Challenges and Prospects of Artificial Intelligence in Aviation: Bibliometric Study

The article considers the possibility of replacing humans with artifi cial intelligence (AI) in industry. An overview of the current capabilities and achievements of AI in industry is presented. Examples of the successful use of AI and its advantages are given. The material describes positions that cannot be replaced by AI. It is emphasized that creative professions, managerial and strategic positions require creative thinking, emotional intelligence and the ability to make complex decisions, which is not yet available to AI. Although AI has signifi cant potential to improve production processes, a complete replacement of humans remains unlikely in the near future due to existing limitations and challenges. The article presents an analysis of the current capabilities and prospects of AI in industry, as well as recommendations for the state and business on the effective implementation of these technologies.

This study investigates the role and impact of artificial intelligence (AI) in the electric power industry through a thematic analysis of corporate communications. As AI technologies proliferate, industries—such as the electric power industry—are undergoing significant transformations. The research problem addressed in this study involves understanding how electric power companies perceive, adopt, and implement AI, as well as the implications of these developments. By employing a qualitative thematic analysis approach, we examined a corpus of corporate communications from innovation leaders, including annual reports and sustainability reports, in the electric power sector. The data spanned 2020 to 2023, capturing a crucial period of AI integration in the industry. Our analysis reveals several key findings. Firstly, there is a clear trend toward increased utilization of AI in various facets of the electric power sector, including grid management, predictive maintenance, and customer service. Companies actively invest in AI technologies to enhance operational efficiency, reduce costs, and improve service quality. Secondly, the corporate discourse has shifted significantly, with companies emphasizing AI’s role in sustainability efforts. Moreover, our analysis identified challenges and concerns associated with AI adoption in the electric power industry. In conclusion, the thematic analysis of corporate communications provides valuable insights into the evolving landscape of AI in the electric power industry. The findings underscore the transformative potential of AI technologies, highlighting opportunities for enhanced efficiency and sustainability. However, they also emphasize addressing challenges to ensure responsible and beneficial AI integration. This study contributes to the growing literature on AI in industries, offering practical implications for electric power companies, policymakers, and stakeholders navigating the AI-driven future of the sector.

A power plant converts energy from a non-electric form to an electric one. Depending on the energy conversion, power plants are classified as minerals, nuclear, solar, geothermal, hydroelectric, and so on. The main goal is to perform this transformation as best as possible. Criteria of safety, efficiency, reliability and affordability are taken into account as benchmarks. The station consists of several units that generate and work together to meet electricity needs. For a fossil fuel power plant, each unit consists of three main components: a boiler, a turbine and a generator. The complexity of the operation is due to the variability of the load and high efficiency required in a wide range of operations. The main difficulties for the management task then arise due to the strong link between process variables and process nonlinearity. The purpose of this article is to analyze and develop a proposal for the introduction of artificial intelligence in the power industry of Ukraine. To achieve this goal, we will offer the following tasks: - analyze existing systems for the use of artificial intelligence in industry; - develop proposals for the introduction of artificial intelligence in the electricity system of Ukraine. In this study, methods of statistical, factorial, historical, comparative, logical, economic-mathematical and systems analysis, the method of expert assessments were used, which allowed to formulate proposals for the introduction of artificial intelligence in the power industry. Artificial neural networks are the first step towards a fundamentally new system of information analysis. They are mathematical and computer models that simulate the work of biological neurons, ie a system of interacting processes, built on the principle of connecting nerve cells of the human brain. It should be noted that they differ from conventional machine algorithms in the ability to learn, memorize and reproduce images, determine patterns, memorize and analyze information and obtain results. Output signals that arrive at the next processor and continue to be converted. Thus, artificial neurons form networks and layers in which connections are created, restored, maintained and destroyed by special signals. With regard to artificial intelligence, there is no generally accepted definition of artificial intelligence, therefore, by artificial intelligence we mean a machine system capable of learning using objective knowledge and experience, to solve creative problems like the human brain and inventive tasks, not going through options, build strategies and apply abstract concepts. Digitalization and artificial intelligence are penetrating most sectors of the economy, including the electricity sector. The digitalization of energy requires the development and widespread use of end-to-end technologies, including industrial Internet, components of robotics, wireless communications, artificial intelligence and others. Thus, artificial intelligence technologies have prospects for development. Of course, significant targeted funding is needed to achieve significant results. The interaction of research institutes and universities with business is very important, where artificial intelligence technologies are also being developed. Attention of the authorities and society, their understanding of the importance of the tasks facing countries to achieve the goal of implementing artificial intelligence. With regard to electricity, the integration of artificial intelligence into the industry will help optimize and increase efficiency in all aspects of energy production, transmission and consumption. It should be noted that the development of electricity is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the power industry

The Foreign Risk Review Modernization Act, a bill introduced to Congress in 2017 that seeks to strengthen the Committee on Foreign Investment in the United States (CFIUS), has the potential to increase restrictions on Chinese companies investing in the U.S. artificial intelligence (AI) industry. Although the bill addresses legitimate national security concerns related to the military applications of AI, it also has the potential to negatively impact the U.S. AI industry and the U.S. economy as a whole. Currently, the combination of a Chinese government focused on strategic technology acquisitions, the open and diffuse nature of AI systems, the off-the-shelf nature of defense technology procurement, the integrated Chinese and U.S. AI industries, and the connection of Chinese tech companies to the Chinese Communist Party create a pipeline for technology transfer from U.S. companies to Chinese government entities. Given the vague nature of what constitutes a national security threat according to CFIUS, past high profile CFIUS action against Chinese companies investing in the U.S. technology sector and an emerging bipartisan consensus that CFIUS needs to be strengthened, there is a strong potential for new foreign investment restrictions. Restricting Chinese investment in the U.S. AI industry, however, may be counterproductive, because it may negatively impact U.S. economic competitiveness by reducing the venture capital pool for AI, potentially driving away top talent and causing AI research and development centers to relocate elsewhere. Instead of increasing restrictions, a smarter policy would be to continue utilizing CFIUS risk mitigation measures in a non-discriminatory manner, while increasing government funding for AI research and development and increasing visas for science and technology graduate students who are foreign nationals.

With the breakthroughs in artificial intelligence technology and the rapid development of intelligent manufacturing, industry and artificial intelligence (AI) are gradually being deeply integrated. On the basis of artificial intelligence, we systematically expounded the generation, definition, characteristics, classification, technical system, and current situation of industrial artificial intelligence (I-AI). Combining existing research and industrial projects, we propose a detailed framework and a reference model for I-AI in industry. The framework contains seven dimensions: objects of I-AI, domain of I-AI, application stages of I-AI, application requirements of I-AI, intelligent technology of I-AI, intelligent function of I-AI, and solutions of I-AI. Secondly, based on the application scenarios of artificial intelligence and industrial convergence, we propose a detailed overall planning for I-AI. Finally, five typical industrial fields are selected, and the I-AI solutions based on TFV (technology and function integration in industrial value chain) unit and 6W1H method are used for new application scenarios of the proposed framework. In addition, a detailed case of implementing for I-AI in port equipment industry is given. The research results of this paper have achieved good results in the related industrial field and can provide some reference for other industrial enterprises to plan, design, implement, and apply artificial intelligence.

Review on artificial intelligence techniques for improving representative air traffic management capability

The Artificial Intelligence (AI) industry has developed rapidly in recent years. AI has become an emerging industry in China. This paper first introduces the characteristics and connotations of AI and then compares the status of the AI industry in more developed cities at home and abroad with that in Tianjin. It finds that the AI industry in Tianjin faces some challenges, including insufficient innovation ability, few AI hardware and software products, lack of benchmarking enterprises, and poor integration with the manufacturing industry. To promote the development of the Tianjin AI industry, it is necessary to take some measures to guarantee the AI enterprise growth. Some measures about independent innovation, talents training, policy support, and AI hardware and software products research and development should be adopted.

Integrating advanced technologies such as artificial intelligence (AI) with traditional agricultural practices has changed how activities are developed in agriculture, with the aim of automating manual processes and improving the efficiency and quality of farming decisions. With the advent of deep learning models such as convolutional neural network (CNN) and You Only Look Once (YOLO), many studies have emerged given the need to develop solutions to problems and take advantage of all the potential that this technology has to offer. This systematic literature review aims to present an in-depth investigation of the application of AI in supporting the management of weeds, plant nutrition, water, pests, and diseases. This systematic review was conducted using the PRISMA methodology and guidelines. Data from different papers indicated that the main research interests comprise five groups: (a) type of agronomic problems; (b) type of sensor; (c) dataset treatment; (d) evaluation metrics and quantification; and (e) AI technique. The inclusion (I) and exclusion (E) criteria adopted in this study included: (I1) articles that obtained AI techniques for agricultural analysis; (I2) complete articles written in English; (I3) articles from specialized scientific journals; (E1) articles that did not describe the type of agrarian analysis used; (E2) articles that did not specify the AI technique used and that were incomplete or abstract; (E3) articles that did not present substantial experimental results. The articles were searched on the official pages of the main scientific bases: ACM, IEEE, ScienceDirect, MDPI, and Web of Science. The papers were categorized and grouped to show the main contributions of the literature to support agricultural decisions using AI. This study found that AI methods perform better in supporting weed detection, classification of plant diseases, and estimation of agricultural yield in crops when using images captured by Unmanned Aerial Vehicles (UAVs). Furthermore, CNN and YOLO, as well as their variations, present the best results for all groups presented. This review also points out the limitations and potential challenges when working with deep machine learning models, aiming to contribute to knowledge systematization and to benefit researchers and professionals regarding AI applications in mitigating agronomic problems.

The application of Artificial Intelligence (AI) is rapidly transforming various industries, and the pharmaceutical industry is no exception. AI is increasingly being used to automate, optimize and personalize various aspects of the pharmacy industry, from drug discovery to drug dispensing. In this context, this paper explores the potential of AI to revolutionize the pharmacy industry, by discussing the current and future applications of AI in the industry. We will examine how AI is being used in drug discovery, personalized medicine, drug safety and quality control, inventory management, and patient counselling. We will also discuss the challenges and limitations of AI in the pharmacy industry, such as data privacy, ethical concerns and regulatory barriers. The paper will argue that AI has the potential to revolutionize the pharmacy industry by enabling faster drug discovery, improving patient outcomes, reducing costs, and increasing the efficiency and accuracy of various pharmacy operations. The old pharmacy system relied on manual processes and human decision-making, while the new AI pharmacy system automates routine tasks, provides personalized treatment plans, and reduces costs while improving patient outcomes. However, it is important to ensure that AI is used ethically and responsibly, and that its impact on the workforce and society is carefully considered. The major benefit of integrating AI into specific applications within the pharmacy field is improved accuracy and efficiency in patient care. Overall, this paper will provide an insight into the future of the pharmacy industry, and the transformative potential of AI in this field.

The rapid advancement of cutting-edge technology has reshaped the global dynamics of the airline sector, elevating the significance of service quality, effectiveness, and protection. As a vital contributor to national economic growth and a public utility, aviation's expansion has directly impacted businesses in the service sector, leading to increased operational opportunities for hotels, restaurants, and travel agencies. This article addresses the challenges within Air Traffic Management (ATM), focusing on the aviation industry's primary concerns. With the continuous growth of aviation traffic, there is a pressing need to enhance security, productivity, and environmental sustainability. The article aims to stimulate business innovation and collaboration in advanced study areas such as dynamic airspace management (DAM), air traffic flow management (ATFM), and collaborative/non-collaborative surveillance. The proposed frameworks, based on Clean Sky and NextGen, incorporate 4D Trajectory Optimization techniques, advanced surveillance technologies, and data link connections to establish a foundation for ATM industry development. Additionally, the research explores adaptive Human-Machine Interface and Interaction (HMI2) forms to automate the assessment and negotiation of aircraft intentions, thereby improving the efficiency and security of ATM operations. The study also addresses specific requirements for cooperative and non-cooperative Detect-and-Avoid (DAA) systems for Remotely Piloted Aircraft Systems (RPAS) within the evolving CNS+A process, ensuring their safe and unrestricted access to all airspace types.

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.

China's Artificial Intelligence (AI) industry has developed rapidly in recent years, with the State Council of China releasing a roadmap in July 2017 with a goal of creating a domestic industry worth 1 trillion Yuan and becoming a global AI powerhouse by 2030. This study evaluates the listed companies in China's AI industry from the perspective of financial performance and analyzes the development status of China's AI industry from a macro perspective. This study selects the more objective and appropriate DEA analysis as the evaluation method according to the characteristics of the AI industry. On the basis of summarizing the development status of the AI industry and AI listed companies, an empirical analysis is carried out. In the data sample, 34 AI listed companies in China's Shanghai and Shenzhen stock markets were selected, and the DEA model with output-orientation model was used to analyze the standard data. The result shows that in the different stock board the efficiency presents different development trends and distribution status.

This article investigates the current and potential future applications of AI in the SmartLynx airlines. The airline industry is a complex system that requires efficient and effective management of various operations, including revenue management, flight operations, customer service, and baggage handling. The use of Artificial Intelligence (AI) has now emerged as a promising solution to address the challenges faced by many other airlines. The aim of this research is to analyze SmartLynx Airlines' operational efficiency in flight operations segments of the Latvian aviation industry and to develop recommendations for improving the current operational strategy for the company through the integration of AI-supported tools with conventional flight operation tools. Research tasks are: (1) to conceptualize theoretical aspects of the use of AI in the aviation industry; (2) to perform empirical research regarding current operational issues and study the use of AI in SmartLynx Airlines to improve these issues; (3) to work out recommendations. The current research employs the quantitative approach – a survey of SmartLynx employees of various departments.

The rapidly developed Artificial intelligence (AI) industry relies on data resources and is often controlled by some tech giants. Although the organizational monopoly in the AI industry is more efficient in text and image generation and transmission in a vast and generative digital market, it still has some problems. The monopoly will cause the issues, such as data and computing resources central control, unequal competition, user discrimination, and so on. This paper will conduct research on these monopoly problems, which not only make a pavement for further research on the academic field of AI but also provide more theoretical evidence for the solutions in regulating AI development in the future. The qualitative analysis will be used, and monopoly issues will be explored with the three aspects, including data monopoly, platform monopoly, and user monopoly, which will reflect the negative impacts of the monopoly in the competition of the AI industry and provide more available solutions for the issues. Especially in the new emerging AI industry, this research has practical significance for guiding an anti-monopoly competition market and maximizing the utilization and fair distribution of data and computing resources.

Knowledge mapping analysis of situational awareness and aviation: A bibliometric study