A comprehensive STPA-PSO framework for quantifying smart glasses risks in manufacturing

The integration of cutting-edge technologies, such as wearables, in complex systems is crucial for enhancing collaboration between humans and machines in the era of Industry 5.0. However, this increased interaction also introduces new challenges and risks, including the potential for human errors. A thorough analysis of the literature reveals an absence of studies that have quantified these risks, underscoring the utmost importance of this research. To address the above gap, the present study introduces the STPA-PSO methodology, which aims to quantify the risks associated with the use of smart glasses in complex systems, with a specific focus on human error risks. The proposed methodology leverages the Systems-Theoretic Process Analysis (STPA) approach to proactively identify hazards, while harnessing the power of the Particle Swarm Optimization (PSO) algorithm to accurately calculate and optimize risks, including those related to human errors. To validate the effectiveness of the methodology, a case study involving the assembly of a refrigerator was conducted, encompassing various critical aspects, such as the Industrial, Financial, and Occupational Health and Safety (OHS) aspects. The results provide evidence of the efficacy of the STPA-PSO approach in assessing, quantifying, and managing risks during the design stage. By proposing a robust and comprehensive risk quantification framework, this study makes a significant contribution to the advancement of system safety analysis in complex environments, providing invaluable insights for the seamless integration of wearables and ensuring safer interactions between humans and machines.

A roadmap for model-based bioprocess development

The bioprocessing industry is undergoing a significant transformation in its approach to quality assurance, shifting from the traditional Quality by Testing (QbT) to Quality by Design (QbD). QbD, a systematic approach to quality in process development, integrates quality into process design and control, guided by regulatory frameworks. This paradigm shift enables increased operational efficiencies, reduced market time, and ensures product consistency. The implementation of QbD is framed around key elements such as defining the Quality Target Product Profile (QTPPs), identifying Critical Quality Attributes (CQAs), developing Design Spaces (DS), establishing Control Strategies (CS), and maintaining continual improvement. The present critical analysis delves into the intricacies of each element, emphasizing their role in ensuring consistent product quality and regulatory compliance. The integration of Industry 4.0 and 5.0 technologies, including Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), and Digital Twins (DTs), is significantly transforming the bioprocessing industry. These innovations enable real-time data analysis, predictive modelling, and process optimization, which are crucial elements in QbD implementation. Among these, the concept of DTs is notable for its ability to facilitate bi-directional data communication and enable real-time adjustments and therefore optimize processes. DTs, however, face implementation challenges such as system integration, data security, and hardware-software compatibility. These challenges are being addressed through advancements in AI, Virtual Reality/ Augmented Reality (VR/AR), and improved communication technologies. Central to the functioning of DTs is the development and application of various models of differing types - mechanistic, empirical, and hybrid. These models serve as the intellectual backbone of DTs, providing a framework for interpreting and predicting the behaviour of their physical counterparts. The choice and development of these models are vital for the accuracy and efficacy of DTs, enabling them to mirror and predict the real-time dynamics of bioprocessing systems. Complementing these models, advancements in data collection technologies, such as free-floating wireless sensors and spectroscopic sensors, enhance the monitoring and control capabilities of DTs, providing a more comprehensive and nuanced understanding of the bioprocessing environment. This review offers a critical analysis of the prevailing trends in model-based bioprocessing development within the sector.

Updating design guidelines for cognitive ergonomics in human-centred collaborative robotics applications: An expert survey

Within the framework of Industry 5.0, human factors are essential for enhancing the work conditions and well-being of operators interacting with even more advanced and smart manufacturing systems and machines and increasing production performances. Nevertheless, cognitive ergonomics is often underestimated when implementing advanced industrial human-robot interaction. Thus, this work aims to systematically update, develop, and validate guidelines to assist non-experts in the early stages of the design of anthropocentric and collaborative assembly applications by focusing on the main features that have positively influenced workers' cognitive responses. A methodology for structured development has been proposed. The draft guidelines have been created starting from the outcomes of a systematic and extended screening of the scientific literature. Preliminary validation has been carried out with the help of researchers working in the field. Inputs on comprehensibility and relevance have been gathered to enhance the guidelines. Lastly, a survey was used to examine in depth how international experts in different branches can interpret such guidelines. In total, 108 responders were asked to qualitatively and quantitatively evaluate the guideline's comprehensibility and provide general comments or suggestions for each guideline. Based on the survey's results, the guidelines have been validated and some have been reviewed and re-written in their final form. The present work highlights that integrating human factors into the design of collaborative applications can significantly bolster manufacturing operations' resilience through inclusivity and system adaptability by enhancing worker safety, ergonomics, and wellbeing.

Defining human-centricity in Industry 5.0 and assessing the readiness of ergonomics/human factors communities in UK

There is a lack of a clear and consistent definition of human-centricity in Industry 5.0. This study identified the definition of human-centricity in Industry 5.0 through a systematic literature review and used it to assess the readiness of Ergonomics/Human Factors communities in the UK. The assessment of the communities readiness was conducted by reviewing UK accredited courses and events of three professional bodies; and interviewing practitioners (n = 8). Eleven themes were identified as elements of human-centricity from the thematic analysis of 30 publications. Gaps that had to be addressed to better equip UK practitioners to support the realisation of human-centricity in Industry 5.0 were also identified.

Analysis of the Effect of Surface Preparation of Aluminum Alloy Sheets on the Load-Bearing Capacity and Failure Energy of an Epoxy-Bonded Adhesive Joint

Surface preparation is an important step in adhesive technology. A variety of abrasive, chemical, or concentrated energy source treatments are used. The effects of these treatments vary due to the variety of factors affecting the final strength of bonded joints. This paper presents the results of an experimental study conducted to determine the feasibility of using fiber laser surface treatments in place of technologically and environmentally cumbersome methods. The effect of surface modification was studied on three materials: aluminum EN AW-1050A and aluminum alloys EN AW-2024 and EN AW-5083. For comparison purposes, joints were made with sandblasted and laser-textured surfaces and those rolled as reference samples for the selected overlap variant, glued with epoxy adhesive. The joints were made with an overlap of 8, 10, 12.5, 14, and 16 mm, and these tests made it possible to demonstrate laser processing as a useful technique to reduce the size of the overlap and achieve even higher load-bearing capacity of the joint compared to sandblasting. A comparative analysis was also carried out for the failure force of the adhesive bond and the failure energy. The results show the efficiency and desirability of using lasers in bonding, allowing us to reduce harmful technologies and reduce the weight of the bonded structure.

Towards a Human-Centric Digital Twin for Human-Machine Collaboration: A Review on Enabling Technologies and Methods

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human-machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human-machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

Challenges and future perspectives for the European grading of pig carcasses - A quality view

This study sought to evaluate pig carcass grading, describing the existing approaches and definitions, and highlighting the vision for overall quality grading. In particular, the current state of pig carcass grading in the European Union (SEUROP system), its weaknesses, and the challenges to achieve more uniformity and harmonization across member states were described, and a broader understanding of pig carcass value, which includes a vision for the inclusion of meat quality aspects in the grading, was discussed. Finally, the noninvasive methods for the on-line evaluation of pig carcass and meat quality (hereafter referred to as pork quality), and the conditions for their application were discussed. As the way pigs are raised (especially in terms of animal welfare and environmental impact), and more importantly, their perception of pork quality, is becoming increasingly important to consumers, the ideal grading of pigs should comprise pork quality aspects. As a result, a forward-looking "overall quality" approach to pork grading was proposed herein, in which grading systems would be based on the shared vision for pork quality (carcass and meat quality) among stakeholders in the pig industry and driven by consumer expectations with respect to the product. Emerging new technologies provide the technical foundation for such perspective; however, integrating all knowledge and technologies for their practical application to an "overall quality" grading approach is a major challenge. Nonetheless, such approach aligns with the recent vision of Industry 5.0, i.e. a model for the next level of industrialization that is human-centric, resilient, and sustainable.

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniques. At its core, this study is in the developmental phase, aiming to create an integrated system that could enable real-time monitoring and analysis of the physical, mental, and emotional states of workers. It provides valuable insights into the impact of Digital Twins (DT) technology and its role in Industry 5.0. With the development of a chatbot trained as an empathic evaluator that analyses emotions expressed in written conversations using natural language processing (NLP); video logs capable of extracting emotions through facial expressions and speech analysis; and personality tests, this research intends to obtain a deeper understanding of workers' psychological characteristics and stress levels. This innovative approach might enable the identification of stress, anxiety, or other emotional factors that may affect worker safety. Whilst this study does not encompass a case study or an application in a real-world setting, it lays the groundwork for the future implementation of these technologies. The insights derived from this research are intended to inform the development of practical applications aimed at creating safer work environments.

IoT-Based Framework for Digital Twins in the Industry 5.0 Era

Digital twins are considered the next step in IoT-based cyber-physical systems; they allow for the real-time monitoring of assets, and they provide a comprehensive understanding of a system behavior, allowing for data-driven insights and informed choices. However, no comprehensive framework exists for the development of IoT-based digital twins. Moreover, the existing frameworks do not consider the aspects introduced by the Industry 5.0 paradigm, such as sustainability, human-centricity, and resilience. This paper proposes a framework based on the one defined as the outcome of a project funded by the European Union between 2010 and 2013 called the IoT Architectural Reference Model (IoT-A or IoT-ARM), with the aim of the development and implementation of a standard IoT framework that includes digital twins. This framework establishes and implements a standardized collection of architectural instruments for modeling IoT systems in the 5.0 era, serving as a benchmark for the design and implementation of an IoT architecture focused on digital twins and enabling the sustainability, resilience, and human-centricity of the information system. Furthermore, a proof of concept of a monitoring digital twin for a vertical farming system has been developed to test the validity of the framework, and a discussion of applications in the manufacturing and service sectors is presented.

The human-centric Industry 5.0 collaboration architecture

While the primary focus of Industry 4.0 revolves around extensive digitalization, Industry 5.0, on the other hand, seeks to integrate innovative technologies with human actors, signifying an approach that is more value-driven than technology-centric. The key objectives of the Industry 5.0 paradigm, which were not central to Industry 4.0, underscore that production should not only be digitalized but also resilient, sustainable, and human-centric. This paper is focusing on the human-centric pillar of Industry 5.0. The proposed methodology addresses the need for a human-AI collaborative process design and innovation approach to support the development and deployment of advanced AI-driven co-creation and collaboration tools. The method aims to solve the problem of integrating various innovative agents (human, AI, IoT, robot) in a plant-level collaboration process through a generic semantic definition, utilizing a time event-driven process. It also encourages the development of AI techniques for human-in-the-loop optimization, incorporating cross-checking with alternative feedback loop models. Benefits of this methodology include the Industry 5.0 collaboration architecture (I5arc), which provides new adaptable, generic frameworks, concepts, and methodologies for modern knowledge creation and sharing to enhance plant collaboration processes. •The I5arc aims to investigate and establish a truly integrated human-AI collaboration model, equipped with methods and tools for human-AI driven co-creation.•Provide a framework for the co-execution of processes and activities, with humans remaining empowered and in control.•The framework primarily targets human-AI collaboration processes and activities in industrial plants, with potential applicability to other societal contexts.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Blockchain Protocols and Edge Computing Targeting Industry 5.0 Needs

"Industry 5.0" is the latest industrial revolution. A variety of cutting-edge technologies, including artificial intelligence, the Internet of Things (IoT), and others, come together to form it. Billions of devices are connected for high-speed data transfer, especially in a 5G-enabled industrial environment for information collection and processing. Most of the issues, such as access control mechanism, time to fetch the data from different devices, and protocols used, may not be applicable in the future as these protocols are based upon a centralized mechanism. This centralized mechanism may have a single point of failure along with the computational overhead. Thus, there is a need for an efficient decentralized access control mechanism for device-to-device (D2D) communication in various industrial sectors, for example, sensors in different regions may collect and process the data for making intelligent decisions. In such an environment, reliability, security, and privacy are major concerns as most of the solutions are based upon a centralized control mechanism. To mitigate the aforementioned issues, this paper provides the opportunities for and highlights some of the most impressive initiatives that help to curve the future. This new era will bring about significant changes in the way businesses operate, allowing them to become more cost-effective, more efficient, and produce higher-quality goods and services. As sensors are getting more accurate, cheaper, and have lower time responses, 5G networks are being integrated, and more industrial equipment and machinery are becoming available; hence, various sectors, including the manufacturing sector, are going through a significant period of transition right now. Additionally, the emergence of the cloud enables modern production models that use the cloud (both internal and external services), networks, and systems to leverage the cloud's low cost, scalability, increased computational power, real-time communication, and data transfer capabilities to create much smarter and more autonomous systems. We discuss the ways in which decentralized networks that make use of protocols help to achieve decentralization and how network meshes can grow to make things more secure, reliable, and cohere with these technologies, which are not going away anytime soon. We emphasize the significance of new design in regard to cybersecurity, data integrity, and storage by using straightforward examples that have the potential to lead to the excellence of distributed systems. This groundbreaking paper delves deep into the world of industrial automation and explores the possibilities to adopt blockchain for developing solutions for smart cities, smart homes, healthcare, smart agriculture, autonomous vehicles, and supply chain management within Industry 5.0. With an in-depth examination of various consensus mechanisms, readers gain a comprehensive understanding of the latest developments in this field. The paper also explores the current issues and challenges associated with blockchain adaptation for industrial automation and provides a thorough comparison of the available consensus, enabling end customers to select the most suitable one based on its unique advantages. Case studies highlight how to enable the adoption of blockchain in Industry 5.0 solutions effectively and efficiently, offering valuable insights into the potential challenges that lie ahead, particularly for smart industrial applications.

NEP+: A Human-Centered Framework for Inclusive Human-Machine Interaction Development

This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform's dual mission: to empower the network-related capabilities of ZeroMQ and to provide software tools and interfaces for prototyping and integration. NEP+ accomplishes this through a comprehensive quality model and an integrated software ecosystem encompassing middleware, user-friendly graphical interfaces, a command-line tool, and an accessible end-user programming interface. This article primarily focuses on presenting the proposed quality model and software architecture, illustrating how they can empower developers to craft cross-platform, accessible, and user-friendly interfaces for various applications, with a particular emphasis on robotics and the Internet of Things (IoT). Additionally, we provide practical insights into the applicability of NEP+ by briefly presenting real-world user cases where human-centered projects have successfully utilized NEP+ to develop robotics systems. To further emphasize the suitability of NEP+ tools and interfaces for developer use, we conduct a pilot study that delves into usability and workload assessment. The outcomes of this study highlight the user-friendly features of NEP+ tools, along with their ease of adoption and cross-platform capabilities. The novelty of NEP+ fundamentally lies in its holistic approach, acting as a bridge across diverse user groups, fostering inclusivity, and promoting collaboration.

Can green finance facilitate Industry 5.0 transition to achieve sustainability? A systematic review with future research directions

Most of the world's rising carbon emission results from industrial activities. Previous industrial revolutions did not put much thought into safeguarding the natural world. Governments worldwide have been continuously implementing regulations and policies for the mitigation of climate change to promote sustainable development. To achieve decarbonization, the climate change discussion is merged with Industry 5.0 (I5.0) where green finance (GF) plays a crucial role. This technological metamorphosis of transition from Industry 4.0 (I4.0) to I5.0 will affect humans and their society. I5.0 forms a symbiotic relationship with different aspects of Society 5.0 (S5.0) such as social (human‒machine centricity), ecological (zero emissions), and technological (green innovations). Thus, the I5.0 transition prioritizes greening the economy in pursuit of achieving S5.0. Through a systematic review of 196 articles, this research study concisely summarizes the rapidly expanding body of information. The research domain gave six major themes: Green Innovations (GI), Green Manufacturing Practices (GMP), Circular Economy (CE), Green Supply Chain Management (GSCM), Emerging Economies, and Net Zero Economy (NZE). Finally, a framework has been provided that illustrates the supporting role of GF for the I5.0 transition eventually followed by S5.0. This study provides an overview of these themes with their propositions and future research directions. The present study addresses the knowledge gap by providing valuable contributions to the burgeoning research domain of I5.0 and GF. Moreover, it aims to garner the attention of different stakeholders to integrate these two concepts of research to attain the goal of sustainable development.

Technological machines and artificial intelligence in nursing practice

This article is a theoretical discourse about technological machines and artificial intelligence, highlighting their effective interactive outcomes in nursing. One significant influence is technological efficiency which positively affects nursing care time, enabling nurses to focus more on their patients as the core of nursing. The article examines the impact of technology and artificial intelligence on nursing practice in this era of rapid technological advancements and technological dependence. Strategic opportunities in nursing are advanced, exemplified by robotics technology and artificial intelligence. A survey of recent literature focused on what is known about the influence of technology, healthcare robotics, and artificial intelligence on nursing in the contexts of industrialization, societal milieu, and human living environments. Efficient, precision-driven machines with artificial intelligence support a technology-centered society in which hospitals and healthcare systems become increasingly technology-dependent, impacting healthcare quality and patient care satisfaction. As a result, higher levels of knowledge, intelligence, and recognition of technologies and artificial intelligence are required for nurses to render quality nursing care. Designers of health facilities should be particularly aware of nursing's increasing dependence on technological advancements in their practice.

Soft skills and their importance in the labour market under the conditions of Industry 5.0

The profound ramifications stemming from a multitude of global events and the ongoing progression of the fourth and fifth industrial revolutions necessitate a broadening of skillsets beyond the mere acquisition of technical and digital proficiencies. The practical, intelligent, responsible, and sustainable utilisation of technologies relies exclusively on human agency. Such employment necessitates a type of cognitive processing that machines find arduous, emphasising the importance of aligning human reasoning with machine intelligence. The significance of digital skills is widely acknowledged within the framework of cultivating suitable sets of employee abilities. Nonetheless, it is imperative to underscore the importance of human skills as they remain irreplaceable by robots. Furthermore, the labour market acknowledges and rewards these skills owing to their capacity to confer flexibility and adaptability, thereby embodying the competing attributes of the future workforce. In light of the prevailing circumstances outlined in Industry 5.0-characterised by an amplified utilisation of technologies and diminished interpersonal interactions resulting from the pervasive impact of the Covid-19 pandemic-this study seeks to provide a theoretical description of the significance of soft skills and their categorisation, while investigating the practical demand for such skills. The dataset used in this study encompasses information pertaining to skill prerequisites extracted from job posts published on a job portal over five years, encompassing 19 000 distinct organisations. The findings of our study revealed that within technologically driven domains, there is a discernible demand for soft skills, such as critical and analytical thinking, problem-solving, communication skills, and creativity with flexibility. Furthermore, our results indicate that individuals must possess balanced proficiency in both soft and digital skills to thrive in a future characterised by technological advancements.

Enabling Trust and Security in Digital Twin Management: A Blockchain-Based Approach with Ethereum and IPFS

The emergence of Industry 5.0 has highlighted the significance of information usage, processing, and data analysis when maintaining physical assets. This has enabled the creation of the Digital Twin (DT). Information about an asset is generated and consumed during its entire life cycle. The main goal of DT is to connect and represent physical assets as close to reality as possible virtually. Unfortunately, the lack of security and trust among DT participants remains a problem as a result of data sharing. This issue cannot be resolved with a central authority when dealing with large organisations. Blockchain technology has been proposed as a solution for DT information sharing and security challenges. This paper proposes a Blockchain-based solution for digital twin using Ethereum blockchain with performance and cost analysis. This solution employs a smart contract for information management and access control for stakeholders of the digital twin, which is secure and tamper-proof. This implementation is based on Ethereum and IPFS. We use IPFS storage servers to store stakeholders' details and manage information. A real-world use-case of a production line of a smartphone, where a conveyor belt is used to carry different parts, is presented to demonstrate the proposed system. The performance evaluation of our proposed system shows that it is secure and achieves performance improvement when compared with other methods. The comparison of results with state-of-the-art methods showed that the proposed system consumed fewer resources in a transaction cost, with an 8% decrease. The execution cost increased by 10%, but the cost of ether was 93% less than the existing methods.

A Human Digital-Twin-Based Framework Driving Human Centricity towards Industry 5.0

This work presents a digital-twin-based framework focused on orchestrating human-centered processes toward Industry 5.0. By including workers and their digital replicas in the loop of the digital twin, the proposed framework extends the traditional model of the factory's digital twin, which instead does not adequately consider the human component. The overall goal of the authors is to provide a reference architecture to manufacturing companies for a digital-twin-based platform that promotes harmonization and orchestration between humans and (physical and virtual) machines through the monitoring, simulation, and optimization of their interactions. In addition, the platform enhances the interactions of the stakeholders with the digital twin, considering that the latter cannot always be fully autonomous, and it can require human intervention. The paper also presents an implemented scenario adhering to the proposed framework's specifications, which is also validated with a real case study set in a factory plant that produces wooden furniture, thus demonstrating the validity of the overall proposed approach.

The Psychosocial Model of Absenteeism: Transition from 4.0 to 5.0

The research provides insight into the factors that determine absenteeism in different types of organizations, in order to facilitate the adjustment of employees and organizations in the transition process from Industry 4.0 to Industry 5.0. The aim of the study is to predict the absenteeism of employees in the context of job characteristics and mental health. Additionally, the research investigated the effect of size, ownership, and sector of the companies on absenteeism, job characteristics, and mental health. The sample included responses from 502 employees of different sociodemographic characteristics that work in various types of organizations, performing white-collar and blue-collar jobs. A short mental health questionnaire-Mental Health Inventory, MHI-5-was used in order to measure mental health. The Job Characteristics Questionnaire was used to measure the employees' perceptions of their job characteristics-job variety, autonomy, feedback, dealing with others, task identity, and friendship. The absenteeism is operationalized with the question: "During the past 12 months, how many days were you absent from work for any reason?". The findings suggest that mental health and job characteristics significantly reduce absenteeism among different sectors. The result showed that the size, ownership, and sector of the organization significantly affect the absenteeism, job characteristics, and the mental health of the employees. The results support the premises of Industry 5.0 and offer a new human-centric approach to absenteeism through the promotion of mental health through long-term organizational strategies and a more inclusive approach to employees' preferences in relation to job characteristics. The study offers a new, double-sided model of absenteeism, determining causal factors from the perspective of personal and organizational factors.

Human-Centric Digital Twins in Industry: A Comprehensive Review of Enabling Technologies and Implementation Strategies

The last decade saw the emergence of highly autonomous, flexible, re-configurable Cyber-Physical Systems. Research in this domain has been enhanced by the use of high-fidelity simulations, including Digital Twins, which are virtual representations connected to real assets. Digital Twins have been used for process supervision, prediction, or interaction with physical assets. Interaction with Digital Twins is enhanced by Virtual Reality and Augmented Reality, and Industry 5.0-focused research is evolving with the involvement of the human aspect in Digital Twins. This paper aims to review recent research on Human-Centric Digital Twins (HCDTs) and their enabling technologies. A systematic literature review is performed using the VOSviewer keyword mapping technique. Current technologies such as motion sensors, biological sensors, computational intelligence, simulation, and visualization tools are studied for the development of HCDTs in promising application areas. Domain-specific frameworks and guidelines are formed for different HCDT applications that highlight the workflow and desired outcomes, such as the training of AI models, the optimization of ergonomics, the security policy, task allocation, etc. A guideline and comparative analysis for the effective development of HCDTs are created based on the criteria of Machine Learning requirements, sensors, interfaces, and Human Digital Twin inputs.

Design of Digital-Twin Human-Machine Interface Sensor with Intelligent Finger Gesture Recognition

In this study, the design of a Digital-twin human-machine interface sensor (DT-HMIS) is proposed. This is a digital-twin sensor (DT-Sensor) that can meet the demands of human-machine automation collaboration in Industry 5.0. The DT-HMIS allows users/patients to add, modify, delete, query, and restore their previously memorized DT finger gesture mapping model and programmable logic controller (PLC) logic program, enabling the operation or access of the programmable controller input-output (I/O) interface and achieving the extended limb collaboration capability of users/patients. The system has two main functions: the first is gesture-encoded virtual manipulation, which indirectly accesses the PLC through the DT mapping model to complete control of electronic peripherals for extension-limbs ability by executing logic control program instructions. The second is gesture-based virtual manipulation to help non-verbal individuals create special verbal sentences through gesture commands to improve their expression ability. The design method uses primitive image processing and eight-way dual-bit signal processing algorithms to capture the movement of human finger gestures and convert them into digital signals. The system service maps control instructions by observing the digital signals of the DT-HMIS and drives motion control through mechatronics integration or speech synthesis feedback to express the operation requirements of inconvenient work or complex handheld physical tools. Based on the human-machine interface sensor of DT computer vision, it can reflect the user's command status without the need for additional wearable devices and promote interaction with the virtual world. When used for patients, the system ensures that the user's virtual control is mapped to physical device control, providing the convenience of independent operation while reducing caregiver fatigue. This study shows that the recognition accuracy can reach 99%, demonstrating practicality and application prospects. In future applications, users/patients can interact virtually with other peripheral devices through the DT-HMIS to meet their own interaction needs and promote industry progress.

Modeling the artificial intelligence-based imperatives of industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective

The recent COVID-19 pandemic has significantly affected emerging economies' global supply chains (SCs) by disrupting their manufacturing activities. To ensure business survivability during the current and post-COVID-19 era, it is crucial to adopt artificial intelligence (AI) technologies to renovate traditional manufacturing activities. The fifth industrial revolution, Industry 5.0 (I5.0), and artificial intelligence (AI) offer the overwhelming potential to build an inclusive digital future by ensuring supply chain (SC) resiliency and sustainability. Accordingly, this research aims to identify, assess, and prioritize the AI-based imperatives of I5.0 to improve SC resiliency. An integrated and intelligent approach consisting of Pareto analysis, the Bayesian approach, and the Best-Worst Method (BWM) was developed to fulfill the objectives. Based on the literature review and expert opinions, nine AI-based imperatives were identified and analyzed using Bayesian-BWM to evaluate their potential applicability. The findings reveal that real-time tracking of SC activities using the Internet of Things (IoT) is the most crucial AI-based imperative to improving a manufacturing SC's survivability. The research insights can assist industry leaders, practitioners, and relevant stakeholders in dealing with the impacts of large-scale SC disruptions in the post-COVID-19 era.

Automatic Bounding Box Annotation with Small Training Datasets for Industrial Manufacturing

In the past few years, object detection has attracted a lot of attention in the context of human-robot collaboration and Industry 5.0 due to enormous quality improvements in deep learning technologies. In many applications, object detection models have to be able to quickly adapt to a changing environment, i.e., to learn new objects. A crucial but challenging prerequisite for this is the automatic generation of new training data which currently still limits the broad application of object detection methods in industrial manufacturing. In this work, we discuss how to adapt state-of-the-art object detection methods for the task of automatic bounding box annotation in a use case where the background is homogeneous and the object's label is provided by a human. We compare an adapted version of Faster R-CNN and the Scaled-YOLOv4-p5 architecture and show that both can be trained to distinguish unknown objects from a complex but homogeneous background using only a small amount of training data. In contrast to most other state-of-the-art methods for bounding box labeling, our proposed method neither requires human verification, a predefined set of classes, nor a very large manually annotated dataset. Our method outperforms the state-of-the-art, transformer-based object discovery method LOST on our simple fruits dataset by large margins.

Collaborative approaches in sustainable and resilient manufacturing

In recent years, the manufacturing sector is going through a major transformation, as reflected in the concept of Industry 4.0 and digital transformation. The urge for such transformation is intensified when we consider the growing societal demands for sustainability. The notion of sustainable manufacturing has emerged as a result of this trend. Additionally, industries and the whole society face the challenges of an increasing number of disruptive events, either natural or human-caused, that can severely affect the normal operation of systems. Furthermore, the growing interconnectivity between organizations, people, and physical systems, supported by recent developments in information and communication technologies, highlights the important role that collaborative networks can play in the digital transformation processes. As such, this article analyses potential synergies between the areas of sustainable and resilient manufacturing and collaborative networks. The work also discusses how the responsibility for the various facets of sustainability can be distributed among the multiple entities involved in manufacturing. The study is based on a literature survey, complemented with the experience gained from various research projects and related initiatives in the area, and is organized according to various dimensions of Industry 4.0. A brief review of proposed approaches and indicators for measuring sustainability from the networked manufacturing perspective is also included. Finally, a set of key research challenges are identified to complement strategic research agendas in manufacturing.

Advanced Control Systems in Industry 5.0 Enabling Process Mining

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new "proof of concept" of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems.

Mist and Edge Computing Cyber-Physical Human-Centered Systems for Industry 5.0: A Cost-Effective IoT Thermal Imaging Safety System

While many companies worldwide are still striving to adjust to Industry 4.0 principles, the transition to Industry 5.0 is already underway. Under such a paradigm, Cyber-Physical Human-centered Systems (CPHSs) have emerged to leverage operator capabilities in order to meet the goals of complex manufacturing systems towards human-centricity, resilience and sustainability. This article first describes the essential concepts for the development of Industry 5.0 CPHSs and then analyzes the latest CPHSs, identifying their main design requirements and key implementation components. Moreover, the major challenges for the development of such CPHSs are outlined. Next, to illustrate the previously described concepts, a real-world Industry 5.0 CPHS is presented. Such a CPHS enables increased operator safety and operation tracking in manufacturing processes that rely on collaborative robots and heavy machinery. Specifically, the proposed use case consists of a workshop where a smarter use of resources is required, and human proximity detection determines when machinery should be working or not in order to avoid incidents or accidents involving such machinery. The proposed CPHS makes use of a hybrid edge computing architecture with smart mist computing nodes that processes thermal images and reacts to prevent industrial safety issues. The performed experiments show that, in the selected real-world scenario, the developed CPHS algorithms are able to detect human presence with low-power devices (with a Raspberry Pi 3B) in a fast and accurate way (in less than 10 ms with a 97.04% accuracy), thus being an effective solution (e.g., a good trade-off between cost, accuracy, resilience and computational efficiency) that can be integrated into many Industry 5.0 applications. Finally, this article provides specific guidelines that will help future developers and managers to overcome the challenges that will arise when deploying the next generation of CPHSs for smart and sustainable manufacturing.

Toward Better Food Security Using Concepts from Industry 5.0

The rapid growth of the world population has increased the food demand as well as the need for assurance of food quality, safety, and sustainability. However, food security can easily be compromised by not only natural hazards but also changes in food preferences, political conflicts, and food frauds. In order to contribute to building a more sustainable food system-digitally visible and processes measurable-within this review, we summarized currently available evidence for various information and communication technologies (ICTs) that can be utilized to support collaborative actions, prevent fraudulent activities, and remotely perform real-time monitoring, which has become essential, especially during the COVID-19 pandemic. The Internet of Everything, 6G, blockchain, artificial intelligence, and digital twin are gaining significant attention in recent years in anticipation of leveraging the creativity of human experts in collaboration with efficient, intelligent, and accurate machines, but with limited consideration in the food supply chain. Therefore, this paper provided a thorough review of the food system by showing how various ICT tools can help sense and quantify the food system and highlighting the key enhancements that Industry 5.0 technologies can bring. The vulnerability of the food system can be effectively mitigated with the utilization of various ICTs depending on not only the nature and severity of crisis but also the specificity of the food supply chain. There are numerous ways of implementing these technologies, and they are continuously evolving.

Technology and orthopaedic surgeons

Modern and innovative technologies are rapidly penetrating the clinical practices of Orthopaedic Surgeons. The ones that have proved successful for clinical use are Additive Manufacturing/3D printing, Artificial Intelligence, Robotics, Smart sensors, and Orthobiologics. Industry 5.0 revolution has helped provide personalised treatment by integrating machines and human beings. In this special issue, we present a collection of excellent articles on these technologies.

Industry 5.0 in Orthopaedics

Background

Industrial revolutions play a major role in the development of technologies in various fields. Currently, the world is marching towards softwarization and digitalization. There is an emerging need for conversion of Industry 4.0 to Industry 5.0 for technological development and implementation of the same in the digital era. In health care, digitalization emerged in Industry 4.0 revolution. To enhance patient care and quality of life, Industry 5.0 plays a major role in providing patient-centric care and customization and personalization of products. The integration of human intelligence with artificial intelligence provides a precise diagnosis and enhances the recovery and functional outcome of the patients.

Materials and methods

In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care.

Results

Industry 5.0 lessens the work of medical professionals and integrates software-based diagnosis and management. It provides cost-effective manufacturing solutions with limited resources compared to Industry 4.0. Industry 5.0 focuses on SMART and additive manufacturing of implants, and the development of bio-scaffolds, prosthetics, and instruments. In this manuscript, the domains and limitations of Industry 5.0 and further research on Industry 6.0 were elaborated on to bring out technologies in better health care.

Conclusion

'The personalization and customization of products' are the hallmarks of this evolving Industry 5.0 revolution. The major uplifts in various domains of industry 5.0 such as advanced automation, digitalization, collaborative robots, and personalization bring this an inevitable mechano-scientific technological revolution in this current medical era.

Digital Stereotypes in HMI-The Influence of Feature Quantity Distribution in Deep Learning Models Training

This paper proposes a concept of Digital Stereotypes, observed during research on quantitative overrepresentation of one class over others, and its impact on the results of the training of Deep Learning models. The real-life observed data classes are rarely of the same size, and the intuition of presenting multiple examples of one class and then showing a few counterexamples may be very misleading in multimodal classification. Deep Learning models, when taught with overrepresentation, may produce incorrect inferring results, similar to stereotypes. The generic idea of stereotypes seems to be helpful for categorisation from the training point of view, but it has a negative influence on the inferring result. Authors evaluate a large dataset in various scenarios: overrepresentation of one or two classes, underrepresentation of some classes, and same-size (trimmed) classes. The presented research can be applied to any multiclassification applications, but it may be especially important in AI, where the classification, uncertainty and building new knowledge overlap. This paper presents specific 'decreases in accuracy' observed within multiclassification of unleveled datasets. The 'decreases in accuracy', named by the authors 'stereotypes', can also bring an inspiring insight into other fields and applications, not only multimodal sentiment analysis.

Industry 5.0: improving humanization and sustainability of Industry 4.0

The widespread digitization and dynamic development of the technologies of the fourth industrial revolution, leading to the dehumanization of industry, have increased the interest of the scientific community in aspects of industrial humanization, sustainability and resilience. Hence, the aim of the article is to identify areas related to humanization and sustainability of the concept of Industry 4.0. A bibliometric analysis of Web of Science using Vosviewer tools, Excell and content analysis of selected papers were applied. The most important results include the determination of the dynamics of the increase in the number of publications in the segment of Industry 4.0 and Industry 5.0. The article indicates the weaknesses of the concept of Industry 4.0, especially in the area of the role of man in smart factories and sustainable development. Thus, the framework of the concept of Industry 5.0 was identified. In addition, the bibliometric analysis carried out allowed the identification of an important stream of employee skill development.

A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications

We are still in the midst of Industry 4.0 (I4.0), with more manufacturing lines being labeled as smart thanks to the integration of advanced ICT in Cyber-Physical Systems (CPS). While I4.0 aims to provision cognitive CPS systems, the nascent Industry 5.0 (I5.0) era goes a step beyond, aiming to build cross-border, sustainable, and circular value chains benefiting society as a whole. An enabler of this vision is the integration of data and AI in the industrial decision-making process, which does not exhibit yet a coordination between the Operation and Information Technology domains (OT/IT). This work proposes an architectural approach and an accompanying software prototype addressing the OT/IT convergence problem. The approach is based on a two-layered middleware solution, where each layer aims to better serve the specific differentiated requirements of the OT and IT layers. The proposal is validated in a real testbed, employing actual machine data, showing the capacity of the components to gracefully scale and serve increasing data volumes.

Green IoT and Edge AI as Key Technological Enablers for a Sustainable Digital Transition towards a Smart Circular Economy: An Industry 5.0 Use Case

Internet of Things (IoT) can help to pave the way to the circular economy and to a more sustainable world by enabling the digitalization of many operations and processes, such as water distribution, preventive maintenance, or smart manufacturing. Paradoxically, IoT technologies and paradigms such as edge computing, although they have a huge potential for the digital transition towards sustainability, they are not yet contributing to the sustainable development of the IoT sector itself. In fact, such a sector has a significant carbon footprint due to the use of scarce raw materials and its energy consumption in manufacturing, operating, and recycling processes. To tackle these issues, the Green IoT (G-IoT) paradigm has emerged as a research area to reduce such carbon footprint; however, its sustainable vision collides directly with the advent of Edge Artificial Intelligence (Edge AI), which imposes the consumption of additional energy. This article deals with this problem by exploring the different aspects that impact the design and development of Edge-AI G-IoT systems. Moreover, it presents a practical Industry 5.0 use case that illustrates the different concepts analyzed throughout the article. Specifically, the proposed scenario consists in an Industry 5.0 smart workshop that looks for improving operator safety and operation tracking. Such an application case makes use of a mist computing architecture composed of AI-enabled IoT nodes. After describing the application case, it is evaluated its energy consumption and it is analyzed the impact on the carbon footprint that it may have on different countries. Overall, this article provides guidelines that will help future developers to face the challenges that will arise when creating the next generation of Edge-AI G-IoT systems.

A Review and Qualitative Meta-Analysis of Digital Human Modeling and Cyber-Physical-Systems in Ergonomics 4.0

Occupational ApplicationsFounded in an empirical case study and theoretical work, this paper reviews the scientific literature to define the role of Digital Human Modeling (DHM), Digital Twin (DT), and Cyber-Physical Systems (CPS) to inform the emerging concept of Ergonomics 4.0. We find that DHM evolved into DT is a core element in Ergonomics 4.0. A solid understanding and agreement on the nature of Ergonomics 4.0 is essential for the inclusion of ergonomic values and considerations in the larger conceptual framework of Industry 4.0. In this context, we invite Ergonomists from various disciplines to broaden their understanding and application of DHM and DT.

COVID-19 and hospitality 5.0: Redefining hospitality operations

The sudden outbreak of COVID-19 has severely affected the global hospitality industry. The hygiene and cleanliness of hotels has become the focal point in the recovery plan during COVID-19. This study investigates the effects of past disasters on the global hospitality industry, and how the industry responded to them. Since past pandemics and epidemics identified hygiene and cleanliness as an important factor, this study further explores the role of technology in ensuring hygiene and cleanliness. Hence, this study further examines the scalability of Industry 5.0 design principles into the hospitality context, leading to Hospitality 5.0 to improve operational efficiency. The study further delineates how Hospitality 5.0 technologies can ensure hygiene and cleanliness in various touchpoints in customer's journey. This study serves as a foundation to understand how synergy between humans and machines can be achieved through Hospitality 5.0. The theoretical and practical implications are discussed.