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An Investigative approach to hard machining of inconel 718 with coated carbide tool
Super alloys sustain good strength at high temperature and pressure conditions. Such newlinematerials have high demand in Aerospace industry, Marine industry, and Nuclear power plants. They have a great demand in Nuclear and Aerospace applications because of it retain its properties at temperature over 700 C. Machinability of nickel based super - alloys is extremely poor, mainly due to their low thermal conductivity, build up edge and self-hardening, which leads to high dynamic cutting forces. They are difficult to machine because of its high shear strength, work hardening and precipitation hardening. High abrasive particles in its microstructure and tendency forming chip to weld to tool and form Built Up Edge (BUE) make it more difficult to machine. Friction between tool and material and its low thermal conductivity newlineresults in high temperature generation. They have Nickel (Ni), Chromium (Cr), Ferrous (Fe) or Cobalt (Co) as base contains. Small amount of Al, Ti, Nb, Ta, W, Mo added to these alloys to sustain at high temperature. Chromium is important alloying element in order to obtain the hot corrosion resistance property. Due to these factors the tool wear is extremely high and increasing the tool life by minutes is an enormous success. To overcome this situation, various newlinematerials have been developed for Inconel 718 machining. Though Ceramic tools, Silicon newlineCarbide whiskers, reinforced alumina tools, carbide tools have been used to machine Inconel 718 but they have failed to produce good surface, better accuracy and minimum tool wear. The present study is to improvise the surface roughness, reduce tool wear and create better machining parameters for extensive use of the material. Taguchi methodology, Grey Relational Analysis (GRA) and Response Surface Methodology (RSM) have been used to analyze the cutting parameters and determine better response parameters for the machining characteristics of Inconel 718. -
Impact of Digital Storytelling on Motivation in Middle School English Classrooms
Motivation is a key factor in the learning process, especially in language acquisition. This research examines the effects of using digital storytelling (DST) on motivation in English classrooms. The study, which used a mixed methods approach, involved 100 middle school students in Bengaluru selected through convenience sampling. Data collection methods included questionnaires and semi-structured interviews. Students were divided into experimental and control groups, with the former receiving DST-integrated instruction and the latter being taught using traditional methods. The results of the quantitative analysis showed a positive influence on motivation in the experimental group compared to the control group. Qualitative results showed that implementing DST increased students' motivation, engagement, and understanding of the English language more effectively than traditional teaching methods. Further research is encouraged to explore the full potential of DST to improve student language skills and motivation. 2024 IGI Global. All rights reserved. -
Impact of Digital Storytelling on Middle School Students' Attitudes Toward English Language Learning
The integration of digital storytelling (DST) into teaching has significantly influenced educational development, especially English language acquisition. This study examines the impact of DST-integrated pedagogy on students' attitudes and perceptions toward learning English. In a quantitative study using an experimental design, 200 middle school students were purposively selected and divided into control and experimental groups. The control group received the traditional method of language teaching, while the experimental group received the DST method. Data were collected through a survey and analysed using descriptive and Wilcoxon test. The results suggest that exposure to DST positively influenced students' attitudes and led to better understanding, engagement, and motivation in learning English in the treatment group. This suggests that incorporating DST into English lessons can improve teaching quality and students' overall progress. Further analysis is needed to fully explore the potential of DST-based instruction in developing language acquisition skills. 2024 IGI Global. All rights reserved. -
Geometry of Variably Inclined Inviscid MHD Flows
A steady plane variably inclined magnetohydrodynamic flow of an inviscid incompressible fluid of infinite electrical conductivity studied. Introducing the vorticity, magnetic flux density, and energy functions along with the variable angle between magnetic field and velocity vector, governing equations are reformulated. The resulting equations are solved to analyze the geometry of the fluid flow. Considering streamlines to be parallel, stream function approach is applied to obtain the pattern for magnetic lines and the complete solution to the flow variables. Next considering parallel magnetic lines, magnetic flux function approach is applied to obtain streamlines and the complete solution of the flow. A graphical analysis of pressure variation is made in all the cases. 2020, Springer Nature Singapore Pte Ltd. -
Machine Learning-Based Driver Assistance System Ensuring Road Safety for Smart Cities
Technologies around smart city and green computing are gaining more and more interest from diversified workforce areas. The transportation system is one of them. The transportation vehicles are operating day and night to provide proper support for the need. This is really tiring for the transportation workers, especially the drivers who are driving the vehicle. A slight negligence of a driver may cause a huge loss. The increasing number of road accidents is therefore a big concern. Research works are going on to comfort the drivers and increase the security features of vehicle to avoid accidents. In this chapter, a model is proposed, which can efficiently detect drivers drowsiness. The discussion mainly focuses on building the learning model. A modified convolution neural network is built to solve the purpose. The model is trained with a dataset of 7000 images of open and closed eyes. For testing purpose, some real-time experiments are done by some volunteer drivers in different conditions, like gender, day, and night. The model is really good for daytime and if the driver is not wearing any glass. But with a glass in the eyes and in night condition, the system needs improvements. 2025 selection and editorial matter, Yousef Farhaoui, Bharat Bhushan, Nidhi Sindhwani, Rohit Anand, Agbotiname Lucky Imoize and Anshul Verma; individual chapters, the contributors. -
Marketing Research and Market-Focused Production as an Effective Business Tool in Power Sector
Businesses must devote part of their resources to conducting market and marketing research to make good decisions, which will help expand any business and utilize resources effectively. Understanding the intended clients is essential to successfully operating and expanding a firm. For marketers to comprehend consumer value about the product being supplied and therefore add value to their consumers, it is crucial to have this understanding. Organizations can better influence customers to buy niche goods or corporate services after thoroughly understanding their objectives, requirements, and values. In this situation, it is required to restructure the physical system and the related control and planning systems to provide production the tools it needs to become more competitive and customer-focused, acting as a positive and active production process instead of a reactive one. One of the finest techniques for understanding consumers is market research. It provides basic information that a company may utilize to inform its marketing strategy, facilitating and enhancing sales and marketing. This paper reviews the impact of effective market and marketing research and market-focused manufacturing in the power sector. 2023 IEEE. -
Organization justice impact on employee work engagement
Research methodology: For the study 200 employees of selected Educational Institutions in North NCR was taken as respondents. Data was collected using standard questionnaire containing standard scaled of distributive, procedural, interactional, trust and employee engagement. The relationships between justice perceptions and work engagement were analyzed using correlations and regression analysis. Findings: The analysis of the study indicates that there is a strong and positive relationship among organization justice and employee engagement. The study also indicates that procedural, interactional and distributive justice are inter related with each other. Further, distributive and interactional justice take precedence over procedural justice in determining job engagement, while distributive justice plays the most important role in determining organization engagement (OE), followed by procedural and interactional justice. Limitations: This paper adds to the very small number of studies that have investigated the role of interactional justice in enhancing job and OEs. It has also established inter-relationships between the three dimensions of organizational justice and their individual roles in determining job and OEs. 2020 SERSC. -
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Determining the Antecedents and Consequences of Brand Experience: A Study to develop a Conceptual Framework
In the marketing literature, one of the most talked- about subjects is brand experience (BE). Through an examination of the numerous studies conducted by BE researchers, this report attempted to determine the significance of BE in the body of recent literature. This paper culminates in the creation of a conceptual framework that prospective investigators might utilize to discern the diverse pathways inside BE. 2024 IEEE. -
Impact of Digital Media Marketing on Consumer Buying Decisions
Digital Marketing has become one of the most discussed topics in the field of management in the recent past. With the advent of social media, digital marketing has even garnered more attention. It has directly or indirectly influenced the buying behaviour of the customers also. This paper has tried to understand the impact of digital marketing in influencing the impulsive buying behaviour of the customers. 2024 IEEE. -
Role of Artificial Intelligence in Influencing Impulsive Buying Behaviour
This research paper investigates the influence of Artificial Intelligence (AI) on impulsive buying behaviour in the digital commerce domain. The study explores how AI algorithms, data analysis, and customized marketing approaches influence impulsive buying decisions, reshaping traditional understandings of this phenomenon. The analysis draws from a confluence of psychological principles, technological advancements, and marketing strategies, aiming to shed light on how AI not only forecasts but also incites impulsive buying behaviours. The study identifies research gaps, such as the integration of AI with emotional triggers, the comparative effectiveness of AI vs. human influence, and cross-cultural and demographic variability. The research methodology involves a descriptive study with a questionnaire-based survey, and data analysis tools such as ANOVA and paired t-tests. This research contributes to the broader discussion on digital-age consumer behaviors, underscoring the revolutionary role of AI in transforming retail experiences and beyond. 2024 IEEE. -
Understanding the use of Regression Analysis in Business Analytics to understand the perceptions of Students about Quality in Higher Education
For a very long time, researchers in a variety of fields have utilized regression analysis as a crucial tool for data analysis and result interpretation. Regression analysis has also been widely employed in the business world to determine what factors influence consumers' decisions to purchase any of the company's products. Comprehending the interplay of these variables will enable the business to conduct a more thorough consumer analysis and boost sales. This essay is an attempt to comprehend students' perceptions on the qualities they consider important while applying to universities. Regression analysis is another approach used in this article to determine how the quality criteria affect the respondents' overall happiness. 2024 IEEE. -
ETHICAL CONFLICTS AMONG THE LEADING MEDICAL AND HEALTHCARE LEADERS
Today, the whole world is fighting the COVID-19 pandemic. In these circumstances, medical professionals are being viewed as the frontline warriors who are risking their lives for the sake of helping, caring, and curing these patients. However, in these difficult times, there are few medical professionals and health care providers who are taking advantage of this situation and taking advantage of distressed and distraught patients at will. A conflict between professional and personal ethical values makes them depressed and puzzled. It is tough for them to maintain a good image of their profession and business. The objectives of this study are to review the ethical conflict amid the ongoing Covid pandemic and post-Covid pandemic (vaccination period) in the context of medical professionals and health care providers. The paper is designed based on a literature review. Almost fifty-two research papers, articles, survey reports, and newspapers were studied in the context of ethics in business/profession. After reviewing moral distress is ongoing and post-pandemic period, the researchers have tried to present the medical professionals and health care providers' critical situation to give priority to their professional ethics or personal interest. School of Engineering, Taylor's University -
Geopolitical Risk, Variability of Oil Price, and the Global Trade Uncertainty: An Economic Perspective
Oil prices are the outcome of a highly integrated, dynamic global system. With geopolitical risk and trade policy uncertainty contribute to the volatile world markets, the study analyses the impact of geopolitical risk, trade policy uncertainty on crude oil price globally. It considers data from 2000 to 2023 and finds out systematic link between the three variables. The data proved a long run impact of trade policy uncertainty and geopolitical risk on the crude oil. This enhances the influence of the variables and leads to implementation of policy measures in global markets. The data are tested through Auto Regressive Distributed Lag (ARDL) model and checked for long run cointegration and short run association. The policy, thus, has been suggested as to improve the transition towards sustainability globally. Though the impact of geopolitical risk and trade policy uncertainty is not strong on crude oil price in world market, it can affect the short run volatility. Thus, mitigation, diversification and transparency are the key factors to strengthen the existing situation in world. 2026, IGI Global Scientific Publishing. All rights reserved. -
Pattern of Carbon Dioxide Emission, Economic Growth and Energy Consumption in South-Asian Countries: An Empirical Analysis
The main aim of this chapter is to analyse the pattern of environmental pollution as represented by per capita carbon dioxide emission (PCCO2), per capita gross domestic product (PCGDP) and per capita energy consumption (PCEC) and their nexus in case of South-Asian countries for the time period 19912014. Econometric tools such as panel co-integration and fully modified ordinary least squares have been used to study the relations. A positive significant relationship has been observed between PCGDP and PCCO2 emission. In addition, an increase in PCEC also has a positively significant impact on PCCO2 emission. Therefore, the governments of all the countries need to come together and take steps to curb the rising carbon emission since neither the problem nor the responsibility is restricted to one country alone. There is a need for countries to increase the consumption of renewable energy and explore alternate options that are fewer dependents on coal or any other fossil fuel. On priority, economies in South-Asian region should focus on sustainable economic activities by balancing growth of economy with clean environment. 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An Empirical and Statistical Analysis of Classification Algorithms Used in Heart Attack Forecasting
The risk of dying from a heart attack is high everywhere in the world. This is based on the fact that every forty seconds, someone dies from a myocardial infarction. In this paper, heart attack is predicted with the help of dataset sourced from UCI Machine Learning Repository. The dataset analyses 13 attributes of 303 patients. The categorization method of Data Mining helps predict if a person will have a heart attack based on how they live their lives. An empirical and statistical analysis of different classification methods like the Support Vector Machine (SVM) Algorithm, Random Forest (RF) Algorithm, K-Nearest Neighbour (KNN) Algorithm, Logistic Regression (LR) Algorithm, and Decision Tree (DT) Algorithm is used as classifiers for effective prediction of the disease. The research study showed classification accuracy of 90% using KNN Algorithm. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Automated Verification of Open/Closed Principle: A Code Analysis Approach
The SOLID principles are foundational to software engineering, focusing on the maintainability, scalability, and extensibility of software systems. The Open/Closed Principle (OCP), a pivotal element among these principles, underscores the need to design software modules that are open for extension yet closed for modification. This research explores automated verification techniques for OCP, addressing the validation of software modules through extensibility and adaptability assessments. The principal objectives involve the development of a code analysis approach and a methodology capable of automating the verification of adherence to OCP in developed codes, providing actionable insights to software developers. The system focuses on specific aspects of OCP, including inheritance, abstraction, and polymorphism, and aims to provide clear indications of where violations occur within a codebase. The implementation uses the Abstract Syntax Tree (AST) analysis to examine class definitions. The automated analysis of Python code using the defined rules offers a clear understanding of OCP adherence. Results are presented in Pandas DataFrames, indicating potential violations and providing developers with actionable insights to enhance code quality and maintainability. Overall, the automated code verification system aims to enhance code quality and adherence to fundamental design principles, paving the way for advancements in automated code analysis and software engineering practices. 2024 IEEE. -
Comparative Analysis of Noise Generated in BGV Homomorphic Encryption: Lattigo vs FHEgen Parameters
Post-quantum cryptography has emerged as a critical field following advances in quantum computing that threaten classical encryption schemes such as RSA and ECC. Fully Homomorphic Encryption (FHE), particularly the Brakerski-Gentry- Vaikuntanathan (BGV) scheme based on the Ring Learning with Errors (RLWE) problem, provides a promising solution for secure computations on encrypted data. A fundamental challenge in BGV implementations is the growth of noise during homomorphic operations, which must remain below a decryption threshold to ensure correctness. This study presents a comparative analysis of noise generation in BGV implementations using two distinct parameter selection approaches: Lattigo's pre-validated generic parameters and FHEgen's automatically generated application-specific parameters. Through empirical measurements using Lattigo v6.1.1, we evaluated five parameter sets across initial noise after encryption, noise expansion during homomorphic multiplication, and overall noise growth patterns. Our results demonstrate that Lattigo N13 achieves marginally lower post-multiplication noise (0.0587 log2 bits, or 4.15% lower in magnitude), though FHEgen achieves substantially higher verified security (210 bits vs. 50-60 bits). However, Lattigo's range of pre-validated parameters (LogN = 12 to LogN = 15) offers greater flexibility for varying computational depths. We conclude that the choice between parameter selection approaches depends on application requirements: FHEgen is preferable for well-defined computational needs with noise optimization priorities, while Lattigo is advantageous when flexibility and extensive validation are critical. This work provides practical insights for FHE practitioners in selecting parameters that balance security, noise management, and computational efficiency. 2025 IEEE. -
She Shores: A Study on the Lives, Challenges and Resilience of Women of the Koli Fishing Community in Mumbai
This study delves into the lives of women from the Koli fishing community in Mumbai, aiming to illuminate their unique life experiences and the daily struggles that often remain hidden beneath their prosperous facade. It endeavours to examine their agency and adaptive strategies employed to navigate these challenges. The research was conducted in Pachubandar, Vasai, located in the western suburbs of Mumbai, which stands as one of the prominent Koli settlements in the city. Employing a qualitative research approach coupled with an exploratory research design, the study engaged ten participants, comprising seven Koli women and three key informants from the community. Additionally, an observational analysis of four retail and wholesale fish markets in Mumbai was conducted to gain insight into the working conditions of Koli fisherwomen. This study adopts a gender-focused perspective to scrutinise the contextual vulnerabilities that shape the lives of Koli women. It underscores the paradox wherein, despite playing a pivotal role in sustaining both their families and the traditional fishing occupation, their contributions often go unnoticed. The Koli women face severe deprivation due to their limited access to property and decision-making authority. They find themselves entangled within traditional norms and patriarchal structures, which impede their access to essential assets and diverse livelihood resources. Although they significantly contribute to the fishery sector, their struggles, needs, and aspirations are frequently disregarded due to their lack of representation and involvement in decision-making bodies. The majority of these women work under precarious conditions, devoid of proper infrastructure, resources, and security. Furthermore, the evolving dynamics within the fishery sector, driven by rapid urbanisation and modernisation, have a profound impact on the lives and traditional livelihoods of Koli women. They now confront issues such as dwindling fish catches due to environmental degradation, heightened market competition, reduced livelihood spaces brought about by shifting urban and coastal landscapes, altered labour relations, and technological advancements. Consequently, they find themselves caught between the conflicting forces of tradition and modernity. The research also sheds light on the strategies devised by Koli women to resist and adapt to the uncertainties and challenges they encounter, ultimately safeguarding their livelihoods through self-organisation. The study emphasises the imperative to acknowledge their contributions as 'visible work' and advocates for the incorporation of gender considerations when formulating policies and development strategies within the fisheries sector. 2024 Meghna Roy.