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Context and background The incorporation of artificial intelligence (AI) into businesses has become increasingly widespread in recent years, particularly in developed countries, fundamentally reshaping the landscape of entrepreneurship and innovation. AI presents numerous opportunities for small businesses, including those involved in cross-border trade. It has the potential to transform various aspects of business operations, such as customer interactions, marketing research, understanding consumer behavior, optimizing routes, and forecasting sales and marketing trends. Additionally, AI can contribute to the formalization of business processes, and assist these businesses in adapting more effectively to the ever-changing dynamics of the market. However, the successful implementation of AI necessitates a meticulous assessment of AI applications to ensure alignment with the objectives and resources of each individual small business. Goal and Objectives: The primary focus of this paper is to explore the potential of Artificial Intelligence in enhancing Africa's cross-border trade. The paper will address the following research questions: What are the effective applications of artificial intelligence in cross-border informal trade in Africa, and what are the limitations? What does the future hold for African countries in terms of utilizing artificial intelligence for cross-border trade? How can African informal cross-border traders and their stakeholders benefit from incorporating artificial intelligence in their activities? Methodology: The research is purely empirical research based on observation and measurement of phenomena, as directly experienced by the researcher. Results: AI can be effectively applied by Informal Cross-Border Traders (ICBT) to enhance their businesses and enhance competitiveness. There are several AI applications accessible to ICBT within their operational context. Although the adoption and utilization of AI in Africa are still in their infancy, there is considerable promise for the future. Africans must address the challenges hindering the adoption and utilization of AI, as technology is advancing rapidly, and opportunities await those who embrace it.

This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their ability to more efficiently identify and reduce contaminants compared to traditional techniques. This paper examines a collection of case studies in which artificial intelligence (AI)-powered devices have been used, showcasing significant developments in the evaluation of water quality and improved levels of treatment efficiency. The present study additionally analyzes the various problems and potential future developments of Artificial Intelligence (AI) and Machine Learning (ML) within this particular domain. These challenges cover issues of scalability, data security, as well as the importance for interdisciplinary collaboration. This paper gives a comprehensive analysis of the impact of AI and ML technologies on water quality management, demonstrating their potential to transform current practices towards greater sustainability and efficiency.

This research paper provides a comprehensive review of the current state-of-the-art and prospects of smart materials for sensing and actuation applications. Smart materials, with their unique ability to respond to external stimuli, have been the subject of extensive research and development in recent years. The paper begins by discussing the various types of smart materials, including piezoelectric materials, shape memory alloys, and electroactive polymers, and their properties and applications in sensing and actuation. the paper covers the advancements in the design and fabrication of smart materials and devices, including the use of nanotechnology and 3D printing. The potential for integration with emerging technologies, such as artificial intelligence and the internet of things, is also explore. The paper provides a comprehensive and in-depth analysis of the state-of-the-art and prospects of smart materials for sensing and actuation applications. The research presented has significant implications for the development of nextgeneration smart materials and devices, with the potential to revolutionize various industries and improve our quality of life.

This research paper explores the opportunities and challenges associated with the use of machine learning and artificial intelligence in advanced materials processing. With the exponential growth of data, advanced analytical techniques and powerful computational tools, machine learning and artificial intelligence can be leveraged to develop novel materials with tailored properties, enhance process optimization, and improve manufacturing efficiencies. However, the integration of these technologies into materials processing systems is not without challenges, including data acquisition and pre-processing, algorithm selection and optimization, and the interpretation of results. This paper provides an overview of the state-of-the-art in machine learning and artificial intelligence for advanced materials processing, highlighting case studies and examples of successful applications, and identifying potential future research directions. The goal of this research is to provide insights and recommendations to accelerate the adoption of these technologies and their impact on the development of advanced materials.

This research paper investigates the future of advanced materials processing, with a focus on the innovations and challenges that lie ahead. The study begins by exploring the current state of advanced materials processing and the latest trends in the field, including the use of advanced manufacturing technologies, such as additive manufacturing, to create complex geometries and novel materials. The paper then examines the challenges facing the field, including the need to develop new processing techniques that can handle a wider range of materials and produce materials with specific properties. The study also analyses the potential impact of emerging technologies, such as artificial intelligence and machine learning, on the future of materials processing. Finally, the paper concludes with a discussion of the key innovations and trends that are likely to shape the future of materials processing, including the use of sustainable materials, the development of new nanomaterials, and the integration of advanced sensors and data analytics into the manufacturing process. Overall, this research paper provides a comprehensive analysis of the future of advanced materials processing and highlights the critical role that innovation will play in shaping the field in the coming years.

Today's world is going through many changes, especially due to globalization and technological advances. Digitization has progressed a lot, especially in the last few years which have seen economic and health issues. This has strengthened the case for using digital alternatives to improve performance at a reduced price. One of the sectors most affected by the digital revolution is banking. Given the ease of life with AI technology, these developments rank among the most important in the world in terms of technological, digital, and computing advances. This paper presents a literature review of the various theories discussed on the topic. The objective is to provide a research avenue on the subject of our study by proposing a theoretical conceptual model bringing together different factors influencing the appropriation of artificial intelligence technologies in the banking sector.

This article is both an editorial introduction to the guest-edited special issue of JoSTrans on Translation Automation and Sustainability, and a position paper in which we propose a model for evaluating the sustainable use of automation technology in translation and beyond. As grounding notions, the article reviews definitions of automation and considers the urgency of sustainability. Thereafter we propose an adaptation of Elkington’s (1997) triple bottom line, giving equal weight to evaluation based on people, planet, and performance, describing each of these elements in turn. Finally, we introduce the articles from this special issue, in which authors describe various aspects of automation technology in translation with a focus on sustainability.

The landscape of power systems is undergoing a transformative shift with the burgeoning inclusion of Distributed Energy Resources (DERs), which, while beneficial in enhancing the sustainability of electricity supply, introduces complexity in grid management. This paper presents a comprehensive framework leveraging Artificial Intelligence (AI) to orchestrate DER operations, thus achieving optimized load balancing and grid stability. A multi-agent system that utilizes machine learning algorithms is proposed, capable of predictive analytics and real-time decision-making. The architecture is underpinned by a robust data layer that assimilates inputs from a myriad of sensors and smart meters, facilitating the dynamic management of DERs. Through the simulation of various scenarios, the system demonstrates significant improvements in load distribution, peak shaving, and voltage regulation. The framework also showcases resilience against fluctuations and anomalies, attributing to the self-learning capability of AI models that continuously refine control strategies. The adaptability of the system is evaluated in the context of grid demand-response initiatives and the integration of intermittent renewable energy sources. Overall, the results indicate a substantial advancement in the operational efficiency of power grids, highlighting the synergy between AI and energy resource management.

Nowadays home automation is based on the automation process or else the Internet of Things. This paper presents combination of artificial intelligence and the Internet of things-based automation system. Here we develop some fundamental algorithms of artificial intelligence and then integrate them with electronic sensors. The main part of the Internet of Things is a Controller, we are using an Arduino based controller module here. All the sensors used in home automation are in turn connect to this Arduino controller. These Sensors will read the data based on the specifications, then that data will send to the microcontroller to analyze and process and later the Artificial Intelligence starts working as per the data received. Our home appliances start acting depending on the sensor's values obtained. For suppose, if the climate is too cool it will automatically switch OFF the FAN (or) AC. Using AI based face recognition system will operate the doors to close and open, also it can give alerts. By using a water level sensor, we will come to know the water level of the tank, with the help of this ta the motor pump will be ON or OFF according to the values of the water level sensor. And using an MQ-08 sensor the smoke value will be measured in the house, if the smoke value will be HIGH, it may give an alert.

This paper provides a thorough analysis of the development of artificial intelligence (AI) in the context of Industry 4.0 and the soon-to-be Industry 5.0. Important conclusions come from the data, such as the startling 900% increase in AI applications between 2010 and 2018, which corresponds to a 60% rise in the proportion of industrial enterprises using AI at that time. Moreover, our analysis shows that Industry 4.0's AI integration has resulted in a notable 200% cost reduction and a cumulative 400% boost in production efficiency. Our study delves into the rapid deployment of critical technologies like 5G connectivity and quantum computing within the framework of Industry 5.0. The usage of 5G connectivity has increased by 200% in only two years, while quantum computing has seen a staggering 1000% growth in acceptance over the course of eight years. These findings demonstrate the fast technological transition occurring in Industry 5.0. Furthermore, by 2033, the research predicts a startling 400% increase in human-machine cooperation and an anticipated 133% decrease in mistake rates. The research highlights how Industry 4.0's deep consequences of AI development and Industry 5.0's revolutionary possibilities will impact manufacturing in the future.