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Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution.

The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs.

This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances.

This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

This paper aims to concentrate on research that has been conducted in the previous decade on metamaterial (MTM)-based sensors for material characterization, which includes solid dielectrics, micro fluids and biomolecules.

There has been a vast advancement in sensors based on MTM since the past few decades. MTM elements provide a sensitive response to materials while having a tiny footprint, making them an appealing alternative for realizing diverse sensing devices.

Related research papers on MTM sensors published in reputable journals were reviewed in this report, with a specific emphasis on the structure, size and nature of the materials characterized. Because electromagnetic wave interaction excites MTM structures, sensing applications around the electromagnetic spectrum are possible.

The paper contains valuable information on MTM sensor technology for material characterization, and this study also highlights the challenges and approaches that will guide future development.

The purpose of this paper is to analyse the social and economic disparities across social groups in rural Uttar Pradesh. The paper demonstrates that the structure of the rural economy in India is characterised by deeply ingrained prejudices and social discrimination. The four-village study undertaken in one of the most populated states in India, Uttar Pradesh, clearly reveals that there is a huge disparity in terms of various social and economic indicators and that the so-called high growth has hardly helped in bettering their lives.

The paper is based on primary data collected from the Census survey of villages exploring socio-economic disparities across social groups by using decomposition models.

The results evidently lend credence to the postulations that a large proportion of the disadvantaged groups are prone to multiple deprivations, both in the society and in labour markets. The inquiry reveals this phenomenon clearly.

From the policy point of view, it is therefore imperative to ensure the direct and focussed provision of basic human requirements in terms of education, employment and income of the state. The implementation of direct policy interventions is an absolute necessity if the state has to guarantee convergence and the inclusive growth process on a sustained basis.

This paper fulfils an identified need to study the inequality among the social groups in terms of education, employment, income and livelihood opportunities in selected villages of four districts of Uttar Pradesh.

This paper aims to evaluate the various dynamic performance parameters of hydrodynamic journal bearings. For this, the bearing’s inner surface is textured with chevron-shaped textures with different texture depths and number of textures in different regions/locations.

In the present study, the effect of chevron-shaped texture having different values of texture depths, locations and number of textures has been numerically simulated. The dynamic performance characteristics have been calculated by solving the fluid flow governing Reynolds equation using the finite element method, assuming iso-viscous and Newtonian fluid.

The obtained results indicate that the bearing stability can be improved with the help of surface texture. Among all the investigated texture locations, the maximum increase in stability threshold speed is observed for fully textured distribution. Moreover, for the chevron-shaped texture considered in the present study, the optimum values of texture depth and number of textures have also been determined for maximum bearing stability.

While designing, designers should focus on those optimum values of texture depth, texture location and number of textures which lead to maximum enhancement in bearing stability.

This study is useful in the appropriate selection of chevron-shaped texture parameters on bearing surface for the maximum bearing stability.

The purpose of this paper is to provide the various steady state parameters of hydrodynamic journal bearings have been determined to get maximum performance enhancement ratio. For this, the bearings inner surface is textured with triangular shape with different texture depths and a number of textures in pressure increasing region. The textured region acts as a lubricant reservoir, which provides additional film-thickness and reduce friction. Therefore, enhance the overall performance of bearing.

In the present study, the effect of triangular shaped texture on the static performance characteristics of a hydrodynamic journal bearing has been studied. Different values of texture depths and a number of textures have been numerically simulated in pressure developing region. The static performance characteristics have been calculated by solving the fluid flow governing Reynolds equation using the finite element method, assuming iso-viscous Newtonian fluid. The performance enhancement ratio, which is the ratio of load carrying capacity (LCC) to the coefficient of friction (COF) has been calculated from results to finalized optimum design parameters.

The paper provides numerically obtained results indicate that surface texturing can improve bearing performance if the textured region is placed in the pressure increasing region. Moreover, surface texturing is the most effective at bearing performance enhancement when the bearing operates at lower eccentricity ratios and texture depth. The performance enhancement ratio, which is the ratio of LCC to the COF is found to be a maximum value of 2.198 at texture depth of 1.5, eccentricity ratio of 0.2 and the textured region located in the increasing pressure region.

The present study is based on a numerical based research approach, which has its limitations. So, researchers are encouraged to investigate the same work experimentally.

The paper includes implications to be beneficial for designers for designing better hydrodynamic journal bearings.

For the triangular shaped texture, considered in the present study, the optimum values of texture depth and texture distribution region have also been determined. While designing, designers should focus on those values of texture depth, texture region and a number of textures, which give the maximum value of performance enhancement ratio, which represents maximum LCC at the lowest value of the COF.

Sustainable supplier selection is of vital importance in sustainability decision of supply chain under carbon neutrality. Multi-criteria decision-making approaches are widely used in sustainable supplier selection and generally classified the involved criteria into three sustainable development (SD) dimensions: Environmental, Social and Economic. During the assignment of appropriate weighted scores to the criteria, most of the methods considered mutually exclusive criteria. However, some criteria cover multidimensions since ambiguity vagueness makes them difficult to classify into one dimension exclusively. The purpose of this paper is to find proper approaches addressed to multidimensional overlapping criteria in the evaluation of suppliers’ sustainability performance.

This study proposes three approaches to resolve the multidimensional overlapping criteria issue by data envelopment analysis (DEA) methods. The first approach uses all dimensional criteria and “dimensional overlapping criteria” in a single DEA model. The second approach consists of two-stage DEA. The first stage is to find SD dimensional performances, which are used in the second stage. The third approach uses an aggregate weight-constrained DEA model with additional constraints. Such approaches are applied to an empirical case study with six dimensions.

Results indicate that the third approach is better than the first two approaches in balancing the development among all dimensions instead of focusing on the superiority dimension to obtain high performance.

Discussing overlapping criteria in the context of sustainable supplier evaluation and other multi-criteria evaluation have a noticeable impact on evaluation systems, but appropriate approaches for this issue are currently under-researched.

The decision-making to outsource and select the most suitable global manufacturing outsourcing partner (MOP) is complex and uncertain due to multiple conflicting qualitative and quantitative criteria as well as multiple alternatives. Vagueness and variability exist in ratings of criteria and alternatives by group of decision-makers (DMs). The paper provides a novel Stochastic Fuzzy (SF) method for evaluation and selection of agile and sustainable global MOP in uncertain and volatile business environment.

Four main selection criteria for global MOP selection were identified such as economic, agile, environmental and social criteria. Total 16 sub-criteria were selected. To consider the vagueness and variability in ratings by group of DMs, SF method using t-distribution or z-distribution was adopted. The criteria weights were determined using the Stochastic Fuzzy-CRiteria Importance Through Intercriteria Correlation (SF-CRITIC), while MOP selection was carried out using Stochastic Fuzzy-VIseKriterijumskaOptimizacija I KompromisnoResenje (SF-VIKOR) in the case study of footwear industry. Sensitivity analysis was performed to test the robustness of the proposed model. A comparative analysis of SF-VIKOR and VIKOR was made.

The worker’s wages and welfare, product price, product quality, green manufacturing process and collaboration with partners are the most important criteria for MOP selection. The MOP3 was found to be the best agile and sustainable global MOP for the footwear company. In sensitivity analysis, significance level is found to have important role in MOP ranking. Hence, the study concluded that integrated SF-CRITIC and SF-VIKOR is an improved method for MOP selection problem.

In a group decision-making, ambiguity, impreciseness and variability are found in relative ratings. Fuzzy variant Multi-Criteria Decision-Making methods cover impreciseness in ratings but not the variability. On the other hand, deterministic models do not cover either. Hence, the stochastic method based on the probability theory combining fuzzy theory is proposed to deal with decision-making problems in imprecise and uncertain environments. Most notably, the proposed model has novelty as it captures and reveals both the stochastic perspective and the fuzziness perspective in rating by group of DMs.

The proposed multi-criteria group decision-making model contributes to the sustainable and agile footwear supply chain management and will help the policymakers in selecting the best global MOP.

To the best of the authors’ knowledge, SF method has not been used to select MOP in the existing literature. For the first time, integrated SF-CRITIC and SF-VIKOR method were applied to select the best agile and sustainable MOP under uncertainty. Unlike other studies, this study considered agile criteria along with triple bottom line sustainable criteria for MOP selection. The novel method of SF assessment contributes to the literature and put forward the managerial implication for improving agility and sustainability of global manufacturing outsourcing in footwear industry.

This study aims to understand how the texture shape, number of textures and addition of nanoparticle additives in lubricants impact the dynamic characteristics of journal bearing by comparing six different texture shapes like triangle, chevron, arc, circle, rectangle and elliptical applied in pressure-increasing region under various geometrical and operating conditions.

The finite element method approach has been employed to solve governing Reynold’s equation, assuming iso-viscous Newtonian fluid, for computation of performance parameters like stiffness and damping coefficient, threshold speed, etc. By using a regression model, the impact of adding nanoparticles Al₂O₃ and CuO to the base lubricant on viscosity variation is calculated for selected temperature ranges and weight fractions of nanoparticles.

The arc-shaped texture with an area density of 28.27%, eccentricity ratio of 0.2 and texture depth of 0.6 exhibited 35.22% higher direct stiffness and 41.4% higher damping coefficient compared to the lowest value in the circle-shaped texture. Increasing the number of arc-shaped textures on the bearing surface with low area density led to declining stiffness and damping parameters. However, with nanoparticle additives, the arc-shaped texture further showed 10.75% and 8.11% improvement in stiffness and 9.99% and 4.87% enhancement in damping coefficient for Al₂O₃ and CuO, respectively, at 90 °C temperature and 0.5% weight fraction.

By understanding the influence of texture shapes on the dynamic characteristics, engineers can design bearings that exhibit improved stability and enhance overall performance.

The purpose of this paper is to provide an analytical model for low carbon supplier development. This study is focused on the level of investment and collaboration decisions pertaining to emission reduction.

The authors’ model includes a fuzzy c-means (FCM) clustering algorithm and a fuzzy formal concept analysis. First, a set of suppliers were classified according to their carbon performances through the FCM clustering algorithm. Then, the fuzzy formal concepts were derived from a set of fuzzy formal contexts through an intersection-based method. These fuzzy formal concepts provide the relative level of investments and collaboration decisions for each identified supplier cluster. A case from the Indian renewable energy sector was used for illustration of the proposed analytical model.

The proposed model and case illustration may help manufacturing firms to collaborate with their suppliers for improving their carbon performances.

The study contributes to the low carbon supply chain management literature by identifying the decision criteria of investments toward low carbon supplier development. It also provides an analytical model of collaboration for low carbon supplier development. Though the purpose of the study is to illustrate the proposed analytical model, it would have been better if the model was empirically validated.

Though the earlier studies on green supplier development program evaluation have considered a set of criteria to decide whether or not to invest on suppliers, these are silent on the relative level of investment required for a given set of suppliers. This study aims to fulfill this gap by providing an analytical model that will help a manufacturing firm to invest and collaborate with its suppliers for improving their carbon performance.