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RETAIL INVESTORS PARTICIPATION IN INDIAN CORPORATE BOND MARKET: A CRITICAL STUDY
A developed and vibrant corporate bond market provides an important means of financial resources to corporate, in addition to bank financing. It brings transparency and prudence into financial system and helps in minimizing risks in financial system through diversification. The main objective of this study is to find out the various issues and hurdles faced by retail investors and come out with appropriate suggestions in this regard. A modest attempt has also been made to understand the present status of the Indian corporate bond market in reference to retail investors participation. The study reveals that, in spite of sincere efforts of policymakers, the participation of retail investors in Indian corporate bond market are negligible. It has also been observed that the important issues of retail investors are related to safety of principal, returns, liquidity and tax benefits. It is expected that the suggestions made in this paper, if implemented, will definitely helpful in attracting retail investors to Indian corporate bond market. 2024 Published by Faculty of Engineering. -
The impact of audit committee independence and competence on investors investment decision making: A study in the Indian context
This study examined the impact of an independent and competent audit committee on the decision-making process of investors in the Indian capital market and adopted a quantitative approach in which cross-sectional data are gathered with the help of a self-administered questionnaire survey. The selection of participants involves a stratified random sample technique, specifically targeting 441 regular investors associated with nine prominent brokerage houses in the Delhi NCR engaged in equity market investments. Descriptive analysis is applied to discern respondent characteristics, whereas correlation and regression analyses are utilised to test and elucidate the relationships and influences among variables in the model. The findings of this study, which indicate a notable correlation between investors' investment decision-making in India and the independence and competence of the audit committee, are in line with the expectation that independence and competence are essential attributes that the audit committee must possess. These characteristics, in turn, have a notable influence on investors' investment decisions. The outcome of this paper fills a gap in the literature by offering insights into the vital attributes of audit committee independence and competence that notably contribute to investment decision-making in India. This research examined the impact of an independent and competent audit committee on the decision-making process of investors in the Indian capital market, which has never been examined before. Thus, the findings shed light on the influence of board independence and competence on the decision-making process of investors in the Indian capital market. 2025, Malque Publishing. All rights reserved. -
On l(T, 1)-colouring of certain classes of graphs
For a given set T of non-negative integers including zero and a positive integer k, the L(T, 1)-Colouring of a graph G = (V, E) is a function c: V(G) ? {0, 0, , k} such that |c(u) ? c(v)| ? T if the distance between u and v is 1 and |c(u) ? c(v)| ? 0 whenever u and v are at distance 2. The L(T,1)-span, ?T,1(G) is the smallest positive integer k such that G admits an L(T, 1)-Colouring. In this article we initiate a study of this concept of L(T, 1)-Colouring by determining the value of ?T,1(G) for some classes of graphs and present algorithms to obtain the L(T, 1)-Colouring of paths and stars. 2020 IJSTR. -
L(t, 1)-colouring of graphs
One of the most famous applications of Graph Theory is in the field of Channel Assignment Problems. There are varieties of graph colouring concepts that are used for different requirements of frequency assignments in communication channels. We introduce here L(t, 1)-colouring of graphs. This has its foundation in T-colouring and L(p, q)-colouring. For a given finite set T including zero, an L(t, 1)-colouring of a graph G is an assignment of non-negative integers to the vertices of G such that the difference between the colours of adjacent vertices must not belong to the set T and the colours of vertices that are at distance two must be distinct. The variable t in L(t, 1) denotes the elements of the set T. For a graph G, the L(t, 1)-span of G is the minimum of the highest colour used to colour the vertices of a graph out of all the possible L(t, 1)-colourings. It is denoted by ?t,1(G). We study some properties of L(t, 1)-colouring. We also find upper bounds of ?t,1(G) of stars and multipartite graphs. I??k University, Department of Mathematics, 2022; all rights reserved. -
Role of Need for Achievement on Decision making and Life Orientation of Young Adults
Purpose-To assess the role of need for achievement on decision making and life orientation of young adults. Design/methodology/approach-The data was collected from the participants using a questionnaire. The sample size is 100 young adults. The sampling technique used is convenience sampling, and the research design is a cross-sectional survey. It was hypothesised that individuals high in achievement motivation will also be high in life orientation level and there will be a positive correlation between achievement motivation and decision making. Findings The results of the study indicate that an individual high in achievement motivation will also be high in life orientation level and a positive correlation is found between achievement motivation and decision making. The other findings are that optimising decision-making styles is positively correlated with achievement motivation and a significant difference in achievement motivation between males and females is found, indicating a higher need for achievement in females as compared to males. Social Implications-The findings of the study are considerable with respect to the personal, professional, and educational development of young adults. As the research suggests, there is a positive relationship between decision-making styles, achievement motivation, and orientation towards life. Therefore, various decision-making styles can be introduced in the behavioural sciences subject domain. Higher achievement needs in females indicate their potential in various professional realms, and such platforms, if provided, can increase women's participation in the workforce, resulting in economic, social, and personal development for women as well as society. Originality/ Value The youth of a country are its greatest assets, and for an aspirational country, there is a need for a highly motivated task force. The research topic focuses on how motivated behaviour occupies a central position in personality and its relationship with decision-making style and orientation towards life. This study focuses on the need of the hour, which is harnessing our youth and exploring more about the achievement-oriented behaviour and optimistic outlook of young adults, which is the demographic dividend of the country. 2022 RESTORATIVE JUSTICE FOR ALL. -
Advancing Road Safety through Driver Drowsiness Detection Using Deep Learning Model
Driver drowsiness poses a significant threat to public safety, contributing to numerous road accidents and fatalities annually. Drowsy drivers exhibit characteristic changes in facial expressions and behaviors, including eye closure, head nodding, and yawning. These indicators can be detected through various techniques, including image processing, computer vision, and machine learning. This research investigates a promising approach: utilizing a ResNet-101 deep convolutional neural network (CNN) for driver drowsiness detection based on eye, head, and mouth states. The model was trained on a vast dataset of 2.2 million images, covering diverse driving conditions. Despite achieving a 69% accuracy, suggesting real-world potential, computational limitations restricted training to only a quarter of the data. This necessitates further research with larger datasets and increased resources to enhance accuracy and robustness. 2024 IEEE. -
Mental Workload Estimation Using EEG
Mental workload contributes considerably to the outcome or the performance of any task. The concern of human workload increases during a human-machine collaboration task or in a multitasking environment. This paper presents a comparative study of machine learning algorithms used to estimate workload using Electroencephalography (EEG) data. An open-access EEG dataset acquired during a 'simultaneous capacity (SIMKAP) experiment' and 'no task' is used to create and validate models for binary classification of workload as present and absent respectively. The paper presents an implementation of various classification models that use EEG data to predict the workload. In this paper, implementation for KNN classifier (57.3%), Random Forest classifier (57.19%), MLP network classifier (58.2%), CNN+ LSTM network classifier (58.68%), and LSTM network classifier (61.08%) has been reported. The paper can be further extended to study operator workload in real-time using a brain-computer interface paradigm for any kind of task in a real-world application. The workload classification can be further used in human-machine tasks to decide task allocation between the system to achieve optimal performance in a complex critical system. 2020 IEEE. -
Impact of Homophily on Patient Empowerment: A Study of Online Patient Support Groups
Internet facility has led to emergence of patient support groups. These have gained prominence as these fulfils important benefits to patients. One such benefit is patient empowerment. These online groups provide opportunity to patients to interact with similar ailments and predicaments and who can understand the pain and discomfort felt by the patient. This provides validation for the patient and patients experiences. How does this homophily impacts patient empowerment? This question has been explored in this study. The methodology is based on an online survey of patients visiting such online platforms. In all 701 patients provided the data. Independent variable (homophily) and dependent variable (patient empowerment) have been measured using a 7-point Likert scale. Findings provide that both are weakly correlated, but this correlation is significant. Regression analysis led to a regression model that is fit statistically. This provides basis to encourage patients to visit online support groups. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Impact of Perceived Social Support on Patient Empowerment: A Study of Online Patient Support Groups
Disease-specific online patient support groups have emerged predominantly in last 30years, and these are being visited by a large number of patents. These platforms obviously bring important benefits to the patients visiting them. An important variable is the perceived social support that patients feel they derive while interacting with healthcare providers and fellow patients over there. Patient empowerment is another variable, and which has been found to be a critical factor in overall well-being of patients. How does the perceived social support felt by patients visiting an online patient support group impact their perceived empowerment? This paper explores this question. Research design is associative, and for which the data has been procured online from the patients visiting online patient support groups. The questionnaire comprises of an independent variable (perceived social support) and a dependent variable (patient empowerment). Validated scales have been used. For analysis, a factor analysis was undertaken to reconfirm the validity of the scales. Thereafter, regression equation has been developed to measure the impact. Results show that the model obtained passes the fitness and the independent variable has a significant positive association with patient empowerment. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Spectroscopic Studies and Theoretical Analysis of Some Selected Heterocycles
Seven derivatives of heterocycles benzimidazole, benzoxazole and benzothiazole were studied, five of which were synthesized and characterized. The molecular geometry and newlinespectroscopic data of the compounds in the ground state were calculated using the density functional theory (DFT/B3LYP) method with the 6-311++G(d,p) basis set. A comparison between the experimental and calculated data was attempted. Molecular electrostatic potential (MEP) and global reactivity parameters were deduced using theoretical calculations. HOMO-LUMO energy gap for each compound was determined by DFT and cyclic voltammetry. The cyclic voltammograms were recorded in acetonitrile solvent using lithium perchlorate as the supporting electrolyte. For all the compounds experimentally determined HOMO LUMO energy gap in polar solvent was lesser than that from DFT calculated energy gap. Using the HOMO-LUMO energy gap, global reactivity parameters were calculated. The effect of solvents of varying polarity on the absorption and emission spectra of the compounds was studied. Large excitation and emission energy differences were observed for all the selected heterocycles. The excitation and fluorescence spectra of selected heterocycles were recorded in eight solvents of different solvent polarity. It is evident from the excitation spectra that on increase of the solvent polarity, a bathochromic shift takes place for and#960;-and#960;* transition, and this is attributed to the high influence of solvent polarity in the excited state of heterocycle newlinecompared to its ground state. The dipole moments in the ground and the first excited state of heterocycle derivatives were newlinecalculated using Lippert-Mataga and Kawski-Chamma-Viallet methods. Guggenheim-Debye method was adopted to calculate ground state dipole moment. The dipole moments of the compounds were also calculated using Time Dependent-Density Functional Theory (TD-DFT). The dipole moment values of the compounds suggested that the excited state has more charge separation and thus becomes more polar. -
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A Study on Graph Colouring with Distance Constraints
In this dissertation, we have studied the variations of graph colouring based on distance constraints. For a given set T of non-negative integers including zero and a positive integer k, the L(T,1)-colouring of a graph G = (V,E) is a function c : V(G) and#8594; newline{0,1,2,...,k} such that |c(u)and#8722;c(v)| and#8712;/ T if the distance between u and v is 1 and |c(u)and#8722; newlinec(v)| and#8805; 1 whenever u and v are at distance 2. The L(T,1)-span, and#955;T,1(G) is the smallest positive integer k such that G admits an L(T,1)-Colouring. We have determined the newlineL(T,1)-span for some classes of graphs for set T whose elements are arranged in arithmetic progression. Further, for any general set T , we have found the bound for L(T,1)- span of a few classes of graphs. We use Python programming to colour certain classes of graphs concerning L(T,1)-colouring and fnd the value of L(T,1)-span. Next, we have explored equitable fractional open neighbourhood colouring, which is an extension of a specifc variation of L(h,k)-Colouring for h = 0 and k = 1. For a newlinepositive integer p, equitable fractional open neighbourhood colouring of a graph G is an newlineassignment of positive integers to the vertices of G such that for each vertex v and#8712;V(G), vertices of N(v) receives at least l1p|N(v)|m distinct colours and N(v) can be partitioned into k-classes V1,V2,...Vk such that ||Vi|and#8722; |Vj|| and#8804; 1 for every i and#824;= j and 1 and#8804; k and#8804; n. The minimum number of colours required to colour G such that it admits equitable fractional open neighbourhood colouring for a fxed p is called the equitable fractional open neighbourhood chromatic number, and#967;eq onc newlinep (G). We have studied some properties of equitable fractional open neighbourhood colouring and explored some classes of graphs which admit equitable fractional open neighbourhood colouring with land#8710;(pG)m colours. Further, we have introduced and examined a variation of perfect graphs, and#967;onc-perfect graphs, with respect to equitable fractional open neighbourhood colouring for the special case of p = 1. -
Gucchi (Morchella esculenta)
This chapter focuses on Morchella esculenta as a nutraceutical and functional food, its habit, habitat, general characteristics, availability, biologically active compounds present and pharmacological and medicinal value. Mushrooms are spore-bearing fleshy fruiting bodies of fungus often present above the ground. Greeks and Romans included mushrooms in their diet. Romans considered mushrooms as the food of supernatural beings, despite the Chinese contemplating them as the elixir of the human being. Functional foods that are prepared from morel mushrooms are of high medicinal properties. The production of M. esculenta worldwide is 1.5 million tonnes of fresh weight and 150 tonnes of dry weight. India and Pakistan are the major morel-producing countries and each country has about 50 tonnes of dry morels. The pharmacological properties of Morchella species show its use in Chinese traditional medicine since 2, 000 years and in Malaysia and Japan to cure several diseases. 2023 Deepu Pandita and Anu Pandita. -
Hybrid Convolutional Neural Network and Extreme Learning Machine for Kidney Stone Detection
When it comes to diagnosing structural abnormalities including cysts, stones, cancer, congenital malformations, swelling, blocking of urine flow, etc., ultrasound imaging plays a key role in the medical sector. Kidney detection is tough due to the presence of speckle noise and low contrast in ultrasound pictures. This study presents the design and implementation of a system for extracting kidney structures from ultrasound pictures for use in medical procedures such as punctures. To begin, a restored input image is used as a starting point. After that, a Gabor filter is used to lessen the impact of the speckle noise and refine the final image. Improving image quality with histogram equalization. Cell segmentation and area based segmentation were chosen as the two segmentation methods to compare in this investigation. When extracting renal regions, the region-based segmentation is applied to obtain optimal results. Finally, this study refines the segmentation and clip off just the kidney area and training the model by using CNN-ELM model. This method produces an accuracy of about 98.5%, which outperforms CNN and ELM models. 2023 IEEE. -
Phishing attack detection using Machine Learning
Phishing is a type of digital assault, which adversely affects people where the client is coordinated to counterfeit sites and hoodwinked to screen their touchy and private data which integrates watchwords of records, monetary data, ATM pin-card data, etc. Recently safeguarding touchy records, it's fragile to cover yourself from malware or web phishing. AI is an investigation of information examination and logical investigation of calculations has demonstrated outcomes. Contradicting phishing sprinters with remarkable perception and felonious outcomes comparable as care shops, and custom against phishing approaches. This paper examines the association of Machine Literacy routes in identifying phishing assaults and records their advantages and drawbacks. There are countless Machine Learning calculations that have been dug to proclaim the relevant decision that act as against phishing apparatuses. We made a phishing section framework that extracts capacities that are expected to descry phishing. We likewise utilize numeric outline, as well as an overall investigation of customary Machine Learning methodologies comparable as Decision Tree, Random Forest, Multi-layer Perceptron's, XG Boost Classifier, SVM, Light BGM Classifier, Cat Boost Classifier, and covering grounded highlights choice, which contains the metadata of URLs and assists with deciding if a site is licit or not. 2022 The Authors -
An Enhanced Pathfinder Algorithm for Optimal Integration of Solar Photovoltaics and Rapid Charging Stations in Low-Voltage Radial Feeders
Most low-voltage (LV) feeders have large distribution losses, poor voltage profiles, and inadequate voltage stability margins owing to their radial construction and high R/X ratio branches, and they may not be able to handle substantial solar photovoltaics (SPVs) and EV penetration. Thus, optimal integration of SPVs and rapid charging stations (RCSs) can solve this problem. This paper offers an extended pathfinder algorithm (EPFA) with guiding elements and three followers' life lifestyle procedures based on animal foraging, exploitation, and killing. First, the EV load penetration was used to evaluate the LV feeder performance. Subsequently, the required RCSs and SPVs were appropriately integrated to match the EV load penetration and optimise feeder performance. An Indian 85-bus real-time system was used for simulations. The losses and GHG emissions increased by 150% and 80%, respectively, without the SPVs and RCS for zero-to-full EV load penetration. RCSs allocation alone reduced the losses by 40.1%, whereas simultaneous SPVs and RCSs allocation reduced the losses by 66%. However, the GHG emissions decreased by 13.7% and 54.33%, respectively. This study shows that SPVs and RCS can enhance the LV feeder performance both technically and environmentally. In contrast, EPFA outperformed the other algorithms in terms of the global solution and convergence time. The Author(s). -
Dynamic optimal network reconfiguration under photovoltaic generation and electric vehicle fleet load variability using self-adaptive butterfly optimization algorithm
Currently, electrical distribution networks (EDNs) have used modern technologies to operate and serve many types of consumers such as renewable energy, energy storage systems, electric vehicles, and demand response programs. Due to the variability and unpredictability of these technologies, all these technologies have brought various challenges to the operation and control of EDNs. In this case, in order to operate effectively, it is inevitable that effective power redistribution is required in the entire network. In this paper, a multi-objective based dynamic optimal network reconfiguration (DONR) problem is formulated using power loss and voltage deviation index considering the hourly variation of load, photovoltaic (PV) power, and electric vehicle (EV) fleet load in the network. This paper introduces recently introduced meta-heuristic butterfly optimization algorithm (BOA) and it's improve variant of self-adaptive method (SABOA) for solving the DONR problem. The simulation study of IEEE 33-bus EDN under different conditions has proved the effectiveness of DONR, and its adoptability for real-time applications. In addition, by comparing different performance indicators (such as mean, standard deviation, variance, and average calculation time) of 50 independently run simulations, the efficiency of SABOA can be evaluated compared with other heuristic methods (HMs). Comparative studies show that SABOA is better than PSO, TLBO, CSA and FPA in the frequent occurrence of global optimal values. 2021 Walter de Gruyter GmbH, Berlin/Boston 2021. -
Self-adaptive Butterfly Optimization for Simultaneous Optimal Integration of Electric Vehicle Fleets and Renewable Distribution Generation
Fuel prices and environmental concerns have prompted an increase in the use of electric vehicle (EV) technology in recent years. Charging stations (CSs) are a great way to support this shift to sustainability. This has increased the demand for EV charging on electrical distribution networks (EDNs). However, optimal EV charging stations along with renewable energy sources (RES) integration can maintain EDN performance. This paper proposes a novel hybrid approach based on self-adaptive butterfly optimization algorithm (SABOA) for optimal integration of EV CSs and RES problems under various EV load growth scenarios. A multi-objective function is created from distribution losses, GHG emissions, and VSI. The ideal locations for CSs and RES are found using SABOA while minimizing the proposed multi-objective function. The simulation results on IEEE 33-bus EDN validate the suggested technique's superiority in terms of global optima. This type of hybrid strategy is required for optimal real-time integration of EV CSs and RES, taking into account emerging high EV load penetrations. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Butterfly Optimization Algorithm-Based Optimal Sizing and Integration of Photovoltaic System in Multi-lateral Distribution Network for Interoperability
In this paper, a new and simple nature-inspired meta-heuristic search algorithm, namely butterfly optimization algorithm (BOA), is proposed for solving the optimal location and sizing of solar photovoltaic (SPV) system. An objective function for distribution loss minimization is formulated and minimized via optimally allocating the SPV system on themain feeder. At the first stage, the computational efficiency of BOA is compared with various other similar works and highlights its superiority in terms of global solution. In thesecond stage, the interoperability requirement of SPV system while determining the location and size of SPV system among multiple laterals in a distribution system is solved without compromises in radiality constraint. Various case studies on standard IEEE 33-bus system have shown the effectiveness of proposed concept of interline-photovoltaic (I-PV) system in improving the distribution system performance in terms of reduced losses and improved voltage profile via redistributing the feeder power flows effectively. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Caste, Cricket, and Community Fraternal Intersections in Blue Star
[No abstract available]