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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]

Introducing Artificial Intelligence (AI) tools is one of the development trends in complex industrial systems in the industry 4.0 environment. Unique challenges in system operations need to be handled by effective operation support systems. The knowledge-based operation support systems are developing rapidly in recent years. The paper aims at highlighting the concerns of knowledge acquisition and representation in one of the knowledge-based methodologies, the Multilevel Flow Modelling (MFM). A procedure of knowledge acquisition and representation for building MFM models is proposed to aim at improving the overall model quality and consistency. An interface linking systems' instrumentations to MFM functions are introduced. The new reasoning engine is used for MFM based real-time cause-consequence reasoning about dynamic plant situations. The model verification and validation, and the model performance evaluation analysis method are proposed. This paper also provides case studies that illustrate the effectiveness of intelligent operation support by applying MFM to an off-shore water injection system. It demonstrates that the procedure of knowledge acquisition and representation can facilitate the model builders, and ensure the quality of the models used for operation support. [ABSTRACT FROM AUTHOR]

Industry 4.0 process fault detection and diagnosis(FDD) is built on the foundations of Industrial Internet of Things(IIoT) for sensing and artificial intelligence for recognizing patterns. Despite the performance and application of various learning-based algorithms in multiple sectors, their black-box nature makes industrial experts skeptical. So, this paper proposes an eXplainable Fault Detection, Diagnosis, and Correction(XFDDC) Framework to create best-fit FDD models that are explainable. The XFDDC framework is designed to explain the FDD model predictions using eXplainable Artificial Intelligence(XAI) techniques. The proposed framework was applied to the bench-marked Tennessee Eastman Process(TEP) dataset. On evaluation, an XGBoost model yielded better Fault Detection Rate(FDR) and F1 score against popular transparent and complex models like Naive Bayes, K-Nearest Neighbors, Random Forest and a rule-based version of XGBoost. To explain the predictions of the XGBoost model, the XFDDC framework suggests the use of feature-based XAI techniques. So, the TreeSHAP algorithm is applied on the XGBoost model to generate local and global explanations as part of fault diagnosis. The proposed framework also recommends counterfactual explanations to provide action recommendations for correcting the fault situation. Thus, a best-fit explainable XGBoost Fault Detection, Diagnosis, and Correction (XGBoost-XFDDC) model is created. [ABSTRACT FROM AUTHOR]

Human motion analysis plays a crucial role in industry 4.0 and, more recently, in industry 5.0 where human-centered applications are becoming increasingly important, demonstrating its potential for enhancing safety, ergonomics and productivity. Considering this opportunity, an increasing number of studies are proposing works on the analysis of human motion in an industrial context, taking advantage of the rise of artificial intelligence technologies and sensor technologies. The objective of this work is to provide a review of recent studies exploring these technologies in the analysis of human movement while specifically considering industrial context. First, a taxonomy of key human motion analysis applications is proposed, presenting statistical insights to reveal trends and highlighting lacks in current research. Furthermore, this work identifies benchmark datasets acquired in various industrial case studies and associated sensors. Many recommendations for selecting optimal sensors and valuable benchmarks are proposed. Then, the paper outlines the current trend of utilizing hybrid deep learning methodologies in human movement analysis while underscoring the performance and limitations of these proposed methods, considering industrial constraints such as real-time recognition and frugality. Finally, challenges and future works are highlighted, focusing on the opportunities to address problems related to the complex industrial environment in order to achieve reliable performances. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 marks a transformative era for businesses and industries, characterized by advanced technologies like automation, Internet of Things (IoT), artificial intelligence (AI), smart factories, and cyber-physical systems. This revolution promises significant advantages, including enhanced productivity, sustainable progress, and heightened resilience. However, the integration of Industry 4.0 is challenged by the need for a skilled workforce with expertise in areas such as information technology and data analytics. Higher education institutions (HEIs) play a vital role in equipping future professionals with these skills, necessitating curriculum updates and infrastructure enhancements. Simultaneously, the importance of education for sustainable development (ESD) has been underscored by global initiatives like the Sustainable Development Goals (SDGs). ESD instills a sense of responsibility for economic, ecological, and equitable well-being. As digital technologies blur the lines between industries, education faces the challenge of adapting to evolving demands. The integration of AI tools in education has emerged as a catalyst for reshaping learning experiences, fostering innovation, and preparing individuals for the digital age. AI chatbots such as ChatGPT have garnered widespread attention and possess the potential to revolutionize various aspects of education. However, their integration raises ethical concerns, necessitates curriculum redesign, requires strategies for continuous learning, and demands alignment with industry standards. While the potential of AI integration in education is promising, there is a notable gap in the existing literature when it comes to exploring the ethical implications, the influence of AI on ESD, the impact on the structure of Blooms Taxonomy, collaboration between academia and industry, strategies for continuous learning, and the effective integration of AI tools for personalized learning. This paper aims to critically examine the integration of AI tools, with a specific emphasis on ChatGPT, in education within the context of ESD. It delves into the transformative potential, ethical considerations, imperatives for continuous learning, and the role of industry partnerships. By providing insights and strategies, this paper contributes to the ongoing discussion about the evolving nature of education in a technologically driven world, equipping academic institutions to navigate the complexities and opportunities associated with AI integration in education more effectively. [ABSTRACT FROM AUTHOR]

• A comprehensive investigation of laser welding and forming process as two critical parts of smart manufacturing. • An intelligent Aluminum Laser welded blanks (ALWBs) factory based on industry 4.0. • Sensor based monitoring in forming and welding of Aluminum alloys. • A novel model has been proposed as a roadmap for applying the idea of Industry 4.0 in forming of Aluminum Laser Welded Blanks (ALWB). The aim of this review paper is a comprehensive investigation of the laser welding and forming process as two critical parts of smart manufacturing and welding intelligent systems in Industry 4.0, especially for forming Aluminum Laser Welded Blanks (ALWB). In the modern era, manufacturing companies and their business models have a major impact on economic development and social relationships. Since Industry 4.0 has become a commonly accepted term for research centers and universities, both businesses and researchers are interested in the initiative. On the other side, aluminum has proven more challenging to weld than the other metals accounting for the most part for its tendency to form porosity. In this regard, monitoring using sensor-based approachesarehat centralize the information in real time is gaining ground. In addition, this ensures the accuracy and quality of the final product. There are several critical factors such as laser power, laser speed, focal point, laser scanning, and frequency that play a key role in laser technology which is important to control them toward the zero-defect manufacturing system. On the other hand, robustness accounting for geometrical defects (ex. underfill amount/angle at the toe as well as the number of admissible porosity defects in the weld about formability) are the critical parameters in the forming process. Here, a comprehensive review paper is conducted insight into the function of different sensors to signals from the welding and forming process. Finally, a novel model has been proposed as a roadmap for applying the idea of Industry 4.0 in forming Aluminum Laser Welded Blanks (ALWB). [ABSTRACT FROM AUTHOR]

This study aims to synthesize the extant literature on supply chain (SC) resilience and draw a roadmap for resilient SCs by employing a systematic literature review (SLR). The paper utilizes an SLR methodology to draw upon important themes and sub-themes. This is based on a pool of papers published in peer-reviewed journals. Major themes identified included SC networks and structures, Industry 4.0 and digitalization, innovation and entrepreneurship, capabilities and strategies, SC risk management, circular economy and sustainability. SC networks and structures, as well as the role of information technology and Industry 4.0, are an important theme of research in the literature. The literature at this juncture has mainly highlighted the role of big data analytics, blockchain technology, and artificial intelligence in SC resilience. In addition, the important capabilities highlighted by researchers include SC flexibility, SC capacity, SC robustness, SC relationship management and SC leadership for improving SC resilience. The important themes and sub-themes identified in the study would help identify the strategic fields of action for creating resilient SCs. Particularly, the conceptualization of resilience within the SCs would help to uncover the factors that play an important role in improving the SCs. Further, the study also focuses on the interplay between sustainability and resilience. The present study will help managers to understand the factors that affect resilience in SCs. [ABSTRACT FROM AUTHOR]

The take-off of a Fourth Industrial Revolution (4IR) is at the core of a vibrant academic debate with an increasingly larger number of studies investigating the effects of the latest generation of new technologies, namely artificial intelligence (AI), flexible automation, additive manufacturing, big data, etc., — hereinafter defined as intelligent technologies. A great deal of attention has been paid to the effects associated with the adoption of such transformative technologies, whilst the impact associated with their production, and in particular with the development of related technological knowledge, has remained almost unexplored. Using patent data at country level for a sample of industrialized economies between 1990 and 2014, this paper seeks to quantify the productivity spillovers associated with intelligent technologies. We show that the elasticity of aggregate productivity to the stock of knowledge related to intelligent technologies is statistically significant and economically important, ranging between 0.01 and 0.06. Since these innovations have increased by a factor of 3 from the early 1990s, based on our most conservative estimates, knowledge related to intelligent technologies would account for a range between 3 and 8% of observed productivity change. We also document that the time pattern of productivity spillovers associated with intelligent technology patents may conform to a productivity J-curve , corroborating the view that such innovations could behave as General Purpose Technologies (GPTs). [ABSTRACT FROM AUTHOR]

• We describe the development of Pharma 4.0, a prospective cyber–physical-based framework for drug discovery and development. Pharma 4.0, a technology ecosystem in drug development analogous to Industry 4.0 in healthcare, is transforming the traditional approach to drug discovery and development, aligning product quality with less time to market, and creating intelligent stakeholder networks through effective collaborations. The wide range of potential Pharma 4.0 networks have produced several conceptualizations, which have led to a lack of clarity and definition. The main emphasis of this paper is on the clinical trial stage of drug development in the Pharma 4.0 era. It highlights the merged computerized technologies that are currently used in clinical research, and proposes a framework for integrating Pharma 4.0 technologies. The impact of and barriers to employing the proposed framework are discussed, highlighting its potential and some future research applications. [ABSTRACT FROM AUTHOR]

• Revealing the concept, connotation, and state-of-the-art architecture of HSM. • A comprehensive survey on key enablers and applications of HSM. • Identification of challenges and future research directions towards HSM. • Enhancing the research, implementation, and application of HSM. As businesses started to embrace Industry 4.0, along came the Fifth Industrial Revolution. Industry 5.0 complements the existing Industry 4.0 paradigm for the not-too-distant future by highlighting research and innovation as drivers for a transition to a sustainable, human-centric, and resilient industry. The core values of Industry 5.0 including human-centricity, sustainability, and resilience have prompted formal discussions that manufacturing should be human-centric. Human-centric smart manufacturing (HSM) takes full advantage of human flexibility, machine precision, and new-generation information technologies to construct a super smart, sustainable, and resilient manufacturing system. Nowadays, extensive research on the concept, architecture, enabling technologies, and applications of smart manufacturing have been conducted. However, research on HSM is relatively lacking, yet starting to gain traction rapidly. Hence, this paper primarily carries out a systematic literature review on HSM to identify promising research topics with high potential for further investigations. Firstly, this paper reveals the concept and connotation of HSM, on which the state-of-the-art architecture of HSM is introduced. Then, several promising Industry 4.0 technologies are identified as the key enablers that can continue to support the construction and operation of HSM, where a comprehensive survey and discussion on these technologies are conducted. Thirdly, what's new in HSM applications and how HSM will reshape the entire lifecycle of industrial products are explored. Finally, this paper identifies limitations, barriers, and challenges that will be encountered during the developing, operating, and maintaining HSM, and provides valuable research directions to continuously improve HSM. [ABSTRACT FROM AUTHOR]

Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. The majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance is the current trend of smart maintenance, followed by most maintenance engineers. The challenging task in predictive maintenance is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, a data-driven approach for predictive maintenance is taking a new flight towards smart manufacturing. Several researchers have published work related to fault diagnosis in rotating machines, mainly exploring a single type of fault. However, a consolidated review of literature that focuses more on the "multi-fault diagnosis" aspect of rotating machines is lacking. There is a need for a study that would systematically cover all the aspects right from sensor selection, data acquisition, feature extraction, multi-sensor data fusion to the systematic review of AI techniques employed in multiple fault diagnosis. In this regard, this paper attempts to achieve the same by implementing a systematic literature review on a Data-driven approach for multi-fault diagnosis of Industrial Rotating Machines using the "Preferred Reporting Items for Systematic Reviews and Meta-Analysis" (PRISMA) method. The PRISMA method is a collection of guidelines for the composition and structure of systematic reviews and other meta-analyses. This paper identifies the foundational work done in the field and gives a comparative study of different aspects related to multi-fault diagnosis of industrial rotating machines. The paper also identifies the major challenges, research gap. It gives solutions using recent advancements in AI in implementing multi-fault diagnosis, giving a strong base for future research in this field. [ABSTRACT FROM AUTHOR]

In the Industry 4.0 era, a myriad of sensors of diverse nature (temperature, pressure, etc.) is spreading throughout the entire value chain of industries, being potentially exploitable for multiple purposes, such as Predictive Maintenance (PdM): the just-in-time maintenance of industrial assets, which results in reduced operating costs, increased operator safety, etc. Nowadays, industrial processes require to be highly configurable, in order to proactively adapt their operation to diverse factors such as user needs, product updates or supply chain uncertainties. This limits current Industry 4.0-PdM solutions, typically consisting of ad-hoc developments intended for specific scenarios, i.e. they are designed to operate under certain conditions (configurations, employed sensors, etc.), being unable to manage changes in their setup. This paper presents a general Deep Learning (DL) architecture, MachNet, which deals with such heterogeneity and is able to address PdM problems of a diverse nature. The modularity of the proposed architecture enables it to deal with an arbitrary number of sensors of different types, also allowing the integration of prior information (age of assets, material type, etc.), which clearly affects performance and is often neglected. In practice, our architecture effortlessly adapts to the assets' specifications and to different PdM problems. That is, MachNet becomes an architectural template that can be instantiated for a given scenario. We tested our proposal in two different PdM-related problems: Health State (HS) and Remaining-useful-Life (RuL) estimation, achieving in both cases comparable or superior performance to other state-of-the-art approaches, with the additional advantage of the generality that MachNet offers. [ABSTRACT FROM AUTHOR]

The wider availability of low-cost sensing and data acquisition technologies means that real-time sensing of tribological parameters is becoming increasingly viable. Consequently, the potential to use these technologies to monitor in-service tribological components has increased significantly. This paper presents a review of a number of state-of-the-art in sensors for measuring friction, wear and lubricant properties. It also elaborates on the use of sensor coatings as an emerging area for directly probing the tribological interface. It is concluded that sensors will find ever increasing uses in condition monitoring" applications. However, sensing and tribology is beginning to evolve towards "Tribotronics" where combining the sensing of machine elements that have conventionally been passive with computational capability, or even embedded intelligence, along with actuation can create active machine elements, optimised to operate with say minimum power loss in all situations of duty. Additionally, it is noted that by incorporating sensing and responsive capabilities, functional surfaces can also become part of a bigger connected systems particularly in association with Industry 4.0. Increased use of sensors in tribological components alongside machine learning and artificial intelligence, will also support the shift in industrial tribological analytics. [ABSTRACT FROM AUTHOR]

In the manufacturing industry, a hydraulic system harnesses liquid fluid power to create powerful machines. Under the trend of Industry 4.0, the predictive maintenance of hydraulic systems is transforming to more intelligent and automated approaches that leverage the strong power of artificial intelligence and data science technologies. However, due to the knowledge-intensive and heterogeneous nature of the manufacturing domain, the data and information required for predictive maintenance are normally collected from ubiquitous sensing networks. This leads to the gap between massive heterogeneous data/information resources in hydraulic system components and the limited cognitive ability of system users. Moreover, how to capture and structure useful domain knowledge (in a machine-readable way) for solving domain-specific tasks remains an open challenge for the predictive maintenance of hydraulic systems. To address these challenges, in this paper we propose a virtual knowledge graph-based approach for the digital modeling and intelligent predictive analytics of hydraulic systems. We evaluate the functionalities and effectiveness of the proposed approach on a predictive maintenance task under real-world industrial contexts. Results show that our proposed approach is capable and feasible to be implemented for digital modeling, data access, data integration, and predictive analytics. • A domain ontology HSO is built to formalise domain knowledge about hydraulic systems. • A virtual knowledge graph system is explored to facilitate data integration. • An intelligent predictive maintenance approach is proposed to predict degradation. • The effectiveness of the proposed approach is evaluated on a real-world case study. [ABSTRACT FROM AUTHOR]

To build intelligent manufacturing systems that react flexibly in case of failures or unexpected circumstances, manufacturing capabilities of production systems must be utilized as much as possible. Artificial Intelligence (AI) and, in particular, automated planning can contribute to this by enabling flexible production processes. To efficiently leverage automated planning, an almost complete planning domain description of the real-world is necessary. However, creating such planning descriptions is a demanding and error-prone task that requires high manual efforts even for domain experts. In addition, maintaining the encoded knowledge is laborious and, thus, can lead to outdated domain descriptions. To reduce the high efforts, already existing knowledge can be reused and transformed automatically into planning descriptions to benefit from organization-wide knowledge engineering activities. This paper presents a novel approach that reduces the described efforts by reusing existing knowledge for planning and scheduling in Industry 4.0 (I4.0). For this purpose, requirements for developing a converter that transforms existing knowledge are derived from literature. Based on these requirements, the SWS2PDDL converter is developed that transforms the knowledge into formal Planning Domain Definition Language (PDDL) descriptions. The approach's usefulness is verified by a practical evaluation with a near real-world application scenario by generating failures in a physical smart factory and evaluating the generated re-planned production processes. When comparing the resulting plan quality to those achieved by using a manually modeled planning domain by a domain expert, the automatic transformation by SWS2PDDL leads to comparable or even better results without requiring the otherwise high manual modeling efforts. • Literature-based requirements for transforming semantic models into planning domains. • Literature-based requirements for using AI planning in business process management. • Applying design science for developing a converter for reusing semantic models. • Using AI planners to manually created and automatically converted planning domains. • Evaluation on a smart factory for verifying the suitability of the converter. [ABSTRACT FROM AUTHOR]

The paper aims at investigation of the nexus between Industry 4.0 (I4.0) and environmental sustainability in addition to exploring the long-run and short-run effects of Industry 4.0 on CO 2 emissions. For this end, energy consumption, internet and communication technology (ICT) exports, research and development (R&D), artificial intelligence (AI), ICT technology patents and bitcoin are taken as control variables of Industry 4.0 for a panel of 9 countries that contribute to 62% of the total CO 2 emissions in the world. For this purpose, the paper follows two approaches. First, the paper proposes utilization of AI and ICT technology patents, technology-related R&Ds and ICT exports as variables of I4.0 in addition to investigating the effects of economic growth, energy consumption and bitcoin. Second, to control structural changes and nonlinearity in the cointegrating relations and existence of degenerate cointegration, Fourier panel bootstrap autoregressive distributed lag model (FPBARDL) is utilized. Afterwards, FPBARDL based long, short and strong causality analyses are conducted. The empirical findings revealed positive impacts of all I4.0-related variables on emissions in the long-run. Though I4.0 related AI and ICT innovation has no direct effect in the short-run, its effects are determined through increased energy consumption towards emissions. The strong positive effects of energy consumption and positive effects of economic growth, ICT exports and R&D are observed both in the short and long-run. In addition to positive impacts of I4.0 on environment, the findings favor insufficiency of policies focusing on lowering emissions in I4.0 context.Policy recommendations include strong commitment to energy efficiency and renewable energies and trade policies with environmental concerns. [ABSTRACT FROM AUTHOR]

Energy intensive industries can be classified into those that process metal, glass, ceramics, paper, cement, and bulk chemicals. They are associated with significantly high proportions of carbon emissions, consume a lot of energy and raw materials, and cause energy wastage as a result of heat escaping from furnaces, reheating of products, and rejection of parts. In alignment with UN sustainable development goals of industry, innovation, infrastructure and responsible consumption and production, it is important to ensure that the energy consumption of EIIs are monitored and reduced such that their energy efficiency can be improved. Towards this aim, it is possible to employ the concepts of digitalisation and smart manufacturing to identify the critical areas of improvement and establish enablers that can help improve the energy efficiency. The aim of this research is to review the current state of digitalisation in energy-intensive industries and propose a framework to support the realisation of sustainable smart manufacturing in Energy Intensive Industries (EIIs). The key objectives of the work are (i) the investigation of process mining and simulation modelling to support sustainability, (ii) embedding intelligence in EIIs to improve energy and material efficiency and (iii) proposing a framework to enable the digital transformation of EIIs. The proposed five-layer framework employs data acquisition, process management, simulation & modelling, artificial intelligence, and data visualisation to identify and forecast energy consumption. A detailed description of the various phases of the framework and how they can be used to support sustainability and smart manufacturing is demonstrated using business process data obtained from a machining industry. In the demonstrated case study, the process management layer utilises Disco for process mining, the simulation layer utilises Matlab SimEvent for discrete-event simulation, the artificial intelligence layer utilises Matlab for energy prediction and the visualisation layer utilises grafana to dashboard the e-KPIs. The findings of the research indicate that the proposed digital life-cyle framework helps EIIs realise sustainable smart manufacturing through better understanding of the energy-intensive processes. The study also provided a better understanding of the integration of process mining and simulation & modelling within the context of EIIs. [Display omitted] • Framework to support the digital life cycle management of Energy Intensive Industries. • Investigate the interplay between process mining and simulation modelling to support sustainability. • Intelligence needs to be embedded to improve energy efficiency and material efficiency. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence as an important tool in length metrology from nano scale. • AI methods enable classification, prediction, decision-making and optimization. • New ideas of AI for preparation and realization of measurement process are presented. • An example showing AI in analysis of interference fringes. • Artificial Intelligence supports an operator in measurement process. The applications of modern length and angle metrology continue to pose new challenges for manufacturers and operators of measuring equipment, starting from nano scale. One method to support measurement processes is the use of artificial intelligence. As an essential part of Industry 4.0 and Metrology 4.0 strategies, it actively supports humans in executing measurement activities. The use of artificial intelligence in this area is determined by technological and social factors, as the shortage of highly skilled operators is becoming increasingly acute. The paper discusses the possibilities of using artificial intelligence in coordinate metrology. Ideas including the selection of the measurement strategy, conditions, filtering techniques, as well as self-verification or even self-calibration were presented. Schemes of procedures using local and global databases of applications and measuring instruments are presented. A new area application in the analysis of interference fringes is shown, with method descriptions and experimental results. [ABSTRACT FROM AUTHOR]

In this paper we introduce the current thrust of development in machine learning and artificial intelligence, fueled by advances in statistical learning theory over the last 20 years and commercial successes by leading big data companies. Then we discuss the characteristics of process manufacturing systems and briefly review the data analytics research and development in the last three decades. We give three attributes for process data analytics to make machine learning techniques applicable in the process industries. Next we provide a perspective on the currently active topics in machine learning that could be opportunities for process data analytics research and development. Finally we address the importance of a data analytics culture. Issues discussed range from technology development to workforce education and from government initiatives to curriculum enhancement. [ABSTRACT FROM AUTHOR]

In the railway domain, rolling stock maintenance affects service operation time and efficiency. Minimizing train unavailability is essential for reducing capital loss and operational costs. To this aim, prediction of failures of rolling stock equipment is crucial to proactively trigger proper maintenance activities. Indeed, predictive maintenance is a golden example of the digital transformation within Industry 4.0, which affects several engineering processes in the railway domain. Nowadays, it may leverage artificial intelligence and machine learning algorithms to forecast failures and schedule the optimal time for maintenance actions. Generally, rail systems deteriorate gradually over time or fail directly, leading to data that vary extremely slowly. Indeed, ML approaches for predictive maintenance should consider this type of data to accurately predict and forecast failures. This paper proposes a methodology based on Long Short-Term Memory deep learning algorithms for predictive maintenance of railway rolling stock equipment. The methodology allows us to properly learn long-term dependencies for gradually changing data, and both predicting and forecasting failures of rail equipment. In the framework of an academic-industrial partnership, the methodology is experimented on a train traction converter cooling system, demonstrating its applicability and benefits. The results show that it outperforms state-of-the-art methods, reaching a failure prediction and forecasting accuracy over 99%, with a false alarm rate of ∼ 0. 4 % and a mean absolute error in the order of 1 0 − 4 , respectively. [ABSTRACT FROM AUTHOR]

• Advanced and efficient image-text multimodal fusion approach. • Clever use of matrix transformation to achieve alignment of different modal features. • Using attention mechanism to ensure the parallelism of the model and improve the training speed. • The unimodal and multimodal features are stitched together to ensure the complementarity of the modalities. • The pain point problem of difficult to obtain domain datasets is solved by constructing a generic dataset. Combining Artificial Intelligence (AI) to process rich media information has become an important part of Industry 4.0. Sentiment recognition in AI aims to analyze user emotions contained in rich media to facilitate service enhancement. Previous research on sentiment recognition has mainly focused on academia, and few have discussed algorithmic applications and innovations in industry. In this paper, we propose a general approach for multimodal sentiment recognition for images and text. The method provides a new approach for processing rich media information by fully considering the internal features of each modality itself as well as the correlations between the modalities. In the dataset constructed in this paper, the accuracy rate is improved by more than 4% compared with the method using single modality. The effectiveness and generality of the method in multimodal sentiment recognition is demonstrated by extending the experiments with a multimodal public dataset. [Display omitted] [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology is far from being comprehensive and mature, resulting in their piecemeal implementation in practice where some functions are automated by DTs, and others are still performed by humans. This piecemeal implementation of DTs often leaves practitioners wondering what roles (or functions) to allocate to DTs in a work system, and how might it impact humans. A lack of knowledge about the roles that humans and DTs play in a work system can result in significant costs, misallocation of resources, unrealistic expectations from DTs, and strategic misalignments. To alleviate this challenge, this paper answers the research question: When humans work with DTs, what types of roles can a DT play, and to what extent can those roles be automated? Specifically, we propose a two-dimensional conceptual framework, Levels of Digital Twin (LoDT). The framework is an integration of the types of roles a DT can play, broadly categorized under (1) Observer, (2) Analyst, (3) Decision Maker, and (4) Action Executor, and the extent of automation for each of these roles, divided into five different levels ranging from completely manual to fully automated. A particular DT can play any number of roles at varying levels. The framework can help practitioners systematically plan DT deployments, clearly communicate goals and deliverables, and lay out a strategic vision. A case study illustrates the usefulness of the framework. • Digital Twin human interaction • Digital Twin can play multiple roles in a work system. • Levels of Digital Twin framework provides a common language for understanding. • Digital Twin must be evaluated for each case – a "one size fits all" does not work. [ABSTRACT FROM AUTHOR]

The recognition of human actions based on artificial intelligence methods to enable Human–Robot Collaboration (HRC) inside working environments remains a challenge, especially because of the necessary huge training datasets needed. Meanwhile, Digital Twins (DTs) of human centered productions are increasingly developed and used in the design and operation phases. As instance, DTs are already helping industries to design, visualize, monitor, manage, and maintain their assets more effectively. However, few works are dealing with using DTs as a dataset generator tool. Therefore, this paper explores the use of a DT of a real industrial workstation involving assembly tasks with a robotic arm interfaced with Virtual Reality (VR) to extract a digital human model. The DT simulates assembly operations performed by humans aiming to generate self-labeled data. Thereby, a Human Action Recognition dataset named InHARD-DT was created to validate a real use case in which we use the acquired auto-labeled DT data of the virtual representation of the InHARD dataset to train a Spatial–Temporal Graph Convolutional Neural Network with skeletal data on one hand. On the other hand, the Physical Twin (PT) data of the InHARD dataset was used for testing. Obtained results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Process intensification 4.0 (PI4.0) framework allows system-level transformations. • PI4.0 incorporates a data approach based on the design principles of industry 4.0. • An iterative design strategy is presented as the backbone of PI4.0. • Machine Learning/advanced computing accelerate knowledge discovery, driving PI4.0. • Different case studies are reviewed that support the philosophy behind PI4.0. This paper reviews system-level transformations converging into the next generation of Process Intensification strategies defined as PI4.0. Process Intensification 4.0 uses data-driven algorithms to understand other physical and chemical processes that improve equipment design, predictive control, and optimization. Following this, an overview of the use of Artificial Intelligence techniques, particularly Machine Learning for the acceleration of equipment design, process optimization, and streamlining, is presented. This work will highlight and discuss the emerging framework of the integration between Circular Chemistry, Industry 4.0, and Process Intensification and how the data obtained from this integration is at the core of the next generation of Process Intensification strategies. This is supported by a discussion of different cases that apply data-driven models enabled by Machine Learning as a mean to enhance an intensified system (product synthesis, equipment or methods). [Display omitted] [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

• Traditional elevator fault monitoring methods are less timely and inaccurate. • Flink performs better than Spark Streaming in terms of elevator data processing. • A finite state machine is used to monitor the operation of the elevator control system. Under "Industry 4.0", the implementation and application of big data and Artificial Intelligence (AI) technology in the elevator industry has become more and more common. With the surge of the elevator operation data and higher requirements for its real-time performance, the traditional elevator fault monitoring is inaccurate, which needs to be solved urgently. In this paper, a fault monitoring and diagnosis method of elevator based on AI and big data is proposed. Firstly, the elevator system and its fault types and causes are analyzed. Then in order to select the best big data processing tools, the performance of Flink and Spark Streaming is compared. The results show that Flink features faster computing speed and is more suitable for handling big data. Thirdly, a pattern recognition algorithm based on finite state machine (FSM) is proposed to monitor the running state for whole elevator control system. Finally, simulation experiment has been made. Graphical Abstract [Display omitted]. [ABSTRACT FROM AUTHOR]

Palm oil is a renewable resource that has the potential to replace fossil fuel and petrochemical for a better sustainable system. However, there is room for improvement in the current operation of the palm oil industry to achieve better sustainability development. The industrial revolution toward automation and artificial intelligence (AI) is the new trend known as the fourth industrial revolution (Industry 4.0). Unfortunately, the palm oil industry has been moving slowly in this revolution. This paper aims to conduct a detailed review of the current state of the palm oil industry development toward Industry 4.0. A novel Hazard and Operability Analysis (HAZOP) approach is adopted i) to ensure a detailed evaluation of the existing problems, and ii) to identify potential implementation of Industry 4.0 technologies in the palm oil industry. HAZOP is a common approach used in chemical engineering to systematically evaluate process safety and identify the possible improvement of the existing system. The same concept is applied in this paper to investigate the possible adaptation of Industry 4.0 technologies to improve the palm oil industry. Existing Industry 4.0 technologies and features were evaluated to identify feasible adaptation in the industry. The HAZOP review proposed 23 recommendations to improve the palm oil industry with Industry 4.0 technologies to achieve a higher standard in sustainable production. A total of 13 specific Industry 4.0 features were identified as the potential development gaps for palm oil industry stakeholders, which included the adaptation of Internet-of-Things sensors, cloud computing, blockchain, and smart imaging processing technologies. • Evaluation of limitation and problems of current palm oil industry. • Discussion of adaptation potential of Industry 4.0 in palm oil industry. • HAZOP approach literature review to identify research gaps in palm oil industry. • Potential Industry 4.0 features to be implemented in palm oil industry is proposed. [ABSTRACT FROM AUTHOR]

• Data is a business asset and this must be properly governed. • Data governance is critical for Industry 4.0. • Specification and description of data governance requirements for Industry 4.0. • Proposal of a reference framework for constructing data governance systems. • Data governance reference framework based on TOGAF, ISO-42010 and RAS standards. The fourth industrial revolution, or Industry 4.0, represents a new stage of evolution in the organization, management and control of the value chain throughout the product or service life cycle. This is mainly based on the digitalization of the industrial environment by means of the convergence of Information Technologies (IT) and operational Technologies (OT) through cyber-physical systems and the Industrial IoT (IIoT) and the use of data generated in real time for gaining insights and making decisions. Therefore data becomes a critical asset for Industry 4.0 and must be managed and governed like a strategic asset. We rely on Data Governance (DG) as a key instrument for carrying out this transformation. This paper presents the design of a specific governance framework for Industry 4.0. First, this contextualizes data governance for Industry 4.0 environments and identifies the requirements that this framework must address, which are conditioned by the specific features of Industry 4.0, among others, the intensive use of big data, the cloud and edge computing, the artificial intelligence and the current regulations. Next, we formally define a reference framework for the implementation of Data Governance Systems for Industry 4.0 using international standards and providing several examples of architecture building blocks. [ABSTRACT FROM AUTHOR]

The latest technological developments epitomized in Industry 4.0 have created a disruptive effect on the production/service systems and value chains. Industry 4.0, building on the integration of information and communication technologies, Internet of things, robotics, additive manufacturing, and artificial intelligence, aims for developing autonomous and dynamic operations to enable the mass production of highly customized products. Industry 4.0 and quality management share the same objective, that is, improving process performance, yet through different trajectories. However, notwithstanding Industry 4.0 developments, quality management models have remained stagnant and failed to keep abreast of these advancements. This paper evaluates the alignment of quality management models with Industry 4.0. The paper shows that quality models are not congruent with Industry 4.0. The former build on the paradigm that supports establishing formal systems, compliance with specifications, and meeting requirements which are quite irrelevant to Industry 4.0. The paper compares the features of Industry 4.0 against quality models, highlights inadequacies in quality models, and develops recommendations for future quality models. This study is the first to review quality management models in light of the Industry 4.0 developments. [ABSTRACT FROM AUTHOR]

• An Intelligent IoT precision irrigation approach was implemented at farm and district scales. • The precision irrigation system was made up of the integration of intelligent agents, crop models, agent-based modeling and Internet of Things technologies. • The behavior of each irrigation agent was influenced by their own interests and neighbors. • The proposed approach is promising for the design of distributed systems in agriculture. The fourth industrial revolution in agriculture seeks the automation of traditional practices, using modern smart technologies. Advances in electronics, computation and the internet of things are integrated for improving field inputs management. The aim of this paper is to present the design and implementation of an intelligent IoT-multiagent precision irrigation approach for improving water use efficiency in irrigation systems. The study site was the large-scale irrigation and drainage district of Chicamocha and Firavitoba (Usochicamocha) located in Boyacá - Colombia, where water is distributed from the Chicamocha riverbed. In the proposed system, irrigation is supervised and controlled in each field by an intelligent irrigation agent that autonomously prescribes and applies water amounts with agronomical criteria. The methodology was applied with real (cyber-physical) and virtual (simulated) intelligent agents and was extended to eleven pump stations that supply water to 5911 fields. Using a MQTT protocol, hundreds of irrigation intelligent agents report water prescriptions and crop characteristics to a master agent in each pump station, who creates a regional irrigation map to manage georeferenced field information and performs negotiation of water resources between agents according to supply availability. Field maps and intelligent irrigation agents can be visualized using devices with internet access. Results demonstrated that irrigation amounts were correctly applied on the fields, thus improving the water use efficiency. This technology is a novel support to decision-making in water resources management applications at field and district scales. [ABSTRACT FROM AUTHOR]

Recent trends in intelligent manufacturing are transforming shop floor environments into digital factories, thanks to a pervasive integration of information and communication technologies in production lines. Industrial processes become the source of high-volume heterogeneous data, paving the way to create manufacturing intelligence by means of machine learning and data-driven methodologies. In such settings, predictive diagnostics play a crucial role, as they promise to predict future critical conditions in the production process. Unfortunately, the diffusion of data-driven predictive maintenance methodologies is limited by (i) the absence of timely ground-truth knowledge (i.e., class labels), required in the learning phase of data-driven supervised approaches, and (ii) the limited availability of data-mining expertise among application-domain experts, required to harness the power of machine learning techniques. Innovative data-driven services are needed to support domain experts in (i) applying powerful self-learning intelligent techniques with limited technical expertise and (ii) easily understanding results and choices operated by such intelligent techniques, to increase trust by means of transparency. To this aim, this paper presents UDaMP, an integrated platform to support manufacturing intelligence by providing a transparent, self-tuning, unsupervised discovery and assisted data labelling service for predictive maintenance, specifically targeted at cyclic industrial processes. UDaMP includes (i) production-cycle-aware feature engineering, (ii) unsupervised discovery of production-cycle categories, (iii) self-tuning of the optimal number of categories, (iv) human-readable characterisation of production-cycle categories, and (v) assisted data labelling for domain experts. Scalable clustering algorithms automatically discover groups of production cycles sharing common time-independent properties. A self-tuning strategy is integrated to automatically configure the specific input parameter and select the best approach for the data under analysis. Each cluster is then locally characterised through the data distribution of the top 10 most relevant features to support domain experts in uncovering its meaning. Experimental evaluation of UDaMP has been performed on real-world data collected in two different industrial settings. • Semi-supervised labelling of production cycles for predictive maintenance. • Wide applicability to any cycle-based production process without ground truth labels. • Exploitable by domain experts thanks to transparent self-tuning techniques. [ABSTRACT FROM AUTHOR]

• Current states of DT research focusing on product design are presented. • The challenge and potential application of DT in product design are discussed. • The directions of the potential DT studies in product design are discussed. In the era of digitalization, there are many emerging technologies, such as the Internet of Things (IoT), Digital Twin (DT), Cloud Computing and Artificial Intelligence (AI), which are quickly developped and used in product design and development. Among those technologies, DT is one promising technology which has been widely used in different industries, especially manufacturing, to monitor the performance, optimize the progresses, simulate the results and predict the potential errors. DT also plays various roles within the whole product lifecycle from design, manufacturing, delivery, use and end-of-life. With the growing demands of individualized products and implementation of Industry 4.0, DT can provide an effective solution for future product design, development and innovation. This paper aims to figure out the current states of DT research focusing on product design and development through summarizing typical industrial cases. Challenges and potential applications of DT in product design and development are also discussed to inspire future studies. [ABSTRACT FROM AUTHOR]

To implement an Industry 4.0 Framework in an ongoing industrial manufacturing process to prepare it for complex Artificial Intelligence use cases is not an easy task. The automotive industry is undergoing a large transformation due to a variety of disruptive factors through the introduction of CASE (Connected, Autonomous, Shared, Electrified) technologies. Production improvements will enable future development of CASE capabilities. This paper presents an Industry 4.0 framework, named BiDrac, that integrates computing, communication and control under an Industrial Cyber–Physical​ System (ICPS) ecosystem. It combines Artificial Intelligence and Industrial Internet of Things (IIoT) inside the Industry 4.0 paradigm for the Predictive Maintenance of an Automotive Paint Shop Process inside an Industrial Cloud (IC) as an open platform that provides community of cloud-based solutions to enable the development of new digital solutions. Several real use cases of the proposed platform have been included in the results section to illustrate the potential of the proposed framework. [ABSTRACT FROM AUTHOR]

Data-driven models for industrial energy savings heavily rely on sensor data, experimentation data and knowledge-based data. This work reveals that too much research attention was invested in making data-driven models, as supposed to ensuring the quality of industrial data. Furthermore, the true challenge within the Industry 4.0 is with data communication and infrastructure problems, not so significantly on developing modelling techniques. Current methods and data infrastructures for industrial energy savings were comprehensively reviewed to showcase the potential for a more accurate and effective digital twin-based infrastructure for the industry. With a few more development in enabling technologies such as 5G developments, Internet of Things (IoT) standardization, Artificial Intelligence (AI) and blockchain 3.0 utilization, it is but a matter of time that the industry will transition towards the digital twin-based approach. Global government efforts and policies are already inclining towards leveraging better industrial energy efficiencies and energy savings. This provides a promising future for the development of a digital twin-based energy-saving system in the industry. Foreseeing some potential challenges, this paper also discusses the importance of symbiosis between researchers and industrialists to transition from traditional industry towards a digital twin-based energy-saving industry. The novelty of this work is the current context of industrial energy savings was extended towards cutting-edge technologies for Industry 4.0. Furthermore, this work proposes to standardize and modularize industrial data infrastructure for smart energy savings. This work also serves as a concise guideline for researchers and industrialists who are looking to implement advanced energy-saving systems. Image 1 • Various countries transitioning towards Industry 4.0 via industrial analytics and energy savings. • Enabling technologies can lead to the deployment of new industrial data infrastructures. • Advances in digital twins, AI, blockchain and IoT can serve as enabling technologies. • Global policies are inclining towards improving industrial energy efficiency. • Communication between researchers and industrialist is crucial for implementation. [ABSTRACT FROM AUTHOR]

In the Healthcare 4.0 era, recent breakthroughs in emerging and disruptive technologies, such as Artificial Intelligence (AI), machine learning and robotics, have significantly changed the way in which healthcare services are delivered to patients. Advancements in patient monitoring systems and wearable devices have created more powerful and flexible real-time remote patient observation. Healthcare provision has experienced remarkable improvements facilitated by cloud computing and wireless technologies, while wireless sensor networks now play an important role in mobile technologies for e-healthcare. In this paper, we propose a mobile-based healthcare system for the Healthcare 4.0 era. The proposed approach enables patients to self-authenticate, using their own mobile and wearable devices, establishing a session key between owned devices. After mutual authentication, the cloud server verifies each user. The security of the approach is described through the AVISPA tool. We analyze the embedded security and functionality which improves the healthcare system and calculate the relative communication and computation costs. Results show that the proposed approach provides greater security compared to other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry. • Genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning are the most widely used AI methods in AEC. • Optimization, simulation, uncertainty, project management, and bridges are the most commonly addressed topics/issues using AI methods/concepts. • Future research opportunities lie in applying robotic automation to AEC problems. • There are future research opportunities also in using convolutional neural networks (CNNs) for solving AEC problems. [ABSTRACT FROM AUTHOR]

• A FANET based on a fleet of drones provides the connectivity to IoT-enabled devices. • A battery charge station supplied by a wind generator enables persistent mission without any human intervention. • Reinforcement Learning optimally manage the use of the fleet to meet bandwidth request. • Reinforcement Learning reduces the wasted energy and enables the use of the wind generator. • Wireless power transfer based charge station has been used to face hostile environmental conditions. The optimal management of a fleet of drones is proposed in this paper for providing connectivity to sensors and actuators in Industrial Internet of Things (IIoT) scenarios. The persistent mission without any human intervention on the battery charge is obtained by means of an on-field wind generator supplying a charge station that adopts resonant wireless power transfer. The objective of the fleet management is to provide the best connectivity over the time considering the variability of both the bandwidth request and the wind energy availability. The optimal management is performed by a system controller adopting reinforcement learning (RL) for deciding the number of drones to take off and, consequently, the instantaneous provided bandwidth. A constant charge time of drone battery represents a key element of the system because this enables to strongly reduce the complexity of the system controller task. To this purpose, an adaptive current control for the charge station is introduced to compensate charge time variabilities due to the coupling factor changes caused by misalignments that can occur between a pad and a drone. The results have highlighted that the RL provides good performance improvement in case of green generation. An important aspect arose from this study is the ability of RL to increase the saved energy even if it is not considered as a target of the controller. [ABSTRACT FROM AUTHOR]

Polyacrylonitrile (PAN) is a versatile man-made polymer and has been used in a large array of products since its first mass production in the mid 40s. Among all applications of PAN the widely used application is in manufacture of precursor fiber for fabrication of carbon fibers. The process of PAN-based carbon fiber production comprises fiber spinning, thermal stabilization and carbonization stages. Carbon fiber properties are significantly dependent on the quality of PAN precursor fiber and in particular the process parameters involved in thermal stabilization. This paper is the first comprehensive review that provides a general understanding of the links between PAN fiber structure, properties, and its stabilization process along with the use of mathematical modelling as a powerful tool in prediction and optimization of the processes involved. Since the promise of the mathematical modelling is to predict the future behaviour of the system and the value of the variables for the unseen or unmeasured domain of variables; and in the era of industry 4.0 rise, this review will be valuable in further understanding of the intricate processes of carbon fiber manufacture and utilising the advanced mathematical modelling using machine learning techniques to predict and optimize a range of critical factors that control the quality of PAN and resultant carbon fibers. [ABSTRACT FROM AUTHOR]

The adoption of emerging technological trends and applications of the Internet of Things (IoT) in the industrial systems is leading towards the development of Industrial IoT (IIoT). IIoT serves as a new vision of IoT in the industrial sector by automating smart objects for sensing, collecting, processing and communicating the real-time events in industrial systems. The major objective of IIoT is to achieve high operational efficiency, increased productivity, and better management of industrial assets and processes through product customization, intelligent monitoring applications for production floor shops and machine health, and predictive and preventive maintenance of industrial equipment. In this paper, we present a new and clear definition of IIoT, which can help the readers to understand the concept of IIoT. We have described the state-of-the-art research efforts in IIoT. Finally, we have highlighted the enabling technologies for IIoT and recent challenges faced by IIoT. [ABSTRACT FROM AUTHOR]

Inventions and new ideas are at the center of societal transformation. Inventions have been historically protected by a system of intellectual property law of which patents are at the heart. Whilst patent law is still deeply moored in its roots in the industrial revolution, to a greater extend it has been able to adapt to the successive revolutions like the computing albeit with some challenges. The world is now at an unprecedented threshold of the most far reaching revolution whose consequences to patent law in particular are so far reaching that its impact is still unknown. This is the AI revolution. This paper begins with an analysis of the AI revolution from available literature and research. It notes that there are divergent views among scholars about the impact of AI on various elements of patent law. It posits that this dissonance might be a result of most scholars treating AI as one homogenous block without distinguishing the various phases in the evolution of AI. It thus proposes a new sui generis conceptual framework – [ the Multi-Level Model] as a suitable basis for insightful conceptual analysis of AI impact on patent law with focus on two key questions; patentability and inventorship. [ABSTRACT FROM AUTHOR]

This paper discusses possible changes in cement standards to cope with megatrends: climate change and industry 4.0. Most standards are prescriptive, defining cements by composition of SCMs, hence the number of types increase, and composition limits widen. Cements are classified by compressive strength of fixed water/cement mortars. This pragmatic approach ignores physical effects, that results in variable mixing water demand. It hinders the development of cements with low water demand, products of optimized particle engineering and dispersants, which allows formulate cements with lower clinker fraction. Also, most standard tests are difficult to automate to generate large datasets crucial to train artificial intelligence. Performance-based standards are an alternative, but simple and progressive approach is recommended to ease transition. Cement types should not be solely defined by composition but rather classified by performance characteristics including durability and environmental. Combined water fraction, cwf , may be a good parameter to replace strength class. [ABSTRACT FROM AUTHOR]