Search

Decarbonization of steelmaking has stagnated while it has a considerable share of global greenhouse gas emissions and a growing demand. Digitalization is seen as a viable option to reduce emissions and costs of the sector in the near term and life cycle assessment (LCA) as a comprehensive framework to evaluate changes in production practices. In this study, we analyze the potential impact of using optimization algorithms to improve the operation of a steelmaking plant in Spain. Specifically, we study the potential effects of optimizing the sequence in which steel is produced to minimize losses during casting. The global warming (GW) impacts and economic costs are quantified using a dynamic LCA model, considering uncertainty and temporal variability using an open‐source LCA framework. The results indicate, on average, modest savings in costs and are inconclusive regarding GW emissions. Most of the cost savings come from a reduction in the use of additives and electricity, which are wasted when the steel is scrapped during casting. The methodological framework has proven useful in quantifying and interpreting the potential effects of digitalization. The implemented solution, tested in an industrial setting, allows an automated evaluation of production at the plant using the LCA model, facilitating the use of sustainability criteria in decision‐making. [ABSTRACT FROM AUTHOR]

Within Industry 4.0, the integration of Industrial IoT (IIoT) marks a transformative phase for modern industries, fostering the development of smart factories and intelligent manufacturing systems. Despite the substantial growth of IIoT, a critical research gap persists in web security within IIoT environments. This paper addresses this gap by proposing an explainable and robust machine learning-based web attack detection system to ensure IIoT web application security using ToN-IoT and NF-ToN-IoTv2 datasets that accurately reflects IIoT traffic. Given eXplainable Artificial Intelligence’s (XAI) capability to instill trustworthiness and transparency in learning models, the authors opted for the SHAP technique for feature selection, leveraging its global insights to explain feature contributions to the system’s decision-making. Compared to the existing works, the system demonstrated strong performance across various metrics, including accuracy, recall, precision, specificity, F-value, FPR, FNR, AUC-ROC curve, MCE, training, and prediction times, in binary and multi-classification scenarios. [ABSTRACT FROM AUTHOR]

The advent of the fourth industrial revolution, characterized by artificial intelligence (AI) as its central component, has resulted in the mechanization of numerous previously labor-intensive activities. The use of in silico tools has become prevalent in the design of biopharmaceuticals. Upon conducting a comprehensive analysis of the genomes of many organisms, it has been discovered that their tissues can generate specific peptides that confer protection against certain diseases. This study aims to identify a selected group of neuropeptides (NPs) possessing favorable characteristics that render them ideal for production as neurological biopharmaceuticals. Until now, the construction of NP classifiers has been the primary focus, neglecting to optimize these characteristics. Therefore, in this study, the task of creating ideal NPs has been formulated as a multi-objective optimization problem. The proposed framework, NPpred, comprises two distinct components: NSGA-NeuroPred and BERT-NeuroPred. The former employs the NSGA-II algorithm to explore and change a population of NPs, while the latter is an interpretable deep learning-based model. The utilization of explainable AI and motifs has led to the proposal of two novel operators, namely p-crossover and p-mutation. An online application has been deployed at https://neuropred.anvil.app for designing an ideal collection of synthesizable NPs from protein sequences. [ABSTRACT FROM AUTHOR]

The breakthrough brought by generative adversarial networks (GANs) in computer vision (CV) applications has gained a lot of attention in different fields due to their ability to capture the distribution of a dataset and generate high-quality similar images. From one side, this technology has been rapidly adopted as an alternative to traditional applications and introduced novel perspectives in data augmentation, domain transfer, image expansion, image restoration, image segmentation, and super-resolution. From another side, we found that due to the lack of industrial datasets and the limitation for acquiring and accurately annotating new images, GANs form an exciting solution to generate new industrial image datasets or to restore and augment existing ones. Therefore, we introduce a review of the latest trend in GANs applications and project them in industrial use cases. We conducted our experiments with synthetic images and analyzed most of GAN's failures and image artifacts to provide training's best practices. [ABSTRACT FROM AUTHOR]

The paper presents the use of adaptive neural networks for carrying out simulation optimisation using digital models (discrete event simulation models) created in accordance with the Industry 4.0 concept. The digital models reflect different problems in industrial engineering. The simulation optimisers use an adaptive neural network to find the best settings of the digital models according to defined objective functions for each model. We compared the effectiveness (using different evaluation criteria) of the adaptive neural network (ANN) optimisation method used on 6 different discrete event simulation models. We compared adaptive neural networks with 11 optimisation methods -- pseudo gradient, metaheuristic, evolutionary and swarm optimisation methods (and their combinations). The ANN method demonstrated the ability to efficiently find the global optimum of the objective function in different cases of the objective function -- the ANN method is in the top 5 best tested methods from the 12 optimisation methods. [ABSTRACT FROM AUTHOR]

This study contributes to the understanding of Construction 4.0 (C4.0) adoption in developing countries, with a specific focus on the Mekong Delta region of Vietnam. Due to a lack of innovative methods in construction, developing nations struggle to promote sustainability. To cover this research gap, prior studies have addressed the critical success factors (CSFs) and motivations but neglected the complexity and interconnections of CSFs. This study proposes a Fuzzy Delphi – Analytic Hierarchy Process analysis to discern the most influential factors for successful C4.0 adoption. The results underscore that the motivation to reduce the use of natural resources and establish an effective logistics network supersedes cost-related considerations. Collaboration, information sharing, and strategic management emerge as pivotal CSFs. This study provides valuable insights for policymakers and practitioners aiming to promote sustainable construction practices through C4.0 technology in a developing context. [ABSTRACT FROM AUTHOR]

Timely and accurate misalignment fault detection in rotor bearing systems is crucial for reliable and efficient operation, especially in Industry 4.0 based condition monitoring. The method described in this paper uses artificial neural networks (ANN) and empirical modelling to identify misalignment defects in rotor bearing systems. To capture the dynamic behaviour of the rotor bearing system under different operating conditions and fault scenarios, an empirical model based on Dimensional Analysis (DA) is developed. This model is trained using vibration data obtained from a well-aligned system. By comparing the predicted response with the actual vibration response, an accuracy of 8% to 10% is achieved. However, accurately quantifying the severity of misalignment poses challenges due to system nonlinearities. To address this issue, an ANN is employed to learn the mapping between vibration features and misalignment levels. To train the ANN, a comprehensive dataset is created through experiments conducted on a test rig. Artificial misalignment is introduced to the rotor, and vibration signals are analysed under various conditions of misalignment, rotor speed, and static load. The trained ANN is validated using experimental data, demonstrating an impressive accuracy of 99.46%. This integrated approach holds significant potential for predictive maintenance, leading to improved operational efficiency. [ABSTRACT FROM AUTHOR]

This study propose the use of AI enabled machine learning algorithms with the Bag‐of‐Word (BoW) methods for the detection of intrusions by analysing the system call patterns. Host based Intrusion Detection System can make use of system call patterns to differentiate between normal and anomalous program behaviours. First, the system call patterns are pre‐processed with different approaches like BoW, BoW with Boolean value, BoW with Probability value and BoW with TF‐IDF. Next machine learning algorithms are used to evaluate the performance of classifier models. We used J48 (C4.5), Random Forrest, RIPPER, KNN, SVM, and NaiveBayes ML algorithms. This process was carried out on ADFA‐LD and on our proposed virtual machine monitor (VMM) malware attack data set for analysis. The proposed work is evaluated based on detection accuracy and false alarm rate metrics. Random Forrest algorithm performs better compared with other ML algorithms in terms of intrusion detection accuracy and false alarm rate on ADFA and VMM malware data set. The proposed data set provide better results compared with ADFA‐LD analysed using ML algorithms. The classifier model trained with ADFA and VMM malware system call data sets may do predictive analytics in detecting security issues for Industry 4.0 systems. [ABSTRACT FROM AUTHOR]

In light of Industry 4.0, rapid analysis and optimization of manufacturing processes are emerging as a vital demand of smart manufacturing factories. Ergonomics is an essential aspect of the ongoing screening of working conditions and a fundamental variable in Industry 4.0, as it calls for a flexible manufacturing system to strengthen the competitiveness of factories in the global market. This paper proposes a new augmented reality-based ergonomic evaluation platform: Augmented Reality-based Ergonomic Platform (ARE Platform). Introducing the Augmented Reality technology by superimposing virtual planning objects into the physically existing production environment. Utilizing the motion capture system collects data for a set of ergonomic indexes (RULA, OWAS, and NIOSH), accessibility and visibility verification. ARE platform could be used in the verification phase of smart manufacturing systems to evaluate the level of risk to workers' bodies during operations in real-time. The platform reduces the time and economic cost of verification and satisfies the rapid response of ergonomic evaluation and feedback in the context of smart manufacturing. Finally, the developed ARE platform is validated in two rigorous automotive assembly cases in the laboratory. Meanwhile, the ergonomic assessment results are analysed and reported for a people-oriented manufacturing system. [ABSTRACT FROM AUTHOR]

Human errors are prevalent in manual assembly line statistics, but humans are still a necessary and numerous working force for assembly tasks. Since faster and shorter production cycles lead to higher mental efforts, an alternative to decrease the cognitive load and errors of manual assembly lines is using augmented reality (AR). AR increases system interaction and assists operators in the step-by-step of any tasks performed. Its use allows granular production data, enabling an increase in quality control and timely detection of assembly problems. However, AR alone is not enough to avoid demotivation and lack of engagement in the task, which is repetitive. Gamification can become an auxiliary resource to increase operators' motivation and engagement. This paper presents a system model to integrate AR and gamification for a manual assembly line of printed circuit boards (PCBs) called GRAAL, used to develop an actual system for PCB assembly, which can guide operators using AR but also tracks down their efficiency and efficacy which is then used for gamifying their performance. A prototype system was implemented and assessed by 34 specialist respondents. TAM 3 (Technology Acceptance Model 3) Questionnaire was used to assess the system, which allowed us to find that GRAAL was well accepted (all scores were above the center of a Likert scale from 1 to 7 values), suggesting that GRAAL may be useful as an auxiliary resource to manual assembly lines. We conclude that modeling a system that merges AR and gamification is feasible but a non-trivial task in industry. The system's features are dependent on conditioning factors such as Application Environment, Application Context, Application Audience, AR hardware, and gamification elements. [ABSTRACT FROM AUTHOR]

Recently, Metaverse is gaining prominence within the field of radiology due to its potential to revolutionize image visualization. Radiologists can harness its capabilities to access dynamic, highly detailed results, thereby enhancing diagnostic precision. Digital twins, at the core of the Metaverse, are digital replicas of real-world objects and entities. They serve as the foundational building blocks, enabling the creation of virtual counterparts for everything within the Metaverse. To ensure the reliability of these digital twins, blockchain technology offers a multi-dimensional data storage solution, reinforcing data integrity and trustworthiness. It is used to ensure a transparent and 3D visualization of each communication and interaction for further looking up any criticality if present in the network. With the rapid increase in value and volume of data, the evolution of the metaverse faces a number of privacy and security concerns. Furthermore, the metaverse in Industry 4.0 is a trending topic that further needs to focus on its security challenges at its initial stage. Fortunately, blockchain is considered as one of the significant solutions. The aim of this paper is to propose a secure and efficient fuzzy evaluation matrix by computing the trust values along with integrating with blockchain mechanism in industry 4.0 enabling metaverse environment. The proposed mechanism is validated against various security concerns such as broken authentication, eavesdropping, personal information leakage, data injection, and unauthorized access. The proposed mechanism showed the validation rate against existing schemes with an improvement of 94% in comparison of several security metrics. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations. [ABSTRACT FROM AUTHOR]

Inspections of concrete bridges across the United States represent a significant commitment of resources, given their biannual mandate for many structures. With a notable number of aging bridges, there is an imperative need to enhance the efficiency of these inspections. This study harnessed the power of computer vision to streamline the inspection process. Our experiment examined the efficacy of a state-of-the-art Visual Transformer (ViT) model combined with distinct image enhancement detector algorithms. We benchmarked against a deep learning Convolutional Neural Network (CNN) model. These models were applied to over 20,000 high-quality images from the Concrete Images for Classification dataset. Traditional crack detection methods often fall short due to their heavy reliance on time and resources. This research pioneers bridge inspection by integrating ViT with diverse image enhancement detectors, significantly improving concrete crack detection accuracy. Notably, a custom-built CNN achieves over 99% accuracy with substantially lower training time than ViT, making it an efficient solution for enhancing safety and resource conservation in infrastructure management. These advancements enhance safety by enabling reliable detection and timely maintenance, but they also align with Industry 4.0 objectives, automating manual inspections, reducing costs, and advancing technological integration in public infrastructure management. [ABSTRACT FROM AUTHOR]

The demand for green hydrogen as an energy carrier is projected to exceed 350 million tons per year by 2050, driven by the need for sustainable distribution and storage of energy generated from sources. Despite its potential, hydrogen production currently faces challenges related to cost efficiency, compliance, monitoring, and safety. This work proposes Hydrogen 4.0, a cyber–physical approach that leverages Industry 4.0 technologies—including smart sensing, analytics, and the Internet of Things (IoT)—to address these issues in hydrogen energy plants. Such an approach has the potential to enhance efficiency, safety, and compliance through real-time data analysis, predictive maintenance, and optimised resource allocation, ultimately facilitating the adoption of renewable green hydrogen. The following sections break down conventional hydrogen plants into functional blocks and discusses how Industry 4.0 technologies can be applied to each segment. The components, benefits, and application scenarios of Hydrogen 4.0 are discussed while how digitalisation technologies can contribute to the successful integration of sustainable energy solutions in the global energy sector is also addressed. [ABSTRACT FROM AUTHOR]

Human-centered strategies, focused on resilience and sustainability are the pillars of Industry 5.0. In the purposes of the Cuban Government included in the Social Economic Development Plan to 2030, the intention of the objectives that define the fifth industrial revolution is presented in a dispersed manner. The intention of this article is to arouse in the reader the interest of analysis referred to the possible convergence, among others, of: the continuity of Industry 4.0 to 5.0, the strategic importance of science and innovation in the development of nations, the need for clean industries, the circular economy and recycling, learning and requalification of the workforce. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution (4IR) has brought about significant advancements in technology that have impacted various industries, including the railway sector. This paper explores the implications of the 4IR for the railway industry and identifies the essential soft skills that rail organisations need in order to thrive in this era. A systematic literature review (SLR) was conducted to establish a theoretical foundation, followed by a survey administered to professionals in the rail sector to prioritise the identified soft skills. Comparative methods were employed to analyse the survey data and to determine the relative importance of the 4IR soft skills. The findings revealed a range of key soft skills, such as adaptability, critical thinking, and change management, in which it is crucial that railway professionals excel in the 4IR era. The paper discusses the significance of these soft skills and their potential impact on rail organisations, and provides recommendations for fostering their development. This research contributes to a more nuanced understanding of the soft skills that are necessary for the railway sector’s successful adaptation to the 4IR. [ABSTRACT FROM AUTHOR]

SCADA systems are used in industries to perform control and monitoring of industrial processes in real-time. The advent of the Internet of Things (IoT), and the Industrial IoT (IIoT) in particular, has brought new disruptive technology that has the potential to drive the industrial digitization agenda well beyond the capabilities demonstrated by conventional SCADA systems. Industries, particularly manufacturing industries and utilities keep thriving to be competitive and the adoption of new technologies in the form of IIoT is set to improve efficiency and productivity through enhanced real-time data analytics and production availability. This paper examines the opportunities and challenges presented by integrating IIoT into existing SCADA systems. Potential solutions to the challenges are presented together with future research outlooks that have a bearing on the SCADA/IIoT integration efforts. [ABSTRACT FROM AUTHOR]

The fact is that hundreds of holes are drilled in the assembly process of furniture sets, so intelligent drilling is a key element in maximizing efficiency. Increasing the feed rate or the cutting speed in materials characterized by a higher machinability index is necessary. Smart drilling, that is, the real-time adjustment of the cutting parameters, requires the evolution of cutting process variables. In addition, it is necessary to control and adjust the processing parameters in real time. Machinability is one of the most important technological properties in the machining process, enabling the determination of the material's susceptibility to machining. One of the machinability indicators is the unit cutting resistance. This article proposes a method of material identification using the short-time Fourier transform in order to automatically adjust cutting parameters during drilling based on force signals, cutting torque and acceleration signals. In the tests, four types of wood-based materials were used as the processed material: medium-density fiberboard, chipboard, plywood board and high-pressure laminate. Holes with a diameter of 10 mm were drilled in the test materials, with variable feed rate, cutting speed and thickness of cutting layer. An innovative method for determining the value of unit cutting resistance was proposed. The results obtained were used to determine the machinability index. Based on the test results, it was shown that both the selected signal measures in the time and frequency domains and the unit cutting resistance are constant for a given material of a workpiece and do not depend on the drilling process parameters. In this article, the methodology is proposed, which can be used as an intelligent technique to support the drilling process to detect the material being machined using data from sensors installed on the machine tool. The work proposes the fundamentals for material identification based on the analysis of force signals and the magnitude of force derivatives. The proposed methodology shows effectiveness, which proves that it can be used in intelligent drilling processes. Hybrid wood-based material structures consisting of different materials are becoming more and more common in building structures for strength, economic and environmental reasons. Due to the difference in the machinability of interconnected materials, cutting parameters must be optimized in real time during machining. Currently, with the rapid development of Industry 4.0, the on-line identification of parameters is becoming necessary to improve the process flow in industrial reality. The proposed methodology can be used as an intelligent technique to support the drilling process in order to detect the material being processed using data from sensors installed on the machine tool. [ABSTRACT FROM AUTHOR]

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. [ABSTRACT FROM AUTHOR]

The agro-food industries produce substantial waste that has an adverse effect on the ecosystem. Nevertheless, these byproducts have abundant polyphenols with various bioactivities. Although conventional extractions (CE) are used for extraction, they require a long extraction time and more solvents, affecting product quality. Hence, this article reviewed emerging thermal extraction technologies (subcritical water, supercritical fluid, microwave, high pressurized liquid extraction, etc.) and their combinative effects (for instance, integrated subcritical-microwave, integrated supercritical fluid-hot-pressurized liquid extraction) that have been applied to extract polyphenols from agro-byproducts in the last 5 years. The fundamental mechanisms, their applications, shortcomings, and future investigations on augmenting these technologies are presented. The review showed that integrating cutting-edge technology could facilitate the development of more polyphenol extraction from agro-byproducts as there are many benefits, including increased extractability, reduced impurities, preserved thermosensitive compounds, and consume low energy. Hence, it is proposed that the next 5 years should explore combined novel thermal extraction technologies. The techno-economic analysis must be considered to fully investigate the implementation of these technologies in the polyphenol extraction from byproducts. Finally, waste valorization can inspire creativity and agro-business creation, particularly when integrated with food industry 4.0. [ABSTRACT FROM AUTHOR]

This paper explores and experimentally compares the effectiveness of robot-stopping approaches based on the speed and separation monitoring for improving fluency in collaborative robotics. In the compared approaches, a supervisory controller checks the distance between the bounding volumes enclosing human operator and robot and prevents potential collisions by determining the robot's stop time and triggering a stop trajectory if necessary. The methods are tested on a Franka Emika robot with 7 degrees of freedom, involving 27 volunteer participants, who are asked to walk along assigned paths to cyclically intrude the robot workspace, while the manipulator is working. The experimental results show that scaling online the dynamic safety zones is beneficial for improving fluency of human-robot collaboration, showing significant statistical differences with respect to alternative approaches. [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]

Manufacturers have traditionally relied on regular maintenance and inspections to ensure the proper functioning of all technical devices, machines, and components. In the era of Industry 4.0, these precautionary measures are supplemented and extended considerably by applying predictive maintenance. This form of maintenance is almost symbolic of Industry 4.0 and promises tremendous success. However, reliable data collection, integrating Industry 4.0 into the pre-existing manufacturing infrastructure, and analysing production machine status and production-related data present real challenges. A solution presented in this paper addressing these issues is an FPGA-based edge device development for data analysis and fault detection. In the proposed system, the MFCC method is implemented on a Pynq-Z2 board as pre-processing IP cores used to convert the vibration data collected to detect and diagnose machinery-bearing faults into 2D feature representations, which are then utilised for fault prediction based on optimised CNN network architecture. The Pynq-Z2 platform can be connected to the internet network directly through the Ethernet port and remotely controlled, making it perfect for IoT applications. We opted for the NCS2 accelerator from Intel to implement the proposed CNN network that will be connected to the Pynq-Z2 platform through the USB port. An experimental test through an IIoT application was carried out to validate the proposed system. The results showed that the system could perform pre-processing and prediction tasks in less than 40 ms. [ABSTRACT FROM AUTHOR]

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements. [ABSTRACT FROM AUTHOR]

The digital economy refers to the economic activities that emerge from connecting individuals, businesses, devices, data, and operations through digital technology. It includes online transactions across multiple sectors and technologies, such as the Internet, mobile technology, big data, and information and communications technology. The digital economy differs from a traditional economy because it relies on digital technology, online transactions, and its transformative effect on traditional industries Digital innovations such as the Internet of Things (IoT), Artificial Intelligence (AI), Virtual Reality, Blockchain, and autonomous vehicles all play a part in creating a digital economy. For this study, various papers on "Digital Economy" and "Industry 4.0" are identified from Scopus, Google Scholar and other research platforms and further studied briefly. This review paper has been developed after studying the digital economy and its needs in the Industry 4.0 Environment. The defining trends, key enablers, features and challenges associated with the digital economy towards Industry 4.0 are discussed briefly. Finally, the paper identifies and discusses the significant requirements of Industry 4.0 fulfilled through the digital economy. Today, customers are becoming aware of goods and services online and are turning to the industry for long-term solutions by deploying digital technologies. The digital economy is built on hyper connectivity, the growing interconnectedness of individuals, organisations, and machines due to the Internet, mobile technologies, and the IoT. Industry 4.0 technologies are automation, data exchange, cloud computing, robotics, big data, AI, the IoT, and other technological trends are all part of the industrial sector's digital transformation, which aims to achieve industrial objectives and intelligent manufacturing practices that engage with customers, emerging technologies, and innovation. The economy is becoming more digital, changing how products and services are provided and consumed. A new road map is being provided by Industry 4.0 services to help many industries adapt their conventional methods and support the new revolution. In the digital economy, scaled-up, integrated ecosystems that leverage software platforms to generate value, build resilience, and stimulate innovation via networked goods, assets, people, and processes rapidly replace old, linear value chains with partner participation. [ABSTRACT FROM AUTHOR]

The importance of the capital goods producing sector is analyzed in two economies with different levels of economic development, Germany, and Mexico, during the years 2005 and 2015. The methodology of vertically integrated sectors is used in order to determine the innovative effort that is incorporated directly and indirectly in each sector, which allows us to identify three elements that could be the cause of the backwardness of the Mexican economy compared to the German: firstly, the Mexican economy is characterized by its low innovative capacity, reflecting a poor effort seen as R&D expenditure; second, absence of a capital goods producing sector with important connections with the productive structure and, third, lack of an industrial policy that promotes and encourages innovative efforts, which is aggravated within the current context of accelerated technological change worldwide. [ABSTRACT FROM AUTHOR]

The recent tailings storage facility (TSF) dam failures recorded around the world have concerned society in general, forcing the mining industry to improve its operating standards, invest greater economic resources, and implement the best available technologies (BATs) to control TSFs for safety purposes and avoid spills, accidents, and collapses. In this context, and as the era of digitalization and Industry 4.0 continues, monitoring technologies based on sensors have become increasingly common in the mining industry. This article studies the state of the art of implementing sensor technologies to monitor structural health and safety management issues in TSFs, highlighting advances and experiences through a review of the scientific literature on the topic. The methodology applied in this article adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and utilizes scientific maps for data visualization. To do so, three steps were implemented: (i) a quantitative bibliometric analysis, (ii) a qualitative systematic review of the literature, and (iii) a mixed review to integrate the findings from (i) and (ii). As a result, this article presents the main advances, gaps, and future trends regarding the main characteristics of the sensor technologies applied to monitor TSF structural health and safety management in the era of digitalization. According to the results, the existing research predominantly investigates certain TSF sensor technologies, such as wireless real-time monitoring, remote sensors (RS), unmanned aerial vehicles (UAVs), unmanned survey vessels (USVs), artificial intelligence (AI), cloud computing (CC), and Internet of Things (IoT) approaches, among others. These technologies stand out for their potential to improve the safety management monitoring of mine tailings, which is particularly significant in the context of climate change-related hazards, and to reduce the risk of TSF failures. They are recognized as emerging smart mining solutions with reliable, simple, scalable, secure, and competitive characteristics. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to identify the basis of the technological anxiety phenomenon by defining the differences and similarities in terms of barriers of the implementation of Industry 4.0 technologies across industrial processing sector. Design/methodology/approach: This paper presents a qualitative, exploratory research, and the authors apply the cross-case study method. The study is based on interviews with representatives of 11 medium-sized and large companies from industrial processing sector; specifically, the authors focus on three industries: automotive, food and furniture. Findings: The research showed that there are similarities as well as differences in terms of identified barriers between individual industries. Taking into account the various dimensions of technological anxiety, similarities are visible, in particular, in the case of Internal processes and infrastructure and human resources, while in the other two dimensions, i.e. strategic planning and standards and security, differences between the sectors were noted. Practical implications: The developed list of barriers can be a starting point for middle and senior managers of manufacturing companies to understand the sources of technological anxiety. The planning and introducing preventive and protective tools during Industry 4.0 implementation may reduce the occurrence of technological anxiety and thus ensure a smoother adoption of technologies 4.0, while respecting the organizational culture. Originality/value: This work contributes to in-depth understanding of multifaced technological anxiety phenomenon. This paper classifies dimensions of existing barriers, increases the awareness on the difficulties during transformation process and, thus enables the improvement of the use of company's internal potential. [ABSTRACT FROM AUTHOR]

Purpose: This conceptual paper aims to develop the vision of a novel policy tool called the digital industrial cluster (DIC) thought for a future context shaped by digital economies and Industry 4.0. The DIC departs from the industrial cluster's precepts and take them into the digital agglomeration phenomenon. Design/methodology/approach: This research uses a discursive and theoretical approach. To create a clear picture related to the research topic, this paper reviews the literature on how industrial clusters deal with digital transformation. The review takes a particular interest in industrial clusters, information and communication technologies, Industry 4.0 and policy tools for digital agglomeration. Findings: The research develops the vision of the DIC as a new policy tool, building its foundations on both industrial clusters and digital economies; furthermore, the DIC was compared with other policy tools based on digital agglomeration to understand the similarities, differences and advantages of the former. Finally, this paper discussed where the DIC could find its way toward implementation and what externalities could be expected from doing so. Practical implications: This research could be useful for policymakers and cluster organizations looking to deploy policy tools that take advantage of industrial clusters and digital transformation. Besides, the theoretical foundations presented could lead researchers to empirical identification of early incarnations of the DIC. Originality/value: This paper develops theoretical principles for a new policy tool that connects industrial clusters, digital agglomeration and Industry 4.0 for the first time. [ABSTRACT FROM AUTHOR]

As the rate of digitalisation grows in the manufacturing sector, there is a need to address the manner in which design for the human experience is conceptualised. In industrial systems, user experience/user interface (UX/UI) as a paradigm does not fit in squarely, as industrial technologies require a blend of humans and technology for productivity. Thus, the aim of this article is to highlight the challenges and fundamental issues related to human-machine interaction (HMI) design in smart manufacturing from the disciplines of Human Factors and Interaction Design in terms of major themes: (1) designing for operator experience, (2) meaning-based analytics and design for complexity, and (3) human-centered design process for enabling reliability, maintainability, availability, and safety. These various challenges are depicted using a case study of HMI concept design for monitoring an additive manufacturing setup. The article concludes by discussing future steps for addressing HMI design in the manufacturing sector. [ABSTRACT FROM AUTHOR]

Industry 4.0 represents the most advanced stage of organization of industrial companies, allowing them to respond to an uncertain and changing environment, particularly as accentuated by the recent crisis resulting from COVID-19. Management innovation (MI) contributes to this process of permanent adaptation. The MI implementation phase is a critical step in MI generation that can generate many potential obstacles. This study focuses on these obstacles while considering the different activities (or subprocesses) embedded in this phase and the different actors involved in this complex process. We conducted a longitudinal case study in real time to investigate the implementation of MI internally generated by a multinational industrial company. Our results show that the obstacles encountered during the MI implementation phase may differ depending on the different activities and actors of this phase, thus leading us to question current implementation frameworks. This paper contributes by refining the theoretical model of MI generation and providing a better understanding of the obstacles that occur during the MI implementation phase. From a managerial perspective, this paper highlights key management principles to overcome the obstacles identified. [ABSTRACT FROM AUTHOR]

Unpredictable natural and man-made disasters highlight importance of humanitarian supply chain (HSC) to serve people and affected areas. The main challenges of applying effective relief operations are creating "swift trust" and "coordination" between aid organizations. Implementation of Industry 4.0 facilitates coordination and swift trust within HSC performance. The study intends to assess the readiness status of swift trust and coordination between stakeholders as well as to recommend the most suitable Industry 4.0 tools for improving relief operations. Firstly, a comprehensive set of critical success factors for implementing Industry 4.0 tools are introduced. The factors are categorized into limited groups using Exploratory Factor Analysis. In the next step, hierarchy fuzzy expert system is designed for assessing the readiness status of swift trust and coordination as well as to suggest the most suitable Industry 4.0 tool for enhancing HSC performance within given case study. The framework was applied for three aid organizations to address the pandemic disease in Iran. The outcome denotes that the organization has the highest readiness in logistic and transparency while information quality received the lowest readiness value. In addition to that, the organization should invest on the development of Industry 4.0 enablers including "Internet of Things and Big Data Analytics". The study extends organizational information process theory within HSC for reaching competitive advantage by information processing. The study suggests theoretical and practical implications by introducing a comprehensive set of critical success factors for implementation of Industry 4.0 and providing practical advice for enhancing HSC performance. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Industry 4.0, the integration of advanced wireless technologies into manufacturing processes holds the promise of unprecedented connectivity and efficiency. In particular, the data transmission in a heavy industry environment needs stable connectivity with mobile operators. This paper deals with the performance study of 4G and 5G mobile signal coverage within a complex factory environment. For this purpose, a cost-effective and portable measurement setup was realized and used to provide long-term measurement campaigns monitoring and recording several key parameter indicators (KPIs) in 4G/5G downlink and upload. To support the reproducibility of the provided study and other research activities, the measured dataset is publicly available for download. Among others findings, the obtained results show how the performance of 4G/5G is influenced by a heavy industry environment and of the time of day on the network load. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use. Thus far, the IoT physical layer communication challenges have been met with various communications protocols that provide varying strengths and weaknesses. This paper summarizes the network architectures of some of the most popular IoT wireless communications protocols. It also presents a comparative analysis of some of the critical features, including power consumption, coverage, data rate, security, cost, and quality of service (QoS). This comparative study shows that low-power wide area network (LPWAN)-based IoT protocols (LoRa, Sigfox, NB-IoT, LTE-M) are more suitable for future industrial applications because of their energy efficiency, high coverage, and cost efficiency. In addition, the study also presents an Industrial Internet of Things (IIoT) application perspective on the suitability of LPWAN protocols in a particular scenario and addresses some open issues that need to be researched. Thus, this study can assist in deciding the most suitable IoT communication protocol for an industrial and production field. [ABSTRACT FROM AUTHOR]

Recently, with the fourth industrial revolution, the research cases that search for optimal design points based on neural networks or machine learning have rapidly increased. In addition, research on optimization is continuously reported in the field of fuel cell research using hydrogen as fuel. However, in the case of optimization research, it often requires a large amount of training data, which means that it is more suitable for numerical research such as CFD simulation rather than time-consuming research such as actual experiments. As is well known, the design range of fuel cell flow channels is extremely small, ranging from hundreds of microns to several millimeters, which means the small tolerance could cause fatal performance loss. In this study, the general optimization study was further improved in terms of reliability by considering stochastic tolerances that may occur in actual industry. The optimization problem was defined to maximize stack power, which is employed as objective function, under the constraints such as pressure drop and current density standard deviation; the performance of the optimal point through general optimization was about 3.252 kW/L. In the reliability-based optimization problem, the boundary condition for tolerance was set to 0.1 mm and tolerance was assumed to occur along a normal distribution. The optimal point to secure 99% reliability for the given constraints was 2.918 kW/L, showing significantly lower performance than the general optimal point. [ABSTRACT FROM AUTHOR]

The scientific community has shown considerable interest in Industry 4.0 due to its capacity to revolutionise the manufacturing sector through digitalisation and data-driven decision-making. However, the actual implementation of Industry 4.0 within complex industrial settings presents obstacles that are typically beyond the scope of mainstream research articles. In this paper, a comprehensive case-study detailing our collaborative partnership with a leading medical device manufacturer is presented. The study traces its evolution from a state of limited digitalisation to the development of a digital intelligence platform that leverages data and machine learning models to enhance operations across a wide range of critical machines and assets. The main business objective was to enhance the energy efficiency of the manufacturing process, thereby improving its sustainability measures while also saving costs. The project encompasses energy modelling and analytics, Fault Detection and Diagnostics (FDD), renewable energy integration and advanced visualisation tools. Together, these components enable informed decision making in the context of energy efficiency. [ABSTRACT FROM AUTHOR]

The rapid expansion of the Internet of Things (IoT) has paved the way for the development of smart systems, with Wireless Sensor Networks (WSNs) serving as the underlying infrastructure. While it exists in a miniature form, IoT-based WSN today stands as the revolution of the future, promising tremendous influence on society. However, the limited resources of these networks pose various challenges, particularly in routing, with congestion being a significant issue that affects their efficiency. Although previous studies are available on congestion management in WSNs, research specifically focused on IoT-based WSNs and addressing the root causes of congestion is none. In order to address this gap, this article conducts a thematic review of the current literature to identify congestion management strategies and forecast future trends. The search identified 86 studies, among which 47 articles were analyzed. The six final themes were discovered: artificial intelligence approach, customized classical method, hybrid approach, cross-layering approach, SDN-based approach, and RPL routing advancement. The findings establish a comprehensive taxonomy model as a conceptual framework for future research in congestion management strategies for IoT-based WSNs routing. This taxonomy aids academic researchers as well as industrial practitioners and highlights crucial areas for future research on congestion issues from the perspective of Industry 4.0. [ABSTRACT FROM AUTHOR]

This research breaks new ground by utilizing the advanced natural language processing (NLP) capabilities of OpenAI's GPT-3.5 Turbo model for the extraction of voice of customer (VoC) data from online customer support interactions on Twitter. Traditional methods of VoC extraction have typically fallen short in capturing the richness and nuance of customer sentiment. Contemporary machine learning (ML) approaches, while improved, still struggle to interpret the contextual subtleties of digital customer communications effectively. This study showcases the innovative deployment of GPT-3.5 Turbo, demonstrating its superior performance in extracting VoC through a deeper understanding of conversational context and a more intuitive, chat-based data processing. Furthermore, the large-scale, multilingual processing capabilities of this model offer a more comprehensive and inclusive analysis of VoC. The study ties these advancements to Lean Six Sigma 4.0, illustrating how the integration of GPT-3.5 Turbo's transformative capabilities can elevate the customer-centric approach of Lean Six Sigma in the era of Industry 4.0. This innovative exploration points to a significant evolution in VoC analysis, offering potential for more insightful, real-time data–driven customer service strategies and a more substantial foundation for decision-making in product development and process improvement. Future research is encouraged to validate these preliminary findings and to investigate ethical considerations associated with the use of such advanced NLP models. [ABSTRACT FROM AUTHOR]

The application of adhesively bonded joints has increased in a large variety of different industrial sectors over the last decades. In comparison to other joining techniques, adhesively bonded joints can offer easier assembly, weight reduction, lower stress concentration, etc. Additive Manufacturing (AM) as a transformational approach in Industry 4.0 can even improve the structural performance of such joints in terms of more weight reduction, making more homogenous stresses in the adhesive layer of the bonded area. In this work, an adhesively bonded single lap joint (SLJ) with metallic sandwich structures and a pyramidal lattice core as adherend by using AM was designed, produced and tensile tested. The result of this study showed that in addition to the significant savings in the weight of the adhesively bonded joint, the additive manufactured sandwich adherend produces a more homogenous stress distribution compared to conventional adherends with the same material. Furthermore, two different finite element (FE) approaches were used for modelling the fracture behaviour of the additive manufactured joint, namely a solid element formulation and a simplified model using shell-beam formulation. The simplified shell-beam formulation reduces the modelling and calculation efforts massively and shows a good agreement with experimental results. [ABSTRACT FROM AUTHOR]

Amidst globalization and the Fourth Industrial Revolution, indigenous language preservation and promotion gain paramount significance. These languages encapsulate rich cultural, historical, and ecological value, embodying unique knowledge systems and identities. Nevertheless, challenges stemming from historical, social, resource, and institutional constraints impede preservation, with digital media technology thus emerging as a pivotal tool in this endeavour. This paper meticulously explores the role of digital media technology in indigenous language preservation during the Fourth Industrial Revolution. Through a comprehensive review of reputable literature, including scholarly articles, case studies, and expert insights, the paper underscores the imperative of language preservation, delving into the obstacles hindering preservation efforts and spotlighting the pivotal role of digital media technology in rejuvenating indigenous languages. The paper also examines various digital tools and applications that facilitate preservation, emphasizing their accessibility and engagement, while also underscoring the importance of collaborative efforts involving indigenous communities, linguists, and technology experts. Ethical considerations, encompassing intellectual property rights and informed consent, are of paramount importance in indigenous language preservation, and thus this paper also consolidates insights derived from the findings and provides recommendations for effective preservation strategies, underlining the necessity of ethical guidelines and emphasizing the indispensable role of government support. The study concludes by addressing prospects and challenges in indigenous language preservation, providing a comprehensive view of safeguarding linguistic heritage in the digital age. [ABSTRACT FROM AUTHOR]