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The global transport sector, a significant contributor to energy consumption and greenhouse gas (GHG) emissions, requires innovative solutions to meet sustainability goals. Artificial intelligence (AI) has emerged as a transformative technology, offering opportunities to enhance energy efficiency and reduce GHG emissions in transport systems. This study provides a comprehensive review of AI's role in optimizing vehicle energy management, traffic flow, and alternative fuel technologies, such as hydrogen fuel cells and biofuels. It explores AI's potential to drive advancements in electric and autonomous vehicles, shared mobility, and smart transportation systems. The economic analysis demonstrates the viability of AI-enhanced transport, considering Total Cost of Ownership (TCO) and cost-benefit outcomes. However, challenges such as data quality, computational demands, system integration, and ethical concerns must be addressed to fully harness AI's potential. The study also highlights the policy implications of AI adoption, underscoring the need for supportive regulatory frameworks and energy policies that promote innovation while ensuring safety and fairness. [ABSTRACT FROM AUTHOR]

This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities. [ABSTRACT FROM AUTHOR]

The maritime industry, central to global trade, faces critical challenges related to energy efficiency and environmental sustainability due to significant energy loss from waste heat in marine engines. This review investigates the potential of waste heat recovery (WHR) technologies to enhance operational efficiency and reduce emissions in marine systems. By analyzing major WHR methods, such as heat exchangers, Organic Rankine Cycle (ORC) systems, thermoelectric generators, and combined heat and power (CHP) systems, this work highlights the specific advantages, limitations, and practical considerations of each approach. Unique to this review is an examination of WHR performance in confined marine spaces and compatibility with existing ship components, providing essential insights for practical implementation. Findings emphasize WHR as a viable strategy to reduce fuel consumption and meet environmental regulations, contributing to a more sustainable maritime industry. [ABSTRACT FROM AUTHOR]

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä's Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI's potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) represent a transformative advancement in transportation technology, promising to enhance travel efficiency, reduce traffic accidents, and revolutionize our road systems. Central to the operation of AVs is the integration of artificial intelligence (AI), which enables these vehicles to navigate complex environments with minimal human intervention. This review critically examines the potential dangers associated with the increasing reliance on AI in AV navigation. It explores the current state of AI technologies, highlighting key techniques such as machine learning and neural networks, and identifies significant challenges including technical limitations, safety risks, and ethical and legal concerns. Real-world incidents, such as Uber's fatal accident and Tesla's crash, underscore the potential risks and the need for robust safety measures. Future threats, such as sophisticated cyber-attacks, are also considered. The review emphasizes the importance of improving AI systems, implementing comprehensive regulatory frameworks, and enhancing public awareness to mitigate these risks. By addressing these challenges, we can pave the way for the safe and reliable deployment of autonomous vehicles, ensuring their benefits can be fully realized. [ABSTRACT FROM AUTHOR]

The maritime industry, responsible for moving approximately 90% of the world's goods, significantly contributes to environmental pollution, accounting for around 2.5% of global greenhouse gas emissions. This review explores the integration of artificial intelligence (AI) in promoting sustainability within the maritime sector, focusing on shipping and port operations. By addressing emissions, optimizing energy use, and enhancing operational efficiency, AI offers transformative potential for reducing the industry's environmental impact. This review highlights the application of AI in fuel optimization, predictive maintenance, route planning, and smart energy management, alongside its role in autonomous shipping and logistics management. Case studies from Maersk Line and the Port of Rotterdam illustrate successful AI implementations, demonstrating significant improvements in fuel efficiency, emission reduction, and environmental monitoring. Despite challenges such as high implementation costs, data privacy concerns, and regulatory complexities, the prospects for AI in the maritime industry are promising. Continued advancements in AI technologies, supported by collaborative efforts and public–private partnerships, can drive substantial progress towards a more sustainable and efficient maritime industry. [ABSTRACT FROM AUTHOR]

The rapid development and deployment of autonomous vehicles (AVs) present unprecedented opportunities and challenges in the transportation sector. While AVs promise enhanced safety, efficiency, and convenience, they also introduce significant cybersecurity vulnerabilities due to their reliance on advanced electronics, connectivity, and artificial intelligence (AI). This review examines the current state of cybersecurity in autonomous vehicles, identifying major threats such as remote hacking, sensor manipulation, data breaches, and denial of service (DoS) attacks. It also explores existing countermeasures including intrusion detection systems (IDSs), encryption, over-the-air (OTA) updates, and authentication protocols. Despite these efforts, numerous challenges remain, including the complexity of AV systems, lack of standardization, latency issues, and resource constraints. This review concludes by highlighting future directions in cybersecurity research and development, emphasizing the potential of AI and machine learning, blockchain technology, industry collaboration, and legislative measures to enhance the security of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Urban centers, replete with diverse amenities and opportunities, simultaneously grapple with the challenges brought on by rapid urbanization, notably in the realms of transport and logistics. A pivotal move towards energy- efficient and sustainable systems is essential to mitigate these challenges. In this landscape, machine learning (ML), and particularly recurrent neural networks (RNNs), emerge as powerful tools for effectively addressing these urban complexities. This comprehensive review zeroes in on the deployment of RNNs within sustainable urban transportation and logistics, highlighting their adeptness in processing sequential data, a critical component in various forecasting and optimization tasks. We commence with a foundational understanding of RNNs, segueing into their successful applications in urban transport and logistics. This review also critically examines the constraints of current methodologies and potential avenues for enhancement. We scrutinize the application of RNNs across several areas, encompassing the energy shift in both passenger and freight transport, logistics management, integration of low- and zero-emission vehicles, and the energy dynamics of transport and logistics. Additionally, the role of RNNs in traffic and infrastructure planning is explored, particularly in forecasting traffic flow, congestion patterns, and optimizing energy usage. The crux of this review is to amalgamate and present the existing knowledge on the instrumental role of RNNs in facilitating the transition to energy-efficient urban transportation and logistics. Our goal is to highlight effective strategies, pinpoint challenges, and map out avenues for future research in this domain. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics. [ABSTRACT FROM AUTHOR]