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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.

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 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.

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.

This study aims to analyze the effect of interrupted rolling on microstructures and mechanical properties of Mg–8Li–xGr composite is investigated.

Graphene reinforced composite was developed by using stir casting route and rolled with different reduction in thickness such as 50, 75 and 90%. Microstructure, hardness and tensile characteristics of the rolled samples were evaluated.

Investigation on microstructures of rolled composite depicts that increase in rolling reduction % resulted in fine elongated grains and decreased aspect ratio. Further, it was also observed that increasing percentage of rolling reduction promotes the dissolution of ß Li phase and as a result the ductility of composite decreases. Interrupted rolled samples showcase higher hardness when compared with as-cast composite. Composite rolled with 90% reduction displays higher yield strength of 219 MPa. Hardening capacity of composites decreases with increase in reduction percentage due to the effective reduction in grain size.

Investigation on the influence of interrupted rolling on microstructures and mechanical properties of Mg graphene composite. The in-depth understanding of this will help to improve its wide spread application.

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 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.

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.