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The main objective of financial sector reform in India in the early 1990s was to invite more competition. Consequently, with the entry of private and foreign banks, the competitive banking environment witnessed widespread changes like new technology, new products and services, new business practices and obviously new regulatory norms to tackle the new developments. This research evaluates the impact of competition in the banking industry on the profitability of Indian banks. Using annual data of twelve public sector and nineteen private sector banks for the period 2013 to 2022, the researchers investigate the effect of competition on banking profitability. Competition is measured by Lerner index, whereas financial performance is measured by Net Interest Margin (NIM). The results of the study indicate that there is a strong effect of competition of banking profitability during the study period. [ABSTRACT FROM AUTHOR]

Financial inclusion is an important issue to the policymakers of a country. In India, financial inclusion has also been considered as a top priority since its independence. In this study, the researchers attempt to find out the vital and influential socio-economic factors that impel financial inclusion in the Malda district in West Bengal. The Principal Component Analysis (PCA) and Mixed Effect Generalized Ordinal Logistic Regression Model have been applied on the primary survey database, collected from Malda district through multi-stage sampling, in order to understand the influence of different socio-economic factors towards financial inclusion. From the results of regression models, the study observes that income, age, literacy, occupation and area of residence (urban, semi-urban and rural) are significant influential socio-economic factors towards financial inclusion. In case of the Muslim community, age gets a unique weight and for non-Muslim community mainly the two elements, income and literacy, are highly dominating factors towards financial inclusiveness. [ABSTRACT FROM AUTHOR]

In this paper we consider the general problem of managing an array of sensors in order to track multiple targets in the presence of clutter. There are three complicating factors. The first is that because of physical limitations (e.g., communication bandwidth) only a small subset of the available sensors can be utilized at any one time. The second complication is that the associations of measurements to targets/clutter are unknown. The third complication is that the total number of targets in the surveillance region is unknown and possibly time varying. It are these second and third factors that extend previous work [14]. Hence sensors must be utilized in an efficient manner to alleviate association ambiguities and to allow accurate estimation of the states of a varying number of targets. We pose the problem as a bi-criterion optimization with the two objectives of 1) controlling the posterior Cramer-Rao lower bound ((PCRLB) which provides a measure of the optimal achievable accuracy of target state estimation), and 2) maximizing the probability of detecting new targets. Only recently have expressions for multitarget PCRLBs been determined [7], and the necessary simulation techniques are computationally expensive. However, in this paper we show the existence of a muititarget information reduction matrix (IRM) which can be calculated off-line in most cases. Additionally, we propose some approximations that further reduce the computational load. We present solution methodologies that, in simulations, are shown to determine efficient utilization strategies for the available sensor resources, with some sensors selected to track existing targets and others given the primary task of surveillance in order to identify new threats.

Initializing and maintaining a track for a low observable (LO) (low SNR, low target detection probability and high false alarm rate) target can be very challenging because of the low information content of measurements. In addition, in some scenarios, target-originated measurements might not be present in many consecutive scans because of mispointing, target maneuvers, or erroneous preprocessing. That is, one might have a set of noninformative scans that could result in poor track initialization and maintenance. In this paper an algorithm based on the expectation-maximization (EM) algorithm combined with maximum likelihood (ML) estimation is presented for tracking slowly maneuvering targets in heavy clutter and possibly noninformative scans. The adaptive sliding-window EM-ML approach, which operates in batch mode, tries to reject or weight down noninformative scans using the Q-function in the M-step of the EM algorithm. It is shown that target features in the form of, for example, amplitude information (AI), can also be used to improve the estimates. In addition, performance bounds based on the supplemented EM (SEM) technique are also presented. The effectiveness of new algorithm is first demonstrated on a 78-frame long wave infrared (LWIR) data sequence consisting of an F1 Mirage fighter jet in heavy clutter. Previously, this scenario has been used as a benchmark for evaluating the performance of other track initialization algorithms. The new EM-ML estimator confirms the track by frame 20 while the ML-PDA (maximum likelihood estimator combined with probabilistic data association) algorithm, the IMM-MHT (interacting multiple model estimator combined with multiple hypothesis tracking) and the IMM-PDA estimator previously required 28, 38, and 39 frames, respectively. The benefits of the new algorithm in terms of accuracy, early detection, and computational load are illustrated using simulated scenarios as well.

Most present-day radar systems use monopulse techniques to extract angular measurements of subbeam accuracy. The familiar 'monopulse ratio' is a very effective means to derive the angle of a single target within a radar beam. For the simultaneous estimation of the angles of two closely-spaced targets, a modification on the monopulse ratio was derived in [7], while [21] presented a maximum likelihood (ML) technique via numerical search. In this paper it is shown that the ML solution can in fact be found explicitly, and the numerical search of [21] is unnecessary. However, the ML solution requires the signal to noise ratio (SNR) for each target to be known, and hence we generalize it so it requires only the relative SNR. Several versions of expectation maximization (EM) joint angle estimators are also derived, these differing in the degree to which prior information on SNR and on beam pattern are assumed. The performances of the different direction-of-arrival (DOA) estimators for unresolved targets are studied via Monte Carlo, and it is found that most hove similar performance: this is remarkable since the use of prior information (SNR, relative SNR, beam pattern) varies widely between them. There is, however, considerable performance variability as a function of the two targets' off-boresight angles. A simple combined technique that fuses the results from different approaches is thus proposed, and it performs well uniformly.

Financial system plays an important role in an economy. The banking sector in India has been undergoing reforms for a long time with the aim to develop competitiveness, increase inclusiveness, create big-sized banks and improve operational efficiency. The present investigation looks at the efficiency of the public sector banks for the period 2012-2018, using data envelopment analysis under the assumption of constant returns to scale. The two-input, two-output model is used to determine the efficiency levels on the basis of Minimum Distance to Strong Efficient Frontier as proposed by Aparicio et al. (2007). In order to capture the effect of non-performing assets (NPAs), efficiency is computed under the restricted (with NPAs not included) and unrestricted model (with NPAs considered as an undesirable output). The study finds that majority of the banks have been consistently performing quite well compared to their peers and overall industry. The correlation in the ranking of banks under the two models shows high and significant value in all the years. [ABSTRACT FROM AUTHOR]

The present study deals with the issue of attitude towards organic food in the background of increasing awareness about health and environmental protection. The investigation is based on 446 respondents whose responses are collected on a 5-point Likert scale. The overall finding shows that the attitude is on the higher side. The exploration of data shows that there is significant difference towards attitude when age, income, family type and profession are considered. There is, however, no significant difference in case of gender, educational qualification and domicile. [ABSTRACT FROM AUTHOR]

The purpose of this research is to investigate how willing are the postgraduate students in using online education. This investigation which is based on a sample of 471 students of higher education applies the Technology Acceptance Model explores the relationship between various dimensions of this model in students' willingness to use digital education system. The Partial Least Square Structural Equation modeling (PLS-SEM) indicates that perceived benefits and user friendliness have direct impact on students' willingness to use online education platform. In addition, perceived benefits and user friendliness have positive effect on students' attitude which in turn strongly influence their willingness to use online mode of education. [ABSTRACT FROM AUTHOR]

Financial inclusion is accepted worldwide as a pathway of inclusive growth. This study focuses on the aspect of 'usage of banking services' towards financial inclusion and looks into the effect of religious practices of communities of Malda district that is relatively backward and Muslim dominated. Primary data is collected from five hundred respondents using a structured questionnaire on the basis of multistage sampling method. Results of factor analysis show that knowledge about products and satisfaction about banking services are the prominent factors that give incentive to people to avail banking services. Moreover, the two extracted latent variables viz. customer experience on banking service and customer attitude towards banking products are two important aspects that affect 'usage' of such services. Contrary to the general perception, the study finds relative less significance of staff behavior and banking transaction time in the use of banking services. The study finds no significant difference in the usage of banking services between the Muslim and non-Muslim communities. [ABSTRACT FROM AUTHOR]

The present study explores the effect of dividend payment behavior on firm value. The research looks into the effect of varying dividend behavior on Tobin's Q, a proxy for firm value. For determining the dividend behavior of the corporates, total sample of 269 companies is distributed into four quartiles based on average dividend yield. The result shows that dividend payout ratio and change in dividend fail to have a significant effect on firm value in the least dividend-yield companies. However, it is interesting to see that for the high dividend paying companies, dividend payout and change in dividend affect the firm value positively suggesting evidence of clientele effect in the Indian context. [ABSTRACT FROM AUTHOR]