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This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR). [ABSTRACT FROM AUTHOR]

The fusion of quantum computing and artificial intelligence (AI) heralds a transformative era for Industry 4.0, offering unprecedented capabilities and challenges. This paper delves into the intricacies of quantum AI, its potential impact on Industry 4.0, and the necessary change management and innovation strategies for seamless integration. Drawing from theoretical insights and real-world case studies, we explore the current landscape of quantum AI, its foreseeable influence, and the implications for organizational strategy. We further expound on traditional change management tactics, emphasizing the importance of continuous learning, ecosystem collaborations, and proactive approaches. By examining successful and failed quantum AI implementations, lessons are derived to guide future endeavors. Conclusively, the paper underscores the imperative of being proactive in embracing quantum AI innovations, advocating for strategic foresight, interdisciplinary collaboration, and robust risk management. Through a comprehensive exploration, this paper aims to equip stakeholders with the knowledge and strategies to navigate the complexities of quantum AI in Industry 4.0, emphasizing its transformative potential and the necessity for preparedness and adaptability. [ABSTRACT FROM AUTHOR]

This document is a review of advanced digital technologies in modeling and control of technological processes. It highlights the importance of international collaboration in scientific research and the sharing of knowledge and resources. The special issue includes studies in various fields such as mining, mineral processing, vision and image processing systems. The papers cover topics such as power quality data collection, energy generation estimation, predictive models for fault detection, virtual soft sensors, autonomous transportation control systems, machine learning for the oil and gas industry, optical inspection systems, image encryption, critical velocity prediction, computational fluid dynamics, and steel defect recognition. The contributions provide valuable insights and potential solutions for improving efficiency and sustainability in different industries. [Extracted from the article]

Nanomanufacturing and digital manufacturing (DM) are defining the forefront of the fourth industrial revolution—Industry 4.0—as enabling technologies for the processing of materials spanning several length scales. This review delineates the evolution of nanomaterials and nanomanufacturing in the digital age for applications in medicine, robotics, sensory technology, semiconductors, and consumer electronics. The incorporation of artificial intelligence (AI) tools to explore nanomaterial synthesis, optimize nanomanufacturing processes, and aid high-fidelity nanoscale characterization is discussed. This paper elaborates on different machine-learning and deep-learning algorithms for analyzing nanoscale images, designing nanomaterials, and nano quality assurance. The challenges associated with the application of machine- and deep-learning models to achieve robust and accurate predictions are outlined. The prospects of incorporating sophisticated AI algorithms such as reinforced learning, explainable artificial intelligence (XAI), big data analytics for material synthesis, manufacturing process innovation, and nanosystem integration are discussed. [ABSTRACT FROM AUTHOR]

The next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a quantum-safe "Software-Defined-Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined Networking" (SDN) control plane controls many "Software-Defined-Deterministic Wide-Area Networks" (SDD-WANs), realized with FPGAs. The SDN control plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI)-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks, significantly reduces buffer-sizes, and supports ultra-reliable-low-latency communications in the forwarding-plane. The forwarding-plane can: (i) Encrypt/Authenticate M2M flows using quantum-safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "guaranteed intrusion detection systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets; (iii) Provide guaranteed immunity to external cyber-attacks, and exceptionally strong immunity to internal cyber-attacks; (iv) Save USD 100s of billions annually by exploiting FPGAs; and (v) Enable hybrid Classical-Quantum networks, by integrating a "quantum key distribution" (QKD) network with a classical forwarding plane with exceptionally strong cyber-security, determined by the computational hardness of cracking Symmetric Key Cryptography. Extensive experimental results for an SDD-WAN over the European Union are reported. [ABSTRACT FROM AUTHOR]

Today's factories are considered as smart ecosystems with humans, machines and devices interacting with each other for efficient manufacturing of products. Industry 4.0 is a suite of enabler technologies for such smart ecosystems that allow transformation of industrial processes. When implemented, Industry 4.0 technologies have a huge impact on efficiency, productivity and profitability of businesses. The adoption and implementation of Industry 4.0, however, require to overcome a number of practical challenges, in most cases, due to the lack of modernisation and automation in place with traditional manufacturers. This paper presents a first of its kind case study for moving a traditional food manufacturer, still using the machinery more than one hundred years old, a common occurrence for small- and medium-sized businesses, to adopt the Industry 4.0 technologies. The paper reports the challenges we have encountered during the transformation process and in the development stage. The paper also presents a smart production control system that we have developed by utilising AI, machine learning, Internet of things, big data analytics, cyber-physical systems and cloud computing technologies. The system provides novel data collection, information extraction and intelligent monitoring services, enabling improved efficiency and consistency as well as reduced operational cost. The platform has been developed in real-world settings offered by an Innovate UK-funded project and has been integrated into the company's existing production facilities. In this way, the company has not been required to replace old machinery outright, but rather adapted the existing machinery to an entirely new way of operating. The proposed approach and the lessons outlined can benefit similar food manufacturing industries and other SME industries. [ABSTRACT FROM AUTHOR]

Industry 4.0 is a term used to refer to the fourth industrial revolution, characterized by the introduction of new technologies, such as the Internet of Things, Big Data, and artificial intelligence (AI). As the number of connected devices in industrial settings grows, energy optimization of such sensors becomes increasingly essential. This paper proposes an energy optimization framework for sensor nodes in Industry 4.0. The framework is based on energy efficiency, energy conservation, and energy harvesting principles. It is designed to optimize the energy consumption of sensor nodes while maintaining their performance. The framework includes dynamic power management, scheduling, and harvesting techniques to reduce energy consumption while maintaining performance. In addition, the framework provides a comprehensive approach to energy optimization, including advanced analytics and AI to predict energy consumption and optimize energy use. The proposed model reached 96.93% sensitivity, 91.36% false discovery rate, 11.28% false omission rate, 90.12% prevalence threshold, and 91.24% threat score. The proposed framework is expected to improve the performance of sensor nodes in Industry 4.0, enabling increased efficiency and cost savings. [ABSTRACT FROM AUTHOR]

By reviewing the current state of the art, this paper opens a Special Section titled "The Internet of Things and AI-driven optimization in the Industry 4.0 paradigm". The topics of this section are part of the broader issues of integration of IoT devices, cloud computing, big data analytics, and artificial intelligence to optimize industrial processes and increase efficiency. It also focuses on how to use modern methods (i.e. computerization, robotization, automation, machine learning, new business models, etc.) to integrate the entire manufacturing industry around current and future economic and social goals. The article presents the state of knowledge on the use of the Internet of Things and optimization based on artificial intelligence within the Industry 4.0 paradigm. The authors review the previous and current state of knowledge in this field and describe known opportunities, limitations, directions for further research, and industrial applications of the most promising ideas and technologies, considering technological, economic, and social opportunities. [ABSTRACT FROM AUTHOR]

In the third industrial revolution (IR), mechatronics engineering has emerged, which is the merger of mechanical and electronics engineering along with information technology to an extent. In the current industrial revolution (fourth), the amalgamation of mechanical with Artificial intelligence (AI) has been used to simulate human behaviour in machines. In the earlier decades, the application of AI in mechanical engineering has expedited and the increased volume of related research papers is a proof of this fact. The broad domain of mechanical engineering has a lot of potential for machine learning applications in order to detect errors, minimise rejection, improve product quality, optimise a system, and so on, as it has not been intensely explored although. This article’s goal is to give a structured evaluation of works that employ vision systems and to attract researchers to analyse its potential integration with machine learning (ML) techniques from the industry 4.0 viewpoint. Current trends and research gaps in the area of manufacturing in collaboration with ML are also discussed at appropriate places. A general procedure to carry out research in the field of mechanical engineering is also presented. The results of various studies highlighted in this paper confirm that the implementation of AI in ME has provided an optimum solution to many mechanical problems that improved product quality and minimised the associated cost. Finally, the authors have led a direction to empower researchers in order to explore various areas in mechanical engineering from Industry 4.0 and AI perspective and enable possible topics for future research. The article concludes by providing challenges that may be faced in the coming future about the use of machine learning in mechanical engineering problems. [ABSTRACT FROM AUTHOR]

Digital twin is the digital representation of an entity, and it drives Industry 4.0. This paper presents a bibliometric analysis of digital twin in the supply chain to help researchers, industry practitioners, and academics to understand the trend, development, and focus of the areas of digital twin in the supply chain. This paper found several key clusters of research, including the designing of a digital twin model, integration of a digital twin model, application of digital twin in quality control, and digital twin in digitalization. In the embryonic stage of research, digital twin was tested in the production line with limited optimization. In the development stage, the importance of digital twin in Industry 4.0 was observed, as big data, machine learning, Industrial Internet of Things, blockchain, edge computing, and cloud-based systems complemented digital twin models. Digital twin was applied to improve sustainability in manufacturing and production logistics. In the current prosperity stage with high annual publications, the recent trends of this topic focus on the integration of deep learning, data models, and artificial intelligence for digitalization. This bibliometric analysis also found that the COVID-19 pandemic drove the start of the prosperity stage of digital twin research in the supply chain. Researchers in this field are slowly moving towards applying digital twin for human-centric systems and mass personalization to prepare to transit to Industry 5.0. [ABSTRACT FROM AUTHOR]

The development of information technology has had a significant impact on various areas of human activity, including medicine. It has led to the emergence of the phenomenon of Industry 4.0, which, in turn, led to the development of the concept of Medicine 4.0. Medicine 4.0, or smart medicine, can be considered as a structural association of such areas as AI-based medicine, telemedicine, and precision medicine. Each of these areas has its own characteristic data, along with the specifics of their processing and analysis. Nevertheless, at present, all these types of data must be processed simultaneously, in order to provide the most complete picture of the health of each individual patient. In this paper, after a brief analysis of the topic of medical data, a new classification method is proposed that allows the processing of the maximum number of data types. The specificity of this method is its use of a fuzzy classifier. The effectiveness of this method is confirmed by an analysis of the results from the classification of various types of data for medical applications and health problems. In this paper, as an illustration of the proposed method, a fuzzy decision tree has been used as the fuzzy classifier. The accuracy of the classification in terms of the proposed method, based on a fuzzy classifier, gives the best performance in comparison with crisp classifiers. [ABSTRACT FROM AUTHOR]

The successful implementation of Human–Robot Collaboration (HRC) has become a prominent feature of smart manufacturing environments. Key industrial requirements, such as flexibility, efficiency, collaboration, consistency, and sustainability, present pressing HRC needs in the manufacturing sector. This paper provides a systemic review and an in-depth discussion of the key technologies currently being employed in smart manufacturing with HRC systems. The work presented here focuses on the design of HRC systems, with particular attention given to the various levels of Human–Robot Interaction (HRI) observed in the industry. The paper also examines the key technologies being implemented in smart manufacturing, including Artificial Intelligence (AI), Collaborative Robots (Cobots), Augmented Reality (AR), and Digital Twin (DT), and discusses their applications in HRC systems. The benefits and practical instances of deploying these technologies are showcased, emphasizing the substantial prospects for growth and improvement in sectors such as automotive and food. However, the paper also addresses the limitations of HRC utilization and implementation and provides some insights into how the design of these systems should be approached in future work and research. Overall, this paper provides new insights into the current state of HRC in smart manufacturing and serves as a useful resource for those interested in the ongoing development of HRC systems in the industry. [ABSTRACT FROM AUTHOR]

This research paper is devoted to the issue of changes in healthcare sector under the influence of Industry 4.0. The main objective of the research is to estimate the impact of Industry 4.0 concept elements in healthcare sector on national economy and social environment within the prospects and synergies for bigger use of Industry 4.0 technologies in healthcare sector. The paper draws attention to identifying the synergies brought about by Industry 4.0 in the healthcare sector. To reach this goal, methods such as analysis, comparison, synthesis, and logical deduction are to be used. The results have shown that healthcare sector is a sector that is most in need of the technological convergence regarding the factors of digitization, automation, robotics that have brought the possibility of greater and more transparent data connectivity. [ABSTRACT FROM AUTHOR]

In today's industry, the fourth industrial revolution is underway, characterized by the integration of advanced technologies such as artificial intelligence, the Internet of Things, and big data. One of the key pillars of this revolution is the technology of digital twin, which is rapidly gaining importance in various industries. However, the concept of digital twins is often misunderstood or misused as a buzzword, leading to confusion in its definition and applications. This observation inspired the authors of this paper to create their own demonstration applications that allow the control of both the real and virtual systems through automatic two-way communication and mutual influence in context of digital twins. The paper aims to demonstrate the use of digital twin technology aimed at discrete manufacturing events in two case studies. In order to create the digital twins for these case studies, the authors used technologies as Unity, Game4Automation, Siemens TIA portal, and Fishertechnik models. The first case study involves the creation of a digital twin for a production line model, while the second case study involves the virtual extension of a warehouse stacker using a digital twin. These case studies will form the basis for the creation of pilot courses for Industry 4.0 education and can be further modified for the development of Industry 4.0 educational materials and technical practice. In conclusion, selected technologies are affordable, which makes the presented methodologies and educational studies accessible to a wide range of researchers and solution developers tackling the issue of digital twins, with a focus on discrete manufacturing events. [ABSTRACT FROM AUTHOR]

Meat, as protein-rich food, provides essential nutrition to humans. Meat consumption is expected to increase by 15% over the next decade, requiring the meat industry to boost its capacity. However, the meat cutting industry's harsh working environment and long-term heavy workload result in a labor shortage. Low-margin and low capacity require innovative processing methods. Hence, some novel automation methods are urgently demanded to meet these challenges. Accompanied by the development of Industry 4.0, robotization and intelligent systems are progressively applied in the meat cutting industry, which can provide a flexible, adaptive, and sustainable processing approach. The paper selected 50 papers using the systematic literature review method. This paper provides an overview of the current research and commercial applications of cutting robotization from livestock, poultry, and fish in the meat industry. Additionally, intelligent sensing technologies, advanced cutting techniques, and a novel manufacturing concept called the meat factory cell, are emphasized to promote efficiency, scalability, and modularization. Finally, we discuss the potential applications of digital technologies in the meat cutting industry and the focal points of future research aiming to promote cutting robotization. The meat cutting process can be highly robotized by integrating dexterous cutting robots, advanced sensing techniques, and digital systems, driving the transformation from manual labor to robotic, efficient, and intelligent manufacturing. Consequently, robotization and intelligent digital systems can provide a brand-new manufacturing method to the meat cutting industry and advance the Meat factory 4.0. [Display omitted] • Advanced perception techniques assist robots to perform adaptive cutting. • Intelligent robotic systems in the meat cutting industry are reviewed. • Commercial applications regarding meat cutting robotization are presented. • Advanced cutting techniques are highlighted to promote cutting performance. • Potential applications of advanced digital systems are provided for sustainability. [ABSTRACT FROM AUTHOR]

A cyber-physical system (CPS) is an interconnected physical system that can be monitored, controlled, and operated remotely. A systems-based analysis of CPS is critical for enhancing operational safety and security. Such analysis is particularly important in light of the evolving Fourth Industrial Revolution (Industry 4.0) which is largely driven by CPS, Internet of Things (IoT), algorithmic systems, automation, and smart manufacturing. As a result of industries' and societies' growing dependence on CPSs, their safe and secure applications need to be scrutinized to avoid any threats or harm to processes and human lives. In order to achieve this target, the current work analyzes the literature to investigate challenges and opportunities present in the CPSs. The analysis attempts to answer the following Research Questions (RQs): (RQ1) What are the prominent trends in the field of safety and security of CPSs? (RQ2) What are the opportunities in Industry 4.0 technologies related to safety? (RQ3) What characteristics of CPSs are likely to cause safety and security issues? (RQ4) What are the emerging safety and security issues introduced by CPSs? (RQ5) How were these issues addressed in the literature? CPSs can address some traditional safety issues due to characteristics like high efficiency and control. However, a CPS is challenged by heterogeneity, dependencies, complexity, unattended nature, increased machine intelligence, autonomous reconfiguration, and uncertainties. Safety and security challenges include opaque system failure, complex socio-technical system, human-machine interface, cyber-physical attacks, unsecured remote configuration, lack of standards, and resilience. The paper is structured under the following key headings: bibliometric results, opportunities from Industry 4.0 technologies, issues introduced by Industry 4.0 technologies, literature proposed solutions, and research challenges. Based on the assessment of about 900 relevant publications extracted from two databases (Web of Science (WoS) Core Collection and Engineering village (EV)), the documents were categorized into eight major divisions: (i) Safety Opportunities, (ii) Hazardous Characteristics, (iii) Fault Occurrence, (iv) Cyber-physical attacks, (v) Human factors, (vi) occupational Health and safety, (vii) standards, and (viii) Reliability and resiliency. Proactive strategies were classified under four options: identification, evaluation, prevention, and management. The paper also raises some research needs and unanswered questions that require further analysis to address how security and safety mechanisms can introduce more complexity in the system that can lead to new risks. The paper concludes by emphasizing digital process safety as part of the engineering curriculum to address the process industry's need for digital solutions and to make process safety learning a conscious choice. [ABSTRACT FROM AUTHOR]

Industry 4.0 has introduced new technological advances such as Additive Manufacturing (AM), Artificial Intelligence (AI), Augmented Reality (AR), Cyber--physical systems (CPS), Blockchain, Cybersecurity and many more. It is also helping organisations to take on challenges like demand fluctuations and market volatility. Industry 4.0 is also the interconnection of computers, materials, and AI with minimal human intervention in decision-making. Although Industry 4.0 has tremendous potential for industrial growth, it has many challenges, such as technical integration, human resource issue, supply chain issues and data security. These challenges are expected to be addressed in the future by Industry 5.0 Industry 5.0 introduced various new technologies such as Predictive maintenance, Hyper customisation in the Industry, Cyber--Physical Cognitive systems, and the introduction of Collaborative Robots. Industry 5.0, with its human-centric approach, helped overcome the various challenges of Industry 4.0. In Industry 4.0, the focus was on sustainability and production volume, whereas in Industry 5.0, the primary focus is on human centricity. This paper's literature review has been done by considering the findings and results discussed in various latest literature. We have listed various technological developments in Industry 5.0 and the advancements to the technologies of Industry 4.0. Industry 5.0 has tackled various issues faced in Industry 4.0, such as data privacy and technical integration issues. Resilience is also one of the critical aspects of Industry 5.0, which contributed towards hyper individualisation and helped make individualised products with specific user needs. This study would provide a base for the researchers and industries to get acquainted with Industry 4.0 technologies, their challenges, the technological advances, and the ways Industry 5.0 overcame the challenges of Industry 4.0. This paper also lists the recent application of these technologies, which would provide an idea of how these technologies can be implemented. Further, the limitations of these studies have been listed, providing an opportunity for future research in this domain. This paper provides a structured review of the technologies of Industry 4.0, the challenges they faced and how they can be removed through the technological advancements of Industry 5.0. [ABSTRACT FROM AUTHOR]

Research background: The climate crisis is one of the greatest challenges the world is facing in the 21st century. The global response to this problem must comprise transitions in land and ecosystems, energy, urban, infrastructure, and industrial systems and a transformation in enterprise and risk management. With the global pressure to fight climate change and achieve the Sustainable Development Goals, the innovative potential of Industry 4.0 technologies (blockchain, Big Data analytics, the Internet of Things, cloud computing, artificial intelligence, 3D additive manufacturing and technology platforms) has been subject to enormous interest among researchers and practitioners. Additionally, the COVID-19 pandemic has accelerated technology investment and digital transformation in many business areas, potentially including sustainability risk management. Purpose of the article: The purpose of this paper is to explore the scope of Industry 4.0 technology implementation in sustainability risk management and its financial impact. Methods: Based on the survey research, the authors verified if enterprises implementing Industry 4.0 technologies during the last five years are benefiting from their investment depending on the number of technologies. Due to the ordinal measurement scale of the analyzed variables, the verification of hypotheses was carried out using the Mann-Whitney U test. Findings & value added: In the opinion of the managers surveyed, the number of Industry 4.0 technologies implemented in the sustainability risk management process does not affect the enterprise's aggregate Financial Performance Indicator, individual financial indicators (ROA, ROE, OPM, NPM and FLR) and the enterprise's ability to manage financial risk. This study contributes to the literature on the technological transformation of the sustainability risk management process and its financial importance. The design and implementation of sustainable development strategies imply a long-term perspective, including the achievement of risk management effects in this area. The implementation of one technology, let alone two or more, is a difficult and time-consuming management challenge before synergy effects are achieved in an integrated technological eco-system. [ABSTRACT FROM AUTHOR]

In the current context of industry development through the implementation of the concepts of Internet of Things (IoT), Big Data, Artificial Intelligence technology, welded structures manufacturing processes must adapt to the new trends. The main advantages of implementing these concepts in the industry consist of increasing process efficiency, increasing productivity and achieving better management through product customization, intelligent monitoring applications for production and machine health, and predictive and preventive maintenance of industrial equipment. Thus, intelligent welding manufacturing and systems (IWMS) should be the norm for manufacturing systems. The paper presents the implementation of four new robotic cells, with the possibility of online monitoring and control of all their equipment. Moreover, all the information necessary for the manufacturing process as well as all the documents elaborated are available in Cloud and can be permanently accessed by authorized persons. Starting with the process of design, elaboration of technical documentation, welding, control up to packaging and delivery to the beneficiary, all the activities are standardized and the status of the manufacturing process can be supervised. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to predict artificial intelligence (AI) technology development and the impact of AI utilization activity on companies, to identify AI strategies dealing with the broad innovation activity of AI, and to construct the strategic decision-making framework of AI strategies for a small- and medium-sized enterprise (hereafter SME), to improve strategic decision-making practices of AI strategy in SMEs. Design/methodology/approach: This study used the multiple methods on the design of two data collection stages. The first stage is an expertise-based approach. It organized the three groups of expert panels and conducted the Delphi survey on them in combination with the brainstorming of technology, innovation and strategy in the fourth industrial revolution. The second stage is in the complement approach of expertise-based results. It used the literature review to involve the analysis of academic and practical papers, reports and audio materials relating to technology development, innovation types and strategies of AI. Additionally, it organized the four semi-structured interviews. Finally, this study used the mind-map and decision tree to conduct each analysis and synthesize each analytical result. Findings: This study identifies the precondition and four paths of AI technological development classifying into specialized AI, AI convergence with other technologies, general AI and AI control methods. It captures the impact of non- and technological innovation through AI on companies. Second, it identifies and classifies the six types of AI strategy: the bystander, capability-building, capability-holding, management-enhancing, market-enhancing and new-market-creating strategy. By using the decision tree, it constructs the strategic decision-making framework containing six AI strategies. Actionable points, strategic priorities and relevant instruments are suggested. Research limitations/implications: The strategic decision-making framework covering from AI technology development to utilization in a SME can help understand the strategic behaviours in SMEs. The typology of six AI strategies implies the broad innovation behaviours in SMEs. It can lead to further research to understand the pattern of strategic and innovation behaviour on AI. Practical implications: This practical study can help executives, managers and engineers in SMEs to develop their strategic practices through the strategic decision framework and six AI strategies. Originality/value: This practical study elicits the six types of AI strategy and constructs the strategic decision-making framework of six AI strategies from AI technology development to utilization. It can contribute to improving the practices of strategic decision-making in SMEs. [ABSTRACT FROM AUTHOR]

This paper reviews the trends in income inequality over the last 40 years, and proposes a new explanation for their evolution – that it is hoped will be tested empirically by many studies in the years ahead – to see whether the observed increases in inequality of the last four decades have been caused by an aggravation of its traditional causes (such as land an human capital concentration), or by a widespread increase in instability in five key areas affecting inequality, the Human Development Index and human capabilities. The five areas where a sharp increase in instability has been observed concern: (a) the financial sector; (b) industry 4.0, especially the development of robotics and artificial intelligence; (c) diseases such as ebola, aids and covid; (d) number of conflicts and (e) a growing environmental crisis. A key unanswered question in the above proposed overall explanation is whether these five crises are interconnected and whether there is a primum movens that explains this series of problematic events. [ABSTRACT FROM AUTHOR]

Disassembly of e-waste has received significant attention over the past decades to extract value-added parts or components for recovery or reuse. It is imperative to develop automatic disassembly to replace human workers thus safeguarding them against the hazardous environment. Most scholars investigate the disassembly of e-waste from a technical perspective on laboratory scale. Few types of research related to its development track and scaled application are completed. This paper attempts to fill this gap by analyzing the disassembly of Waste Electrical and Electronic Equipment (WEEE) in a strategic perspective from manual operation, (semi)-automation to intelligent disassembly through a systematic literature review. The main barriers to automating the recycling industry lie in the high complexity and uncertainty of end-of-life (EOL) products that perplex the automatic handling and planning. Intelligent systems integrated in cognitive robots are helpful to handle the uncertainty through learning and revision processes. This work has three objectives: first, to map out what research has been carried out in the field of WEEE disassembly and the necessity for disassembly automation; second, to conduct a systematic literature review for the state of the art of automatic disassembly and discuss the barriers to its industrial application; third, to propose a perspective for integrating Industry 4.0 technologies with disassembly automation to promote flexibility and efficiency, providing a new scheme for future treatment of WEEE. [ABSTRACT FROM AUTHOR]

Cutting-edge technologies, with a special emphasis on the Internet of Things (IoT), tend to operate as game changers, generating enormous alterations in both traditional and modern enterprises. Understanding multiple uses of IoT has become vital for effective financial management, given the ever-changing nature of organizations and the technological disruptions that come with this paradigm change. IoT has proven to be a powerful tool for improving operational efficiency, decision-making processes, overall productivity, and data management. As a result of the continuously expanding data volume, there is an increasing demand for a robust IT system capable of adeptly handling all enterprise processes. Consequently, businesses must develop suitable IoT architectures that can efficiently address these continually evolving requirements. This research adopts an incremental explanatory approach, guided by the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). A rigorous examination of 84 research papers has allowed us to delve deeply into the current landscape of IoT research. This research aims to provide a complete and cohesive overview of the existing body of knowledge on IoT. This is accomplished by combining a rigorous empirical approach to categorization with ideas from specialized literature in the IoT sector. This study actively contributes to the ongoing conversation around IoT by recognizing and critically examining current difficulties. This, consequently, opens new research possibilities and promotes future developments in this ever-changing sector. [ABSTRACT FROM AUTHOR]

Although there are methods of artificial intelligence (AI) applied to virtual reality (VR) solutions, there are few studies in the literature. Thus, to fill this gap, we performed a systematic literature review of these methods. In this review, we apply a methodology proposed in the literature that locates existing studies, selects and evaluates contributions, analyses, and synthesizes data. We used Google Scholar and databases such as Elsevier’s Scopus, ACM Digital Library, and IEEE Xplore Digital Library. A set of inclusion and exclusion criteria were used to select documents. The results showed that when AI methods are used in VR applications, the main advantages are high efficiency and precision of algorithms. Moreover, we observe that machine learning is the most applied AI scientific technique in VR applications. In conclusion, this paper showed that the combination of AI and VR contributes to new trends, opportunities, and applications for human-machine interactive devices, education, agriculture, transport, 3D image reconstruction, and health. We also concluded that the usage of AI in VR provides potential benefits in other fields of the real world such as teleconferencing, emotion interaction, tourist services, and image data extraction. [ABSTRACT FROM AUTHOR]

The widespread adoption of advanced technologies, such as Artificial Intelligence (AI), Machine Learning, and Robotics, is rapidly increasing across the globe. This accelerated pace of change is drastically transforming various aspects of our lives and work, resulting in what is now known as Industry 4.0. As businesses integrate these technologies into their daily operations, it significantly impacts their work tasks and required skill sets. However, the approach to technological transformation varies depending on location, industry, and organization. However, there are no published methods that can adequately forecast the adoption of technology and its impact on society. It is essential to prepare for the future impact of Industry 4.0, and this requires policymakers and business leaders to be equipped with scientifically validated models and metrics. Data-driven scenario planning and decision-making can lead to better outcomes in every area of the business, from learning and development to technology investment. However, the current literature falls short in identifying effective and globally applicable strategies to predict the adoption rate of emerging technologies. Therefore, this paper proposes a novel parametric mathematical model for predicting the adoption rate of emerging technologies through a unique data-driven pipeline. This approach utilizes global indicators for countries to predict the technology adoption curves for each country and industry. The model is thoroughly validated, and the paper outlines highly promising evaluation results. The practical implications of this proposed approach are significant because it provides policymakers and business leaders with valuable insights for decision-making and scenario planning. [ABSTRACT FROM AUTHOR]

The so‐called age of AI, industry 4.0, the fourth industrial revolution, etc. all attempt to conjure into existence a new technological paradigm. Should we believe the hype? This paper draws on neo‐Schumpeterian and régulation theory to widen the scope of this debate and examine techno‐economic and institutional discontinuities. In exploring these discontinuities, this paper argues, first, that growth regimes are not necessarily tenable as indicators of new paradigms, and, second, that there are (infra)structural discontinuities between the ICT/post‐Fordist era and those of the AI/platform era. Platformisation entails a distinct institutional logic, regime of accumulation (RA) and mode of social régulation (MSR). The clusters of technological and institutional changes behind this shift have not yet been sufficiently addressed by economic geography and associated literatures. In reconceptualising the shift in terms of (infra)structural discontinuity, the paper synthesises neo‐Schumpeterian and régulation theory to identify both technological and institutional changes in the régulation of capitalist accumulation. [ABSTRACT FROM AUTHOR]

New technologies are developed inside today's companies with the ascent of Industry 4.0 paradigm; Artificial Intelligence applied to Predictive Maintenance is one of these, helping factories automate their systems in detecting anomalies. The deviation of statistical features from standard operating conditions computed on collected data is a common investigation technique that companies use. The information loss due to transformation from raw data to extracted features is a problem of this approach. Furthermore, a common Predictive Maintenance framework requires historical data about failures that often do not exist, neglecting the possibility of applying it. This paper uses Artificial Intelligence as Machine Learning models to recognize when something changes in the data's behavior collected up to that moment, also helping companies to gather a preliminary dataset for future Predictive Maintenance implementation. The aim concerns a framework in which several sensors are used to collect data by adopting a sensor fusion approach. The architecture is composed of an optimized software system able to enhance the computation scalability and the response time regarding novelty detection. This article analyzes the proposed architecture, then explains a proof-of-concept development using a digital model; finally, two real cases are studied to show how the framework behaves in a real environment. The analysis done in this paper has an application-oriented approach; hence a company can directly use the framework in its systems. [ABSTRACT FROM AUTHOR]

Climate change, the growth in world population, high levels of food waste and food loss, and the risk of new disease or pandemic outbreaks are examples of the many challenges that threaten future food sustainability and the security of the planet and urgently need to be addressed. The fourth industrial revolution, or Industry 4.0, has been gaining momentum since 2015, being a significant driver for sustainable development and a successful catalyst to tackle critical global challenges. This review paper summarizes the most relevant food Industry 4.0 technologies including, among others, digital technologies (e.g., artificial intelligence, big data analytics, Internet of Things, and blockchain) and other technological advances (e.g., smart sensors, robotics, digital twins, and cyber-physical systems). Moreover, insights into the new food trends (such as 3D printed foods) that have emerged as a result of the Industry 4.0 technological revolution will also be discussed in Part II of this work. The Industry 4.0 technologies have significantly modified the food industry and led to substantial consequences for the environment, economics, and human health. Despite the importance of each of the technologies mentioned above, ground-breaking sustainable solutions could only emerge by combining many technologies simultaneously. The Food Industry 4.0 era has been characterized by new challenges, opportunities, and trends that have reshaped current strategies and prospects for food production and consumption patterns, paving the way for the move toward Industry 5.0. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected by an IoT network, along with a collection of enabling technologies, such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, there has been an inevitable increase in research proposals over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security. [ABSTRACT FROM AUTHOR]

This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]

This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines. [ABSTRACT FROM AUTHOR]

Background: Consumer and advanced consumption culture in modern society is an era that focuses on individual personality and value, and "my own customized products", or customized marketing strategies, are actively being developed throughout the industry. Recently, IT technologies that can support personalized services such as artificial intelligence, ubiquitous systems, and marketing automation have been recognized for their potential, directly or indirectly affecting distribution industries affected by personal consumption culture. Accordingly, customized products or services, i.e., customization, are attracting attention as an effective methodology to cope with such market changes. Objectives: Among the necessities used by modern women, cosmetics account for an endless interest in beauty and maintaining physical and mental health, and as the cosmetics market expands, it is considered that the cosmetics industry needs a clearer and in‐depth study on the cosmetics submarket to satisfy consumers' diverse needs. Methods: This review paper is a literature review, and a narrative review approach has been used for this study. A total of 300 to 400 references were selected using representative journal search websites such as PubMed, Google Scholar, Scopus, ResearchGate, LitCovid, DBPia, and RISS, of which a total of 42 papers were selected in the final stage based on 2013 to 2022 using PRISMA flow diagram. Results: This study suggested to indicate the changes in the cosmetics market due to the emergence of cosmetics curation services after the coronavirus disease‐19 pandemic, advanced changes in consumer purchase patterns following the 4th Industrial Revolution, and significant future prospects of cosmetics curation services. Conclusion: As the beauty and cosmetology industry is expected to develop in the future, it will grow as a centerpiece of the beauty industry and symbolizes nationalized cultural pride. Therefore, this review article will be continuing to promote customization as a premium beauty service for dermatological condition in Republic of Korea through corporate analysis. [ABSTRACT FROM AUTHOR]

Predictive maintenance is a field of study whose main objective is to optimize the timing and type of maintenance to perform on various industrial systems. This aim involves maximizing the availability time of the monitored system and minimizing the number of resources used in maintenance. Predictive maintenance is currently undergoing a revolution thanks to advances in industrial systems monitoring within the Industry 4.0 paradigm. Likewise, advances in artificial intelligence and data mining allow the processing of a great amount of data to provide more accurate and advanced predictive models. In this context, many actors have become interested in predictive maintenance research, becoming one of the most active areas of research in computing, where academia and industry converge. The objective of this paper is to conduct a systematic literature review that provides an overview of the current state of research concerning predictive maintenance from a data mining perspective. The review presents a first taxonomy that implies different phases considered in any data mining process to solve a predictive maintenance problem, relating the predictive maintenance tasks with the main data mining tasks to solve them. Finally, the paper presents significant challenges and future research directions in terms of the potential of data mining applied to predictive maintenance. This article is categorized under:Application Areas > Industry Specific ApplicationsTechnologies > Internet of Things [ABSTRACT FROM AUTHOR]

The potential benefits of Industry 4.0 have led to an increased interest in smart manufacturing. To facilitate the self-diagnosis and adaptive ability in smart milling system, a digital twin–driven intelligent algorithm for monitoring in-process milling parameters is proposed here. The algorithm can extract the radial width of cut, axial depth of cut, cutter runout parameters, and cutting constants in the end milling process at the same time only by using force sensor. It is an important breakthrough in this paper to converge two different force models to realize cyber-physical fusion for identifying milling parameters in the milling process. By using the convolution force model, digital twin technology can extract the approximate solution of milling parameters in the machining process in advance, so as to narrow the range of solution. Furthermore, the subsequent artificial intelligence algorithm can find the accurate solution of the current milling parameters in a short calculation time by cyber-physical fusion with the numerical force model considering cutter runout effect. Milling experiments are carried out to validate the proposed algorithm. It is shown that due to the complementary advantages of the convolution force model and numerical force model, the algorithm proposed in this paper can give consider to the identification accuracy and calculation efficiency. [ABSTRACT FROM AUTHOR]

In a dynamic and rapidly changing world, customers' often conflicting demands have continued to evolve, outstripping the ability of the traditional factory to address modern-day production challenges. To fix these challenges, several manufacturing paradigms have been proposed. Some of these have monikers such as the smart factory, intelligent factory, digital factory, and cloud-based factory. Due to a lack of consensus on general nomenclature, the term Factory of the Future (or Future Factory) has been used in this paper as a collective euphemism for these paradigms. The Factory of the Future constitutes a creative convergence of multiple technologies, techniques, and capabilities that represent a significant change in current production capabilities, models, and practices. Using the semi-narrative research methodology in concert with the snowballing approach, the authors reviewed the open literature to understand the organizing principles behind the most common smart manufacturing paradigms with a view to developing a creative reference that articulates their shared characteristics and features under a collective lingua franca, viz., Factory of the Future. Serving as a review article and a reference monograph, the paper details the meanings, characteristics, technological framework, and applications of the modern factory and its various connotations. Amongst other objectives, it characterizes the next-generation factory and provides an overview of reference architectures/models that guide their structured development and deployment. Three advanced communication technologies capable of advancing the goals of the Factory of the Future and rapidly scaling advancements in the field are discussed. It was established that next-generation factories would be data rich environments. The realization of their ultimate value would depend on the ability of stakeholders to develop the appropriate infrastructure to extract, store, and process data to support decision making and process optimization. [ABSTRACT FROM AUTHOR]

Purpose: This paper reviews the synergy of Industry 4.0 and additive manufacturing (AM) and discusses the integration of data-driven manufacturing systems and product service systems as a key component of the Industry 4.0 revolution. This paper aims to highlight the potential effects of Industry 4.0 on AM via tools such as digitalisation, data transfer, tagging technology, information in Industry 4.0 and intelligent features. Design/methodology/approach: In successive phases of industrialisation, there has been a rise in the use of, and dependence on, data in manufacturing. In this review of Industry 4.0 and AM, the five pillars of success that could see the Internet of Things (IoT), artificial intelligence, robotics and materials science enabling new levels of interactivity and interdependence between suppliers, producers and users are discussed. The unique effects of AM capabilities, in particular mass customisation and light-weighting, combined with the integration of data and IoT in Industry 4.0, are studied for their potential to support higher efficiencies, greater utility and more ecologically friendly production. This research also illustrates how the digitalisation of manufacturing for Industry 4.0, through the use of IoT and AM, enables new business models and production practices. Findings: The discussion illustrates the potential of combining IoT and AM to provide an escape from the constraints and limitations of conventional mass production whilst achieving economic and ecological savings. It should also be noted that this extends to the agile design and fabrication of increasingly complex parts enabled by simulations of complex production processes and operating systems. This paper also discusses the relationship between Industry 4.0 and AM with respect to improving the quality and robustness of product outcomes, based on real-time data/feedback. Originality/value: This research shows how a combined approach to research into IoT and AM can create a step change in practice that alters the production and supply paradigm, potentially reducing the ecological impact of industrial systems and product life cycle. This paper demonstrates how the integration of Industry 4.0 and AM could reshape the future of manufacturing and discusses the challenges involved. [ABSTRACT FROM AUTHOR]

Since the beginning of the 21st century, with the development of information technology, researchers in various fields have gradually increased their research on human emotion and behavior. The current research mechanism used in emotion and behavior research is artificial intelligence technology. Through the literature survey and data analysis in related fields, it is found that the acquisition of human emotions and behaviors will be carried out through facial feature algorithm for point capture and combined with machine learning for output detection and analysis. Among them, the detection process requires machine learning of artificial intelligence first. This paper firstly analyzes and summarizes the advantages of Python programs at this stage and completes the preliminary work of system construction by setting and installing platform parameters. In the research process, this paper uses the existing algorithm to apply the σ E value algorithm to the samples and conducts preliminary tests. The overall detection values in the test data are relatively average, and there are still differences in the samples. At the same time, we compare the U E and T E detection algorithms according to the output Y value of the algorithm in the machine learning. The detection rate of some emoticons in the U E algorithm is high, but the detection rate of other emoticons is low. Finally, according to the limitation of the output method in the mathematical formula, a new algorithm σ x of taking the weighted sum and taking the logarithm and then taking the square root is proposed again. According to the statistical analysis, the overall average value of the final algorithm has been improved, and the overall detection rate is about 80%; compared with the T E and U E algorithms, the overall detection frequency fluctuates less. The σ x algorithm in the frequency fluctuation data table in the paper is also superior to the existing algorithms in machine learning, sample testing, and data in the frequency fluctuation. Our next direction will be to use the Python main program to perform AI automatic facial emotion detection work by combining the new algorithm σ x with the V value, DWT, and CNN algorithm in the facial recognition feature through machine learning. [ABSTRACT FROM AUTHOR]

In a rapidly changing World, changes in the field of law are inevitable, both at the theoretical level and in the domain of legal regulation. This, first of all, refers to basic doctrinal notions and legal definitions, which represent the basis for the construction and development of legal institutes. In this context, a special place is occupied by the thing, as a starting category of property law that regulates relations related to the appropriation. In many legal systems, but also in legal doctrine, it is not entirely clear what and under what conditions can be appropriated. This is, on the one hand, a consequence of legal regulation lagging behind changes in society, and on the other, legal science lagging behind other scientific fields and technological development. Serbian civil law is specific in this regard. In legal theory, which has been developing since the beginning of the 20th century under the strong influence of German doctrine and the German Civil Code of 1896, the prevailing view is that things are only matter (a material part of the environment). There is no general conceptual definition in the current laws. That gap can be filled by applying the old rules from the Serbian Civil Code of 1844, which contains a definition that, due to its breadth, is much more suitable for the era in which we live, than the one that the doctrine took over from German law. According to the SCC, “the name of a thing is understood in the legal sense of everything that is not a human being, or is not a person, and serves the needs of a human being.” Such determination is closer to the position of fundamental sciences and modern integrative scientific disciplines of the new generation, according to which everything in the human environment is consists of matter, energy and information. Certain changes in the understanding of things occurred under the pressure of time and practical needs. Instead of meeting the changes, legal regulation and legal science react belatedly and intervene to what has been a part of reality for a long time. The gap between the normative and the real is alleviated by the fact that the other two elements of the Universe (energy and information) are also included under the traditional general notion of things (based on the thesis that thing is exclusively matter). Paradoxically, in many countries this was done in the domain of criminal law or under its influence. However, even that change is not enough. It is necessary to make another step towards what is the new reality in the 21st century and accept the fact that energy and (physical, structural) information should not be subsumed under a general term, because they are already part of the thing, they are inside it (as its constituents). Instead, a more abstract and at the same time comprehensive concept should be built. Summa summarum, in the spirit of modern science and in the broadest property law sense, a thing is every component of an environment that can be physically and legally subject to appropriation. Such a notion is sufficiently abstract and broad enough that it can encompass elements that at the current level of development of science and technology are considered to be constituent of everything existing, as well as some new ones, to which the evolution of scientific thought will lead. It can also represent a framework for finding answers to some new legal challenges. For instance, for complex problems related to physical information and the information code of matter such as DNA, which is followed by questions from the domain of biomedical engineering, issues related to the digital shadow of living matter and its reproduction, and in that context, with the so-called bio-informational property. Special attention is required for what is directly related to the fourth industrial revolution, above all the Internet of Things (IoT). Under the influence of technical and technological progress, the boundaries between subjects and objects of law are shifting or even being erased. Some things (understood in a modern way, as a synthesis of matter, energy and information) have or gradually acquire some kind of legal subjectivity. These include humanoid robots, various forms of artificial intelligence, etc. Virtual reality (Cyber space) and its (intangible, digital) entities, such as avatars, digital fashion, digital immortality, etc., represent a special area with which the modern concept of things should be connected. The paper also discusses some important issues related to notions and definitions, with the reasons for their rethinking and methodological approaches to that issue, among which the new scientific encyclopedism and interdisciplinarity are particularly significant. [ABSTRACT FROM AUTHOR]

Smart city discourses often invoke the Panopticon, a disciplinary architecture designed by Jeremy Bentham and popularly theorized by Michel Foucault, as a model for understanding the social impact of AI technologies. This framing focuses attention almost exclusively on the negative ramifications of Urban AI, correlating ubiquitous surveillance, centralization, and data consolidation with AI development, and positioning technologies themselves as the driving factor shaping privacy, sociality, equity, access, and autonomy in the city. This paper describes an alternative diagram for Urban AI—the Polyopticon: a distributed, polyvalent, multi-modal network of synthetic intelligences. It posits that fourth industrial revolution technologies change the political, social, and psychodynamic relationships of sentience and witness in the city, shifting the effects of watching and watched beyond the exclusive domain of top-down surveillance and discipline. The Polyopticon poses a more expansive and ambivalent spectrum of possibilities for Urban AI scenarios, one that undermines the totalizing, singular, and cerebral notion of intelligence that so often characterizes Urban AI and smart city critiques. [ABSTRACT FROM AUTHOR]

The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed. [ABSTRACT FROM AUTHOR]

This paper deals with the prediction of the future location of vehicles, which is attracting attention in the era of the fourth industrial revolution and is required in various fields, such as autonomous vehicles and smart city traffic management systems. Currently, vehicle traffic prediction models and accident prediction models are being tested in various places, and considerable progress is being made. However, there are always errors in positioning when using wireless sensors due to various variables, such as the appearance of various substances (water, metal) that occur in the space where radio waves exist. There have been various attempts to reduce the positioning error in such an Internet of Things environment, but there is no definitive method with confirmed performance. Of course, location prediction is also not accurate. In particular, since a vehicle moves rapidly in space, it is increasingly affected by changes in the environment. Firstly, it was necessary to develop a spatial positioning algorithm that can improve the positioning accuracy. Secondly, for the data generated by the positioning algorithm, a machine learning method suitable for position prediction was developed. Based on the above two developed algorithms, through experiments, we found a means to reduce the error of positioning through radio waves and to increase the accuracy of positioning. We started with the idea of changing the positioning space itself from a three-dimensional space into a two-dimensional one. With changes in the time and space of radio wave measurement, the location was measured by transforming the spatial dimension to cope with environmental changes. This is a technology that predicts a location through machine learning on time series data using a direction angle classification technique. An experiment was conducted to verify the performance of the proposed technology. As a result, the accuracy of positioning was improved, and the accuracy of location prediction increased in proportion to the learning time. It was possible to confirm the prediction accuracy increase of up to 80% with changes. Considering that the accuracy result for location prediction presented by other researchers is 70%, through this study, the result was improved by 10% compared to the existing vehicle location prediction accuracy. In conclusion, this paper presents a positioning algorithm and machine learning methodology for vehicle positioning. By proving its usefulness through experiments, this study provides other researchers with a new definition of space for predicting the location of a vehicle, and a machine learning method using direction angles. [ABSTRACT FROM AUTHOR]

In the 21st century, as part of the fourth industrial revolution, artificial intelligence (AI), is one of the most important and well-known technology. AI also has made it possible to execute human tasks without the need for humans. However, there is one important concern with consciousness. This is because consciousness is one of the most defining qualities between AI and humans. So, this study presents a literature review and bibliometric analysis of artificial and mind consciousness research around the world from 2000 to 2022 in order to provide researchers and scholars around the world with an overview of the results and trends in artificial and mind consciousness research. A textual query on two databases; Scopus (289 papers), and Web of Science (303 papers) using the term "artificial consciousness" OR "mind consciousness" was performed on 10 June 2022 retrieving 509 scholarly papers from 2000 to 2022 related to artificial and mind consciousness studies for in-depth analysis. Bibliometric analysis were performed using Rstudio software version 4.2.0 and biblioshiniy for bibliometrix to visualize and analyze trends of artificial and mind consciousness research. This bibliometric analysis was analyzed the annual scientific publication growth, the most productive authors, most frequent word has been using, most famous journal name, and which countries has highest collaboration with other country. According to the findings of the analysis, there is significant inconsistency in global trends in annual scientific production, with the number of publications increasing and decreasing. Among all countries, United States (USA) contributed the most publications in the field of artificial and mind consciousness research. According to the findings has show the most relevant authors is Kelley TM (Scopus), and Patel AD (WoS). Moreover, the most relevant journals articles in artificial and mind consciousness studies is Procedia Computer Science (Scopus), and Journal Of Consciousness Studies (WoS). The implication of this study is can help new researchers in this field by providing information on relevant publications and authors to consult when conducting research on this topic. Furthermore, this research helps other researchers understand current trends in this area of study. As a result, the justification for doing this study is to provide the first bibliometric analysis and to fill research gaps in bibliometric studies of artificial consciousness and the mind by providing information in the form of a literature review, overview, and guidelines. [ABSTRACT FROM AUTHOR]

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit. [ABSTRACT FROM AUTHOR]