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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.

This study aims to propose and evaluate a simpler technology to reduce harmful emissions from diesel engines by preheating the fuel before injection into the combustion chamber.

A spring-type heater coil with suitable insulation was installed on the high-pressure fuel pipeline to preheat the fuel. Experiments were conducted at a standard injection timing of 23° before top dead center, across 25%, 50%, 75% and 100% of full load. The fuel was preheated to 100°C, 160°C and 220°C for each engine load. Engine performance, emissions and thermal balance were analyzed for preheated and unheated diesel.

This study found that preheated fuel improved combustion characteristics, with higher pressure rise and net heat release rates during diffusion combustion. Brake thermal efficiency increased by 8.75% to 10.58%, and brake-specific fuel consumption decreased by up to 9.18%. Emissions significantly dropped: nitrogen oxides by up to 51%, smoke density by up to 63%, carbon monoxide by up to 67% and hydrocarbon by up to 25%. Thermal balance results showed increased useful work and reduced heat losses, particularly at higher preheating temperatures.

This research presents a novel and simpler approach to enhancing diesel engine performance and reducing emissions by preheating the fuel. The findings demonstrate significant improvements in efficiency and substantial reductions in harmful emissions, highlighting the potential of preheated fuel as a viable solution for cleaner diesel engine operation.

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.

Plant-based meat analogues (PBMAs) hold significant promise as a sustainable solution to meet future protein demands, replicating the taste and nutritional value of meat. However, the present reliance on extrusion technology in PBMA production limits the exploration of more accessible and affordable methods. The current investigation aims to meet the market demand for a scalable and cost-effective processing approach by exploring saturated steam-assisted technology that could broaden the production volume of PBMAs, thereby supplementing protein security and planet sustainability.

A one-factor-at-a-time (OFAT) approach is employed to evaluate the effect of ingredients and process conditions on the governing quality attributes (texture, colour and sensory).

Among the ingredients, monosodium glutamate (MSG) and nutritional yeast (NY) significantly enhanced the hardness and chewiness of saturated steam-assisted plant-based meat analogues (ssPBMAs) followed by potato protein isolate (PPI), defatted soy flour (DSF) and salt. The addition of PPI and DSF led to a decrease in lightness (L* value) and an increase in the browning index (BI). Sensory evaluations revealed that higher concentrations of DSF imparted a noticeable beany flavour (>20%), whereas PPI (30%) improved the overall sensory appeal. Increased levels of NY (10%) and MSG (5%) enhanced the umami flavour, enhancing consumer preference. Higher thermal exposure time (TTi) (45 min) and temperature (TTe) (120 °C) during processing resulted in softer products with reduced L* values. These findings establish a foundation for selecting and optimizing the ingredients and processing parameters in ssPBMA production.

The novelty of the current study includes process behaviour of selected ingredients such as PPI, NY, MSG, DSF, salt and adopted process conditions, namely, dough processing time (DPT), protein network development time (PNDT), TTi and TTe on the quality of ssPBMAs.

The research paper comprehensively investigates the gear tooth deflection of standard thermoplastic gears with steel gear as the driver and driven companions. An accurate mapping of characteristic contact regions between the meshing gears was done, and the behaviour of the gear tooth in the premature and prolonged contact zones was studied.

The study employs the finite element method to conduct a quasi-static 2D analysis of meshing gear teeth. The finite element model was created in AutoCAD and analysed using the ANSYS 19.1 simulation package.

In the polymer-polymer gear combinations, premature and prolonged contact primarily occurs along the addendum radii of meshing gears, whereas a novel contact phenomenon was observed in the coast side for polymer-metal and metal-polymer combinations, exhibiting a path perpendicular to the standard drive side contact. As well, the deflection of the tooth alters the load distribution across the interlocking gears, leading to a decrement in the root stresses.

The Lewis bending equation demonstrates that bending stresses depend solely on the applied load and the geometry of the tooth. It does not consider the effects of deflection. However, the computational results showed that the gear tooth deflection caused by different gear pair combinations also affects the bending stresses. The contact stresses observed in the polymer-polymer gear combination were observed to be within the material’s proportional limit. However, when a steel gear is paired with a polymer gear, the contact stresses exceed the proportional limit due to coast side contact.

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.