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This study aims to measure the competitiveness of top apparel exporting nations competing with China in different apparel product categories across the global environment.

Compound annual growth rate, trade competitiveness, market share percentages, revealed comparative advantage and its variant normalized revealed comparative advantage using two-, four- and six-digit harmonized system codes for the period of 2016–2021 were used to understand the comparative advantage of competing apparel exporting nations.

The findings revealed that China still holds a more decisive comparative advantage than its competitors over the majority of the product categories within the knitted or not knitted apparel and clothing accessories. The other competing nations hold better export competitiveness over China in specific categories. However, that is not sufficient to be the “Next China.”

The study has important implications for different stakeholders of the global apparel industry, such as governments, industry officials, policymakers, investors, researchers and students. The study’s limitations arise from using product categories as competitiveness indicators, notably relying on a macro level approach for measurement while the micro level perspective is not analyzed, which constitutes a significant limitation of the study.

This research thoroughly analyzes the competitive position of the top ten apparel-exporting countries in the global market.

The purpose of this paper is to use the corresponding magnetic sensor and detection method to detect and image the defects of small diameter pipelines. Urban gas pipeline is an energy transportation tool for urban industrial production and social life, which is closely related to urban safety. Preventing the occurrence of urban gas pipeline transportation accidents and carrying out pipeline defect detection are of great significance for the urban economic and social stability. To perform pipeline defect detection, the magnetic flux leakage internal detection method is generally used in the detection of large-diameter long-distance oil and gas pipelines. However, in terms of the internal detection of small-diameter pipelines, due to the heavy weight, large structure of the detection device and small pipe diameter, the detection is more difficult.

In order to solve the above matters, self-made three-dimensional magnetic sensor and three-dimensional magnetic flux leakage imaging direct method are proposed for studying the defect identification. Firstly, for adapting to the diameter range of small-diameter pipelines, and containing the complete information of the defect, a self-made three-dimensional magnetic sensor is made in this paper to improve the accuracy of magnetic flux leakage detection. And on the basis of it, a small diameter pipeline defect detection system is built. Secondly, as detection signal may be affected by background magnetic field interference and the jitter interference, the complete ensemble empirical mode decomposition with adaptive noise method is utilized to screen the detected signal. As a result, the useful signal is reconstructed and the interference signal is removed. Finally, the defect contour inversion imaging of detection is realized based on the direct method of three-dimensional magnetic flux leakage imaging, which includes three-dimensional magnetic flux leakage detection data and data segmentation recognition.

The three-dimensional magnetic flux leakage imaging experimental results shown that, compared to the actual defects, the typical defects, irregular defects and crack groove defects can be analyzed by the magnetic flux leakage defect contour imaging method in qualitative and quantitative way respectively, which provides a new idea for the research of defect recognition.

A three-dimensional magnetic sensor is made to adapt the diameter range of small diameter pipeline, and based on it, a small-diameter pipeline defect detection system is built to collect and display the magnetic flux leakage signal.

Stock markets are essential for households for wealth creation and for firms for raising financial resources for capacity expansion and growth. Market participants, therefore, need an understanding of stock price movements. Stock market indices and individual stock prices reflect the macroeconomic environment and are subject to external and internal shocks. It is important to disentangle the impact of macroeconomic shocks, market uncertainty and speculative elements and examine them separately for prediction. To aid households, firms and policymakers, the paper proposes a granular decomposition-based prediction framework for different time periods in India, characterized by different market states with varying degrees of uncertainty.

Ensemble empirical mode decomposition (EEMD) and fuzzy-C-means (FCM) clustering algorithms are used to decompose stock prices into short, medium and long-run components. Multiverse optimization (MVO) is used to combine extreme gradient boosting regression (XGBR), Facebook Prophet and support vector regression (SVR) for forecasting. Application of explainable artificial intelligence (XAI) helps identify feature contributions.

We find that historic volatility, expected market uncertainty, oscillators and macroeconomic variables explain different components of stock prices and their impact varies with the industry and the market state. The proposed framework yields efficient predictions even during the COVID-19 pandemic and the Russia–Ukraine war period. Efficiency measures indicate the robustness of the approach. Findings suggest that large-cap stocks are relatively more predictable.

The paper is on Indian stock markets. Future work will extend it to other stock markets and other financial products.

The proposed methodology will be of practical use for traders, fund managers and financial advisors. Policymakers may find it useful for assessing the impact of macroeconomic shocks and reducing market volatility.

Development of a granular decomposition-based forecasting framework and separating the effects of explanatory variables in different time scales and macroeconomic periods.

The purpose of this paper is to showcase the utilization of the magnetohydrodynamics-microrotating Casson’s nanofluid flow model (MHD-MRCNFM) in examining the impact of an inclined magnetic field within a porous medium on a nonlinear stretching plate. This investigation is conducted by using neural networking techniques, specifically using neural networks-backpropagated with the Levenberg–Marquardt scheme (NN-BLMS).

The initial nonlinear coupled PDEs system that represented the MRCNFM is transformed into an analogous nonlinear ODEs system by the adoption of similarity variables. The reference data set is created by varying important MHD-MRCNFM parameters using the renowned Lobatto IIIA solver. The numerical reference data are used in validation, testing and training sets to locate and analyze the estimated outcome of the created NN-LMA and its comparison with the corresponding reference solution. With mean squared error curves, error histogram analysis and a regression index, better performance is consistently demonstrated. Mu is a controller that controls the complete training process, and the NN-BLMS mainly concentrates on the higher precision of nonlinear systems.

The peculiar behavior of the appropriate physical parameters on nondimensional shapes is demonstrated and explored via sketches and tables. For escalating amounts of inclination angle and Brinkman number, a viable entropy profile is accomplished. The angular velocity curve grows as the rotation viscosity and surface condition factors rise. The dominance of friction-induced irreversibility is observed in the vicinity of the sheet, whereas in the farthest region, the situation is reversed with heat transfer playing a more significant role in causing irreversibilities.

To improve the efficiency of any thermodynamic system, it is essential to identify and track the sources of irreversible heat losses. Therefore, the authors analyze both flow phenomena and heat transport, with a particular focus on evaluating the generation of entropy within the system.

The purpose of this article is to provide information about interactions between pink-pigmented facultative methylotroph (PPFM) organisms and plants, their molecular mechanisms of methylotrophic metabolism, application of PPFMs in agriculture, biotechnology and bioremediation and also to explore lacuna in PPFMs research and direction for future research.

Research findings on PPFM organisms as potent plant growth promoting organisms are discussed in the light of reports published by various workers. Unexplored field of PPFM research are detected and their application as a new group of biofertilizer that also help host plants to overcome draught stress in poorly irrigated crop field is suggested.

PPFMs are used as plant growth promoters for improved crop yield, seed germination capacity, resistance against pathogens and tolerance against drought stress. Anti-oxidant and UV resistant properties of PPFM pigments protect the host plants from strong sunshine. PPFMs have excellent draught ameliorating capacity.

To meet the ever increasing world population, more and more barren, less irrigated land has to be utilized for agriculture and horticulture purpose and use of PPFM group of organisms due to their draught ameliorating properties in addition to their plant growth promoting characters will be extremely useful. PPFMs are also promising candidates for the production of various industrially and medicinally important enzymes and other value-added products. Wider application of this ecofriendly group of bacteria will reduce crop production cost thus improving economy of the farmers and will be a greener alternative of hazardous chemical fertilizers and fungicides.

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The purpose of this paper is to develop optimal estimation procedures for the stress-strength reliability (SSR) parameter R = P(X > Y) of an inverse Pareto distribution (IPD).

We estimate the SSR parameter R = P(X > Y) of the IPD under the minimum risk and bounded risk point estimation problems, where X and Y are strength and stress variables, respectively. The total loss function considered is a combination of estimation error (squared error) and cost, utilizing which we minimize the associated risk in order to estimate the reliability parameter. As no fixed-sample technique can be used to solve the proposed point estimation problems, we propose some “cost and time efficient” adaptive sampling techniques (two-stage and purely sequential sampling methods) to tackle them.

We state important results based on the proposed sampling methodologies. These include estimations of the expected sample size, standard deviation (SD) and mean square error (MSE) of the terminal estimator of reliability parameters. The theoretical values of reliability parameters and the associated sample size and risk functions are well supported by exhaustive simulation analyses. The applicability of our suggested methodology is further corroborated by a real dataset based on insurance claims.

This study will be useful for scenarios where various logistical concerns are involved in the reliability analysis. The methodologies proposed in this study can reduce the number of sampling operations substantially and save time and cost to a great extent.

Marketing is a hot topic, and the purpose of this study is to investigate how shareholding strategies can be applied to achieve strategic synergy between firms in vertical supply chains to improve retailers’ marketing efforts from a long-term perspective.

This study constructs Stackelberg models to analyze the operating mechanisms of shareholding supply chains under forward, backward and cross-shareholding strategies. The authors analyze the effects of shareholding on prices, marketing efforts and profits, and explore the strategic preferences and outcomes of different supply chain members.

Forward/backward shareholding plays the same role as cross/nonshareholding in supply chains because the effect of the retailer’s shareholding is offset by the power status of the manufacturer, and the retailer can still profit when wholesale prices are higher than selling prices in certain cases. A manufacturer’s shareholding in a retailer can benefit consumers and improve marketing efforts by reducing retailers’ marketing costs, while a retailer’s shareholding in a manufacturer has no such effect. None of all shareholding strategies can coordinate the interests of all members; however, an effective rebate policy can resolve this problem.

The results reveal the operational mechanism of shareholding supply chains and provide reference values for managers who want to improve marketing efforts and economic performance using a shareholding strategy.

Staffing is one of the most influential human resource (HR) activities and is the primary method of hiring and retaining human resources. Among staffing’s several activities, recruitment and selection are one of the most crucial activities. It is possible to rehire former firm employees using the talent management strategy known as “boomerang recruitment”. The boomerang recruitment trend has tremendously grown because many employees who believe they are qualified for the position now wish to return to their old employers. According to data, boomerang employees can be 50% less expensive than conventional ways of hiring. The purpose of this study is to identify the generic critical factors that play a role in the boomerang hiring process based on the literature review. Next, the objective is to determine the relative weight of each of these factors, rank the candidates, and develop a decision-making model for boomerang recruitment.

This paper focuses on the grey-based multicriteria decision-making (MCDM) methodology for recruiting some of the best candidates out of a few who worked for the organization earlier. The grey theory yields adequate findings despite sparse data or significant factor variability. Like MCDM, the grey methods also incorporate experts' opinions for evaluation. Furthermore, sensitivity analysis is also done to show the robustness of the suggested methodology.

Seven (7) recruitment criteria for boomerang employees were identified and validated based on the opinions of industry experts. Using these recruitment criteria, three candidates emerged as the top three and created a pool out of six. In addition, this study finds that Criteria 1 (C1), the employee's past performance, is the most significant predictor among all other criteria in boomerang hiring.

Since the weights and ratings of attributes and alternatives in MCDM methods are primarily based on expert opinion, a significant difference in expert opinions (caused by differences in their knowledge and qualifications) may impact the values of the grey possibility degree. However, enough attention was taken while selecting the experts for this study regarding their expertise and subject experience.

The proposed method provides the groundwork for HR management. Managers confronted with recruiting employees who want to rejoin may use this model. According to experts, each attribute is not only generic but also crucial. In addition, because these factors apply to all sectors, they are industry-neutral.

To the best of the authors’ knowledge, this is the first study to apply a grey-based MCDM methodology to the boomerang recruitment model. This study also uses an example to explain the computational intricacies associated with such methods. The proposed system may be reproduced for boomerang recruiting in any sector because the framework is universal and replicable. Furthermore, the framework is expandable to include new criteria for different work.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

The purpose of this study is to examine the impact of mandatory corporate social responsibility (CSR) spending on firm value in the Indian context.

Using firm-level data over the period 2012–2017, this study uses the difference-in-differences (DID) technique combined with matching to control for potential endogeneity of the decision to comply with the CSR Act since the Act in its current form is applicable as a comply-or-explain obligation.

The results of this study suggest that mandatory CSR spending has a positive and statistically significant impact on firm value. These results remain robust to alternative econometric techniques such as regression discontinuity design (RDD) and randomization inference test as well as to alternative empirical specifications. Furthermore, the study demonstrates that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problem and lower institutional holdings.

This study explicitly considers the “comply-or-explain” flexibility option, in terms of spending on CSR, provided to Indian firms for the initial two to three years and investigates whether spending on CSR helps firms enhance their firm value. The study also finds that the positive effect of CSR spending on firm value is more pronounced for firms with higher information asymmetry problems and lower institutional holdings.