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This study aims to investigate the optimal process parameters of the wire-cut electrical discharge machining (WCEDM) for the machining of the GZR-AA7475 hybrid metal matrix composite (HMMC). HMMCs are prepared with 2 Wt.% graphite and 4 Wt.% zirconium dioxide reinforced with aluminium alloy 7475 (GZR-AA7475) composite by using the stir casting method. The objective is to enhance the mechanical properties of the material while preserving its unique features. WCEDM with a 0.18 mm molybdenum wire electrode is used for machining the composite.

To conduct experimental studies, a Taguchi L27 orthogonal array was adopted. Input variables such as peak current (I_p), pulse-on-time (T_ON) and flushing pressure (P_F) were used. The effect of process parameters on the output responses, such as material removal rate (MRR), surface roughness rate (SRR) and wire wear ratio (WWR), were investigated. The grey relational analysis (GRA) is used to obtain the optimal combination of the process parameters. Analysis of variance (ANOVA) was also used to identify the significant process parameters affecting the output responses.

Results from the current study concluded that the optimal condition for grey relational grade is obtained at T_ON = 105 µs, I_p = 100 A and P_F = 90 kg/cm². Peak current is the most prominent parameter influencing the MRR, whereas SRR and WRR are highly influenced by flushing pressure.

Identifying the optimal process parameters in WCEDM for machining of GZR-AA7475 HMMC. ANOVA and GRA are used to obtain the optimal combination of the process parameters.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

This study aims to investigate the influence of disclosing environmental, social and governance (ESG) factors on financial performance, taking into account the moderating effect of the COVID-19 pandemic.

A sample of the top 100 non-financial firms listed on the Bombay Stock Exchange, for the years 2019–2022, has been considered. Suitable panel regression models have been used to assess the impact of non-financial disclosure on accounting and market measures of firm performance. In addition, a panel data moderating effect model is used to assess the moderating impact.

The outcomes of the study partially favour the value-creation role of ESG disclosure. Specifically, the disclosure of already established ESG metrics, particularly social and governance aspects, positively impacts the market performance while environmental transparency negatively impacts the accounting performance. Of the three ESG components, only extended governance disclosure adds to market value. Results of the moderation effect reveal a significant impact of the pandemic on the ESG disclosure–financial performance relation. However, a more pronounced effect before the pandemic is observed. The results are robust to endogeneity.

This study sheds light on the financial consequences of ESG disclosure within the context of an emerging nation. This is done by using a novel holistic ESG reporting framework to obtain more accurate results. Furthermore, the study distinguishes itself by examining the long-term moderating influence of the unexpected COVID-19 crisis on the ESG disclosure–financial performance relation.

Investment decisions hold immense significance for investors and eventually affect their portfolio performance. Investors are advised to weigh the costs and benefits associated with every decision in order to make rational investment decisions. However, behavioral finance research reveals that investors' choices often stem from a blend of economic, psychological and sociological factors, leading to irrationality. Moreover, environmental, social and corporate governance (ESG) factors, aligned with behavioral finance hypotheses, also sway opinions and stock prices. Hence, this study aims to identify how individual equity investors prioritize key determinants of investment decisions in the Indian stock market.

The current research gathered data from 391 individual equity investors through a structured questionnaire. Thereafter, a fuzzy analytic hierarchy process (F-AHP) was used to meet the purpose of the research.

Information availability, representative heuristics belonging to psychological factors and macroeconomic indicators falling under economic factors were discovered to be the three most prioritized criteria, whereas environmental issues within the realm of ESG factors, recommendations of brokers or investment consultants of sociological factors, and social issues belonging to ESG factors were found to be the least prioritized criteria, respectively.

Only active and experienced individual equity investors were surveyed in this study. Furthermore, with a sample size of 391 participants, the study was confined to individual equity investors in one nation, India.

This research has implications for individual investors, institutional investors, market regulators, corporations, financial advisors, portfolio managers, policymakers and society as a whole.

To the best of the authors' knowledge, no real attempt has been made to comprehend how active and experienced individual investors prioritize critical determinants of investment decisions by taking economic, psychological, sociological and ESG factors collectively under consideration.

Barriers to adequate healthcare in rural areas remain a grand challenge for local healthcare systems. In addition to patients' travel burdens, lack of health insurance, and lower health literacy, rural healthcare systems also experience significant resource shortages, as well as issues with recruitment and retention of healthcare providers, particularly specialists. These factors combined result in complex change management-focused challenges for rural healthcare systems. Change management initiatives are often resource intensive, and in rural health organizations already strapped for resources, it may be particularly risky to embark on change initiatives. One way to address these change management concerns is by leveraging socio-technical simulation models to estimate techno-economic feasibility (e.g., is it technologically feasible, and is it economical?) as well as socio-utility feasibility (e.g., how will the changes be utilized?). We present a framework for how healthcare systems can integrate modeling and simulation techniques from systems engineering into a change management process. Modeling and simulation are particularly useful for investigating the amount of uncertainty about potential outcomes, guiding decision-making that considers different scenarios, and validating theories to determine if they accurately reflect real-life processes. The results of these simulations can be integrated into critical change management recommendations related to developing readiness for change and addressing resistance to change. As part of our integration, we present a case study showcasing how simulation modeling has been used to determine feasibility and potential resistance to change considerations for implementing a mobile radiation oncology unit. Recommendations and implications are discussed.

Post COVID-19 crisis, healthcare has become a priority for every government. Furthermore, the pandemic has also made us realise why do we need an affordable healthcare delivery service at the grassroots level. As a result, the Government of India has come out with the Ayushman Sahakar scheme. This scheme has been launched by the Union Government with an aim to assist the cooperatives in the creation of healthcare infrastructure in this country. It is pertinent to note that the cooperatives in the last few years have transformed rural areas and have pushed them out of poverty. As a result, it will be interesting to see the impact cooperatives will have in the field of healthcare.

The authors in this work have discussed the history of healthcare cooperatives in India. After this, the authors have analysed the government schemes and legal provisions which regulate the functioning of healthcare cooperatives in this country. In the next part, the authors studied the Ayushman Sahakar scheme. The authors have discussed the features of the scheme and the impact it has generated in the field of healthcare. Lastly, the author has discussed the challenges which healthcare cooperatives face in this country and how we can overcome those challenges.

The purpose of this paper is to study Al-based green composite. To make composite samples of aluminium alloy (AA3105) with different weight percentages of rice husk ash (RHA) and eggshell (ES) particles as reinforcement, stir casting method was used.

Several other aspects, including the weight percent of reinforcing agent particles, the applied stress and the sliding speed, were taken into consideration. During the course of the wear test, the sliding distance that was recorded varied from a minimum of 1,000 m all the way up to a maximum of 3,135 m (10, 15, 20, 25 and 30 min). The typical range for normal loads is 8–24 N, and their speed is 1.58 m/s.

With the AA/ES/RHA composite, the wear rates decreases when the grain size of the reinforcing particles enhanced. Scanning electron microscopy images of worn surfaces show that at low speeds, delaminating and ploughing are the main causes of wear. At high speeds, ploughing is major cause of wear. Composites with better wear-resistant properties can be used in wide range of tribological applications, especially in the automotive industry. It was found that hardness increases at the same time as the weight of the reinforcement increases. Tensile and hardness were maximized at 10% reinforcement mix in Al3105.

In this work, ES and RHA has been used to develop green metal matrix composite to support green revolution as promoted/suggested by United Nations thus reducing the environmental pollution.

The need for precise synthesis of customized designs has resulted in the development of advanced coating processes for modern nanomaterials. Achieving accuracy in these processes requires a deep understanding of thermophysical behavior, rheology and complex chemical reactions. The manufacturing flow processes for these coatings are intricate and involve heat and mass transfer phenomena. Magnetic nanoparticles are being used to create intelligent coatings that can be externally manipulated, making them highly desirable. In this study, a Keller box calculation is used to investigate the flow of a coating nanofluid containing a viscoelastic polymer over a circular cylinder.

The rheology of the coating polymer nanofluid is described using the viscoelastic model, while the effects of nanoscale are accounted for by using Buongiorno’s two-component model. The nonlinear PDEs are transformed into dimensionless PDEs via a nonsimilar transformation. The dimensionless PDEs are then solved using the Keller box method.

The transport phenomena are analyzed through a comprehensive parametric study that investigates the effects of various emerging parameters, including thermal radiation, Biot number, Eckert number, Brownian motion, magnetic field and thermophoresis. The results of the numerical analysis, such as the physical variables and flow field, are presented graphically. The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as fluid parameter increases. An increase in mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid.

Intelligent materials rely heavily on the critical characteristic of viscoelasticity, which displays both viscous and elastic effects. Viscoelastic models provide a comprehensive framework for capturing a range of polymeric characteristics, such as stress relaxation, retardation, stretching and molecular reorientation. Consequently, they are a valuable tool in smart coating technologies, as well as in various applications like supercapacitor electrodes, solar collector receivers and power generation. This study has practical applications in the field of coating engineering components that use smart magnetic nanofluids. The results of this research can be used to analyze the dimensions of velocity profiles, heat and mass transfer, which are important factors in coating engineering. The study is a valuable contribution to the literature because it takes into account Joule heating, nonlinear convection and viscous dissipation effects, which have a significant impact on the thermofluid transport characteristics of the coating.

The momentum boundary layer thickness of the viscoelastic polymer nanofluid decreases as the fluid parameter increases. An increase in the mixed convection parameter leads to a rise in the Nusselt number. The enhancement of the Brinkman number and Biot number results in an increase in the total entropy generation of the viscoelastic polymer nanofluid. Increasing the strength of the magnetic field promotes an increase in the density of the streamlines. An increase in the mixed convection parameter results in a decrease in the isotherms and isoconcentration.

The purpose of the study is to conceptualize the model of the predictors of consumer willingness to purchase green products. This study used the underpinning theories related to consumer willingness by integrating the green concept in deriving the consumer willingness to purchase green products. Based on the underpinning theories of marketing strategies, it was found that marketing mix was still fundamental in business. Therefore, green marketing mix was proposed to describe the consumer's green purchase willingness. Furthermore, corporate social responsibility (CSR) plays an important role as the key to organizational strategy. Thus, CSR is also included in the proposed framework. As this is a conceptual paper, further empirical study needs to carry out to verify the proposed hypotheses. This study contributes to the market practitioners or entrepreneurs in terms of re-considering marketing mix and CSR in deriving customer willingness to purchase green products. This study extends the literature of behavioural intention by integrating green marketing strategies with CSR in determining consumer willingness to purchase.

This study aims to investigate the determinants of the food security status of participants and non-participants of livestock extension services living under similar socioeconomic conditions as livestock farming households in the Feed the Future zone of Bangladesh.

Cross-sectional data of 906 farm-households extracted from a total of 2064 from the Feed the Future representative Bangladesh Integrated Households Survey 2018 were used. A triple hurdle model combined with a structural equation model were used to analyze the data. The causal relationship between food security status, livestock extension services, technology adoption and women's empowerment was investigated by estimating structural equation modeling with second-order latent factors.

The results indicate that livestock extension services have increased livestock technology adoption and have a positively significant (p < 0.01) relationship with household wealth, food security, welfare and women's empowerment.

The results suggest that livestock extension services have an impact on new technology adoption and enhancing women's empowerment; thus, the services should be widely made available in the region.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0647.