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Heat transfer innovation is essential in modern society because thermal management systems need effective heating and cooling processes. It is also an essential component in the vehicle industry and other types of transportation, in addition to automobile industry, aviation technology, the computer industry, and the manufacturing industry. By the inspiration of importance of magnetohydrodynamic hybrid nanofluid over a stretching cylinder with the influence of Williamson fluid and porous medium is examined in this current study. To convert the PDEs into ODEs, suitable self-similarity transformation is used. After applying transformations, for graphical purpose we have used the bvp5c technique. The impact of active parameters affecting the fluid's capacity to transfer significance is demonstrated in graphs and tables. In the result section we noticed on the velocity outlines decreased for increasing M parameter. The Cf and Nu increased for larger values of the M and curvature parameters. Additional properties of M and Rd parameter inputs result in improved temperature profiles. [ABSTRACT FROM AUTHOR]

The term "smart highway" refers to the way advanced technologies and smart systems are built into roads to make them safer, more efficient and more environment-friendly. Smart highways have sensors, cameras and other monitoring systems that collect and send information about traffic flow, weather and road hazards in real time. This information is subsequently processed and utilized to optimize traffic flow, enhance road safety and minimize congestion. Moreover, smart roads contain technologies such as charging stations for electric vehicles and intelligent lighting systems, which further improve their sustainability and efficiency. This paper provides a concise summary of the intelligent highway idea and illustrates the possible benefits of this unique approach to transportation infrastructure. [ABSTRACT FROM AUTHOR]

The largest disruptive component of traffic management system is road transport. Intelligent Transportation Systems (ITS) necessitate exact traffic forecast. However, transport departments continue to struggle to choose an ITS prediction method. A user must be able to appropriately utilize estimation model information. This paper covers contemporary estimation approaches, explains their fundamental concepts and evaluates different traffic estimation approaches for the benefit of decision makers. Each method is classified as Machine Learning (ML), Artificial Intelligence (AI), Deep Learning (DL) or hybrid algorithms. Many surveys are model or datadriven. This study is the first to examine traffic prediction using a variety of algorithmic and methodological approaches to key traffic parameters. Predictive evaluation of dependent variables is done and each algorithm's traffic characteristics are examined in chronological order. The application and effectiveness of each strategy are summarized, and the issues found by analysis are addressed by the review. Hybrid Computational Intelligence (CI)-ML and DL traffic prediction systems are found to be better than previous approaches. Open questions and future possibilities for DL and hybrid traffic prediction systems are also discussed. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is advancing rapidly, and this has improved the efficiency of many businesses and industries, including the transportation industry. AI in transportation might minimize travel, lower emissions and make roadways safer. Some examples of AI technologies used in the transportation sector include: Artificial Neural Networks (ANN), Genetic Algorithms (GA), Simulated Annealing (SA), Artificial Immune System (AIS), Ant Colony Optimizer (ACO) and Bee Colony Optimizer (BCO). The paper explores how AI is being used to address some of the most pressing issues in transportation today, including traffic control and safety. The limitations of AI transport are also discussed. [ABSTRACT FROM AUTHOR]

Computerized image processing techniques are extremely useful in agriculture. The technology can help detect plant diseases and improve cultivation quality. The study examines the advantages and disadvantages of previous research on the subject. To find the most effective image processing methods for diagnosing plant diseases, cutting-edge techniques are examined. To find plant pathogens, many computerized image processing methods are used. This review compares the results and many different approaches to develop algorithms such as Support Vector Machines (SVM) and Deep Learning Neural Networks (DLNN), which are important in the detection and classification of leaf diseases. [ABSTRACT FROM AUTHOR]

The paper studies the approaches to detecting, evaluating and categorizing plant diseases from digital images in the visible spectrum using appropriate processing techniques. Despite the fact that disease symptoms might appear anywhere on the plant, only approaches that looked at obvious symptoms in leaves and stems were examined. This was designed for various reasons: to keep the report short and because methods dealing with roots, seeds, and fruits have some unique characteristics that would necessitate a separate survey. The concepts chosen are organized into three categories based on their goal: detection, severity quantification and categorization. Each classification is further categorized based on the algorithm's primary technical solution. The paper also examines and contrasts the benefits and drawbacks of different prospective strategies. Image acquisition, image preprocessing, feature extraction and neural network-based categorization are a few of the techniques included. Researchers working on both vegetable pathology and pattern recognition can benefit from this study, which provides a detailed and accessible summary of this vital field of research. [ABSTRACT FROM AUTHOR]

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making. [ABSTRACT FROM AUTHOR]

In August of 2022, the United States Department of Treasury sanctioned the virtual currency mixer Tornado Cash, an open-source and fully decentralised piece of software running on the Ethereum blockchain, subsequently leading to the arrest of one of its developers in the Netherlands. Not only was this the first time the Office of Foreign Assets Control (OFAC) extended its authority to sanction a foreign 'person' to software, but the decentralised nature of the software and global usage highlight the challenge of establishing jurisdiction over decentralised software and its global user base. The government claims jurisdiction over citizens, residents, and any assets that pass through the country's territory. As a global financial center with most large tech companies, this often facilitates the establishment of jurisdiction over global conduct that passes through US servers. However, decentralised programs on blockchains with nodes located around the world challenge this traditional approach as either nearly all countries can claim jurisdiction over users, subjecting users to criminal laws in countries with which they have no true interaction, or they limit jurisdiction, thereby risking abuse by bad actors. This article takes a comparative approach to examine the challenges to establishing criminal jurisdiction on cryptocurrency-related crimes. [ABSTRACT FROM AUTHOR]

The integration of deep learning (DL) techniques with geographical information system (GIS) offers a promising avenue for gaining novel insights into environmental phenomena by using the capabilities of spatial, temporal, and spectral resolutions, as well as data integration. The integration of these two technologies can result in the development of a highly efficient system for assessing environmental conditions by analyzing the interplay between texture, size, pattern, and process. This viewpoint has gained appeal across various academic disciplines. GIS heavily relies on processors, especially for tasks such as 3D computations, map rendering, and route calculation. In contrast, DL has the capability to efficiently analyze vast quantities of data. DL has garnered significant attention in recent times due to its potential for delivering a wide range of promising outcomes. Moreover, there is clear evidence of the increasing utilization of deep learning techniques across various fields, including GIS. The objective of this study is to provide an overview of the application of DL techniques in the field of GIS. This paper presents a concise review of the fundamental DL ideas that are pertinent to GIS, with a focus on the most current research findings. The present study investigates the various uses and technology of remote sensing in diverse domains, including mapping, hydrological modeling, disaster management, and transportation route planning. This study offers insights into contemporary framework approaches and proposes avenues for further research. [ABSTRACT FROM AUTHOR]

Bitcoin and Ethereum blockchains are exploding with data. While blockchain is becoming increasingly popular, the sheer volume of data obscures important concerns about safety, and privacy. Data analysis can aid in identifying problems and recommending possible solutions, and is also essential to make blockchain useful in a wide range of industries. The paper reviews the current state of research in four key areas: security, privacy, performance and price prediction, and compiles relevant literature. Finally, it predicts future trends and challenges in this field, which will serve as a roadmap for researchers. Blockchain data analysis will increasingly rely on Machine Learning. [ABSTRACT FROM AUTHOR]