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An Efficient Security-Enabled Routing Protocol for Data Transmission in VANET Using Blockchain Ripple Protocol Consensus Algorithm
The security quality in Vehicular Ad-hoc NETworks (VANET) has improved as a result of recent developments in Intelligent Transportation Systems (ITS). However, within the current VANET system, providing a cheap computational cost with a high serving capability is a significant necessity. When a vehicle user goes between one Roadside Unit (RSU) to another RSU region in the current scenario, the current RSU periodically needs re-authentication of the vehicle user. This increases the computational complexity of the system. The gathering and broadcast of existing traffic event information by automobiles are critical in Vehicular Ad-hoc Networks (VANET). Traditional VANETs, on the other hand, have several security concerns. This work develops a blockchain-based authentication protocol to address the aforementioned difficulty. To address critical message propagation issues in the VANET, we invent a novel type of blockchain. We develop a local blockchain for exchanging real-world event messages among cars within a countrys borders, which is a novel sort of blockchain ideal for the VANET. We describe a public blockchain RPCA that records the trustworthiness of nodes and messages in such a distributed ledger suitable for secure message distribution. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Dynamics of Sustainable Economic Growth in Emerging Middle Power Economies: Does Institutional Quality Matter?
The present study investigates the relevance of Institutional structures quality as a determinant of the GDP of the Emerging Middle Power Economies (MIKTA) which constitute predominantly middle-income countries, namely Mexico, South Korea, Indonesia, Turkey, and Australia over the timeframe of 19852016. In addition to institutional variables such as Government Stability, Bureaucratic Quality and Socioeconomic Conditions, the study uses productive factors (per worker capital, human capital) and a macroeconomic indicator (inflation) to show the GDP of the above-mentioned countries. The impact that institutional variables taken have on Efficient Environmental resources, Sustainability and their management has shown to have an impact on the rate of growth of the middle-income economies. To estimate a long-run relation, the study employs the Autoregressive Distributed Lag model, also known as the ARDL model, bringing in controls for cointegration, nonstationary, heterogeneity and cross-sectional dependency and accounts for a mixed order of integration of variables. The model indicates that capital per worker, socio-economic conditions, bureaucratic quality, human capital and inflation have a long-run effect on the GDP of a country. The paper concludes with a positive impact of institutional variables during both, the short-run and the long-run, for the de-pendent variable. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Quality Efficacy Issues in Mangoes: Decoding Retailers Supply Chain
This research article tries to uncover the elements and compelling reasons causing supply chain inefficacy concerning low quality at the retailer level of the mangoes supply chain in Karnataka. The descriptive research approach was used in work. The research was conducted in the biggest mango-producing areas of Karnataka. Factors were discovered by factor analysis. A systematic questionnaire was used to determine how much the mango sector may improve supply chain efficacy. Contingent on the factor analysis, four variables for low quality were identified: functional difficulties, knowledge, Manpower, and resources. It was also discovered that the functional component is the compelling factor causing supply chain inefficacy. The study is confined to the retailer level of the Mango supply chain, focusing on four Mango-producing districts in Karnataka. Furthermore, the measures for the key causes under each aspect causing hindrances in supply chain efficiency in terms of quality have been discovered. There is a scarcity of materials to enhance the supply chain efficiency of merchants in Indias mango business. This research attempted to address a literature gap and help practitioners improve the mango supply chain in underdeveloped nations. This paper also serves the 2nd goal, Zero Hunger, End starvation, improve food security and nutrition, and promote sustainable agriculture of sustainable development. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Revisiting the Nexus Between Suicides and Economic Indicators: An Empirical Investigation
In India, as of 2021, there was a 7.2% increase in suicides. With the economic burden inflicted by the pandemic and increasing suicides, a systematic investigation needs to be done. This empirical investigation uses the Autoregressive Distributed Lag (ARDL) model to obtain long and short-term estimates for the relationship between suicides and prominent economic indicators. The findings suggest that economic indicators like GDP per capita growth, age dependency ratio, and unemployment rate have a significant dynamic relationship with suicides. In this regard, preventive measures can be formulated and implemented in such a way that focuses on improving the countrys economic scenario which will in turn reduce suicides. Organizations and governments can plan training and mental health care programs for farmers, workers, and students. Mental health care services require attention from the government so that at a macro level the problem of increasing suicides can be handled. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Individuals Attitude Concerning Memes: A Study Reference to Active Social Media Users
Todays digital world, memes are more popular among the youngsters to express ones emotion. Nowadays, almost everyone is largely relying on social media to send memes and emojis in order to share the information in a humorous way. Lots of research article are reviewed about the social media memes, internet memes and this paper summarizes the usage of social media and following the trending pattern in communicating or sharing information through the social media platform. This study is aimed to study about the individuals attitude towards memes and its effect among the social media users. Through survey, total of 193 samples were collected by using questionnaire and statistical tools like Reliability test, Chi square and Correlation analysis have been used to interpret the collected data. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Secure and Private Federated Learning through Encrypted Parameter Aggregation
This chapter is dedicated to cross-silo private parameter aggregation. ML/DL has demonstrated promising results in a variety of application domains, especially when vast volumes of data are collected in one location, such as a data center or a cloud service. The goal of FL is to improve the quality of ML/DL models while minimizing their drawbacks. Participating devices in an FL task could range in size from a single smartphone or watch to a global corporation housing multiple data centers. It was originally believed that just a little amount of information about the original training data would be carried over into subsequent model updates as FL interactions occurred. The differential privacy framework is concerned with restricting the release of private information while sharing the outcomes of computations or queries performed on a dataset. Recently, many researchers have begun to employ differential privacy while training models in a federated setting. 2024 Saravanan Krishnan, A. Jose Anand, R. Srinivasan, R. Kavitha and S. Suresh. -
Attention to Economic Factors and Its Response to Foreign Portfolio Investment: An Evidence from Indian Capital Market
Stock market consists of a variety of investors. Among these, Foreign Portfolio Investors (FPIs) is a key investment influx. These investments can change or fluctuate due to several macroeconomic factors which can cause a shift in the dynamics of the markets in India. This paper examines the factors influencing for foreign portfolio investment in long run as well as short run. The sample comprises of 120 monthly observations on Foreign Portfolio Investment (FPIs) and Macro economic variables such as Oil prices (OP), Gross Domestic Product (GDP), Interest Rate (IR), Exchange rate of Indian Rupee with USD (ER), Inflation (CPI), Nifty Index (NSEI), 10year Bond Prices (BP) and Index of Industrial production (IIP) over a period of 10years, spanning from January 2013 to November 2022. The study employed Autoregressive Distributed Lag model (ARDL) to establish the long run association with error correction models. The result indicates that there is long run association between the Foreign Portfolio Investment and macro-economic variables. Among this, NSEI, IIP and ER played a significant role to determine FPI investments in the long run, whereas in the short run, FPI was impacted by ER and NSEI significantly. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Behavioral Bias as an Instrumental Factor in Investment Decision-An Empirical Analysis
Investment decisions are always complex in nature. Investment assets are volatile in nature there are less volatile, medium volatile and high volatile investment assets in the financial market. In the current study how, the behavioral biases of the investors affecting their investment decisions in the less volatile asset classes is examined using an extensive survey method among the IT professionals in the Bangalore city. The relationship between the demographic variables and behavioral biases is tested. Also, a detailed study is conducted to examine the risk-taking behavior of the investors in the less volatile assets. There are basically three type of investors on the basis of their risk-taking behavior i.e. Risk seeking, Risk Neutral and Risk averse investors. Current study reveals that investors in the less volatile asset classes are very much cautious about the risk factor and therefore they are risk averse in nature. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Facile Synthesis of Polymer Dot and Its Antibacterial Action Against Staphylococcus aureus
Antimicrobial resistance (AMR) rising from nosocomial infections is an escalating threat to human life nowadays due to the overuse of drugs. The multidrug-resistant pathogenic bacteria have increased morbidity and mortality rates, becoming a crucial global clinical challenge. Gram-positive Staphylococcus aureus bacteria is one of the nosocomial pathogens that cause severe invasive diseases and skin infections to human health worldwide. Herein, a non-conjugated polymer dot (NCPD) was synthesized from less toxic and biocompatible polyvinyl alcohol (PVA) via hydrothermal treatment. The fluorescence of the polymer dots was enhanced by nitrogen doping. The as-synthesized nitrogen-doped polymer dots (PDs) exhibit excitation-dependent luminescence emission and show green color fluorescence under UV light. The average size of the synthesized functionalized non-conjugated polymer dot is obtained as 4.08nm, and they exhibit an amorphous structure. No antibacterial property was observed for bulk polymer, but the doped polymer dots showed antibacterial activity against Gram-positive Staphylococcus aureus bacteria. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Dandelion Algorithm for Optimal Location and Sizing of Battery Energy Storage Systemsin Electrical Distribution Networks
This paper describes a new way to improve the performance of an EDN by integrating distributed battery energy storage systems (BESs) in the best way possible. This method is based on the Dandelion Algorithm (DA). The search space for BES locations is first predetermined using loss sensitivity factors (LSFs), and then DA is used to determine the optimal locations and sizes. The reduction of real power distribution loss is regarded as the primary objective function, and the impact of BESs is extended to examine the network voltage profile, voltage stability, and GHG emissions. IEEE 33-busEDN is used to calculate the computational efficiency of LSF-DA. Results show that DA is more efficient than Archimedes optimization (AOA), future search algorithm(FSA), pathfinder algorithm(PFA), and butterfly optimization algorithm(BOA) algorithms. Furthermore, the results show that the proposed DA enhances all technological and environmental factors and RDN performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Application Areas, Benefits, and Research Challenges of Converging Blockchain and Machine Learning Techniques
In recent years, machine learning (ML) has become a hot topic of research and application. ML model and huge amount of data growth difficulties still follow ML development. With the lack of new data and constant training, published ML models may soon become obsolete; unscrupulous data contributors may upload incorrectly labelled data, leading to poor training results; and data leakage and abuse are all possible outcomes. These issues can be effectively addressed by using blockchain, a new and rapidly evolving technology. With the advancement of various smart devices and the field of artificial intelligence and machine learning, interdisciplinary collaboration with blockchain technology may be incredibly valuable for future investigations. Collaborative ML and blockchain convergence can be studied here, with emphasis on how these two technologies can be combined and their application areas. On the other hand, look at the existing researchs shortcomings and future enhancements. The Author(s), under exclusive license to Springer Nature Switzerland AG. 2024. -
Wearable Smart Technologies: Changing the Future of Healthcare
Wearable smart technologies are the innovative solutions for the issues of healthcare services. In this chapter, a review of the innovative wearable healthcare devices and applications has been done. Wearable devices are used for supervision and illness control. These innovative wearable technologies can straightforwardly affect the medical dynamic, can upgrade the quality of treatment for patients, and can reduce the expenses incurred in it. The large health record generated by the wearable devices provides an opportunity for data analysts to apply machine learning techniques for prediction on the data generated by sensors. Today's wearable smart technologies are capable of being integrated into eyeglasses, cloths, shoes, belts, watches, etc. Sensors can be inserted in these objects to be worn. The advanced forms of wearable technologies can be attached to the skin of the wearer. A smartphone is mainly utilized to collect data and communicate it to a server situated at a remote area for greater capacity and investigation. Maximum innovations related to wearable technologies are still in the prototyping phase. The study covers almost every aspect of wearable technologies, which could be helpful in the future for innovation and research in this area. 2024 selection and editorial matter, Ankur Beohar, Ribu Mathew, Abhishek Kumar Upadhyay, and Santosh Kumar Vishvakarma -individual chapters, the contributors. All rights reserved. -
Analysing the Ascendant Trend of Veganism: A Comprehensive Study on the Shift towards Sustainable Dietary Choices
Background: Veganism has become a prominent social and culinary movement due to concerns about animal welfare, environmental sustainability, and ones own health. Vegans strive to consume only plant-based meals in order to lessen the suffering of animals, stop the environmental damage caused by the animal agriculture sector, and enhance their own health. Objective: This chapter aspires to understand various dynamics of consumer consciousness towards veganism through social media analysis (Twitter) and research opinions. Materials and Methods: This chapter used a qualitative approach and a three-part methodology. Firstly, a literature review examines the impact of veganism on human health, ethical needs and sustainable food choices. Secondly, the authors extracted tweets and analysed them using data visualisation software- NVivo with the essential parameters being themes, sentiment, world map, and word cloud. Results: Sentiment analysis explained consumer perception towards veganism as a storming blackball result of 36.1 present positive insights. Word map analysis describes veganism as a global phenomenon. The third part analysed the Scopus research data and identified food, diet and meat as major themes in veganism. The Scopus database sentimental analysis also re-emphasised the growing positive insights towards it. Conclusion: This study highlighted the significance of veganism as a sustainable dietary choice for addressing urgent global issues while promoting a thoughtful and compassionate approach to eating. It is also emerging as a powerful tool for positive change in preserving and promoting biodiversity. 2024 selection and editorial matter, Mourade Azrour, Jamal Mabrouki, Azidine Guezzaz, Sultan Ahmad, Shakir Khan and Said Benkirane; individual chapters, the contributors. -
Carbon Nanotubes for Supercapacitors
Supercapacitors are energy storage devices that boast significant capacitance, enhanced energy density, rapid charge/discharge cycles, minimal heat generation, safety, sustainability with no expendable components, and extended durability. Supercapacitors, due to their unique characteristics, are increasingly favoured in consumer electronics and as alternate energy solutions. Carbon nanotubes (CNTs) have emerged as a promising material for supercapacitor electrodes, thanks to their remarkable features like exceptional conductivity, large surface area, robust mechanical strength, and chemical stability. The objective is to offer a comprehensive understanding of the pros and cons of supercapacitor materials involving CNTs and to pinpoint ways to boost their efficiency. This also entails examining how the inherent physical and chemical traits of pure CNTs, such as their size, quality, imperfections, shape, modifications, and treatment processes, influence their capacitance. Moreover, a deeper dive into composites, like CNTs combined with oxides, polymers, and other hybrid materials, aims to customize their composition and characteristics to optimize capacitance while ensuring the devices longevity. This section also compiles the latest studies on various CNT composites as potential supercapacitor electrode materials. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Pathway toDetect Cancer Tumor byGenetic Mutation
Cancer detection is one of the challenging tasks due to the unavailability of proper medical facilities. The survival of cancer patients depends upon early detection and medication. The main cause of the disease is due to several genetic mutations which form cancer tumors. Identification of genetic mutation is a time-consuming task. This creates a lot of difficulties for the molecular pathologist. A molecular pathologist selects a list of gene variations to analyze manually. The clinical evidence strips belong to nine classes, but the classification principle is still unknown. This implementation proposes a multi-class classifier to classify genetic mutations based on clinical evidence. Natural language processing analyzes the clinical text of evidence of gene mutations. Machine learning algorithms like K-nearest neighbor, linear support vector machine, and stacking models are applied to the collected text dataset, which contains information about the genetic mutations and other clinical pieces of evidence that pathology uses to classify the gene mutations. In this implementation, nine genetic variations have been taken, considered a multi-class classification problem. Here, each data point is classified among the nine classes of gene mutation. The performance of the machine learning models is analyzed on the gene, variance, and text features. The gene, variance, and text features are analyzed individually with univariate analysis. Then K-nearest neighbor, linear support vector machine, and stacking model are applied to the combined features of a gene, variance, and text. In the experiment, support vector machine gives better results as compared to other models because this model provides fewer misclassification points. Based on the variants of gene mutation, the risk of cancer can be detected, and medications can be given. This chapter will motivate the readers, researchers, and scholars of this field for future investigations. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Study of Bard-Marangoni Convection in a Microfluid with Coriolis Force
The convection of micro-structured fluid particles and the Coriolis force has been investigated in the problem. The eigenvalues are calculated for upper free velocity and adiabatic temperature boundary conditions and lower rigid velocity and isothermal temperature boundary conditions. The analysis is based on solving linear disturbance equations. The impact of different micropolar fluid variables and the Taylor number based on the convection has also been investigated. The study could observe that while the coupling and micropolar heat conduction parameters along with rotational parameters have a stabilizing effect, the couple stress parameter results in a destabilizing effect. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Selfipendant and Extremal Pendant Graphs
[No abstract available] -
Exploring ARIMA Models with Interacted Lagged Variables for Forecasting
Including interactions among the explanatory variables in regression models is a common phenomenon. However, including interactions existing among lagged variables in autoregressive models has not been explored so far. In this paper, Autoregressive Integrated Moving Average (ARIMA) model with interactions among the lagged variables is proposed for improving forecast accuracy. The methodology for identifying the interacted lagged variables and including them in the ARIMA model is suggested. Using five different data sets of different types, the paper explores the effect of interacted lagged variables in ARIMA model. The experimental results exhibit that when interactions do actually exist, ARIMA model with interactions improves the forecast accuracy as compared to ARIMA model without interactions. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Novel Hybrid Model for Time Series Forecasting Using Artificial Neural Network and Autoregressive Integrated Moving Average Models
Enhancing forecast accuracy while using time series is a potential area of research. Evidences exist in the literature to show that hybrid models can significantly improve the forecasting performance, as they combine the exclusive strengths of different models. This paper presents a novel hybrid model by combining forecasts from Autoregressive Integrated Moving Average (ARIMA) and artificial neural network (ANN) models with suitable weights, thereby improving the forecast accuracy. The methodology employs appropriate error metrics to construct the weights. The paper further demonstrates the efficiency of the proposed methodology through an empirical study, based on two real-world time series data sets. Thus, the new methodology can be used for enhancing the forecast accuracy in a number of fields of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.