutting process and gradually losses these properties as the machining process advances. Similarly, at the beginning of the machining process, amplitude of tool vibrations is considerably low and it increases gradually and peaks at the end of service period of cutting tool while machining. Application of MR damper along with the working methodology in metal cutting is presented, challenges met are analyzed and a scope for development is reviewed.

This study provides corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity. Using an MR damper popularly known for its semi-active damping characteristics is very adaptable and flexible in controlling chatter by providing damping to real-time amplitudes of tool vibration.

This study attempts to implement smart damper in metal cutting to control vibrations.

It is significant to provide corresponding real-time varying damping to control tool vibration which directly influences accuracy and quality of productivity.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The study aims to investigate magnetohydrodynamics thermal convection energy transference and entropy production in an open chamber saturated with ferrofluid having an isothermal solid block.

Analysis of thermal convection phenomenon was performed for an open chamber saturated with a nanofluid having an isothermal solid unit placed inside the cavity with various aspect ratios. The left border temperature is kept at T_c. An external cooled nanofluid of fixed temperature T_c penetrates into the domain from the right open border. The nanofluid circulation is Newtonian, incompressible, and laminar. The uniform magnetic field of strength B at the tilted angle of γ is applied. The finite volume technique is used to work out the non-linear equations of liquid motion and energy transport. For Rayleigh number (Ra=1e+7), numerical simulations were executed for varying the solid volume fractions of the nanofluid (ϕ = 0.01–0.04), the aspect ratios of a solid body (A_s = 0.25–4), the Hartmann number (Ha = 0–100), the magnetic influence inclination angle (γ = 0–π/2) and the non-dimensional temperature drop (Ω = 0.001–0.1) on the liquid motion, heat transference and entropy production.

Numerical outcomes are demonstrated by using isolines of temperature and stream function, profiles of mean Nusselt number and entropy generations. The results indicate that the entropy generation rate and mean Nu can be decreased with an increase in Ha. The inner solid block of A_s = 0.25 reflects the maximum heat transfer rate in comparison with other considered blocks. The addition of nano-sized particles results in a growth of energy transport and mean entropy generations.

An efficient computational technique has been developed to solve natural convection problem for an open chamber. The originality of this research is to scrutinize the convective transport and entropy production in an open domain with inner body. The outcomes would benefit scientists and engineers to become familiar with the investigation of convective energy transference and entropy generation in open chambers with inner bodies, and the way to predict the energy transference strength in the advanced engineering systems.

This research work aims to determine the maximum load a thermoplastic gear can withstand without the occurrence of extended contact. The extended contact of polymer gears is usually overlooked in basic design calculations, although it considerably affects the gear's load-carrying ability. Although various researchers highlighted the phenomenon, an extensive investigation of the extended contact behaviour is limited. Hence the work aims to investigate the premature and extended contact behaviour of thermoplastic gears and its effect in the gear kinematics, bending stiffness, stresses induced and the roll angle subtended by the gear pair.

The work uses finite element method to perform quasi-static two-dimensional analysis of the meshing gear teeth. The FE model was developed in AutoCAD and analysed using ANSYS 19.1 simulation package. A three-dimensional gear model with all the teeth is computationally intensive for solving a static analysis problem. Hence, planar analysis with a reduced number of teeth is considered to reduce the computational time and difficulty.

The roll angle subtended at the centre by the path of approach is higher than the path of recess because of the increased load sharing. The contact stress profile followed a unique R-F-R-F pattern in the premature and extended contact regions due to the driven tip-driver flank surface contact. A non-dimensional parameter was formulated correlating the young's modulus, the load applied and deflection induced that can be utilised to predict the occurrence of premature and extended contact in thermoplastic gears.

The gear rating standards for polymer gears are formulated from the conventional metal gears which does not include the effect of gear tooth deflection. The work attempts to explain the gear tooth deflection for various standard thermoplastics and its effect in kinematics. Likewise, a new dimensionless number was introduced to predict the extended contact that will help in appropriate selection of load reducing the possibility of wear.

Debriefs are a type of workplace meeting that often use after events and critical incidents. Debriefs are used to review performance, promote shared learning and understanding, and improve future team performance. Similarly, reflexivity refers to the extent to which team members reflect upon and openly discuss group processes, procedures, and actions to improve future team performance. In this chapter, the authors review the separate literatures and explore the relationship between debriefs and reflexivity. While the debrief literature does focus on aspects of reflection, what occurs between the aspects of reflection, planning, and action is left unexplored. The concept of reflexivity fits well with the successful use of debriefs, as reflexivity ensures that reflection results in outcomes and moves beyond just an overview or discussion during debriefing. Additionally, important constructs such as psychological safety and sensemaking are relevant to both debriefs and reflexivity such that open and honest discussion as well as developing shared understanding are necessary for effective debriefing and reflection. Using the constructs of psychological safety and sensemaking, the authors propose a model that situates both reflexivity and effective debriefs in the context of team learning. This model integrates team reflexivity with team debriefs, provides a better understanding of how teams can carry out more effective debriefs, and explains how more effective debriefing and greater team reflexivity lead to enhanced learning and improvement in team performance.

Consumers are increasingly choosing social media over other channels and mechanisms for grievance redressal. However, not all social media grievances elicit a response from businesses. Hence, in this research the authors aim to explore the effect of the complainant's social characteristics and the complaint's social and content characteristics on the likelihood of receiving a response to a grievance from the business on social media.

The authors build a conceptual model and then empirically test it to explore the effect of the complainant's characteristics and the complaint's characteristics on the likelihood of response from a business on social media. The authors use data of consumer grievances received by an Indian airline operator on Twitter during two time periods – the first corresponding to lockdown during Covid-19 pandemic, and the second corresponding to the resumption of business as usual following these lockdowns. The authors use logistic regression and the hazard rate model to model the likelihood of response and the response delay, respectively, for social media customer grievances.

Complainants with high social influence are not more likely to get a response for their grievances on social media. While tagging other individuals and business accounts in a social media complaint has negative effect on the likelihood of business response in both the time periods, the effect of tagging regulatory bodies on the likelihood of response was negative only in the Covid-19 lockdown period. The readability and valence of a complaint were found to positively affect the likelihood of response to a social media grievance. However, the effect of valence was significant only in lockdown period.

This research offers insights on what elicits responses from a service provider to consumers' grievances on social media platforms. The extant literature is a plenty on how firms should be engaging consumers on online media and how online communities should be built, but scanty on grievance redressal on social media. This research is, therefore, likely to be useful to service providers who are inclined to improve their grievance handling mechanisms, as well as, to regulatory authorities and ombudsmen.

The purpose of this study is to refine the microstructure and improve the corrosion behaviour of aluminium alloy AA5083 by subjecting it to friction stir processing (FSP).

FSP trials are conducted as per central composite design, by varying tool rotation speed, tool traverse speed and shoulder diameter at three levels. The microstructure is examined and the hardness is measured for both the base material and the processed workpieces. The corrosion behaviour of the base material and processed workpieces is studied using potentiodynamic polarization technique for three different testing temperatures, and the corrosion current and corrosion rate are calculated.

The results reveal that FSP refined the grains, dispersed secondary phases, increased the hardness and improved the corrosion resistance of most of the friction stir processed specimens than the base material at all the three testing temperatures. Grain refinement and fine dispersion of ß phase improves the hardness and corrosion resistance of most of the FSPed specimens. However partial dissolution of ß phase decreases the hardness in some of the specimens. Most of the FSPed specimens displayed more positive potential than the base material at all the testing temperatures representing a higher nobility than the base material, as a result of fine dispersion of secondary phase particles in the matrix. Large pits formed on the surface of the base specimen indicating a higher corrosion rate at all three testing temperatures. The SEM image of FSPed specimens reveals the occurrence of very few pits and minimal corrosion products on the surface, which indicates lower corrosion rate.

The corrosion mechanism of the friction stir-processed AA5083 specimens is found to be a combination of activation and concentration polarization.