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The purpose of this paper is to investigate a supply chain consisting of a producer and multiple suppliers of a type of component needed for the production of a certain product. The effects of carbon emission taxes, quality of components and human inspection errors as well as the collaboration among the supply chain members are considered.

A mathematical model is formulated for a non-collaborative supply chain, and the optimal policy is shown to be the solution of a constraint optimization problem. The mathematical model is modified to the case of a collaborative supply chain and to account for inspection errors. Algorithms are provided, and a numerical example is given to illustrate the determination of the optimal policy.

This study offers a new conceptual and analytical model that analyzes the production problem from a supply chain perspective. Human resource management practices and environmental aspects were incorporated into the model to reduce risk, optimally select the suppliers and properly maximize profit by accounting for human inspection error as well carbon emission taxes. Algorithms describing the determination of the optimal policy are provided.

This study provides practical results that can be useful to researchers and managers aiming at designing sustainable supply chains that incorporate economic, environmental and human factors.

This study can be useful to researchers and managers aiming for designing sustainable supply chains that incorporate economic and human factors.

This empirical study aims to investigate the erosion wear performance of two different 3D-printed materials (acrylonitrile butadiene styrene [ABS] and polylactic acid [PLA]) with various micro textures. The two different textures (prism and square) were created over the surfaces of both materials by using the 3D-printed technique.

The erosion experiments on both materials were performed by using Ducom Erosion Jet Tester. Erosion tests were performed at four different impacting velocities (15, 30, 45 and 60 m/s) with the four different particle sizes (17, 39, 63 97 µm) at the impact angles (30°–90°) for the time duration of 5, 10, 15 and 20 min. The two different textures prism and cone were used for performing the erosion experiments. Taguchi’s orthogonal L16 (mixed level) was used to reduce the number of experiments and to determine the impact of these parameters on erosion wear performance of both 3D-printed materials.

The PLA with cone texture was found to be best (against erosion) than the ABS cone and prism textures due to their high hardness (68 HV). Also, the average signal to noise (S/N) ratio for PLA and ABS was measured as 56.4 and 44.4 dB, respectively. As the value of the S/N ratio is inversely proportional to the erosion rate, the PLA has the least erosion rate as compared to the ABS. The sequence of erosion wear influencing parameters for both materials was in the following order: velocity > erodent size > texture > impact angle > time interval.

Both PLA and ABS with different micro textures for erosion testing were studied with Taguchi’s optimization method, and the erosion mechanisms are well analyzed by using scanning electron microscopy and Image J techniques.

This paper aims to obtain a symmetrical step-down topology with lower equivalent capacity and wider step-down range under the condition of the same output. Two new symmetrical step-down topologies of star-connected autotransformers are proposed in this paper. Taking the equivalent capacity as the main parameter, the obtained topologies are modeled and analyzed in detail.

This paper adopts the research methods of design, modeling, analysis and simulation verification. First, the star-connected autotransformer is redesigned according to the design objective of symmetrical step-down topology. In addition, the mathematical model of two topologies is established and a detailed theoretical analysis is carried out. Finally, the theoretical results are verified by simulation.

Two symmetrical star-connected autotransformer step-down topologies are designed, the winding configurations of the corresponding topology are presented, the step-down ranges of these three topologies are calculated and the influence of step-down ratio on the equivalent capacity of autotransformer are analyzed. Through analysis, the target step-down topologies are obtained when the step-down ratio is [1.1, 5.4] and [1.1, 1.9] respectively.

Because the selected research object is only a star-connected autotransformer, the research results may lack generality. Therefore, researchers are encouraged to further study the topologies of other autotransformers.

This paper includes the implications of the step-down ratio on the equivalent capacity of autotransformers and the configuration of transformer windings.

The topologies designed in this paper enable star-connected autotransformer in the 12-pulse rectifier to be applied in step-down circumstances rather than situations of harmonic reduction only. At the same time, this paper provides a way that can be used to redesign the autotransformer in other multi-pulse rectifier systems, so that those transformers can be used in voltage regulation.

This study investigates a production process that requires N kinds of components for the production of a finished product. The producer orders the various kinds of components from different suppliers and receives the orders in lots at the beginning of each production cycle. Similar to situations often encountered in real life, the lead times are random variables with known probability distributions so that a production cycle starts whenever all N kinds of components become available. Each of the lots received at the start of a production run contains both perfect and imperfect quality components. Once all N kinds of components become available, the producer initiates a screening process to detect the imperfect components. The production of the finished product uses only perfect quality components. The imperfect components are removed from inventory whenever the screening process is completed. The percentage of components of perfect quality present in each lot is a random variable with a known probability distribution.

This production process is described and modeled mathematically and the optimal production/ordering policy is derived based on the mathematical model.

The formulated mathematical model resulted in the determination of the optimal policy consisting of the optimal number of finished items ordered to be produce during each production run, the number of components ordered from each supplier, and the reorder point. The derived closed form expression for the optimal lot size depends on the minimum of the number of perfect quality components in a lot, whereas the reorder point is determined based on the maximum lead time.

The modeling approach and results of this study provide practical implications that may be beneficial to both production and supply chain managers as well as researchers.

This modeling approach that incorporates decision-making related to the logistics of acquiring the components and accounts for the probabilistic nature of the lead times and quality of components addresses a gap in the logistics/production literature.

This paper aims to depict the erosion performance of two HVOF-coated micron layers (Colmonoy-88 and Stellite-6) on pump impeller steel (SS-410) by using Taguchi's method. Taguchi's array (L16) was used to optimize the erosion wear (in terms of weight loss) by using four influencing parameters such as rotational speed, solid concentration, average particle size and time which were varied at four different levels.

The experiments were carried out by using a Ducom slurry tester with rotational speed in the range of 750-1,500 rpm, solid concentration of 35-65 per cent by weight, time period of 75-210 min and average particle sizes in the range of < 53 to 250 µm. Bottom Ash with a nominal size range of < 53 to 250 µm was used as erodent. The process parameters were optimized by using Taguchi's method. The ANOVA method was used to validate the results given by Taguchi's method.

The results revealed that the presence of both carbides and borides and the additional presence of Cr in Colmonoy-88 coating enhancing the slurry erosion resistance of Colmonoy-88 coating. Moreover, the chromium and tungsten carbide particles help in increasing the bond strength between the coating and the substrate material. Further, it was also found that the time was the most dominant factor as compared to other factors.

The very less work has been reported on optimization of erosion wear response of Colmonoy-88 and Stellite-6 coatings by using different design of experiment techniques. Further, the erosion wear mechanism of both coatings has been studied by using image j analysis software.

Amidst the backdrop of a wide array of structural developments that have revolutionized the competitive landscape of Indian commercial banking, this paper aims to empirically examine the role of two external monitoring mechanisms – competition and concentration on financial stability and further highlights the significance of bank-level heterogeneity in the nexus.

The study uses the Lerner index, defined through a translog specification, as a measure of market power. A system generalized method of moments technique accounts for the dynamic associations among the competition-concentration-stability nexus. The study further examines the moderating effect of ownership, size and capitalization on the nexus. The study also uses the Boone indicator and comments on the competition-bank stability relationship after controlling for bank governance.

The findings indicate that banks are less stable in a more competitive and higher concentrated environment. Exploring bank-level heterogeneity, first, the authors report that as competition increases, state-owned banks have greater incentives to undertake risky activities than private and foreign banks, which point to implicit sovereign guarantees that characterize the former. Second, the authors document an adverse influence of competition on the soundness of larger banks consistent with the “too-big-to-fail” assertion. Third, results corroborate the disciplinary role of regulatory capital and lend support to stricter capital norms under Basel III in a more competitive environment.

This paper is perhaps the first to capture competition and concentration in a single model; to reconcile conflicting evidence on competition-risk nexus; to shed light on the joint effect of competition and Basel accords for Indian banks.

The purpose of this study to investigate the effects and possible future prediction of COVID-19. The dataset considered in this study to investigate the effects and possible future prediction of COVID-19 is constrained as follows: age, gender, systolic blood pressure, HDL-cholesterol, diabetes and its medication, does the patient suffered from heart disease or took anti-cough agent food or sensitive to cough related issues and any other chronic kidney disease, physical contact with foreign returns and social distance for the prediction of the risk of COVID-19.

This work implemented a meta-heuristic algorithm on the aforementioned dataset for possible analysis of the risk of being infected with COVID-19. The authors proposed a simple yet effective Risk Prediction through Nature Inspired Hybrid Particle Swarm Optimization and Sine Cosine Algorithm (HPSOSCA), particle swarm optimization (PSO), and sine cosine algorithm (SCA) algorithms.

The simulated results on different cases discussed in the dataset section reveal which category of individuals may happen to have the disease and of what level. The experimental results reveal that the proposed model can predict the percentage of risk with an overall accuracy of 88.63%, sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%) and Gmean (88.12%) with 41 and 146 true positive and negative, 18 and 6 false positive and negative cases, respectively. The proposed model provides a quite stable prediction of risk for COVID-19 on different categories of individuals.

The work for the very first time developed a novel HPSOSCA model based on PSO and SCA for the prediction of COVID-19 disease. The convergence rate of the proposed model is too high as compared to the literature. It also produces a better accuracy in a computationally efficient fashion. The obtained outputs are as follows: accuracy (88.63%), sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%), Gmean (88.12%), Tp (41), Tn (146), Fb (18) and Fn (06). The recommendations to reduce disease outbreaks are as follow: to control this epidemic in various regions, it is important to appropriately manage patients suspected of having the disease, immediately identify and isolate the source of infection, cut off the transmission route and prevent viral transmission from these potential patients or virus carriers.

The present study assesses the commercial bank profit efficiency and its relationship to banking sector competition in a rapidly growing emerging economy, India from 2009 to 2019 using stochastic frontier analysis (SFA).

Lerner indices, conventional and efficiency-adjusted, quantify competition. Two SFA models are employed to calculate alternative profit efficiency (inefficiency) scores: the two-step time-decay approach proposed by Battese and Coelli (1992) and the recently developed single-step pairwise difference estimator (PDE) by Belotti and Ilardi (2018). In the first step of the BC92 framework, profit inefficiency is calculated, and in the second step, Tobit and Fractional Regression Model (FRM) are utilized to evaluate profit inefficiency correlates. PDE concurrently solves the frontier and inefficiency equations using the maximum likelihood process.

The results suggest that foreign banks are less profit efficient than domestic equivalents, supporting the “home-field advantage” hypothesis in India. Further, increasing competition drives bank managers to make riskier lending and investment choices, decreasing bank profit efficiency. However, this effect varies depending on bank ownership and size.

Literature on the competition bank efficiency link is conspicuously scant, with a focus on technical and cost efficiency. Less is known regarding the influence of competition on bank profit efficiency. The article is one of the first to examine commercial bank profit efficiency and its relationship to banking sector competition. Additionally, the study work represents one of the first applications of the FRM presented by Papke and Wooldridge (1996) and the PDE provided by Belotti and Ilardi (2018).

The purpose of this study is to examine the impact of corporate social responsibility (CSR) expenditure and business responsibility report (BRR) on a firm’s financial performance. Additionally, the study explores whether CSR expenditure and firm performance are related linearly or otherwise. The study also assesses the influence of mandating CSR expenditure on a firm’s performance.

The study is set in India and uses a nine-year data set from 165 companies listed on the Bombay Stock Exchange. Data compilation and analysis are done by using content analysis and panel data regressions.

The main findings of the study are that the effect of CSR expenditure on firm performance in India is non-linear and can be characterized as parabolic for investigated firms. While some performance indicators suggest a U-shaped relationship, others show an inverted U-type pattern, making a definitive conclusion elusive in either direction. BRR scores themselves have a positive impact on firm performance. Mandatory CSR expenditure affects the financial performance negatively, but the market performance improves in general.

The study provides new insights on the relationship between CSR expenditure, BRR scores and firm performance from India, which is not only a notable emerging market but also has other gripping characteristics. It has a prolific history of philanthropy, and yet, it is the first country in the world to mandate CSR expenditure in recent times. The equation between reported economic progress and general quality of life remains intriguing, and yet the number of studies on the effects of CSR expenditure on firm performance are no match to the volume of ongoing and completed works in more developed markets. This study attempts to trim the gap and provide some useful insights for managers, policymakers and stakeholders, apart from prompting further research.

Manufacturing industries are facing dynamic challenges in today’s highly competitive world. In the recent past, integrating Industry 4.0 with the lean six sigma improvement methodologies has emerged as a popular approach for organizational excellence. The research aims to explore and analyze critical success factors of lean six sigma integrated Industry 4.0 (LSSI).

This research study explores and analyzes the critical success factors (CSFs) of LSSI. A three-phase study framework is employed. At first, the CSFs are identified through an extensive literature review and validated through experts’ feedback. Then, in the second phase, the initial list of CSFs is finalized using the fuzzy DELPHI technique. In the third phase, the cause-effect relationship among CFSs is established using the fuzzy DEMATEL technique.

A dyadic relationship among cause-and-effect category CSFs is established. Under the cause category, top management commitment toward integrating LSSI, systematic methodology for LSSI and organizational culture for adopting changes while adopting LSSI are found to be topmost CSFs. Also, under the effect category, organizational readiness toward LSSI and adaptability and agility are found to be the uppermost CSFs.

The study offers a framework to understand the significant CSFs for LSSI implementation. Insights from the study will help industry managers and practitioners to implement LSSI and achieve organizational excellence.

To the best of the authors’ knowledge, CSFs of LSSI are not much explored in the past by researchers. Findings will be of great value for professionals in developing long-term operations strategies.