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Unveiling the Dynamics of Initial Public Offerings: A Comprehensive Review of IPO Pricing, Performance, and Market Trends
Initial Public Offerings (IPOs) serve as pivotal moments in the financial markets, representing a company's transition from private to public ownership. The importance of IPOs lies in their capacity to raise substantial capital, facilitating business expansion and development. This paper conducts an in-depth analysis of Initial Public Offerings (IPOs) in India spanning the period from 2018 to 2022, with a particular focus on their listing day performance. The study categorizes IPOs into various issue price ranges, revealing substantial variability in listing day returns across these categories. It underscores the importance of pricing strategy, emphasizing the need for companies to carefully assess their issue prices to align with market demand. Furthermore, the analysis highlights the varying levels of risk associated with IPO investments based on issue price ranges, advocating for diversification and thorough due diligence. In addition, the paper emphasizes the dynamic nature of IPO markets, influenced by factors beyond pricing, and encourages a balanced approach that considers both potential rewards and challenges. This research provides valuable insights for stakeholders, guiding companies, investors, and analysts in making informed decisions in the dynamic world of IPOs. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Study of buoyancy and surface tension driven convection in nanofluid
This thesis presents a detail study of linear and non-linear analysis of buoyancy and surface tension driven convection in nanofluid. The linear Rayleigh-Bernard / Rayleigh-Bernard Marangoni convection in nanofluids in the presence of external constraints like mangnetic feild, rotation and internal heat generation is investigated. -
Study of buoyancy and surface tension driven convention in nanaofluid
This thesis presents a detail study of linear and non-linear analysis of buoyancy and surface tension driven convection in nanofluid. The linear Rayleigh-Bard / Rayleigh- Bard Marangoni convection in nanofluids in the presence of external constraints like magnetic field, rotation and newlineinternal heat generation is investigated. The effect of temperature and volumetric concentration modulation of nanoparticles at the boundary and gravity modulation are studied on the onset of Rayleigh- Bard newlineconvection. The obtained results are discussed qualitatively and presented newlinegraphically. The problem discussed have important applications in the field of oceanography, geophysics, nuclear fuel, astrophysics, geothermal reservoirs, engineering and space situations with g-jitter connected with gravity simulation studies.Given the rising relevance of nanofluid application, we discuss four newlineproblems in this thesis whose detail summary is presented below: (i) LINEAR AND WEAKLY NON-LINEAR ANALYSIS OF GRAVITY MODULATION ON THE ONSET OF RAYLEIGH BARD CONVECTION IN NANOFLUID The effect of modulation of gravity or time-periodic body force on newlinethe onset of Rayleigh-Bard convection in nanofluid is studied using newlinelinear and non-linear analysis. The stability of the fluid layer heated from newlinebelow is analysed by considering time-periodic body acceleration. This newlinehappens generally in the vehicles and satellites associated with studies of newlinemicro gravity simulation. In order to study the effect of gravity modulation newlineon the system stability limit, linear and weakly non-linear analysis is performed. Normal mode technique and perturbation method is applied to study linear stability. The critical Rayleigh number and wave number is newlinecalculated by taking modulation of small amplitude. It is found that the critical thermal Rayleigh number can be increase or decrease by a massive amount depending upon the distribution of nanoparticles.In this thesis we considered bottom heavy distribution of nanoparticles. -
Enhancing mobility management in 5G networks using deep residual LSTM model
Mobility management is an essential component of 5G networks to provide mobile users with seamless connectivity and efficient cell transition. However, increasing user mobility, device density, and the diversity of service requirements all pose significant challenges to achieving optimal mobility management. This article describes a novel method for improving mobility management in 5G networks that employs a deep residual Long Short-Term Memory model. Deep learning and LSTM, a type of recurrent neural network, are used in the proposed model to identify temporal dependencies and patterns in user mobility data. The model learns to predict future user locations and mobility patterns by training on historical mobility data, allowing for proactive resource allocation and handover decisions. We incorporate residual connections into the LSTM architecture, inspired by the residual learning framework, to address the inability of traditional LSTM models to capture complex temporal dynamics. This allows the model to effectively incorporate long-term dependencies and improves prediction accuracy. Furthermore, we incorporate the mLSTM model into the mobility management framework of 5G networks. The model continuously obtains real-time user location updates and predicts future user positions, allowing for proactive handover decisions. The network can optimize resource allocation, reduce handover latency, and improve user experience by leveraging anticipated mobility patterns. We test the proposed method by simulating it extensively with real-world mobility traces. The results show that the mLSTM model accurately predicts user mobility and outperforms conventional methods in transition performance. The model is not affected by changing network conditions, user mobility patterns, or service specifications. 2024 Elsevier B.V. -
Citizen data in distributed computing environments: Privacy and protection mechanisms
Data security is paramount in the increasingly connected world. Securing data, while in transit and rest, and while under usage, is essentialfor deriving actionable insights out of data heaps. Incorrect or wrong data can lead to incorrect decisions. So, the confidentiality and integrity of data have to be guaranteed through a host of technology-inspired security solutions. Organizational data is kept confidentially by the businesses and governments, often in distant locations (e.g., in cloud environments), though more sensitive data is normally kept in house. As the security mechanisms are getting more sophisticated, cyber security attacks are also becoming more intensive, so there is a constant battle between the organisations and the hackers to be one step ahead of the other. In this chapter, the aim is to discuss various mechanisms of accomplishing citizens ' data confidentiality and privacy and to present solution approaches for ensuring impenetrable security for personal data. 2021 by IGI Global. All rights reserved. -
Load balancing with availability checker and load reporters (LB-ACLRs) for improved performance in distributed systems
Distributed system has quite a lot of servers to attain increased availability of service and for fault tolerance. Balancing the load among these servers is an important task to achieve better performance. There are various hardware and software based load balancing solutions available. However there is always an overhead on Servers and the Load Balancer while communicating with each other and sharing their availability and the current load status information. Load balancer is always busy in listening to clients' request and redirecting them. It also needs to collect the servers' availability status frequently, to keep itself up-to-date. Servers are busy in not only providing service to clients but also sharing their current load information with load balancing algorithms. In this paper we have proposed and discussed the concept and system model for software based load balancer along with Availability-Checker and Load Reporters (LB-ACLRs) which reduces the overhead on server and the load balancer. We have also described the architectural components with their roles and responsibilities. We have presented a detailed analysis to show how our proposed Availability Checker significantly increases the performance of the system. 2014 IEEE. -
A Secure Deep Q-Reinforcement Learning Framework for Network Intrusion Detection in IoT-Fog Systems
IoT-Fog system security depends on intrusion detection system (IDS) since the growing number of Internet-of-Things (IoT) devices has increased the attack surface for cyber threats. The dynamic nature of cyberattacks often makes it difficult for traditional IDS techniques to stay up to date. Because it can adapt to changing threat landscapes, deep Q-reinforcement learning (DQRL) has become a potential technique for ID in IoT-Fog situations. In this paper, an IDS system for IoT-Fog networks based on DQRL is proposed. The suggested solution makes use of fog nodes' distributed computing power to provide real-time IDS with excellent accuracy and minimal latency. With feedback from the network environment, the DQRL agent learns to recognize and categorize network traffic patterns as either normal or intrusive. Adaptive exploration techniques, effective reward functions, and deep neural networks for feature extraction are adopted by the system to improve predictive performance. The evaluation findings show that, in terms of detection accuracy, precision, recall and f-measure, the proposed DQRL provides flexibility to changing threat patterns as compared to conventional IDS techniques. A vast array of cyberattacks, such as malware infections, denial-of-service (DoS) attacks, and command-and-control communications, are successfully recognized and categorized by the system. It is possible that the suggested solution will be crucial in safeguarding IoT-Fog networks and preventing cyberattacks 2024 IEEE. -
Experimental Augmentation of Heat Transfer in a Shell and Tube Heat Exchanger using Twisted Tape with baffles and hiTrain Wire Matrix Inserts - A Comparative Study
Heat transfer, a mere process of exchange of heat due to a temperature gradient, plays a vital role in industries and domestic applications. Among all the heat exchangers, Shell and Tube Exchanger are used predominantly due to their compact and robust design. For a given design to increase the heat transfer characteristics needs a research investigation. Among all augmentation techniques, a passive method found widely used as it avoids mechanical modification of the existing heat exchanger and addresses only on flow geometry. Twisted tape inserts are extensively used to change the flow geometry of fluid on the tube side. The present research work intended on utilising twisted tape, twisted tape with baffles and hiTrain wire matrix inserts. Experimental investigation reveals that inserts efficiently disturb the tube side fluid flow, in turn, increases pressure drop which increases the fluid wall shear and hence enhances the substantial increase in tube side heat transfer rate. At lower Reynolds number twisted tape with baffles has comparatively higher heat transfer coefficient, and at higher Reynolds, number hiTrain wire has comparatively higher heat transfer coefficient. Friction factor decreases linearly from twisted tape with baffles to hiTrain wire matrix as Reynolds number increases. Published under licence by IOP Publishing Ltd. -
Blind separation of speech from aortic regurgitation signals using Dhoulaths method
Conducting auscultation of traumatically distressed patients has always been demanding for medical professionals. The challenge calls for an innovative solution enabling doctors to conduct precise diagnoses despite other sound interference. This suggested study presents an entirely non-invasive and convenient method designed to aid doctors in routine diagnostic procedures. This study is centred on the segregation of aortic regurgitation heart sounds from speech. The mixture utilised for the study is a combination of speech and aortic regurgitation signals. The method applied for the study is a revised procedure of Blind Source component separation utilising a solo sensor method. With this technique, doctors are not compelled to prevent patients from articulating their pain or discomfort while diagnosing heart sounds. Doctors can offer a consoling word to patients while the auscultation is in progress without worrying about how the speech sounds affect the diagnosis. For babies, timely detection of heart-related issues can be life-saving. With Dhoulaths method, the distressing sounds of a babys cries can be effectively separated, thereby offering doctors clear audio of heartbeats. The study was conducted to ascertain if heartbeats can be segregated from the signals of speech or cries. This segregation procedure has succeeded in arriving at an enhanced level of clarity. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Industrial Applications of Hybrid Nanocatalysts and Their Green Synthesis
Abstract: The era of industrial revolution has been hugely dependent on a myriad of catalysts. The present era has contributed another dimension to this by the advent of nanocatalysts. The last decades saw even more fine tuning with the use of hybrid nanocatalysts by the integration of a plethora of functionalities into a single nanoparticle. The extremely high surface area, low toxicity, easy recovery and reusability, high product output and possibilities of green synthesis makes hybrid nanocatalysts significant in various fields like bioremediation, fuel cell production, cleaner energy production, dye degradation etc. Metal based hybrid nanocatalysts are highly appealing due to their extremely high surface over volume ratio, entailing unique electronic properties and access to more reaction sites. The recent years have seen more thrust being given to greener modes of synthesis of nanocatalysts, rather than the classical modes (which uses hazardous chemicals), aligning with sustainability goals.The current review is an attempt to explore the myriad uses of magnetic, metal and metal oxide hybrid nanocatalysts and their green synthesis methods. Optimizing the use of hybrid nanocatalysts in various domains would definitely help us achieve the SDGs of the United Nations for a more sustainable life on this planet. Graphical Abstract: [Figure not available: see fulltext.] Highlights: Types of hybrid nanocatalysts have been described. Industrial applications of hybrid nanocatalysis has been summarized. Ways of greener synthesis of hybrid nanocatalysts for environmental sustainability depicted. Advantages and limitations of hybrid nanocatalysts have been evaluated. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
The Role of Corporations in Achieving Ecological Sustainability: Evaluating the Environmental Performance of Corporations
Industrial development of the past 200 years has brought immeasurable wealth and prosperity. However, it has also caused an unintended eco-logical degradation. Hence, the focus of environmental law has shifted from the creation of global frameworks to deal with environmental problems to comply with those frameworks. As a result, the primary actors in environmental law have shifted from the state and the global community to corporations. As a consequence, environmental policies must develop along legally holistic lines. The role corporations have had in achieving ecological sustainability is poorly understood. In the backdrop of the above issues, the chapter examines the implications of ecologically sustainable development for corporations. It articulates corporate ecological sustainability through the concepts of environmental management and ecologically sustainable competitive strategies. It further examines the implications that these concepts have for a corporation in the long run. 2020 by IGI Global. -
National Education Policy 2020: Equity and inclusion in India's education system
The term "equity" in education refers to justice and fairness in the allocation of educational resources and opportunities. In order to achieve educational equity, it is necessary to remove the structural obstacles that prevent students from realizing their full potential. Obstacles like socioeconomic inequalities, prejudice, and unequal resource distribution often act as barriers to quality education. In this background, the present chapter will critically analyze a few significant opportunities offered by the New Education Policy 2020, such as three language formulas, privatization, NEP financing, special education zones policy implications, and challenges in implementation. Even though the opportunities and milestones offered by NEP 2020 are irrefutable, apprehensions pertaining to its scope and usefulness also exist, questioning the sanguinity of the policy. 2024, IGI Global. All rights reserved. -
The mediating influence of leadership behaviour on the relationship between organizational health and work engagement
Efficient leadership and a healthy teaching environment are the two factors that determine how school teachers conduct themselves professionally. Work engagement not only reflects the teachers' performance but also implies the performance of the pupils and the school and it depends on how congenial their working conditions are. The present study intended to assess the extent to which the leadership behaviour of principals mediated the effect of organizational health on the work engagement of 516 secondary school teachers working in Bengaluru, India. The organizational health inventory was employed to quantify the organizational health of schools at institutional, managerial and technical levels, the Utrecht tool was used to measure teachers' work engagement through their vigor, dedication and absorption, and the leadership behaviour of principals was measured in terms of consideration and initiating behaviour. The findings implied a positive relationship between organizational health and teachers' work engagement. Further, while leadership behaviour indeed mediated the impact of organizational health on work engagement, the mediating effect was only partial. The results imply that the teachers' work engagement cannot be entirely attributed to the school management and working conditions, which implies scope for further research on the factors affecting work engagement among the teachers. 2020 by authors. -
A study on work engagement of secondary school teachers in relation to their psychological well-being, leadership behaviour of principals and organizational health /
Organizational success is determined by work engagement and psychological well-being of the workforce. Efficient leadership and a healthy teaching environment determine the professional conduct of school teachers. Work engagement not only reflects teachers’ performance but also implies the performance of pupils and the school. Work engagement depends on the congeniality of the working conditions. The present study explores work engagement of 516 secondary school teachers working in Bengaluru, India. -
A Study on work engagement of secondary school teachers in relation to their psychological well-being, leadership behaviour of principals and organizational health
Organizational success is determined by work engagement and psychological well-being of the workforce. Efficient leadership and a healthy teaching environment determine the professional conduct of school teachers. Work engagement not only reflects teachers performance but also implies the performance of pupils and the school. Work engagement depends on the congeniality of the working conditions. The present study explores work engagement of 516 secondary school teachers working in Bengaluru, India. The Work and Well-being survey (UWES) was used to measure teachers work engagement by assessing their vigour, dedication, and absorption. The scale of psychological well-being scale (developed by Ryff) was employed to evaluate in terms of self-acceptance, positive relation with others,autonomy,environmental mastery, purpose in life and personal growth.The leadership behaviour of principals questionnaire was used to measure in terms of consideration and initiating structure. The Organizational health Inventory was employed to quantify the Organizational health at the institutional, managerial, and technical levels. Results from the regression analysis suggest that work engagement of teachers was positively correlated and significantly influenced by psychological well-being, leadership behaviour of principals and organizational health. -
Cognitive outcomes prediction in children using machine learning and big data analytics
This study explores the potential of machine learning (ML) and big data analytics in predicting cognitive outcomes in children, aiming to enhance early identification and intervention strategies. Leveraging a diverse dataset comprising neurocognitive assessments, genetic markers, socio-economic factors, and environmental variables, our research employs advanced ML algorithms to develop predictive models. The interdisciplinary approach integrates neuroscience, psychology, and data science to discern patterns and correlations within the expansive dataset. The study emphasizes the importance of early cognitive assessment for optimal child development and academic success. By harnessing the power of big data, our models seek to uncover nuanced relationships that traditional methodologies may overlook. Preliminary results indicate promising accuracy in predicting cognitive outcomes, offering a valuable tool for educators, healthcare professionals, and policymakers. Additionally, the model's interpretability allows for a deeper understanding of the factors influencing cognitive development. Ethical considerations, privacy safeguards, and data governance are integral components of this research, ensuring responsible use of sensitive information. The implications of this study extend beyond academia, with the potential to inform educational policies, personalized learning strategies, and targeted interventions for at-risk populations. As technological advancements continue, the integration of ML and big data analytics in predicting cognitive outcomes heralds a new era in pediatric research, promoting proactive approaches to support children's cognitive well-being. 2024 IEEE. -
Artificial immune system based frameworks and its application in cyber immune system: A comprehensive review
Computer science has always mixed the concepts of biology and computers to enhance the way in which systems are designed. Artificial Immune System (AIS) is a Computational Intelligence strategy dependent on an organically enlivened computational system that can be utilized for taking care of complex computational issues. It tends to be seen that AIS is an incredibly various locale of research, going from the modeling immune systems to complex algorithms for specific applications. This paper exhibits an exhaustive survey of different frameworks developed in the artificial immune system and its application. Reviews of frameworks in AIS are uncommon and henceforth this paper gives an inside out audit of progressing research and challenges in AIS. We start by presenting AIS and give a thorough survey of different systems in AIS and its application in anomaly detection. We investigate the utilization of AIS in the Intrusion Detection System named the Cyber Immune System(CIS) and compares various AIS works applied to CIS. We conclude with various future extensions in the area of AIS research. 2019 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
Enhanced AIS Based Intrusion Detection System Using Natural Killer Cells
Intrusion detection system is used to monitor the system and network activities to identify anomalies and attacks so that integrity, availability, and confidentiality can be preserved. Here an intrusion detection system based on Artificial Immune System is proposed based on Natural Killer (NK) cells with immunological memory. NK cells are created and each NK cells detection radius is determined using the negative selection algorithm and is trained to detect various attacks. Effective cells with high fairness values are proliferated and distributed to the network using clonal selection algorithm. In this paper, two types of NK cell are used-a Heavyweight NK cell (HWNK) and a number of Lightweight NK cells (LWNK). The incoming data is vectorized and Major Histocompatibility Complex Class I (MHC1) is created. Then based on this MHC1, any of the receptors i.e. Activating Receptor or Inhibiting Receptor is activated. If it is the signature of an attack, Activating Receptor is activated. Activating receptor activation results in either cytokine release or apoptosis. Here cytokine release means an alarm is generated informing the administrator and apoptosis stands for dropping of the packet. If Inhibiting Receptor is activated, it's a normal packet there is no action taken. The technique proposed yields high accuracy, better detection rate and quick response time. 2020 River Publishers. All Rights Reserved. -
A generic cyber immune framework for anomaly detection using artificial immune systems
Intrusion detection systems play a significant role in computer security. Artificial immune systems are the prime contender in developing an anomaly-based intrusion detection system due to their simplicity. The fundamental goal of this paper is to create a generic framework for an artificial immune system which is fast and accurate in detecting anomalies using artificial immune system concepts. Natural killer cells in the immune system and their quick response to foreign pathogens inspired the adaptation of those cells into an artificial immune system based framework. A natural killer cell-based framework is proposed to improve the accuracy and speed of anomaly detection. The structure of the proposed framework includes major histocompatibility complex class 1 representation, affinity calculation, cell generation, and cell proliferation. This framework addresses the overlapping and hole problem while creating natural killer cells to increase the system's performance. The negative selection algorithm and the positive selection algorithm generate the cells that enhance the anomaly detection technique and give high precision. The parameter response time introduced in this paper is crucial for an intrusion system to be used in real-time. 2022 Elsevier B.V.