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Post listing IPO returns and performance in India: An empirical investigation
Objectives: (a) To analyse the performance of Indian IPOs in the short term. (b) To determine the significance of abnormal return of the IPOs. (c) To study the impact of over-subscription, profit after tax, promoters' holdings, issue price and market returns on IPO performance. Design/ Methodology/Approach: This research paper is based on empirical analysis. All the 52 IPO's listed in the NSE (National Stock Exchange, India) during the year 2018 to 2020 were considered for the study. The study is based on secondary data. The daily share price and Nifty-50 index value were taken from NSE website (www.nseindia.com) and other relevant data from red-herring prospectus of the respective company. The research / statistical tools used are: Market adjusted short run performance model, Wealth relative model, 't' test and regression analysis. Scope of the study: The scope of the study is limited to the IPO's listed only in the National Stock Exchange (NSE), India. Period of study: The study covers a period from January 2018 to December, 2020. Limitation of the study: The study considers only the influence of the external factors on the performance of IPOs. Findings: The average IPO return on the first trading day is 13.52%, ranging from -23.15% to 82.16% with standard deviation of 26.72%. The average IPO return on the third trading day was the highest and is found to be14.52%, ranging from -19.22% to 117.55% with standard deviation of 18.57%. The analysis reveals that the over subscription impacts the IPO performance and the other factors namely, issue price, Profit after Tax, market returns and promoters holdings do not influence IPO returns. Originality / Value: This is an original work that analyses the listing gain or loss and the post listing performance of IPO's in India and other factors that might influence the listing gain or loss. Copyright 2021. T. Ramesh Chandra Babu and Aaron Ethan Charles Dsouza. Distributed under Creative Commons Attribution 4.0 International CC-BY 4.0 -
Exchange rate, stock price and trade volume in US-China trade war during COVID-19: An empirical study
This article aims to examine the influence of international trade wars on the majority of stock market operations, both directly and indirectly affected. The impact of the trade war on the exchange rates of the participating countries was similarly negative. This article seeks to trace the conversion standards' footprints in the United States, China, and India using several indexes such as the Shanghai Composite Index, Dow Jones index, and Nifty 50. The cost of closing down various indices on a daily basis, as well as the conversion standard upsides of the participating currencies, are all examined in this study. Furthermore, utilizing the OLS and GARCH models, this work provides insights into measuring the uncertainties about the impact of exchanging scale on financial exchange. According to the findings of OLS, changes in the swapping scale have had a minor impact on the daily closing costs of stock records in the individual countries. The conversion standard, on the other hand, has a major impact on trade volumes in all three stock markets. When compared to the SSE and DJI equities, the GARCH model predicts that the contingent shift will be less shocking, resulting in a smaller impact on Nifty trade volume. To replicate the impact of trade wars during the Covid-19 crisis, the final results imply that data from domestic and international financial transactions must include securities market transactions. Author This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). -
Physical Co-location: an intersection of problem-solving and vicarious learning
Scholars have examined Revans' problem-solving praxeology in many contexts but have not fully explored the concept in the case of physical co-location. Hence, we focussed on investigating Revans' conceptualisation in a co-located context by paying particular attention to the different forms of learning' that emerged from it. The research setting for this study involved two coworking spaces in Bangalore, India, whose constituents were co-located start-ups and established enterprises. Held from January to March 2020, the study involved conducting exploratory, semi-structured interviews with twelve firms. The findings suggested that in a co-located environment, a) firms learnt vicariously' from a rich, external knowledge base during the enquiry-led Alpha phase b) firms learnt experientially', through learning by doing and reflecting in the implementation-focussed Beta phase c) firms learnt through the process of emergence that resulted from personal reflection and team interaction, in the revelatory Gamma phase. This study lends a novel direction in acknowledging that vicarious learning, that is, learning through the experience of others, serves as a starting point for problem-solving in a co-located context. We demonstrate that firms gain familiarity with the problem through vicarious sources, that is, from those experienced co-located firms who had journeyed on a similar path. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Optimising QoS with load balancing in cloud computing applying dual fuzzy technique
Cloud computing has become a necessity when the internet usage has increased drastically. This research paper objective is to optimise quality of service in cloud computing using dual fuzzy technique. With the competition to provide the best quality service at cloud data centre, we are analysing the parameters of average response time, average completion time, average CPU utilisation and job success. Cloud-sim simulator along with the mathematical model is used to provide reliable and valid result. To achieve the best result, the load in data centre needs to be efficiently distributed, so that it is managed to process maximum service requests with the best service response time and very few failures. In this paper, we applied dual fuzzy technique for the load balancing in the cloud data centre and the findings were extensive and support the proposed technique. With this technique, cloud computing service provider can provide better quality service. Copyright 2021 Inderscience Enterprises Ltd. -
Addiction treatment in India: Legal, ethical and professional concerns reported in the media
As per the Magnitude of Substance Use in India 2019 survey report, over 57 million of the Indian population is in need of professional help for alcohol use disorders and around 7.7 million for opioid use disorders. The increasing demand for addiction treatment services in India calls for professionalising every aspect of the field. Frequent human rights violations and various unethical practices in Indian addiction treatment facilities have been reported in the mass media. This study is a content analysis of newspaper reports from January 1, 2016 to December 31, 2019 looking into legal, ethical and professional concerns regarding the treatment of substance use disorders in India. The content analysis revealed various human rights violations, the use of improper treatment modalities, the lack of basic facilities at treatment settings, and the presence of unqualified professionals in practice. Indian Journal of Medical Ethics 2021. -
Inventory model for deteriorating items with ramp type demand under permissible delay in payment
Permissible delay in payment is a common method of payment often used by the suppliers and it generally leads to higher sales and ultimately higher revenue. This method is significant in the case of deteriorating products. In this paper, an inventory model for the deteriorating items with price and time-dependent ramp type demand is presented with shortages allowed and partially backlogged. The solution procedure is illustrated by numerical examples. The concavity of the profit function with respect to the decision variable is discussed analytically. Numerical analysis shows that the profit per unit time increases with the delay payment facility. Copyright 2021 Inderscience Enterprises Ltd. -
Forecasting intraday stock price using ANFIS and bio-inspired algorithms
The main focus of this study is to explore the predictability of stock price with variants of adaptive neuro-fuzzy inference system (ANFIS) and suggests a hybrid model to enhance the prediction accuracy. Two variants of ANFIS model are designed which includes genetic algorithm-ANFIS (GA-ANFIS) and particle swarm optimisation-ANFIS (PSO-ANFIS) to forecast stock price more accurately. The standard ANFIS is tuned employing GA and PSO algorithm. The experimental data used in this investigation are stocks traded per minute price of four companies from NSE. Sixteen technical indicators are calculated from the historical prices and used as inputs to the developed models. Prediction ability of the developed models is analysed by varying number of input samples. Numerical results obtained from the simulation confirmed that the PSO-ANFIS model has the potential to predict the future stock price more precisely than GA-ANFIS as well as other earlier methods. Copyright 2021 Inderscience Enterprises Ltd. -
Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems
Metro rail systems are increasingly becoming relevant and inevitable in the context of rising demand for sustainable transportation methods. Metros are therefore going to have a consistently expanding user-base and hence user satisfaction will require meticulous planning. Usage forecast is clearly an integral component of metro planning as it enables forward looking and efficient allocation of resources leading to greater commuter satisfaction. An observation from studying the usage of Kochi Metro Rail Ltd. is that there is a consistently occurring temporal pattern in usage for every station. But the patterns differ from station to station. This hinders the search for a global model representing all stations. We propose a way to overcome this by using station memorizing Long Short-Term Memory (LSTM) which takes in stations in encoded form as input along with usage sequence of stations. This is observed to significantly improve the performance of the model. The proposed architecture with station parameter is compared with algorithms like SVR (support vector regression) and neural network implementation with the best architecture to testify the claim. The proposed model can predict the future flow with an error rate of 0.00127 MSE (mean squared error), which is better than the other models tested. CTU FTS 2021. -
Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis
Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved. -
Accident prevention system using real time embedded technology
Two different aspects are presented in the proposed system: a transmitter and a recipient. The velocity boundary is controlled immediately after entering the emitter area by receiving a signal from the RF transmitter. A few meters even before the area, the significantly impacted might be put for this purpose. The surveillance program contains an alcoholic detector, an eye detector, and a smoke detector. GPS and GSM for the detection of incidents on mobile phones. The electromechanical device monitors the information as a consequence of the impact by transmitting it to the microprocessor ATmega330Q. GPS of your smart telephone will then communicate with both the satellite to acquire latitude and longitudinal data as well as the incident names will be transmitted to the families, fire departments, etc. which are already defined. 2021, SciTechnol, All Rights Reserved. -
Effect of Organizational Culture during Crises on adoption of virtual classrooms: An extension of UTAUT model
This study aims to understand the impact of organizational culture in the context of obligatory adoption of a virtual classroom (VC) during the COVID pandemic. The academic crisis created by the pandemic resulted in obligatory adoption of VCs, without being mandated by top management. Organizational culture was tested by this crisis, and thus created a unique opportunity to examine adoption. This research examines Organizational Culture during Crises (OCC) as an antecedent to the Unified Theory of Acceptance and Use of Technology (UTAUT) model to evaluate the factors that determine the intention to adopt a virtual classroom across multiple disciplines by the faculty of a reputed Indian university. Data collected from a sample of 353 respondents was analyzed to test the research model using Structural Equation Modeling (SEM). The findings of the study reveal that OCC plays a positive and significant role in determining the intention of faculties to adopt a virtual classroom. We also found that OCC also significantly influences performance expectancy, effort expectancy, facilitating conditions and social influence. The results imply proper framing of policies by top management of Higher Education Institutions (HEI) for the smooth adoption of virtual classrooms by faculty when confronted by crises. 2021 The Author(s). Published with license by Taylor & Francis Group, LLC. -
A Strategic Review on Use of Polyhydroxyalkanoates as an Immunostimulant in Aquaculture
Background and Objective: Increasing concerns over the use of antibiotics in aquaculture have emerged researchers to focus on short chain fatty acids and other biocompatible molecules as alternatives for disease prophylaxis and treatment. Polyhydroxyalkanoates well studied as biopolymeric materials for using in packaging and biomedicine were not focused much for their abilities to act as antimicrobial agents in aquaculture until recent years. Application studies of polyhydroxyalkanoates as aquafeed additives have highlighted their promising roles as ecofriendly alternatives for commercial antibiotics with strong immunomodulatory effects in fish-es and shrimps. The major aim of this review was to explore up-to-date scientific research studies on use of polyhydroxyalkanoates as aquafeed additives and their immunomodulatory effects. Results and Conclusion: Up-to-date, limited scientific literatures have been published on the use of polyhydroxyalkanoates and their copolymers as alternatives to antibiotics in aquaculture. This research field includes a great scope of development due to the promising immu-nomodulatory and antimicrobial activity of polyhydroxyalkanoates against common pathogens in aquaculture, as reported in literatures. Although several hypothesis and research data for explaining the mechanisms behind their immunostimulatory effects were suggested by various researchers, genetic and molecular bases underlying these phenomena are yet to be explored. Further research and development in this area can introduce these biopolymers as the most promising eco-friendly alternatives for antibiotics in aquaculture. Conflict of interest: The authors declare no conflict of interest. 2021. All Rights Reserved. -
Entropy generation analysis of radiative Williamson fluid flow in an inclined microchannel with multiple slip and convective heating boundary effects
The main theme of the current work is to investigate the flow and heat transport characteristics of non-Newtonian Williamson fluid in an inclined micro-channel along with entropy generation analysis. The significance of the thermal radiation, convective boundary condition, and multiple slip effects is explored. The entropy generation of the system has been analyzed by adopting the 2nd law of thermodynamics. The rheological expressions of the Williamson fluid model are also taken into account. The nonlinear system is tackled by using the finite element method. An appropriate comparison has been made with previously published results in the literature as a limiting case of the considered problem. The comparison confirmed an excellent agreement. Detailed discussion of the significance of effective parameters on Bejan number, entropy generation rate, temperature and velocity is presented through graphs. The numerical results portray that the entropy generation and Bejan number have escalating behavior to the higher value of angle of inclination. Furthermore, the Bejan number changing its behavior at two points for different values of Reynolds number. IMechE 2021. -
Dissecting the Dichotomy of Skill and Social Justice Theory of Law School Legal Aid Clinics in the USA and India: A Re-look of the Past and the Present
With the mushrooming of legal aid clinics across institutions imparting legal education, there exists a conundrum as to their actual objectives. With passage of time, social justice theory is losing ground and skill development theory has gained greater predominance. In order to understand the objectives behind establishing legal aid clinics, the article traces its inter-linkages with the theory of social justice. In doing so, an analysis of the context under which legal aid clinics were established and their relevance to the present day is explored through the article. With the passage of 22 years of establishment of law school legal aid clinics in India, there still exists a dichotomy as to their real purpose and objective. These models of legal aid clinics of the past not only offer insights to develop present models of legal aid clinics, but there is also a need to emulate these models as they are relevant and apt even to this day. The article adapts a comparative approach between India and the USA, chronicling the past and present sojourns of the journey of law school legal aid clinics and the suitability of the social justice theory to the current Indian context. 2021 The West Bengal National University of Juridical Sciences. -
Reimagining Future of Future by redesigning Talent Strategy in the Age of Distraction and Disruption
The coronavirus 2019 (COVID-19) pandemic promoted the development of Industry 4.0 leading to the fifth industrial revolution (Industry 5.0). It brought in new ways of working and the role of the office in the future. It redesigned the workplace to support organizational priorities and resize the footprint creatively. Digitalization and globalization have sparked radical shifts in how employees live and work. In an age of digital disruption, companies and HR leaders are forced to revise organizational on how they organize, recruit, develop, manage and engage the 21st-century workforce. The big questions are: how can HR help business leaders reconstruct the workforce of the future? What effort has the company take to change future work and their workforce today so that it looks different 15 years later? Organizational agility, careers and learning disruption, talent disruption, rethinking performance management and people analytics in addition to creating the right structure, analysis, and standardized people metrics are the key to success and critical drivers to design talent strategy. This study aims to identify the magic ingredient (or strategies) behind managing an organization's talent in creating business success. We further examined and mathematically modelled these strategies in attracting and retaining high-quality employees, developing their skills, and continuously motivating them to improve their performance in the age of distraction and disruption. 354 employees from IT companies participated in the survey. The findings of the study show, as expected, that a compelling employer brand is the most effective talent management strategy of all when it combines three key drivers: organizational culture, organization goodwill and competition for talent. Gender was statistically, significantly and positively associated with the imperatives to reset the future of work agenda. 2021. All Rights Reserved. -
A new framework for contour tracing using Euclidean distance mapping
In this paper, a new fast, efficient and accurate contour extraction method, using eight sequential Euclidean distance map and connectivity criteria based on maximal disk, is proposed. The connectivity criterion is based on a set of point pairs along the image boundary pixels. The proposed algorithm generates a contour of an image with less number of iterations compared to many of the existing methods. The performance of the proposed algorithm is tested with a database of handwritten character images. In comparison to two standard contour tracing algorithms (the Moore method and the Canny edge detection method), the proposed algorithm found to give good quality contour images and require less computing time. Further, features extracted from contours of handwritten character images, generated using the proposed algorithm, resulted in better recognition accuracy. Copyright 2021 Inderscience Enterprises Ltd. -
Acceptance of consumer-oriented health information technologies (chits): Integrating technology acceptance model with perceived risk
This paper is focused on understanding the growing demand for consumer-oriented health information technologies (CHITs) wearable and adult healthcare management apps. This study utilised the Technology Acceptance Model (TAM) and integrated the concept of perceived risk. The structural Equation Modelling (SEM) technique was applied to test the research hypotheses based on the 450 quantitative responses. This study confirms significant relationships between perceived usefulness, perceived ease of use, perceived risk, attitude, behavioural intention, and actual intention in using CHITs. The findings also showed no evidence to conclude that age and education influenced respondents perceived usefulness and perceived ease of the CHITs. This study incorporated the perceived risk to fill a gap in the literature and broaden the current TAM theoretical application in the public health setting. The study findings fill the health-related technology acceptance literature gap and broaden TAM's present application in the public health realm. 2021 Slovene Society Informatika. All rights reserved. -
A self-cooperative trust scheme against black hole attacks in vehicular ad hoc networks
The main objective of the Vehicular Adhoc NETwork (VANET) is to provide secure communications for the vehicles in the network without fixed infrastructures. It inherits all the properties of the MANET. Achieving reliable routing to avoid various routing attacks is the major concern in the vehicular network. Routing attacks degrade the performance of the network. Black hole attack is one of the routing attacks, which drops the data packets without forwarding them to the destination vehicle. Different routing schemes are proposed to provide security against these attacks, which still have security issues. Hence a new self-cooperative trust scheme is proposed in this paper, to detect single as well as collaborative black hole attackers in the network. Two processes: self-detection and cooperative detection, are used to detect attackers in the network. Results show that the proposed scheme has better performance in terms of throughput, PDR and delay. Copyright 2021 Inderscience Enterprises Ltd. -
Experimental evaluation of image segmentation for heart images
The cardiac death is the principal reason of the death in the world.The research work focuses on finding an efficient image segmentation technique for the computer aided detection and also to decrease the noise in the image.The segmentation is implemented with the help of fuzzy C-means clustering along with dual inverse thresholding function and Otsu thresholding.Experimental proof is evaluated with the help of morphological functions and with Gaussian function.The result of the work provides an accurate segmentation for myocardial ischemia in the human heart photo image as well as magnetic resonance imaging. Copyright 2021 Inderscience Enterprises Ltd. -
EFFECTS OF SINUSOIDAL AND NON-SINUSOIDAL TEMPERATURE MODULATION IN A TRIPLE DIFFUSIVE CONVECTION
The Triple Diffusive convection with time-dependent sinusoidal (cosine) and non-sinusoidal (square and triangular) temperature modulation is studied using linear and non-linear analysis. The expression for Rayleigh number and correction Rayleigh number is obtained by using perturbation method which gives the prospect to control the convection. Effects of various parameters of the problem are individually studied for two cases of temperature modulation namely, (i) in-phase and (ii)out-of-phase. Ginzburg-Landau equation using multi-scale method is derived to study the effects of temperature modulation on heat and mass transfer. It is observed that both solutal Rayleigh numbers stabilizes or destabilizes the system depending on the values of the frequency of modulation. 2021 I??k University, Department of Mathematics. All Rights Reserved.