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Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

The purpose of this paper rapid development of various voltage sag compensation techniques in DC bus using ultra-capacitors (UCs) provides satisfactory results when compared with required peak power demand for shorter duration. Later, UCs have been used as floating capacitors [1] [2]. Various UCs are available based on internal resistances which also rely on its manufacturing materials, similar to double layer capacitors.

This paper demonstrates UCs based voltage sag compensation at load side under different working modes of hydraulic pack (HP) in an armored fighting vehicle (AFV). The main sources to supply the HP are 24 V, 400 Ahr battery bank and 20 kW main generator. HP is considered to be the highest power load of a system. 2,500 A inrush current was drawn by HP during initial conditions, and also, this system works in both elevation and azimuth mode. Voltage sag has been varied from 15 to 24 V for different modes. But as per the military standard, electrical systems should operate between 18 and 32 V DC. Because of insufficient terminal voltage, required energy cannot be attained and supplied to the loads. The proposed topology compensated the voltage sag and maintains nominal voltage on a DC bus. The devised circuit has been verified under all possible operating loads such as continuous, intermittent and momentary. The same has been simulated using MATLAB/Simulink and was experimentally verified. The minimum voltage maintained in a DC bus is 22.2 V in simulation, while experimentally, it was 24.2 V.

For getting higher percentage of efficiency, secondary energy system configuration, mainly designed for electrical vehicles, is needed. It was implemented and same was tested with the fighting vehicle system[1]. The proposed configuration comprises of bank of an UC and a battery bank. The system was finally implemented in AFVs.

The goods vehicles made of UCs can hold very minimum energy because of minimum density of energy. The modified AFV can have minimum charging as well as discharging of rate of energy and, thus, power[3][4]. Thus, the proposed idea of modified vehicle system has influence over significant change in the state of charge.

This paper aims to investigate the resultant optimal ultimate tensile strength, elongation, flexural strength and modulus, compression strength and impact strength of fabricated alkali-treated Lagenaria siceraria fiber (LSF)-reinforced polymer matrix composite by optimizing input factors and microstructural characterization by influencing fiber length, fiber concentration and treatment condition of LSF.

The fabrication of LSF-reinforced composite specimens involved surface treatment followed by custom experimental design using a simple hand layup process. The wear analysis was performed by a multi-tribotester TR25 machine, and the developed model was validated by using statistical software Design Expert V.8 and analysis of variance (ANOVA). The surface morphology of the sample was also analyzed by field emission scanning electron microscopy.

The alkali treatment for LSFs had reduced the hemicellulose, and enhanced mechanical performance was observed for 30 wt.% concentration of L. siceraria in epoxy resin. Thermogravimetric analysis revealed thermal stability up to 245°C; microstructure revealed fiber entanglements in case of longer fiber length and compression strength reduction; and the surface-treated fiber composites exhibited reduced occurrences of defects and enhanced matrix–fiber bonding. Enhanced mechanical performances were observed, namely, ultimate tensile strength of 17.072 MPa, elongation of 1.847%, flexural strength of 50.4 MPa, flexural modulus of 3,376.31 GPa, compression strength of 52.154 MPa and impact strength of 0.53 joules.

The novel approach of optimizing and characterizing alkali surface-treated LSF-reinforced epoxy matrix composite was explored, varying fiber length and concentrations for specimens by empirical relations and experimental design to obtain optimal performance validated by ANOVA. Enhanced properties were obtained for: 7 mm fiber length and 30 wt.% concentration of fiber in the composite for alkali-treated fiber.

This paper aims to investigate the impact of the efficiency of working capital management (WCM) on the performance of a sample of Indian companies and explore how the nature of the firm's business influences the significance and direction of this impact.

The data for this study were collected for the period of 2012–2018 for 414 non-financial firms listed on the Bombay Stock exchange. Fixed-effect regression models were run by taking Tobin's Q and return on equity (ROE) as dependent variables, and net trade cycle (NTC) and its components as explanatory variables in the presence of liquidity, leverage, size, age and growth as control variables. Sample firms were segregated into manufacturing, trading and service groups, and regression models were used for all the groups to understand the effect of the nature of a firm's business.

WCM efficiency has a significant impact on the performance of the sample firms. Non-financial Indian firms deliver better financial performance by maintaining lower NTC. Like NTC, its components also impact firm value and profitability. The results report that the significance of the relationship varies depending upon the nature of the firm's business.

The previous research studies had not used a sample of large number of Indian firms. Unlike previous studies, this study reports the influence of the nature of business on the relationship between WCM and firm performance. Further, this paper also examines how the individual components of working capital influence the performance of Indian firms.

The purpose of this study is to introduce working capital efficiency multiplier (WCEM) as a direct profitability measure of working capital management. The existing accounting measures in the literature establish an indirect approach to study the relationship between working capital efficiency and profitability of the firms.

Using the help of a set of companies from CMIE Prowess database, the study introduces WCEM as a direct profitability measure of working capital efficiency.

In this study, a new direct measure of working capital efficiency is introduced which is multiplicative in nature. WCEM is a product of three components, namely, WACC, ratio of the sum of trade receivables and inventories to trade payables and ratio of net working capital (NWC) to net sales.

The importance of direct measure like WCEM could be enormous in performance evaluation of a firm. It can be used as an indicator for choosing a suitable investment opportunity by an investor. This is due to the fact that the firm that is highly efficient in managing working capital is less exposed to liquidity risk. At the same time, the firm is less dependent on external financing. Therefore, such firms eventually create more value for their shareholders. Another indication that WCEM provides is to gauge the bargaining power of the firm and its competitive position in the market. Lower WCEM indicates higher bargaining power of a firm across the value chain, and its superior position relative to its competitors.

Most of the studies on WCM are of the empirical type and there is a complete dearth on theoretical framework. Researchers hereafter can consider WCEM as one of the financial performance variables in place of the existing measures such as return on asset (ROA), return on invested capital (ROIC), return on equity (ROE), gross operating income (GOI) and net operating income (NOI) and thereby can contribute new empirical insights through their research outcomes.

In this paper, a set of safe adaptive genetic algorithms (sGAs) is proposed based on the Splicing/Decomposable encoding scheme and the efficient speed‐up strategies developed by Xu et al.. The proposed algorithms implement the self‐adaptation of the problem representation, selection and recombination operators at the levels of population, individual and component which commendably balance the conflicts between “reliability” and “efficiency”, as well as “exploitation” and “exploration” existed in the evolutionary algorithms. It is shown that the algorithms converge to the optimum solution in probability one. The proposed sGAs are experimentally compared with the classical genetic algorithm (CGA), non‐uniform genetic algorithm (nGA) proposed by Michalewicz, forking genetic algorithm (FGA) proposed by Tsutsui et al. and the classical evolution programming (CEP). The experiments indicate that the new algorithms perform much more efficiently than CGA and FGA do, comparable with the real‐coded GAs — nGA and CEP. All the algorithms are further evaluated through an application to a difficult real‐life application problem: the inverse problem of fractal encoding related to fractal image compression technique. The results for the sGA is better than those of CGA and FGA, and has the same, sometimes better performance compared to those of nGA and CEP.

Bearings and gears are major components in any rotatory machines and, thus, gained interest for condition monitoring. The failure of such critical components may cause an increase in down time and maintenance cost. Condition monitoring using the machine learning approach is a conceivable solution for the problem raised during the operation of the machinery system. The paper aims to discuss these issues.

This paper aims engine gearbox fault diagnosis based on a decision tree and artificial neural network algorithm.

The experimental result (classification accuracy 85.55 percent) validates that the proposed approach is an effective method for engine gearbox fault diagnosis.

This paper attempts to diagnose the faults in engine gearbox based on the machine learning approach with the combination of statistical features of vibration signals, decision tree and multi-layer perceptron neural network techniques.

Several chaotic system-based encryption techniques have been presented in recent years to protect digital images using cryptography. The challenges of key distribution and administration make symmetric encryption difficult. The purpose of this paper is to address these concerns, the novel hybrid partial differential elliptical Rubik’s cube algorithm is developed in this study as an asymmetric image encryption approach. This novel algorithm generates a random weighted matrix, and uses the masking method on image pixels with Rubik’s cube principle. Security analysis has been conducted, it enhances and increases the reliability of the proposed algorithm against a variety of attacks including statistical and differential attacks.

In this light, a differential elliptical model is designed with two phases for image encryption and decryption. A modified image is achieved by rotating and mixing intensities of rows and columns with a masking matrix derived from the key generation technique using a unique approach based on the elliptic curve and Rubik’s cube principle.

To evaluate the security level, the proposed algorithm is tested with statistical and differential attacks on a different set of test images with peak signal-to-noise ratio, unified average changed intensity and number of pixel change rate performance metrics. These results proved that the proposed image encryption method is completely reliable and enhances image security during transmission.

The elliptic curve–based encryption is hard to break by hackers and adding a Rubik’s cube principle makes it even more complex and nearly impossible to decode. The proposed method provides reduced key size.

Introduction: With many new technologies requiring real-time data processing, cloud computing has become challenging to implement due to high bandwidth and high latency requirements.

Purpose: To overcome this issue, edge computing is used to process data at the network’s edge. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. It is used to process time-sensitive data.

Methodology: The authors implemented the model using Linux Foundation’s open-source platform EdgeX Foundry to create an edge-computing device. The model involved getting data from an on-board sensor (on-board diagnostics (OBD-II)) and the GPS sensor of a car. The data are then observed and computed to the EdgeX server. The single server will send data to serve three real-life internet of things (IoT) use cases: auto insurance, supporting a smart city, and building a personal driving record.

Findings: The main aim of this model is to illustrate how edge computing can improve both latency and bandwidth usage needed for real-world IoT applications.

The purpose of this paper is to improve the accuracy of short time load forecasting to ensure the economical and safe operation of power systems. The traditional neural network applied in time series like load forecasting, easily plunges into local optimum and has a complicated learning process, leading to relatively slow calculating speed. On the basis of existing literature, the authors carried out studies in an effort to optimize a new recurrent neural network by wavelet analysis to solve the previous problems.

The main technique the authors applied is referred to as echo state network (ESN). Detailed information has been acquired by the authors using wavelet analysis. After obtaining more information from original time series, different reservoirs can be built for each subsequence. The proposed method is tested by using hourly electricity load data from a southern city in China. In addition, some traditional methods are also applied for the same task, as contrast.

The experiment has led the authors to believe that the optimized model is encouraging and performs better. Compared with standard ESN, BP network and SVM, the experimental results indicate that WS‐ESN improves the prediction accuracy and has less computing consumption.

The paper develops a new method for short time load forecasting. Wavelet decomposition is employed to pre‐process the original load data. The approximate part associated with low frequencies and several detailed parts associated with high frequencies components give expression to different information from original data. According to this, suitable ESN is chosen for each sub‐sequence, respectively. Therefore, the model combining the advantages of both ESN and wavelet analysis improves the result for short time load forecasting, and can be applied to other time series problem.