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The Road to Reducing Vehicle CO2 Emissions: A Comprehensive Data Analysis
In recent years, the influence of carbon dioxide (CO2) releases on the environment have become a major concern. Vehicles are one of the major sources of CO2 emissions, and their contribution to climate change cannot be ignored. This research paper aims to investigate the CO2 emissions of vehicles and compare them with different types of engines, fuel types, and vehicle models. The study was carried out by gathering information about the CO2 emissions of vehicles from the official open data website of the Canadian government. Data from a 7-year period are included in the dataset, which is a compiled version. There is a total of 220 cases and 9 variables. The data is analyzed using statistical methods and tests to identify the significant differences in CO2 emissions among different Car Models. The results indicate that vehicles with diesel engines emit higher levels of CO2 compared to those with gasoline engines. Electric vehicles, on the other hand, have zero CO2 emissions, making them the most environmentally friendly option. Furthermore, the study found that the CO2 emissions of vehicles vary depending on the type of fuel used. The study also reveals that the CO2 emissions of vehicles depend on the model and age of the vehicle. Newer models tend to emit lower levels of CO2 compared to older models. In conclusion, this study provides valuable insights into the CO2 emissions of Cars and highlights the need to adopt cleaner and more sustainable transportation options. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Electrospun nanofibers of 2D Cr2CTx MXene embedded in PVA for efficient electrocatalytic water splitting
The usage of transition metal carbide-based electrocatalysts has proven to be an efficient and effective strategy for enhancing the kinetics of water splitting reactions encompassing the generation of hydrogen (hydrogen evolution reaction, HER) and oxygen (oxygen evolution reaction, OER). In this investigation, we have prepared a composite material by integrating Cr2CTx MXene (derived from Cr2AlC MAX phase) and polyvinyl alcohol (PVA) through electrospinning technique. Carbonization of the MXene-PVA nanofibers resulted in the formation of Cr2CTx/carbon nanofiber (Cr2CTx/CNF) that exhibits high porosity, stability, surface area, and electrocatalytic activity. Systematic examination and optimization for the electrocatalytic water splitting reaction reveales outstanding performance, characterized by substantially lower overpotentials of 265 mV and 250 mV at the constant current density of 10 mA cm?2 with lower Tafel slope values of 85 mV dec?1 and 52 mV dec?1 for HER and OER, respectively. Moreover, this work presents a novel strategy for fabricating non-precious electrocatalyst Cr2CTx/CNF through a cost-effective and straightforward electrospinning and carbonization process, advancing electrocatalytic water splitting applications, especially for oxygen evolution reactions. 2024 The Royal Society of Chemistry. -
Critical Analysis of MoS2-Based Systems for Textile Wastewater Treatment
Indiscriminate discharge of toxic organic contaminant-laden wastewater into water bodies is one of the major issues posing a risk to the environment in general and aquatic living systems in particular. Widely used textile dyes are ubiquitous in the effluents emanating from industries. Photocatalysts, due to their efficiency and eco-friendliness, can be effectively used to remove pollutant dyes from the water bodies. Molybdenum disulfide (MoS2), an emerging co-catalyst, has high photocatalytic activity, strong absorptivity, non-toxicity, and low cost; with a graphene-like structure, it offers functional features similar to graphene: high charge carrier transfer, strong wear resistance, and good mechanical strength. However, in aspects such as cost, abundance, versatile morphologies, and tunable band gap with efficient visible light absorption properties, MoS2 scores over graphene. The present chapter discusses the recent advances in nanostructured MoS2 materials for applications in environmental remediation. Special emphasis has been paid to MoS2 and MoS2-based systems for the photocatalytic degradation of various organic contaminants such as malachite green, methyl orange, rhodamine B, and methylene blue that find extensive use in the textile industry. As a result, MoS2 systems play an essential role in nanocomposites, especially in speeding up photo-induced electron transport and lowering electron recombination rates, making them desirable photocatalysts for the degradation of pollutants. The chapter focuses on addressing SDG 3 (Good Health and Wellbeing), SDG 6 (Clean Water and Sanitation), SDG 7 (Clean and Affordable Energy), SDG 9 (Industry, Innovation, and Infrastructure), SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), and SDG 15 (Life on Land). 2025 Moharana Choudhury, Ankur Rajpal, Srijan Goswami, Arghya Chakravorty and Vimala Raghavan. -
CNN-Bidirectional LSTM based Approach for Financial Fraud Detection and Prevention System
Detecting fraudulent activity has become a pressing issue in the ever-expanding realm of financial services, which is vital to ensuring a positive ecosystem for everyone involved. Traditional approaches to fraud detection typically rely on rule-based algorithms or manually pick a subset of attributes to perform prediction. Yet, users have complex interactions and always display a wealth of information when using financial services. These data provide a sizable Multiview network that is underutilized by standard approaches. The proposed method solves this problem by first cleaning and normalizing the data, then using Kernel principal component analysis to extract features, and finally using these features to train a model with CNN-BiLS TM, a neural network architecture that combines the best parts of the Bidirectional Long Short-Term Memory (BiLS TM) network and the Convolution Neural Network (CNN). BiLSTM makes better use of how text fits into time by looking at both the historical context and the context of what came after. 2023 IEEE. -
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Interpreting the Evidence on Life Cycle to Improve Educational Outcomes of Students Based on Generalized ARC-GRU Approach
Research on the effects of teachers' fatigue on students' learning has been significantly less common than research on the effects of teachers' fatigue on teachers' own performance. Therefore, the purpose of this research is to see if teachers' emotional weariness has any bearing on their students' performance in the classroom. Consideration is given to a student's grades and their impressions of whether or not the system receive assistance from teachers, as well as to the student's general outlook on school, confidence in their own abilities, and faith in the availability of faculty support. Data preparation, feature extraction, and model training are the first steps in the proposed approach. Indicators of the quality of the education being provided are eliminated (by outlier removal and feature scaling). k-mean clustering approach is a technique of clustering which is commonly used in feature extraction. Following feature extraction, GARCH-GRU models are trained. The proposed approach is superior to two popular alternatives, ARCH and GRU. Using the provided method, the system were able to achieve a maximum accuracy of 97.07%. 2024 IEEE. -
Climate Change Impact on Water Resources, Food Production and Agricultural Practices
The greatest threat to human health that exists today is climate change. Ecosystems, societies and biodiversity are seriously at risk from the long term effects due to change in climate, primarily brought on by human activities. Rising temperatures increase evaporation, which causes drought and decreases water availability for ecosystems, drinking water supplies and agriculture. Changed precipitation patterns exacerbate floods, storms and sea levels, contaminating the water supply and harming infrastructure. The effects of rapidly changing climate on water resources must be minimised through sustainable water management techniques, conservation initiatives and International initiatives. The effects of climate change on the long run have been the focus of research because stable weather significantly influences agricultural productivity. Due to agricultures reliance on temperature and rainfall, climate change threatens world food security. Rising temperature results in lower productivity and also promotes the growth of weeds and pests, changes precipitation patterns, which will result in more crop failures and production declines. This work summarises the outcome of climate change on crop and livestock yields, water resources and the economy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications
This book serves as a bridge connecting the theoretical foundations of DRL with practical, actionable insights for implementing these technologies in a variety of industrial contexts, making it a valuable resource for professionals and enthusiasts at the forefront of technological innovation. Deep Reinforcement Learning (DRL) represents one of the most dynamic and impactful areas of research and development in the field of artificial intelligence. Bridging the gap between decision-making theory and powerful deep learning models, DRL has evolved from academic curiosity to a cornerstone technology driving innovation across numerous industries. Its core premiseenabling machines to learn optimal actions within complex environments through trial and errorhas broad implications, from automating intricate decision processes to optimizing operations that were previously beyond the reach of traditional AI techniques. Deep Reinforcement Learning and Its Industrial Use Cases: AI for Real-World Applications is an essential guide for anyone eager to understand the nexus between cutting-edge artificial intelligence techniques and practical industrial applications. This book not only demystifies the complex theory behind deep reinforcement learning (DRL) but also provides a clear roadmap for implementing these advanced algorithms in a variety of industries to solve real-world problems. Through a careful blend of theoretical foundations, practical insights, and diverse case studies, the book offers a comprehensive look into how DRL is revolutionizing fields such as finance, healthcare, manufacturing, and more, by optimizing decisions in dynamic and uncertain environments. This book distills years of research and practical experience into accessible and actionable knowledge. Whether you're an AI professional seeking to expand your toolkit, a business leader aiming to leverage AI for competitive advantage, or a student or academic researching the latest in AI applications, this book provides valuable insights and guidance. Beyond just exploring the successes of DRL, it critically examines challenges, pitfalls, and ethical considerations, preparing readers to not only implement DRL solutions but to do so responsibly and effectively. Audience The book will be read by researchers, postgraduate students, and industry engineers in machine learning and artificial intelligence, as well as those in business and industry seeking to understand how DRL can be applied to solve complex industry-specific challenges and improve operational efficiency. 2024 Scrivener Publishing LLC. -
IoT Framework, Architecture Services, Platforms, and Reference Models
Internet of things (IoT) is spawning a twirl in the world of connected devices by aiding the devices to connect, compute, and coordinate with each other. While the concept of IoT is still embryonic, its outcomes are trailblazing. IoT acts as a facilitator in creating a smart world by connecting devices through sensors and actuators to the Internet. The acceptance of IoT in various sectors indicates that the partakers in an IoT ecology are diverse. This demands common functionalities, interoperability standards, and network protocols across sectors. But there exists an extremity of incongruency in devices, capabilities, and network protocols, and therefore it is imperative to have a complete reference architecture model that necessitates the existing diversities and defines a new monody for the IoT environment. The lack of standard and uniform architectural knowledge, frameworks, and platforms is presently resisting the researchers to reap the benefits that the Internet of things (IoT) offers. This chapter summarizes various Internet of things frameworks, architectures, platforms, and reference models and thereby paves way for businesses to build IoT on it. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Cloud Intrusion Detection Using Hybrid Convolutional Neural Networks
Instead of storing data on a hard drive, cloud computing is seen as the best option. The Internet is used to deliver three different kinds of computing services to users all over the world. One advantage that cloud computing provides to its customers is greater access to resources and higher performance while at the same time increasing the risk of an attack. Intrusion detection systems that can handle a large volume of data packets, analyse them, and generate reports based on knowledge and behaviour analysis were developed as part of this research. As an added layer of protection, the Convolution Neural Network Algorithm is used to encrypt data during end-to-end transmission and to store it in the cloud. Intrusion detection increases the safety of data in the cloud. In this paper demonstrates the data is encrypted and decrypted using a model of an algorithm and explains how it is protected from attackers. It's important to take into account the amount of time and memory required to encrypt and decrypt large text files when evaluating the proposed system's performance. The security of the cloud has also been examined and compared to other existing encoding methods. 2024, Iquz Galaxy Publisher. All rights reserved. -
Does environmental policy stringency improve nature's health in BRICS economies? Implications for sustainable development
In our groundbreaking exploration, we meticulously delve into the relationship between environmental policy stringency, international trade dynamics, and financial openness within the BRICS group (Brazil, Russia, India, China, and South Africa) spanning from 1996 to 2021. With a focus on critical variables such as economic growth and technological innovation, our empirical findings challenge conventional wisdom. Surprisingly, we found that those stringent environmental policies, when standing alone, do not invariably lead to reduce CO2 emissions. Equally interesting is our startling discovery that the anticipated moderating influence of environmental policy stringency, catalyzed by trade and foreign direct investment, on the well-being of our environment does not materialize; contrarily, both trade and foreign direct investment moderating channels exhibit unanticipated positive correlations with CO2 emissions. These revelations provoke us with the presence of a "pollution haven" phenomenon within the BRICS economies. Furthermore, our investigation reveals that, when examined individually, trade and foreign direct investment also appear to contribute to elevated emission levels. These findings provide a resolute solution to our research quandary, underlining the indispensable requirement for cutting-edge and robust environmental policies. These policies must possess the prowess to effectively counteract the adverse environmental consequences stemming from the amalgamation of global trade and financial integration. In doing so, they shall propel BRICS nations toward a future firmly grounded in principles of sustainability and ecological integrity. 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
A reliable inter-domain routing framework for autonomous systems using hybrid Blockchain
Inter-domain networks face several routing challenges, such as security, scalability, and reliability concerns in existing BGP-based systems. These challenges are exacerbated by the increasing number of interconnected networks and the lack of a standardized approach for routing data between them. Hybrid Blockchain-based framework has proposed for inter-domain routing in autonomous systems in this research. The framework combines the use of traditional routing protocols with the distributed ledger technology of Blockchain. It leverages the salient features of both to create a more secure and efficient routing framework. The Blockchain component provides a decentralized and tamper-proof ledger for storing routing information, while the traditional routing protocols handle the actual exchange of data between autonomous systems. The framework is designed to enhance the security of inter-domain routing by incorporating the use of digital signatures and information sharing among participating autonomous systems. Each participating system maintains a copy of the distributed ledger and can verify the authenticity of routing information using digital signatures. It ensures that only legitimate and authorized data is transmitted between autonomous systems, mitigating the risk of malicious attacks or illegitimate routing. The proposed framework obtained 87.73 % Route calculation Speed, 90.41 % Route filtering, 93.77 % Fault tolerance, 94.10 % Load balancing, 95.54 % Hop count, 95.13 % bandwidth consumption, 93.94 % Security Management and 96.29 % Convergence time. The framework employs a consensus mechanism for updating and validating the routing information, ensuring consistency and accuracy in the routing decisions. It also reduces the reliance on a single central authority and distributes the decision-making process among participating systems. 2024 Elsevier Ltd -
Sonochemical assisted impregnation of Bi2WO6 on TiO2 nanorod to form Z-scheme heterojunction for enhanced photocatalytic H2 production
In this work, Bi2WO6/TiO2 nanorod heterojunction was prepared by sonochemical assisted impregnation method. After loading 2 wt% Bi2WO6 on TiO2 nanorods, the photocatalytic hydrogen production rate of 2026 mol/h/g was achieved. Compared to commercial P25 and TiO2 nanorods, ?13 and ?3 folds enhanced activity was observed. The excellent photocatalytic performance of Bi2WO6/TiO2 nanorod photocatalyst was mainly attributed to i) reduction of bandgap due to heterojunction formation, ii) quick transport of photogenerated charge carriers, and iii) efficient charge carrier separation supported by UV-DRS, photocurrent measurement, Impedance study, and photoluminescence spectra analysis. The Z-scheme band alignment for Bi2WO6/TiO2 nanorod heterojunction was proposed based on the Mott-Schottky measurement. This result demonstrated the effective utilization of Z-scheme heterojunction of Bi2WO6/TiO2 for photocatalytic reduction application. 2021 The Society of Powder Technology Japan -
Enhanced Social Media Profile Authenticity Detection Using Machine Learning Models and Artificial Neural Networks
Fake engagement is one of the main issues with online networks or ONSs, which are used to artificially boost an account's popularity, this study examines the effectiveness of seven sophisticated Machine Learning Algorithms, Random Forest Classifier, Decision Tree Classifier, XGBoost, LightGBM, Extra Trees Classifier, and SVM, and got 93% accuracy in Decision Tree Classifier. In order to solve overfitting issues and improve model resilience, the paper proposes Generative Adversarial Networks (GANs) and uses K-Fold Cross-Validation. Furthermore, design a Gan-ANN model that combines Batch Normalization and Artificial Neural Networks (ANN) with GAN-generated synthetic data is investigated. The enhanced dataset seeks to strengthen model performance and generalization when combined with cutting-edge modeling methods. This study aims to improve model scalability, predictive accuracy, and dependability across different machine learning paradigms. 2023 IEEE. -
Selection of cobot for human-robot collaboration for robotic assembly task with Best Worst MCDM techniques
Since the first industrial robot was produced at the beginning of the 1960s, robotic technology has completely changed the sector. Industrial robots are made for various tasks, including welding, painting, assembling, disassembling, picking and placing printed circuit boards, palletizing, packing and labeling, and product testing. Finding flexible solutions that allow production lines to be swiftly re-planned, adjusted, and structured for new or significantly modified product development remains a significant unresolved problem. Today's Industrial robots are still mostly pre-programmed to do certain jobs; they cannot recognize mistakes in their work or communicate well with both a complicated environment and a human worker. Full robot autonomy, including organic interaction, learning from and with humans, and safe and adaptable performance for difficult tasks in unstructured contexts, will remain a pipe dream for the foreseeable future. Humans and robots will work together in collaborative settings such as homes, offices, and factory setups to execute various object manipulation activities. So, it is necessary to study the collaborative robots (cobots) that will play a key role in human-robot collaborations. Multiple competing variables must be considered in a thorough selection process to assess how well industrial cobots will work on an industrial working floor. To select a collaborative robot for the human-robot collaborative application, a straightforward multi-criteria decision-making (MCDM) methodology is based on the best-worst method (BWM). The ranking derived using the BWM method is displayed. The outcomes demonstrated the value of MCDM techniques for cobot selection. 2023 IEEE. -
Flow and heat transport of nanomaterial with quadratic radiative heat flux and aggregation kinematics of nanoparticles
A numerical study of flow and heat transport of nanoliquid with aggregation kinematics of nanoparticles is carried out using the modified Buongiorno model (MBM). The MBM model is composed of random motion nanoparticles, heat diffusion of nanoparticles, and effective properties of nanoliquids. The effects of quadratic variation of density-temperature (quadratic convection), and the quadratic Rosseland thermal radiation are also studied. Inclined magnetism is also taken into account. The aggregation kinematics of nanoparticles is simulated using the modified Krieger-Dougherty model for dynamic viscosity and the modified Maxwell model for thermal conductivity. The main system of nonlinear partial differential equations is solved using the similarity technique and the finite difference method-based algorithm (FDM). The consequence of several key parameters on velocity, nanoparticle volume fraction, wall heat flux, and temperature are found in two cases, namely weak convective heating and strong convective heating. The study reveals that the suspension of the nanoparticles increases the thermal conductivity and, thus, improves the temperature and reduces the heat flux at the plate. The structures of the thermal and velocity surface layer are higher in the case of strong convective heating, while in the case of weak convective heating, the nanoparticle volume fraction layer is thicker. 2021 Elsevier Ltd -
Magnetohydrodynamic flow of Carreau liquid over a stretchable sheet with a variable thickness: The biomedical applications
Purpose: The magnetohydrodynamic (MHD) flow problems are important in the field of biomedical applications such as magnetic resonance imaging, inductive heat treatment of tumours, MHD-derived biomedical sensors, micropumps for drug delivery, MHD micromixers, magnetorelaxometry and actuators. Therefore, there is the impact of the magnetic field on the transport of non-Newtonian Carreau fluid in the presence of binary chemical reaction and activation energy over an extendable surface having a variable thickness. The significance of irregular heat source/sink and cross-diffusion effects is also explored. Design/methodology/approach: The leading governing equations are constructed by retaining the effects of binary chemical reaction and activation energy. Suitable similarity transformations are used to transform the governing partial differential equations into ordinary differential equations. Subsequent nonlinear two-point boundary value problem is treated numerically by using the shooting method based on RungeKuttaFehlberg. Graphical results are presented to analyze the behaviour of effective parameters involved in the problem. The numerical values of the mass transfer rate (Sherwood number) and heat transfer rate (Nusselt number) are also calculated. Furthermore, the slope of the linear regression line through the data points is determined in order to quantify the outcome. Findings: It is established that the external magnetic field restricts the flow strongly and serves as a potential control mechanism. It can be concluded that an applied magnetic field will play a major role in applications like micropumps, actuators and biomedical sensors. The heat transfer rate is enhanced due to Arrhenius activation energy mechanism. The boundary layer thickness is suppressed by strengthening the thickness of the sheet, resulting in higher values of Nusselt and Sherwood numbers. Originality/value: The effects of magnetic field, binary chemical reaction and activation energy on heat and mass transfer of non-Newtonian Carreau liquid over an extendable surface with variable thickness are investigated for the first time. 2020, Emerald Publishing Limited. -
Computational modeling of heat transfer in magneto-non-Newtonian material in a circular tube with viscous and Joule heating
Numerous industrial and engineering systems, like, heat exchangers, chemical action reactors, geothermic systems, geological setups, and many others, involve convective heat transfer through a porous medium. The diffusion rate, drag force, and mechanical phenomenon are dealt with in the DarcyForchheimer model, and hence this model is vital to study the fluid flow and heat transport analysis. Therefore, numerical simulation of the DarcyForchheimer dynamics of a Casson material in a circular tube subjected to the energy losses due to the viscous heating and Joule dissipation mechanisms is performed. The novelty of the present investigation is to scrutinize the convective heat transport characteristics in a circular tube saturated with DarcyForchheimer porous matrix by utilizing the non-Newtonian Casson fluid. The flow occurs due to the elongation of the surface of a tube with a uniform heat-based source/sink. The similarity solution of the nonlinear problem was obtained using dimensionless similarity variables. The effects of operating parameters related to the flow phenomena are analyzed. Further, the friction factor and Nusselt number are also analyzed in detail. The present flow model ensures no flow reversal and acts as a coolant of the heated cylindrical surface; the existence of the magnetic field, as well as an inertial coefficient,acts as the momentum-breaking forces, whereas Casson fluidity buildsit. The Joule heating phenomenon enhances the magnitude of temperature. The thermal field of the Casson fluid is higher at the surface of the circular pipe due to convective thermal conditions. 2021 Wiley Periodicals LLC. -
Effectiveness of exponential heat source, nanoparticle shape factor and Hall current on mixed convective flow of nanoliquids subject to rotating frame
Purpose: The study of novel exponential heat source (EHS) phenomena across a flowing fluid with the suspension of nanoparticles over a rotating plate in the presence of Hall current and chemical reaction has been an open question. Therefore, the purpose of this paper is to investigate the impact of EHS in the transport of nanofluid under the influence of strong magnetic dipole (Hall effect), chemical reaction and temperature-dependent heat source (THS) effects. The Khanafer-Vafai-Lightstone model is used for nanofluid and the thermophysical properties of nanofluid are calculated from mixture theory and phenomenological laws. The simulation of the flow is also carried out using the appropriate values of the empirical shape factor for five different particle shapes (i.e. sphere, hexahedron, tetrahedron, column and lamina). Design/methodology/approach: Using Laplace transform technique, exact solutions are presented for the governing nonlinear equations. Graphical illustrations are pointed out to represent the impact of involved parameters in a comprehensive way. The numeric data of the density, thermal conductivity, dynamic viscosity, specific heat, Prandtl number and Nusselt number for 20 different nanofluids are presented. Findings: It is established that the nanofluid enhances the heat transfer rate of the working fluids; the nanoparticles also cause an increase of viscous. The impact of EHS advances the heat transfer characteristics significantly than usual thermal-based heat source (THS). Originality/value: The effectiveness of EHS phenomena in the dynamics of nanofluid over a rotating plate with Hall current, chemical reaction and THS effects is first time investigated. 2019, Emerald Publishing Limited.