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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 importance of artificial intelligence in the banking industry is reflected in the speed at which financial institutions are adopting and implementing AI solutions to improve their services and adapt to new market demands. The aim of this research is to conduct a bibliometric analysis of the involvement of artificial intelligence in the banking sector to provide a comprehensive overview of the current state of research to guide future directions and support the sustainable development of this rapidly expanding field. Another important objective is to identify research gaps and underexplored areas in the field of artificial intelligence in banking. The methodology used is a bibliometric analysis using VOSviewer, analysing 1089 papers from the Web of Science database. The results of the study provide relevant information for banking professionals but also for policy makers. Thus, the study highlights key areas where banks are using artificial intelligence to gain competitive advantage, thereby guiding practitioners in strategic decision making. Moreover, by identifying emerging trends and patterns in AI adoption, the study helps banking practitioners with foresight, enabling them to anticipate and prepare for future developments in the field. In terms of governmental implications, the study can contribute to the development of more nuanced regulatory frameworks that effectively balance the promotion of AI innovation with the protection of ethical standards and consumer protection. [ABSTRACT FROM AUTHOR]

Today, scenario is one of development & growing field and from which more attention in industry sector. Artificial Intelligence (AI) is one of method from which can improve the industrialization. Using AI in industry can make the industry to depend on stronger, inexpensive and more precise mode of marketing. Artificial Intelligence (AI) is a cognitive science to enables human to explore many intelligent ways to model our sensing and reasoning processes. Industrial AI is a methodical order to allow engineers to methodically expand and organize AI algorithms with repeating and steady successes. In this paper, the key enablers for this transformative technology along with their significant advantages are discussed. [ABSTRACT FROM AUTHOR]

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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]

Bibliometric analysis was performed to study the development of publications related to Industry 4.0 and its key technologies in Vietnam. Comparisons with data from other ASEAN countries, and with global data have been done to identify distinctive characteristics of Industry 4.0 literature from Vietnam. The collection of 1,470 retrieved papers was analysed to answer seven research questions. Our results highlighted some valuable insights of Industry 4.0 literature in Vietnam. The number of papers in Industry 4.0 in Vietnam increased rapidly in recent years, mostly focused on Computer Science, Engineering, and Mathematics. Iran, China, and South Korea were the most productive partner countries with Vietnam in Industry 4.0. Machine learning, artificial intelligence, big data, deep learning, Internet of things, neural networks, and data mining were among the most popular research themes in Industry 4.0 in Vietnam. Vietnam ranked third among 10 Southeast Asian countries, based on the number of published papers in Industry 4.0, but the gap with the two top countries was large. Compared to the global data, the annual growth rate of Industry 4.0 papers in Vietnam, and other Southeast Asian countries was lower. Findings from this work can be helpful for other scholars in establishing potential future research lines related to Industry 4.0 in Vietnam. [ABSTRACT FROM AUTHOR]

Small- and medium-sized enterprises (SMEs) play a fundamental role in the global economy. However, SMEs usually have different characteristics from larger enterprises, e.g., essential resource restrictions, lower performance, and higher environmental impacts. This requires them to search for strategies to be more competitive and sustainable. A possible solution relies on introducing Lean-Green practices. Previous research indicated that digitalization could be an enabler of Lean. Lean can also help to achieve increased environmental performance using the Lean-Green approach. In this study, this important yet under-studied area is investigated as we consider digitalization as an enabler for implementing lean in SMEs, with a focus on Lean-Green practices. A systematic literature review is executed, following a new framework based on topic modelling for extracting the papers. The topic modelling is executed through latent dirichlet allocation (LDA) which is a machine learning technique. In methodological means, this paper represents an example of the frontier of digitalization for research activities. Regarding the investigated focus, the main findings revealed that digitalization is an enabler to Lean and to Lean-Green. As digitalization supports information sharing, it consequently fosters performance measurement systems, improvements, and value chain integration. [ABSTRACT FROM AUTHOR]

Today, manufacturing enterprises are adopting emerging Industry 4.0 technologies to create industrial intelligence-driven smart factories. This trend, in turn, is stimulating the advent of intelligent supply chains that can sync and support the rapid evolution of advanced industrial practices via supply chain digital transformation. Specifically, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as vital breakthrough technologies that can help firms enhance profit margins, reduce supply chain costs, deliver excellent customer service, and make their supply chains intelligent. This paper identifies and analyzes 338 most influential research papers to scientifically examine the linkages among the AI-ML techniques and their applications in the SCM domain through bibliometric and network analysis, descriptive data analysis, and visual representation, thus furnishing a perspicacious knowledge base. The main contribution of this paper is to identify the unexplored potential and the contexts in which AI and ML can be used in managing and transforming supply chains digitally, including the aspects of intelligent and interpretative evolutions. Additionally, a fundamental contribution of this work is a comprehensive mind map that makes it possible to visualize, understand, and simulate the wide spectrum of findings from the bibliometric analyses. Finally, the study presents research gaps, implications, and future scope as a point of reference for researchers and practitioners. [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]

Purpose: The purpose of this publication is to present the applications of usage of business analytics in Industry 4.0. Design/methodology/approach: Critical literature analysis. Analysis of international literature from main databases and polish literature and legal acts connecting with researched topic. Findings: Specifically, the paper discussed how business analytics is employed in predictive maintenance, supply chain optimization, and quality control. Predictive maintenance allows organizations to proactively address equipment failures, thereby reducing downtime and maintenance costs. Supply chain optimization optimizes resource allocation, minimizes costs, and improves customer service through data-driven decision-making. Quality control relies on data analytics to monitor, assess, and enhance product and service quality, ultimately leading to cost reduction and customer satisfaction. It is evident that business analytics is not merely a tool but a strategic imperative for organizations in the era of Industry 4.0. It empowers them to continuously improve their operations, mitigate risks, and stay ahead in a rapidly evolving business landscape. As technology and data analytics capabilities continue to advance, businesses that effectively leverage these tools will be better positioned to thrive in the dynamic and competitive world of Industry 4.0. Originality/value: Detailed analysis of all subjects related to the problems connected with the usage of business analytics in Industry 4.0. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

This study explores the transformative potential of fourth industrial revolution (called Industry 4.0) technologies in the context of poverty elimination, with a particular focus on Africa. Given the multidimensional nature of poverty, which spans economic, social, and environmental aspects, there is a critical need for innovative and sustainable solutions. This paper presents a comprehensive literature review to identify how recent advancements, such as artificial intelligence (AI), the Internet of Things (IoT), robotics, blockchain, big data, and 5G can be harnessed to address various facets of poverty. Drawing on insights from existing research and expert opinions, we propose a conceptual framework that integrates these technologies with strategic policy interventions, infrastructure development, and capacity building. The paper proposes a framework that illustrates the prerequisite requirements before adopting Industry 4.0 technologies in poverty elimination efforts. This framework aims to ensure that the benefits of technological innovations are accessible to the most vulnerable populations, thereby contributing to the broader goals of socioeconomic development and poverty reduction. The work shows that while Industry 4.0 presents a critical opportunity for sustainable development and poverty elimination in Africa, it needs to have essential capacities to optimize the use of observations, visualizations, and mindset management before or when adopting the first stage of Industry 4.0 solutions for poverty elimination. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in mathematics education, focusing on its implications in the 4th Industrial Revolution (4IR) era. Through a comprehensive analysis of 10 relevant studies in Scopus and Google Scholar from 2015 to 2023, this review identifies the research methods, research instruments, participants, and AI tools used in mathematics education. Some key ideas include using AI-driven personalized learning and enhanced mathematics instruction, real-time assessment and feedback, curriculum development, and empowering educators, which were highlighted. The study aligns with the preferred reporting items for systematic reviews and metaanalysis. Based on the analysis, most studies reviewed utilized qualitative research methods. The study indicates that questionnaires were mainly used to gather data from students and teachers who were the most significant participants in the reviewed papers. Further results revealed that ChatGPT were the primary AI tool used in mathematics education, among other AI tools, as identified in this review. Additionally, this review discusses the transformative potential of AI in addressing educational disparities and preparing learners for the demands of 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]

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]

Purpose: This study underscores the accelerated integration of technological solutions within the tourism industry post-pandemic, highlighting the service sector's heightened openness to digital advancements. Authors stress the need for tourism companies to invest in digital technologies and resilient innovation for sustainable Industry 4.0, considering social impact. This research is the perception of the importance of digitalization tools application to tourism sphere especially in the post-pandemic period. Design/methodology/approach: Focusing on Central European travelers, a survey of 553 individuals was conducted to gauge perceptions regarding the importance of digital technologies in tourism. Significantly, gender disparities in touchless technology usage were observed, with women exhibiting less interest compared to men. Moreover, variations in the evaluation of specific technological tools within tourism services indicate diverse preferences among respondents. Findings: What was found in the course of the work? This will refer to analysis, discussion, or results. Research limitations/implications: While the study contributes valuable insights, its limitations, such as generalization and sample size constraints should be acknowledged. Practical implications: The paper delves into the practical implications of these findings for travel agencies, contributing to a broader theoretical understanding of how digital innovations can enrich the travel experience. From a practical perspective, the research encourages prioritizing the development of tourism technologies and investing in advanced capabilities, especially those related to artificial intelligence (AI). Originality/value: The empirical analysis provides valuable insights into the relationship between demographic factors and technology acceptance in the tourism industry. Understanding these dynamics can be incredibly beneficial for businesses and policymakers aiming to improve the adoption and integration of digital technologies in tourism. This knowledge enables not only enhancing travel safety, efficiency, and engagement for travelers but also informs strategic decisions regarding the development of the tourism. [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]

Modern manufacturing faces vastly changing challenges. The current economic situation and technological developments in terms of Industry 4.0 (I4.0) and Industry 5.0 (I5.0) force enterprises to integrate new technologies for more efficient and higher-quality products. Artificial intelligence (AI) and Machine Learning (ML) are the technologies that make machines capable of making human-like decisions. In the long run, AI and ML can add a layer (functionality) to make IoT devices more interactive and user-friendly. These technologies are driven by data and ML uses different types of data for making decisions. Our research focuses on testing a cobot-based quality control (CBQC) system that uses smart fixture and machine vision (MV) to determine the cables inside products with similar designs, but different functionality. The products are IoT modules for small electric vehicles used for interface, connectivity, and GPS monitoring. Previous research describes the methodology of reconfiguration of existing cobot cells for quality control purposes. In this paper, we discuss the testing of the CBQC system, together with creating a pattern database, training the ML model, and adding a predictive model to avoid defects in product cable sequence. Preliminary testing is carried out in the laboratory environment which leads to production testing in SME manufacturing. Results, developments, and future work will be presented at the end of the paper. [ABSTRACT FROM AUTHOR]

This paper shows the critical role of robotics and automation in waste management, presenting a broad analysis of their integration as a transformative step towards Industry 4.0. However, focusing on the challenges faced by growing cities in efficiently handling waste, the study emphasizes smart waste management solutions and the growing demand for innovative. Key components of Industry 4.0, including Artificial Intelligence (AI), Big Data, the Internet of Things (IoT) and Robotics, are explored for their potential to revolutionize waste management practices. The discussion involves the multidimensional impact of these technologies on waste process such as collection, sorting, and disposal processes. Examples such as the Pneumatic Waste Collection System 4.0 (PWC 4.0) and swarm robotics illustrate practical applications, highlighting their involvement to efficiency, sustainability, and inclusivity. By delving into the soft aspects of smart cities and the domains defined by Professor Dr. Rudolf Giffinger, the paper highlights the broader implications of Industry 4.0 in enhancing the quality of life for citizens. The integration of digital technologies into waste management processes aligns with the global agenda of sustainable development and environmental conservation, positioning it as a significant stride towards smarter and more environmentally conscious cities. [ABSTRACT FROM AUTHOR]

This paper is aimed at balancing the interests of business and society in the digital economy, to reduce the social risks of the Fourth Industrial Revolution. The goal of this paper is to study the experience and prospects of the humanisation of AI through the improvement of the practice of corporate social responsibility in Russia. By the example of the experience of Russian regions in 2021, we use econometric modelling to prove that the digital regional economy has a large potential in the sphere of humanisation of AI. The potential for the humanisation of AI in the digital economy of Russian regions is determined by responsible innovations, responsible production and logistics, as well as responsible marketing and sales, which contribute to the implementation of SDGs 9–12. The theoretical significance of the paper lies in its presenting smart region as a socio-economic environment for the humanisation of AI. The scientific novelty of the paper lies in its offering a new—meso-level—view of the humanisation of AI. The advantages of the new view include, first, consideration of socio-economic conditions for the humanisation of AI in a region; second, the most precise identification and correct measuring of the consequences of humanisation of AI for the quality of life in a region. The practical significance of the research results consists in the fact that the new proposed approach to the humanisation of AI, which implies public administration of this process at the level of a region, allows accelerating the considered process. [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]

Time series prediction stands at the forefront of the fourth industrial revolution (Industry 4.0), offering a crucial analytical tool for the vast data streams generated by modern industrial processes. This literature review systematically consolidates existing research on the predictive analysis of time series within the framework of Industry 4.0, illustrating its critical role in enhancing operational foresight and strategic planning. Tracing the evolution from the first to the fourth industrial revolution, the paper delineates how each phase has incrementally set the stage for today's data-centric manufacturing paradigms. It critically examines how emergent technologies such as the Internet of things (IoT), artificial intelligence (AI), cloud computing, and big data analytics converge in the context of Industry 4.0 to transform time series data into actionable insights. Specifically, the review explores applications in predictive maintenance, production optimization, sales forecasting, and anomaly detection, underscoring the transformative impact of accurate time series forecasting on industrial operations. The paper culminates in a call to action for the strategic dissemination and management of these technologies, proposing a pathway for leveraging time series prediction to drive societal and economic advancement. Serving as a foundational compendium, this article aims to inform and guide ongoing research and practice at the intersection of time series prediction and Industry 4.0. [ABSTRACT FROM AUTHOR]

An Industrial Robot is used in industries for transporting, assembly, manufacturing and many more applications. Industrial robots include manufacturing robots, material handling robots, robotic arm and manipulator, mobile robots, assembly robots, etc. In this paper, Mobile Robots are further being discussed. One of the tools that a Mobile Robot uses to function is all with the help of Artificial Intelligence (AI) for performing several tasks autonomously. AI works as the intelligence of the human body for robots. AI is the technology that made it possible for robots to be capable of being totally autonomous. AI marks its presence in the Manufacturing Industry with the 4th Industrial Revolution. AI has several algorithms that help in collecting and analyzing data in order to help robots to function in specific ways. These techniques include Fuzzy Logic, Genetic Algorithm, Neural Network, etc. In this paper, the role of these algorithms in Mobile Robots is discussed. Based on the review of 74 papers and articles, it is observed that there are no review papers discussing the role of nature-based and conventional algorithms used for navigation in Mobile Robots. The use of different AI techniques for specific applications has been discussed in tabular form in this paper. [ABSTRACT FROM AUTHOR]

Rotating machines is an essential part of any manufacturing industry. The sudden breakdown of such machines due to improper maintenance can also lead to the industries' shutdown. The era of the 4th industrial revolution is taking its major shape concerning maintenance strategies, notable being in predictive maintenance. Fault prediction and diagnosis is the major concern in predictive maintenance as this is the major issue faced by all the maintenance engineers. Most of the bibliometric literature review studies that are accessible focus on fault diagnosis in rotating machines, mainly focusing on a single type of fault. However, there isn't a thorough analysis of the literature that focuses on the "multi-fault diagnosis using multi-sensor data" aspect of rotating machines. In this regard, this paper reviews the literature on the "multi-Fault diagnosis using multi-sensor data fusion" of Industrial Rotating Machines employing Machine learning/Deep learning techniques. A hybrid bibliometric approach was used to analyze articles from the "Web of Science" and "Scopus" Database for the last 10 years. The method for literature analysis used, is quantitative as well as qualitative, as not only the traditional approach (bibliometric and network analysis) but also a novel method named ProKnow-C is used, and it entails a number of phases, that includes intelligent and extensive filtering from the large set of results and finally selecting the articles that are more pertinent to the research theme. Based on available publications, an analysis is performed on year-by-year publication data, article types, linguistic distribution of articles, funding sponsors, affiliations, citation analysis and the relationship between keywords, authors, etc. to provide an in-depth vision of research trends in the related area. The paper also focuses on the maintenance strategies, predictive maintenance approaches, AI algorithms, Multi sensor data fusion, challenges, and future directions in "multi-fault diagnosis using multi-sensor data fusion" in rotating machines. The foundational work done in the field, the most prolific papers and the key research themes within the research area are all identified in this bibliometric survey. [ABSTRACT FROM AUTHOR]

Since industry 4.0 was put forward in 2013, industrial process around the world has been moving rapidly towards the age of intelligent manufacturing. Industry 4.0 is known as the fourth industrial revolution dominated by intelligent manufacturing, which has changed the production mode of global manufacturing and triggered far-reaching industrial changes. However, when intelligent machines communicate with each other under industrial 4.0, a large amount of data adopting distributed control will be generated. The infographic in the data is mainly a visual design of industry 4.0 data. Therefore, this paper mainly studies the distributed data optimization processing for industry 4.0. Considering that data leakage is one of the biggest challenges faced by the data storage systems, this paper proposes a data storage method that considers the efficiency and security of data access. The concept of security distance not only guarantees data security but also takes into account the emphasis of different user groups on data security. To minimize data access time, this paper proposes a data access node selection algorithm to minimize data access time while ensuring data security. The simulation proves that compared with baselines, the data access time of the proposed algorithm in random topology and Internet2 topology is less than that of the current data storage algorithm while ensuring data security. The experimental results are simulated on Internet2 topology and random topology with Matlab and Omnet + + simulation platform, showing that the proposed algorithm can select the optimal data storage node under the condition of satisfying the security distance constraint, thus reducing the data access time. [ABSTRACT FROM AUTHOR]

This Special Issue of Energies was devoted to the topic of "Manufacturing Energy Efficiency and Industry 4.0". To a great extent, this issue follows the successful previous Special Issue on "Energy Efficiency of Manufacturing Processes and Systems", which attracted some significant attention from scholars, practitioners, and policy-makers from all over the world. In total, six papers were published. The main topics included energy efficiency improvement in both the manufacturing process and system levels, as well as how this can be facilitated through the use of Industry 4.0. [ABSTRACT FROM AUTHOR]

This document is a summary of an article titled "Industry 4.0, Artificial Intelligence, and Mechanical Engineering towards Industry 5.0" published in the Journal of Engineering, Project & Production Management. The article discusses the role of Industry 4.0, artificial intelligence, and mechanical engineering in the sustainability of industries in the future. It highlights the transition to the Industry 5.0 paradigm, which combines technology with human-centered approaches for the benefit of the workforce and society. The article also includes summaries of several papers that explore various aspects of this transitional paradigm, such as renewable energy sources, heat transfer, machining hard materials, complex metamaterial structures, data logging and analysis in the Internet of Things, internet financing, urban garden designs, risk management in construction projects, and the application of machine learning algorithms in civil engineering. The author expresses gratitude to the members of the International Scientific Committees for their contribution to the peer-review process and hopes that the papers will be valuable to researchers and practitioners in the field. [Extracted from the article]

Purpose: The main purpose of this paper is to analyse the current literature situation in terms of relationships between Industry 4.0 and quality management and TQM. The author wanted to understand what topics and issues can be considered the most relevant referring to the so-called Quality 4.0, what the literature is missing opening avenues for further research. Design/methodology/approach: This research employed a systematic literature review. In total, 75 papers from different sources were reviewed using specific inclusion and exclusion criteria. Findings: Four categories of topics emerged, namely: creating value within the company through quality (big) data, analytics and artificial intelligence; developing Quality 4.0 skills and culture for quality people; customer value co-creation; cyber–physical systems and ERP for quality assurance and control. This paper also tried to understand if there is a definition of Quality 4.0 based on determined methods. Research limitations/implications: Systematic literature review could have introduced some limitations in terms of the number and reliability of reviewed papers. Probably some interesting papers had been not intentionally missed. Practical implications: Consultants and managers in developing and implementing their own Quality 4.0 models could use many practical and discussed implications concerning I4.0 technologies and quality management. Originality/value: This is one of the first papers which employed the systematic literature review for researching Industry 4.0, quality management and TQM relationships. [ABSTRACT FROM AUTHOR]

Background: the advent of Industry 4.0 (I4.0) innovations has revolutionized supply chain management through technologies like the Internet of Things (IoT) and Artificial Intelligence (AI) integrated into procurement processes. Methods: this study addresses a critical knowledge gap by conducting a comprehensive review of 111 papers sourced from the Scopus database. These papers are classified into seven sub-themes encompassing I4.0 or procurement 4.0 (P4.0), big data, IoT, additive manufacturing, blockchain, e-procurement, and AI. Results: the investigation reveals that I4.0 technologies, particularly e-procurement and blockchain, have garnered substantial attention. Such technologies offer diverse value propositions, encompassing streamlined supplier evaluation, lead time reduction, cost optimization, and enhanced data security. Conclusion: the paper underscores pivotal trends and insights for the evolution of Procurement 4.0, illuminating a path toward more efficient supply chain management. [ABSTRACT FROM AUTHOR]

Research background: On the basis of an analysis of the current situation and expectations in the field of implementation of the elements of the Industry 4.0 concept, the purpose of this paper is to identify the effects on the labor market in large manufacturing enterprises in the Slovak Republic. Purpose of the article: The presented work has a theoretical-empirical nature and consists of a theoretical section and a practical section, which includes statistical indicator analysis and quantitative research. In the theoretical section, the paper discusses the issue of Industry 4.0 in general, with a focus on its impact on the labor market, thus laying the groundwork for future research on the subject. Methods: The output of this work is an analysis of selected indicators of the manufacturing industry sector in the Slovak Republic, based on the most recent employment data analysis in the first stage and quantitative research survey in the second stage, with the respondents being manufacturing industry companies operating in the Slovak Republic, and whose primary objective is to determine the current status of the implementation of the elements and technologies of Industry 4.0 in production companies in the Slovak Republic, as well as the factors influencing this situation, such as digital twin simulation modeling, artificial intelligence-based Internet of Manufacturing Things systems, and virtual machine and cognitive computing algorithms. Findings & value added: The research findings indicate that the degree of digitization adopted by businesses in the Slovak Republic is comparatively less robust and more sluggish to adapt. This is primarily attributable to the underdeveloped educational system, population reluctance, self-actualization, and inadequate state support. Recommendations for the Slovak market aim to increase the digital proficiency of businesses and of the general populace through various means, such as reforming legislation, enhancing state support for entrepreneurs, and modifying the education system, constituting the added value of the work. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform's real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond. [ABSTRACT FROM AUTHOR]

Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating artificial intelligence (AI) across various applications to enhance production processes. We cite two critical areas where AI can play a key role in industry: product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution combined with the advent of artificial intelligence brought significant changes to humans' daily lives. Extended research in the field has aided in both documenting and presenting these changes, giving a more general picture of this new era. This work reviews the application field of the scientific research literature on the presence of machine vision in the Fourth Industrial Revolution and the changes it brought to each sector to which it contributed, determining the exact extent of its influence. Accordingly, an attempt is made to present an overview of its use in the Fifth Industrial Revolution to identify and present the changes between the two consequent periods. This work uses the PRISMA methodology and follows the form of a Scoping Review using sources from Scopus and Google Scholar. Most publications reveal the emergence of machine vision in almost every field of human life with significant influence and performance results. Undoubtedly, this review highlights the great influence and offer of machine vision in many sectors, establishing its use and searching for more ways to use it. It is also proven that machine vision systems can help industries to gain competitive advantage in terms of better product quality, higher customer satisfaction, and improved productivity. [ABSTRACT FROM AUTHOR]

The present paper seeks to review the most important ethics that must be taken into consideration in researching the applications of the Fourth Industrial Revolution and to benefit from those applications in the service of mankind and to avoid the risks that can arise by addressing the following points: 1. What is meant by the ethics of scientific research? What is its importance at the university? 2. What are the most important ethics of scientific research and Unethical Deviations? 3. What is meant by the Fourth Industrial Revolution and what are its characteristics? What are the ethical dilemmas for the applications of the techniques of the Fourth Industrial Revolution? 4. Moral dilemmas for the applications of the techniques of the Fourth Industrial Revolution 5. Global interest in the ethics of the Fourth Industrial Revolution and artificial intelligence to confront its moral dilemmas. [ABSTRACT FROM AUTHOR]

Framing of the research. The Fourth Industrial Revolution (I4.0) is dramatically affecting firms' strategies, disrupting their business models. In particular, a bunch of digital technologies like IoT (Internet of Things), cloud platforms, big data, artificial intelligence and data analysis are offering firms the possibility to manage products functions, remotely and globally, kick-starting the design of innovative business models. Purpose of the paper. Using studies that have analyzed the link between business model innovation and ambidexterity as theoretical background, the aim of the paper is to investigate how incumbent BtoB manufacturing firms develop an I4.0 disrupting business model by addressing the related duality between exploration and exploitation (ambidexterity). Methodology. The paper fulfils its purposes by the means of a qualitative investigation, discussing empirical evidence coming from a cross-case analysis of 25 Italian SMEs and medium-large enterprises, selected crossing secondary data and indications coming from a specific panel of ten industry experts. Results. The impact of I4.0 technologies on firms' business models depend heavily on the access to user-firms' data. 21 firms are involved in non-disruptive modifications of the business model; 4 firms are conducting more sophisticated experimentations in result-oriented product-service systems. These firms, that we have named "challengers", are in a privileged position in order to unleash the potential of I4.0, introducing advanced services directly related to the customers' needs. All these challengers adopt a particular form of contextual ambidexterity in which the exploration activities involve specifically selected (key) customers. Practical implications. Managers need to understand which are the pace and extent of change for the various components of the corporate business model to innovate during each specific step of transition towards I4.0 technologies. Research limitations. The main limitation of the study is because the investigated companies were going through a transition phase: therefore, we can't tell what the outcome of this evolutionary journey will be, and if it will be the same for every firm. Originality of the paper. The paper proposes an original framing that contributes theoretically to the literature interfacing business model innovation and ambidexterity management. In particular, the study enhances our knowledge about contextual ambidexterity, a concept as rich in charm as poorly explored in practice. [ABSTRACT FROM AUTHOR]

The Industrial Revolution led to great changes in the world. New technologies were being developed and introduced at a rapid pace, forever changing the way people lived and worked. One of the areas that underwent significant changes due to the Industrial Revolution is agriculture, which is one of the oldest and most traditional form of use of nature and land by humans. Making changes to the agriculture became possible only under the influence of industrial revolutions. Still, even in the early stages, they were not able to radically and visibly transform this important branch of the economy. One of the most visible effects of industrialization on agriculture is the introduction of new technologies. The interdependence between traditional agriculture and modern industrial sectors of the economy is crucial for the overall economic development of the country. The growth of the agricultural sector depends on the growth of industrial demand for agricultural products. Similarly, the growth of the industrial sector depends on an increase in the purchasing power of the agricultural sector for industrial products and an increase in the supply of raw materials for processing. Many developing countries have realized the importance and role of the agricultural sector's industrialization in the country's economic development which ensures food security, optimizes foreign trade balance and raises the welfare of population. The industrial revolutions transformed the agricultural sector from traditional agriculture to mechanized agricultural production and modern precision farming. The fourth industrial revolution stimulated the development of new technologies and methods that changed the global production system. Industry 4.0 has transformed agricultural operations using cyber-physical systems, the Internet of Things, artificial intelligence and machine learning, big data analytics, and the integration of cloud technology into agricultural machinery. Agriculture 4.0 is a digital model of agricultural production, which is based on a highly efficient production process and is the result of long-term technological development. The paper aims to analyze and describe the innovations of Agriculture 4.0, its origin, and characteristics. The paper discusses the current state of the industrial agriculture (industrial agricultural production model, production process, supply chain) sector's strengths and weaknesses. The paper presents the opportunities and challenges of digital agriculture, emphasizing its importance in solving several pressing issues in industrial agriculture. Innovation and technology are essential to maintaining competitiveness in the agricultural sector. Digitization of the agro-food sector creates a highly productive, predictable, and adaptable system to changes. Which, in turn, leads to an increase in food security. Industrial agriculture significantly increases productivity, but at the same time, monoculture and intensive animal husbandry, which is the main production model of modern industrial agriculture, cause great damage to the environment, public health, and animal welfare. In addition, although agricultural production is mechanized and computerized, the lack of digitization and intelligent systems is a major obstacle that hinders the possibility of improving automation. The agri-food supply chain is not managed wisely at the current stage. To solve the mentioned issues, it is necessary to integrate the innovative technologies of the emerging industry 4.0 in agriculture. At the same time, agricultural technologies are also evolving towards a new paradigm -- "Agriculture 5.0 ", which should better combine science and technology with social equity and sustainability. The development of bio-economy in the concept of Society 5.0 implies that the most promising areas of agricultural development are the optimal use of resources, mass personalization and individualization of the final product, the development of creative product differentiation, and the introduction of autonomous automatic decision-making systems based on robotic complexes. The absence of communication barriers in human-machine interaction, including the use of bioinformatics technologies, will allow us to focus on increasing heuristics for innovative solutions in agriculture. [ABSTRACT FROM AUTHOR]

Digital twins (DTs) provide accurate, data-driven, real-time modeling to create a digital representation of the physical world. The integration of new technologies, such as virtual/mixed reality, artificial intelligence, and DTs, enables modeling and research into ways to achieve better sustainability, greater efficiency, and improved safety in Industry 4.0/5.0 technologies. This paper discusses concepts, limitations, future trends, and potential research directions to provide the infrastructure and underlying intelligence for large-scale semi-automated DT building environments. Grouping these technologies along these lines allows for a better consideration of their individual risk factors and use of available data, resulting in an approach to generate holistic virtual representations (DTs) to facilitate predictive analyses in industrial practices. Artificial intelligence-based DTs are becoming a new tool for monitoring, simulating, and optimizing systems, and the widespread implementation and mastery of this technology will lead to significant improvements in performance, reliability, and profitability. Despite advances, the aforementioned technology still requires research, improvement, and investment. This article's contribution is a concept that, if adopted instead of the traditional approach, can become standard practice rather than an advanced operation and can accelerate this development. [ABSTRACT FROM AUTHOR]

Three-dimensional printing has become a fast-growing industry. The first phase of this technology is the design of a 3D object to personalize it and optimize its production. This paper explores the procedural generation of the 3D model. The article aims to present the method of procedurally generating 3D objects in Python. Procedural content generation is the automated creation of content using algorithms. Most often, as part of procedural generation, a small number of input parameters and pseudo-random processes are used to generate content that will meet the requirements. The programming techniques for object customization in Python optimize the manufacturing process. Moreover, procedural generation speeds up the model design, and if developers use 3D scanning methods and artificial intelligence, production can be personalized, which is in line with the concept of Industry 4.0. [ABSTRACT FROM AUTHOR]

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted. [ABSTRACT FROM AUTHOR]

This study conducts a literature review to analyse the incorporation of Industry 4.0 in the construction sector, known as Construction 4.0, in developing countries. This study utilises an effective technique, encompassing academic databases, journals, and conference proceedings, to carefully examine relevant studies published with respect to developing countries. The primary areas of emphasis involve the definition of Construction 4.0. The technologies of execution include six cutting-edge technologies such as Building Information Modelling (BIM), Internet of Things (IoT), robotics, 3D printing, UAVs, and artificial intelligence in construction procedures. This analysis also explores the awareness and understanding of Industry 4.0 in the construction sector (Construction 4.0) in developing countries before identifying where it is being applied therein. Furthermore, obstacles that impede the mainstream adoption in developing countries are identified, including but not limited to such things as insufficient technological infrastructure, skill deficiencies, and budgetary limitations. This review consolidates various studies to provide a thorough comprehension of the present condition of Construction 4.0 in developing nations. As such, this paper aims to provide a guide for future research, policy making, and industry practices in order to promote sustainable and technologically advanced construction methods in these settings. [ABSTRACT FROM AUTHOR]

Purpose: The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management. Design/methodology/approach: The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method. Findings: The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies. Originality/value: This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications. [ABSTRACT FROM AUTHOR]

This special issue (SI) aims to allow researchers and practitioners to share the most recent advances in Industry 4.0-related blockchain technologies from enterprise information systems perspectives. According to a recent study, Industry 4.0 and blockchain will significantly impact future enterprise information systems. In the paper entitled 'A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0', Tan et al. ([11]) proposed a method to facilitate data security. [Extracted from the article]

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]