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This paper attempts to examine the relationship of equity ownership and financial performance of firms in India. The study explored the possibility that whether equity ownership type affects the financial performance of listed Indian firms. The study examined the relationship of equity ownership with accounting as well as market measures of financial performance of the firms. The study sampled the 500 listed companies constituting BSE 500 indices of Bombay Stock Exchange of India. The 397 most actively listed companies on BSE 500 indices of Bombay Stock Exchange of India, which constitute the bulk of trading, were chosen to constitute the sample of the study as of end of 2009-10. The study used Ordinary least square (OLS) to examine the relationship between the equity ownership and financial performance of the Indian listed firms. The findings of the study depict the presence of highly concentrated ownership structure in the Indian market. The results of the regression analyses interestingly indicate that the dispersed equity ownership influences certain dimensions of accounting financial performance measures (i.e. ROA and ROE) but not market performance measures (i.e. Tobin's Q, P/E and P/BV ratios), which indicate that there might be other factors (Behavioral, macro economic, political, contextual) affecting firms performance other than ownership structure. The findings of the study might be relevant for practitioners and investors for taking their financing and investment decisions.

Firm ownership structure has a strong influence on its long-term performance and financial literature documents that high concentration of promoter ownership has a positive bearing on firm performance. This study is an attempt to analyze relationship between promoter's holding and firm performance in an emerging market setting of India. The study specifically examines the endogeneity problem through use of simultaneous equations. To ensure robustness in addition to accounting-based measures (ROA and ROE), a market-based performance measure (Tobin's Q) has been employed. Panel data of 2148 firm-year observations are analyzed from 2003 to 2015 for 179 companies listed on India's broad-based market Index, the S&P BSE 500. The results indicate presence of non-linear relationship between the firm performance and promoter's holdings. Specifically, the study documents the relationship to be of U curve nature and clearly finds evidence of endogenous relationship between promoter's holding and firm performance.

This article aims to investigate the relationship between working capital management and firm performance in an emerging market. The analysis is done over a long window spanning across 2000–2014 by using ordinary least square (OLS), fixed- and random-effects model and generalized method of moments (GMM) on 2,327 firm-year observations, a panel data of 179 companies listed on the S&P BSE 500 Index of Bombay Stock Exchange (BSE). For robustness, in addition to accounting performance, market-based performance measure has also been employed to measure firm performance. This study based on India finds a negative relationship between the working capital management and firm performance, necessitating the need to efficiently manage the working capital for enhanced profitability.

Rotor systems play vital role in many modern day machinery such as turbines, pumps, aeroengines, gyroscopes, to name a few. Due to unavoidable unbalance in the rotor systems, there are lateral and torsional vibrations. Ignoring these effects may cause the system serious damages, which sometimes lead to catastrophic failures. Vibration level in rotor systems is acceptable within a range. Focus in this work is to minimize the vibration level to the acceptable range. One of the ways vibration level can be minimised is by means of providing damping. To accomplish this task in this work a new concept squeeze film damper is made by electro discharge machining which is compact in configuration, is filled with magneto-rheological (MR) fluid and tested out on one support of a Jeffcott rotor. This compact squeeze film damper (SFD) produces damping in a compact volume of the device compared to a conventional SFD. MR fluid is a smart fluid, for which apparent viscosity changes with the application of external magnetic field. This compact damper with MR fluid provides the variable damping force, controlled by an external magnetic field. In this work, proportional controller has been used for providing the control feedback. This MR damper is seen to reduce vibrations in steady state and transient input to the Jeffcott rotor. Parametric study for important design parameters has been done with the help of the simulation model. These controlled dampers can be used for reducing vibrations under different operating conditions and also crossing critical speed.

AbstractLand use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehensiveness of the descriptions of LULC changes, this investigation employed a combination of advanced techniques. Specifically, multitemporal 30 m resolution Landsat-8 satellite imagery was utilized, in addition to the cloud computing capabilities of the Google Earth Engine (GEE) platform. Additionally, the study incorporated the random forest (RF) algorithm. This study aimed to generate continuous LULC maps for 2014 and 2020 for the Shrirampur area of Maharashtra, India. A novel multiple composite RF approach based on LULC classification was utilized to generate the final LULC classification maps utilizing the RF-50 and RF-100 tree models. Both RF models utilized seven input bands (B1 to B7) as the dataset for LULC classification. By incorporating these bands, the models were able to influence the spectral information captured by each band to classify the LULC categories accurately. The inclusion of multiple bands enhanced the discrimination capabilities of the classifiers, increasing the comprehensiveness of the assessment of the LULC classes. The analysis indicated that RF-100 exhibited higher training and validation/testing accuracy for 2014 and 2020 (0.99 and 0.79/0.80, respectively). The study further revealed that agricultural land, built-up land, and water bodies have changed adequately and have undergone substantial variation among the LULC classes in the study area. Overall, this research provides novel insights into the application of machine learning (ML) models for LULC mapping and emphasizes the importance of selecting the optimal tree combination for enhancing the accuracy and reliability of LULC maps based on the GEE and different RF tree models. The present investigation further enabled the interpretation of pixel-level LULC interactions while improving image classification accuracy and suggested the best models for the classification of LULC maps through the identification of changes in LULC classes.