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This paper proposes a 3‐Dimensional Finite Element Model (FEM) for the simulation of magnetic flux distribution in a Magnetically Impelled Arc Butt (MIAB) welding process. The electromagnetic force responsible for the arc rotation in MIAB welding process is governed by the magnetic flux density in the gap, the arc current and the arc length (gap size). To be precise the radial magnetic flux density is a critical factor in arc rotation and weld quality. The aim of this study is to explore the interdependence of the magnetic flux density and the existing current in the coils using finite element code ANSYS. The results of this analysis are verified with the available experimental data for steel pipes (outer dia 50mm and 2mm thickness). The results of the numerical simulation emphasize that the magnetic flux density in the gap between the pipes is proportional to the exciting current.

The purpose of this paper is to locate determinants of the diffusion process of solar energy and examine the relationship between variables affecting diffusion and adoption solar of energy.

The data used in this research have been collected from users and non-users of solar energy products in Punjab. A research model was used to test for factors of diffusion and intention to use solar energy products.

The survey results show that the majority of solar product users are aware of the difference between renewable and non-renewable sources of energy. Roger’s diffusion model containing five components, relative advantage, trialability, observability, complexity and compatibility, shows that relative advantage and trialability significantly influence user’s intention to use solar products. In order to increase adoption and usage of solar energy products, more consideration is required toward raising the reliability and utility of solar products. Demos and trials highly convince users toward solar products. Hence sellers of solar products should give effective trials to increase acceptance of solar products.

This work provides a clear focus which will allow the diffusion and adoption of solar energy product by providing guidance and aspiration as well as providing platform to solar energy manufacture and government for policy making and future decision.

The principal intention behind the activity is to regulate the speed, current and commutation of the brushless DC (BLDC) motor. Thereby, the authors can control the torque.

In order to regulate the current and speed of the motor, the Multi-resolution PID (MRPID) controller is proposed. The altered Landsman converter is utilized in this proposed suppression circuit, and the obligation cycle is acclimated to acquire the ideal DC-bus voltage dependent on the speed of the BLDC motor. The adaptive neuro-fuzzy inference system-elephant herding optimization (ANFIS-EHO) calculation mirrors the conduct of the procreant framework in families.

Brushless DC motor's dynamic properties are created, noticed and examined by MATLAB/Simulink model. The performance will be compared with existing genetic algorithms.

The presented approach and performance will be compared with existing genetic algorithms and optimization of different structure of BLDC motor.

This paper aims to present an optimal variable speed drive of a doubly fed induction motor (DFIM) with minimum losses and reduced inverter capacity. The operation with minimum losses ensures that the DFIM develops the required load torque at desired speed with maximum energy saving. Moreover, the control of rotor voltage ensures the reduced inverter capacity. The water cycle algorithm (WCA) as one of meta-heuristic optimization techniques is used to estimate the optimal rotor voltages to drive the DFIM with minimum losses. The results of WCA are confirmed with other well-known and reliable optimization method such as particle swarm optimization along with classical method.

The DFIM is an efficient alternative solution of synchronous motor (SM) because of its speed is synchronized with both stator and rotor frequencies regardless the load torque. As a result, the speed of variable speed drive associated with DFIM can be controlled through a rotor inverter with reduced capacity rather than SM. The output voltage of rotor inverter is controlled to develop the demanded output power with minimum motor losses.

A complete DFIM drive model is developed under MATLAB/SIMULINK environment using d-q dynamic model to verify the strength and significance of the proposed controller. An experimental setup using a 300 W three-phase wound rotor induction motor is established to validate the mathematical models and theoretical results. The motor performances with proposed rotor voltage control (minimum losses) are compared with conventional method of constant voltage to frequency ratio (V/f constant). It is found that the proposed WCA based on controller achieves significant reductions in motor losses, input power and rotor inverter power.

The paper presents an efficient method to maximize the energy saving of DFIM with a reduced inverter capacity using WCA.

The aim of this research study is to mitigate shading impact on solar photovoltaic array. Photovoltaic (PV) array when getting shaded not only results in appreciable power loss but also exhibits multiple power peaks. Due to these multiple power peaks, the maximum power point tracking (MPPT) controllers’ performance will be affected, as most of the times it ends up in tracking the local maximum power peak and not the global power peak.

The PV panels in an PV array when getting shaded even partially would result in huge power loss. The pattern of shading also plays a crucial role, as it renders a cascaded impact on the overall power output because the cells/panels are connected in series and are parallel. Therefore, during shading, intelligent schemes are needed to appropriately connect and discard the unhealthy and healthy panels in right place with right combination. This research proposes one such scheme to mitigate the shading impact.

To mitigate the shading impact and also to have a smooth power-voltage (P-V) curve, a new series inducing switching scheme is introduced. The proposed scheme not only mitigates the shading impact and enhances the output power but also smoothens the P-V curve that facilitates the MPPTs to track the P-V appropriately.

The research findings are inventive in nature and not copied work. The reference works and the inspirations have been duly cited and credited.

Partial shading causes significant power decreases in the PV systems. The purpose of this paper is to address this problem, connectivity regulation is designed to reduce partial shading problems.

In this approach, the partial shading was estimated and dispersed evenly on the whole array by global shade dispersion technique (GSD). The grey wolf algorithm was implemented for the interconnection of arrays by an efficient switching matrix.

After the implementation of the GSD technique using a grey wolf algorithm, the performance under different shading conditions was analyzed using the MatLab simulation tool. The results were compared with total cross-tied (TCT), Su Do Ku and the proposed method of reconfiguration, where the proposed method improves the maximum power of the PV system appropriately.

This methodology uses any size of PV systems.

Replacement of conventional energy systems with renewable energy systems such as solar helps the environment clean and green.

The GSD interconnection scheme using the grey wolf optimization algorithm has proved an improved output performance compared with the existing TCT and Sudoku based reconfiguration techniques. By comparing with existing techniques in literature, the proposed method is more advantageous for reducing mismatch losses between the modules of any size of the PV array with less operating time.

The purpose of this paper is to present an improved approach for estimation of the rotor position and speed of doubly fed induction generator, which can be used in vector control and direct torque control (DTC) schemes.

Some novel equations are developed for calculation of the rotor position and rotor speed. Such equations do not need to the value of stator flux linkage and just, measured values of the stator voltage and currents as well as rotor current are required to be known.

The simulation results verify the satisfactory steady-state and dynamic performance of proposed approach with both the vector control and DTC schemes. The results show that the proposed estimation approach benefits from the starting on the fly, robustness against the variations of the most of the stator and rotor parameters and immunity against the noise.

The proposed estimation approach is novel and the outcome of the research of authors. It is simple and effective and, no approximation is made in the calculations. The simulation results demonstrate that the proposed scheme can be successfully implemented in various control strategies, e.g. DTC and vector control.

The goal of this research is to create a conceptual model that identifies the relationship between organizational learning and green human resource management practices and its contribution to the circular economy. The focus of this research is to identify the organizational learning and green human resource factors that lead to a more circular economic transformation.

The importance of the interaction of factors benefiting both individuals and organizations has been comprehensively explained by combining two well-known theories: resource-based view theory and organizational learning theory. As learning entails change, a climate of organizational learning combined with green human resource practices will efficiently help firms transition to a circular economy. The authors establish a conceptual model in this study, which they then apply to the small- and medium-sized manufacturing industry and report on in the second paper.

This study concentrates on the factors of organizational learning and green human resource management practices that help to create a circular economy after conducting a thorough literature analysis and consulting with experts.

The conceptual model can be applied to organizations in any industry. In the second part, the authors would investigate the interaction of the indicated factors and build their structural hierarchy using the total interpretive structural modelling method for the small- and medium-sized industries and then present the findings.

The proposed framework might facilitate decision-makers, practitioners and academicians to comprehend the relationship between organizational learning and green human resource management factors and their influence on organizations’ transition into a circular economy. The study’s findings will help HR managers clarify the significant organizational learning and green human resource management factors that play a vital role in transitioning organizations into a circular economy.

This research adds to our understanding of the intricate interactions between organizational learning and green human resource management as they relate to the circular economy. To the best of the authors’ knowledge, the conceptual model illustrating the inter-relationship between organizational learning and green human resource management is the first of its type, and it is a novel notion because no previous research has looked into it.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.