Search results

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.

Current paper is an overview of qualitative research. It starts with discussing meaning of research and links it with a framework of experiential learning. Complexity of socio-political environment can be captured with methodologies appropriate to capture dynamism and intricacy of human life. Qualitative research is a process of capturing lived-in experiences of individuals, groups, and society. It is an umbrella concept which involves variety of methods of data collection such as interviews, observations, focused group discussions, projective tools, drawings, narratives, biographies, videos, and anything which helps to understand world of participants. Researcher is an instrument of data collection and plays a crucial role in collecting data. Main steps and key characteristics of qualitative research are covered in this paper. Reader would develop appreciation for methodiness in qualitative research. Quality of qualitative research is explained referring to aspects related to rigor, worthiness of topic in interpretivist research. This paper presents challenges of qualitative research in terms of thinking of qualitative research, doing of qualitative research, and trustworthiness.

Management of information technology (IT) will continue to be an essential endeavour for organisations as we experience the increasing advancement of technology across different spectres of life. Managers will need to understand how technology is changing their business operations and the emergence of digital consumers who demand more innovative and technologically driven experiences. This book was conceptualised to provide theoretical insight for managers and researchers to understand where we are and where technology is taking us. This book has presented different chapters, highlighting management and practical implications across other technology, sector and countries. These selected chapters provide a wide variety of stimulating insights into the varying challenges and perspectives of management and IT in the digital era. Significantly, we recognise the growing prospects of Artificial intelligence (AI) and how it is revolutionising different sectors, further presenting critical challenges for managers on how to harness the opportunities of this technology for their business prospects.

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.

This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.

In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.

All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.

The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.

The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.

Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.

This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.

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.