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Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

To enhance the efficiency and stability of traffic flow in the vicinity of traffic signals under mixed traffic, a macroscopic model considering the characteristics of the vicinity of traffic signals is proposed from the perspective of cyber-physical systems (CPS). In this model, the effects of traffic lights and vehicle lane changing behavior are analyzed. Among them, the remaining time of the signal light, the distance between the vehicle and the signal light, and the urgency of lane change are considered to build a macro model. Finally, the simulation results show that compared with the traditional macroscopic model, the proposed model can improve the stability of traffic flow in the vicinity of traffic signals and effectively alleviate traffic congestion. [ABSTRACT FROM AUTHOR]

This paper presents a digital twin and cyber-physical systems (CPS) environment for monitoring and analyzing the energy consumption of machine tools. Machine tools, which account for a large portion of the manufacturing industry, should be developed in response to environmental issues through carbon neutrality and digital transformation for the Fourth Industrial Revolution. The digital twin, which is at the core of CPS, must support the visualization and analysis of energy consumption issues from various perspectives, and at the same time, support multi-faceted analyses. In this paper, a digital twin that supports the monitoring and analysis of the energy consumption of machine tools and an established CPS environment are proposed. The digital twin consists of a static model containing structural information, a dynamic model expressing the behavior of the object, a visualization model containing a three-dimensional model, and a configuration model linking those three together. In addition, a CPS environment that supports this is implemented through a commercial internet-of-things platform. Lastly, the practicality of the proposed model and system are verified through case studies and a comparative analysis. This research is expected to prompt innovation in the development of cutting-edge machine tools that are more intelligent and closer to carbon neutrality. [ABSTRACT FROM AUTHOR]

The digital twin factory is the digital twin model of the physical factory. It can accurately reflect the physical world. In the early stage of building an intelligent factory, it is very meaningful to design a digital twin model of a physical factory through process planning, logistics optimization, layout planning, and so on, to shorten the time of building and debugging a physical factory. This paper takes the construction of an intelligent factory for laptop assembly as an example. Firstly, construct the model of the laptop disassembly/assembly graph. Because assembly is the reverse process of disassembly, the algorithm is used to plan the assembly sequence of the laptop. After that, the digital twin model of the laptop assembly line according to the laptop assembly process sequence. Subsequently, time series optimization was used to optimize the bottleneck process of the model, resulting in a 424% increase in output. Lastly, we explain the modules and steps used to build the physical laptop factory. This paper innovatively takes the laptop assembly process sequence as the main line of factory construction and takes the bottleneck process time chain optimization as the breakthrough of capacity improvement, which verifies the important role and significance of digital twin factories in constructing intelligent factories. It provides a basis for the promotion and application of digital twin technology in constructing intelligent factories. [ABSTRACT FROM AUTHOR]

Cyber-physical system (CPS) is an integral part of an internet of things (IoT) with established wide spread applications. An increasing concern towards being highly vulnerable to various forms of dynamic cyber-attacks has been increasingly evolving. A review of existing research methodology showcases complex solutions that can offer sub-optimal security strength when exposed to dynamic cyber-attack forms while increasing the computational burden. Therefore, this manuscript presents a novel yet simplified computational framework capable of determining and resisting critical anomalies within internet-of-cyber physical systems (IoCPS). The presented scheme contributes towards preprocessing following a distinct oversampling method targeting balancing the data. An ensemble machine learning model using a discrete variant of AdaBoost and neural decision tree (NDT) has been implemented to optimize the learning process and improve the threat detection efficiency. The comparative outcome of the proposed study showcases that it offers approximately 7.2% increased threat detection accuracy and approximately 68% reduced response time compared to frequently adopted learning mechanisms towards threat detection over an IoT environment. [ABSTRACT FROM AUTHOR]

Power cyber–physical systems such as multi-area power systems (MAPSs) have gained considerable attention due to their integration of power electronics with wireless communications technologies. Incorporating a communication setup enhances the sustainability, reliability, and efficiency of these systems. Amidst these exceptional benefits, such systems' distributed nature invites various cyber-attacks. This work focuses on the equal power sharing of MAPSs in the event of false data injection (FDI) attacks. The proposed work uses a sliding mode control (SMC) mechanism to ensure timely detection of challenges such as FDI attacks and load change, making MAPSs reliable and secure. First, a SMC-based strategy is deployed to enable the detection and isolation of compromised participants in MAPS operations to achieve equal power sharing. Second, time-varying FDI attacks on MAPSs are formulated and demonstrate their impact on equal power sharing. Third, a robust adaptive sliding mode observer is used to accurately assess the state of the MAPS to handle state errors robustly and automatically adjust parameters for identifying FDI attacks and load changes. Lastly, simulation results are presented to explain the useful ability of the suggested method. [ABSTRACT FROM AUTHOR]

For nonlinear cyber physical systems (CPS) with additional disturbances and system constraint, a resilient self-triggered model predictive control (RST-MPC) strategy is developed, considering the impact of dual-channel deception attacks. The resilient set is first designed to strengthen the resilience of the controlled system against dual-channel deception attacks. Subsequently, the self-triggering mechanism is designed to reduce the computational and communication resource consumption while ensuring system security. Based on this, an RST-MPC algorithm is proposed. Furthermore, sufficient conditions are provided to guarantee the iterative feasibility of the RST-MPC algorithm and the closed-loop stability of the controlled system under dual-channel deception attack. Finally, the design goal of the developed algorithm is verified by the networked nonlinear system. [ABSTRACT FROM AUTHOR]

The integration of cyber (network) with physical world is a big step in cyber-physical systems. This has revolutionised many industries. But this transformation has made cyber-physical systems vulnerable to attacks. One particular type of attack is the adversarial false data injection which injects false data in either the sensor measurements or the corresponding communication channel. A better understanding of how false data injection (FDI) attacks are constructed is crucial for developing strategies to protect against such attacks. In this paper, we consider two models for networked control systems and present an algorithm for constructing FDI attacks in each case and compare with an existing approach. The conditions for the attack to remain stealthy for systems equipped with a $ \chi ^2 $ χ 2 failure detector and the design of attack vectors that satisfy these conditions are discussed in detail. The algorithms are demonstrated by developing FDI attacks for two real-world examples. [ABSTRACT FROM AUTHOR]

Background: Energy systems, as critical infrastructures (CI), constitute Cyber-Physical-Social Systems (CPSS). Due to their inherent complexity and the importance of service continuity of CIs, digitization in this context encounters significant practical challenges. Digital Twins (DT) have emerged over the recent years as a promising solution for managing CPSSs by facilitating real-time interaction, synchronization, and control of physical assets. The selection of an appropriate architectural framework is crucial in constructing a DT, to ensure integration of enabling technologies and data from diverse sources. Objectives: This study proposes a Systematic Literature Review (SLR) to examine technological enablers, design choices, management strategies and Computational Challenges of DTs in Smart Energy Systems (SES) by also analyzing existing architectures and identifying key components. Methods: The SLR follows a rigorous workflow exploiting a multi-database search with predefined eligibility criteria, accompanied by advanced searching techniques, such as manual screening of results and a documented search strategy, in order to ensure its comprehensiveness and reliability, More specifically, research questions are first defined and then submitted as queries to scientific digital libraries (i.e., IEEE Xplore, Scopus, and WoS) selected due to their coverage and reliability (Google Scholar was excluded for the presence of grey literature and non-peer-reviewed material). Then, inclusion and exclusion criteria are established to filter the results and shortlist the significant publications. Subsequently, relevant data are extracted, summarized, and categorized in order to identify common themes, existing gaps, and future research directions, with the aim of providing a comprehensive overview of the current state of DTs for SESs. Results: From the proposed DT-based solutions described in the selected publications, the adopted architectures are examined and categorized depending on their logical building blocks, microservices, enabling technologies, human–machine interfaces (HMI), artificial intelligence and machine learning (AI/ML) implementations, data flow and data persistence choices, and Internet-of-Things (IoT) components involved. Additionally, the integration of edge-cloud computing and IoT technologies in literature are studied and discussed. Finally, gaps, opportunities, future study lines, and challenges of implementing DTs are thoroughly addressed. The results achieved also pave the way for a forthcoming design pattern catalog for DTs in CPSSs capable of supporting the engineering and research communities, by offering practical insights on implementation and integration aspects. Conclusion: The proposed SLR provides a valuable resource for designing and implementing DTs of CPSSs in general and of SESs in particular. Furthermore, it highlights the potential benefits of adopting DTs to manage complex energy systems and it identifies areas for future research. [ABSTRACT FROM AUTHOR]

Purpose: As the construction sector constantly seeks ways to address ever-growing societal demands, the need to embrace innovation and digital technologies (DTs) cannot be overstated. Therefore, the purpose of this study is to assess the success factors influencing the adoption of DTs to achieve sustainable construction in a developing economy such as Nigeria. Design/methodology/approach: A quantitative research approach was conducted with close-ended questionnaires developed and administered to construction professionals based in Lagos State, Nigeria. Data obtained was analyzed using percentages, frequency, mean item score and exploratory factor analysis. Findings: The findings from the mean scores revealed the leading influential success factors which were education and training, methodology of model constructions, organization development, customer satisfaction and profitability and new revenue. Factor analysis revealed three clusters of success factors which were management needs, construction methodology and effective communication. Practical implications: To adequately integrate DTs into construction industry activities and processes, awareness about the technologies must be created and enhanced if already in play. This study posits that the construction sector must accept and implement this new paradigm of innovation to benefit from this disruptive era. Originality/value: This study serves as a foundation for other related studies that are aimed at advocating the efficacy of DTs in the effective and efficient execution of construction activities. The assessment of the success factors influencing the adoption of DTs would help construction organizations and stakeholders to understand the need to embrace and implement smart technologies into the activities, operations and processes of the construction industry. [ABSTRACT FROM AUTHOR]

Abstract Background Energy systems, as critical infrastructures (CI), constitute Cyber-Physical-Social Systems (CPSS). Due to their inherent complexity and the importance of service continuity of CIs, digitization in this context encounters significant practical challenges. Digital Twins (DT) have emerged over the recent years as a promising solution for managing CPSSs by facilitating real-time interaction, synchronization, and control of physical assets. The selection of an appropriate architectural framework is crucial in constructing a DT, to ensure integration of enabling technologies and data from diverse sources. Objectives This study proposes a Systematic Literature Review (SLR) to examine technological enablers, design choices, management strategies and Computational Challenges of DTs in Smart Energy Systems (SES) by also analyzing existing architectures and identifying key components. Methods The SLR follows a rigorous workflow exploiting a multi-database search with predefined eligibility criteria, accompanied by advanced searching techniques, such as manual screening of results and a documented search strategy, in order to ensure its comprehensiveness and reliability, More specifically, research questions are first defined and then submitted as queries to scientific digital libraries (i.e., IEEE Xplore, Scopus, and WoS) selected due to their coverage and reliability (Google Scholar was excluded for the presence of grey literature and non-peer-reviewed material). Then, inclusion and exclusion criteria are established to filter the results and shortlist the significant publications. Subsequently, relevant data are extracted, summarized, and categorized in order to identify common themes, existing gaps, and future research directions, with the aim of providing a comprehensive overview of the current state of DTs for SESs. Results From the proposed DT-based solutions described in the selected publications, the adopted architectures are examined and categorized depending on their logical building blocks, microservices, enabling technologies, human–machine interfaces (HMI), artificial intelligence and machine learning (AI/ML) implementations, data flow and data persistence choices, and Internet-of-Things (IoT) components involved. Additionally, the integration of edge-cloud computing and IoT technologies in literature are studied and discussed. Finally, gaps, opportunities, future study lines, and challenges of implementing DTs are thoroughly addressed. The results achieved also pave the way for a forthcoming design pattern catalog for DTs in CPSSs capable of supporting the engineering and research communities, by offering practical insights on implementation and integration aspects. Conclusion The proposed SLR provides a valuable resource for designing and implementing DTs of CPSSs in general and of SESs in particular. Furthermore, it highlights the potential benefits of adopting DTs to manage complex energy systems and it identifies areas for future research.

With the rapid growth of urbanization and the increasing demand for transportation, urban traffic congestion has become a hindrance to individuals’ travel experience. Urban intersections are one of the primary sources of traffic congestion, and these bottlenecks have a negative impact not only on traffic efficacy but also on the surrounding road traffic in the region. To alleviate urban traffic congestion, cyber-physical systems have been widely implemented in the transportation industry, allowing for the perception, analysis, calculation, and dispatching of urban traffic flow, as well as making urban transportation safe, efficient, and quick. As the system scale and functions increase, system design has become increasingly complex, necessitating a deeper comprehension of the system’s structure and interaction relationships to construct a stable and reliable system. Therefore, this study proposes a method for designing cyber-physical systems for urban traffic intersections based on Model-Based Systems Engineering (MBSE). This method models and analyses exhaustively the system’s requirements, functions, and logical architecture using System Modeling Language (SysML). After the architecture design has been completed, an architecture verification and optimization method based on Failure Mode and Effect Analysis (FMEA) for urban road intersection cyber-physical systems is utilized to analyze the architecture’s reliability by analyzing the failure modes of activities and to optimize the system architecture to improve the design’s efficiency and reliability.

The vertical handover policy of space-air-ground integrated cyber-physical systems based on deep reinforcement learning was studied, in which the challenges of complicated network model and difficulties in acquiring prior knowledge for network topology and model were addressed. By jointly taking the system stability, handover cost and network-using cost into account, the vertical handover policy problem was modeled as a constraint Markov decision process (CMDP), and a sufficient condition to ensure the existence of a feasible solution was derived.Furthermore, a constraint-proximal policy optimization (CPPO) algorithm was proposed to solve the CMDP, and also the distributed learning scheme at base station sides was introduced to accelerate the speed of converging. Simulation results verify the validation and superiority of the proposed vertical handover policy as compared with the baselines.

Summary: In this article, a memory event‐triggered mechanism (METM) based switching‐like control method is designed for a cyber‐physical system (CPS), maintain control performance and improve the communication efficiency under denial of service (DoS) attack. In order to obtain better control effect at small sampling periods, a sampler contains the explicit sampling period is designed by using delta operator approach. First of all, a METM based on an acknowledgement character (ACK) mechanism is proposed, which can reduce event triggered data calculations, increase the transmission instants at the crest or trough of the responses and reduce the negative impact of DoS attack. Next, a switching‐like control strategy for the delta operator system converted from the CPS is designed to prevent DoS attack resulting in open‐loop unstable operation of the system. Further, a switching law is designed to maintain the concealment of the secure channel. Then, sufficient conditions are derived for the CPS in terms of linear matrix inequalities (LMIs). Finally, the effectiveness of the designed method is verified by a simulation example. [ABSTRACT FROM AUTHOR]

Decentralized periodic event‐triggered control can reduce the working time and data transmissions in the sensor nodes for feedback control loops, making it suitable for wireless networked control systems, especially those with large physical scales, such as smart water distribution systems. However, the design of this control approach requires accurate system model, which is normally difficult to obtain. In this work, we assume a digital twin of the original system can be obtained by some modeling and simulation tools, for example, Simscape in MATLAB/Simulink for water distribution systems, and thus present an event‐triggered controller design approach. We first present the design of such an event‐triggered controller for the digital twin; and then analyze the condition under which if both the original system and its digital twin can be satisfied, the same controller can work for the original system and some type of stability can be guaranteed at the same time. The proposed approach is applied in a smart water distribution system to show the feasibility. [ABSTRACT FROM AUTHOR]

This article presents a robust security‐based model predictive control (MPC) framework for a class of discrete‐time nonlinear cyber‐physical systems (CPSs) suffering random deception attacks and persistent actuator saturation. In CPSs, the attacks can significantly degrade the control performance. Naturally, a larger degree of control is required, prompting the consideration of actuator saturation in CPS's structure. In our work, the cyberattacks on the controller‐actuator (C‐A) channel are described as a stochastic process with specific probability and bounded energy. The security‐related constraint is introduced to improve the resistance and robustness against attacks. Mean‐square quadratic boundedness is derived to estimate the robustly invariant set. By placing the saturated input into a convex hull, a robust security‐based MPC optimization problem is addressed less conservatively with linear matrix inequality technique. Furthermore, the feasibility of the proposed method is proved recursively, and the stability and security of the closed‐loop system are established in mean‐square sense. In the end, the laboratory tank and reactor‐separator process are applied to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the last years, there has been a growing interest in the emerging concept of digital twin (DT) as it represents a promising paradigm to continuously monitor cyber–physical systems, as well as to test and validate predictability, safety, and reliability aspects. At the same time, artificial intelligence (AI) is exponentially affirming as an extremely powerful tool when it comes to modeling the behavior of physical assets allowing, de facto, the possibility of making predictions on their potential evolution. However, despite the fact that DTs and AI (and their combination) can act as game-changing technologies in different domains (including the railways), several challenges have to be faced to ensure their effectiveness, especially when dealing with safety-critical systems. This paper provides a narrative review of the scientific literature on DTs for railway maintenance applications, with a special focus on their relationship with AI. The aim is to discuss the opportunities the integration of these two technologies could open in railway maintenance applications (and beyond), while highlighting the main challenges that should be overcome for its effective implementation. [ABSTRACT FROM AUTHOR]

This paper explores secure communication systems with a chaotic carrier. The use of chaotic oscillations instead of regular van der Pol oscillators as a signal carrier is a promising and active research area, providing not only communication systems with new protection principles and organization but also high steganographic efficiency when transmitting short messages. The problem is to select methods and techniques for mixing a useful signal into a chaotic one and its recovery on the receiver side, featuring a set of properties acceptable for implementation and real-world application. We demonstrate application of synergetic control theory (SCT), which provides advanced observer-basing methods for nonlinear dynamic systems as well as explore example of data transmission system consisting of a Genesio–Tesi chaotic oscillator, data signal transmission with a method of nonlinear modulation, and recovering with a single-channel synergetic observer at the receiver side. The paper presents a nonlinear state observer modeling procedure followed by building a MATLAB/Simulink simulation model of the data transmission system for the PC-platform along with software implementation for the Raspberry Pi platform, with simulation and experimental run results showing data transmission rates seem to be acceptable for the considered practical applications. Practical applications and limitations issues are discussed. Future research will be universal modeling procedures for different classes of chaotic generators and whole system experimental hardware implementation. The obtained results can be primarily used in short messages and/or encryption keys secure transmission systems, cyber-physical system component command communications, as well as chaotic carrier system R&D competitive studies and other applications. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) is considered the most modern simulation method when it comes to tightly connect the real and virtual worlds to produce accurate simulation models of constant use to aid stakeholders in decision making. This method has been used more intensively in the manufacturing sector, but its use has spread to other sectors of great relevance in the economy, such as the retail industry. The contribution of this article to existing research is to present a Systematic Literature Review (SLR) addressing the state-of-the-art of the potential use of Digital Twins (DTs) to support decision making in the retail field. The main findings illustrate a considerable appearance of case studies applied directly in the sector, a strong investment in research focused on supply chains, an extensive use of simulation models and sensors, however, that mostly make use of secondary data and are not completely autonomous. Summary tables of the main benefits, opportunities and challenges in applying DTs in the retail sector are also presented. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPSs) offer significant potential to address the evolving demands of industrial development. In the Industry 4.0 era, a framework integrating sensing, data exchange, numerical analysis, and real-time feedback is essential for meeting modern industrial needs. However, implementing this integration presents challenges across multiple domains, particularly in digital analysis, information sensing, and data exchange during corporate transformation. Companies yet to undergo transformation face distinct challenges, including the risks and trial-and-error costs of adopting new technologies. This study focuses on a heavy-duty workpiece processing factory, with a specific emphasis on the painting process. The complexity of this process frequently results in congestion, which is approached as a multi-objective, multi-constraint optimization problem. This paper proposes the Improved Non-dominated Sorting Genetic Algorithm II (INSGA-II) to address the requirements of multi-objective optimization. The proposed approach uses multi-chromosome structures, listeners, and recursive backtracking initialization to optimize the search for solutions under constraints. This enables the factory to automatically streamline production lines based on workpiece processing sequences, leading to increased efficiency. Additionally, a CPS framework focused on simulation modeling has been designed. First, the INSGA-II algorithm processes order data to generate production schedules. Next, the data transmission formats and input-output interfaces are designed. Then, a simulation model is built using the algorithm's results. These components collectively form the CPS framework for this study. The proposed method offers an automated digital solution through the algorithm, enabling verification of its feasibility via the simulation model. As a result, it significantly enhances decision-making speed, reliability, and equipment utilization. [ABSTRACT FROM AUTHOR]

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers' order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters. [ABSTRACT FROM AUTHOR]

The rapid expansion of Cyber-Physical Systems (CPS) is crucial for enhancing connectivity in the smart world. Encompassing smart homes, cities, agriculture, and healthcare, the broad application environment of CPS demands robust security due to diverse devices, communication protocols, and dispersed nodes. In this multi-domain landscape, ensuring authenticity becomes paramount, leading to the introduction the Secured Lightweight Authentication and Key Agreement for Cyber-Physical System (SLAKA_CPS) protocol. This protocol facilitates authentication across heterogeneous CPS devices in a resource-constrained manner, addressing communication and security concerns. Comparative analysis, including computational complexity and communication cost, reveals that SLAKA_CPS outperforms existing systems with a reduction of 11% in computing complexity, 24% in communication, and 50% in storage costs. Formal verification processes such as AVISPA, BAN logic, and ROR model reinforced the effectiveness of the proposed protocol. [ABSTRACT FROM AUTHOR]

With large-scale Cyber-Physical Systems (Internet of Things) deployments, the low-cost networked devices are becoming ubiquitous. Time stamping of events is a basic requirement and it is crucial for distributed/isolated systems/devices to work in unison. The system clocks of these devices are not very stable and their behaviour is affected by ageing and environmental conditions. A clock’s timing behaviour is generally analysed independently by comparison to a highly stable standard reference clock using specialized instruments. Network Time Protocol (NTP) is widely used for time synchronization over computer networks. As the NTP server clock is more stable in comparison to a client’s system clock, the variation in the end device’s clock offset values with respect to the NTP server clock over a long time can be considered for analysing the behaviour of the end device’s clock. This paper presents a method for analysing clock behaviour based on NTP messages. A computer programme is developed, which utilizes an open-source NTP client library to exchange NTP messages and records end device’s system clock’s offset variations over time for a given periodicity to analyse the behaviour. This black box analysis approach does not require any direct access to the end device’s clock circuit. The solution may be useful for analysing clock behaviour for long-term stability under actual environmental conditions, for deciding time synchronization periodicity to optimize the number of synchronization packet exchanges, and for pre-deployment timing stability assessment of devices for deployment scenarios having intermittent/non-availability of periodic time synchronization. [ABSTRACT FROM AUTHOR]

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber–physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber–physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates. [ABSTRACT FROM AUTHOR]

Digital Twins (DTs) are emerging as a fundamental brick of engineering Cyber-Physical Systems (CPSs), but their notion is still mostly bound to specific business domains (e.g. manufacturing), goals (e.g. product design), or applications (e.g. the Internet of Things). As such, their value as general purpose engineering abstractions is yet to be fully revealed. In this paper, we relate DTs with agents and multiagent systems, as the latter are arguably the most rich abstractions available for the engineering of complex socio-technical and CPSs, and the former could both fill in some gaps in agent-oriented software engineering and benefit from an agent-oriented interpretation—in a cross-fertilisation journey. [ABSTRACT FROM AUTHOR]

A Digital Twin (DT) is a frequently updated virtual representation of a physical or a digital instance that captures its properties of interest. Incorporating both cyber and physical parts to build a digital twin is challenging due to the high complexity of the requirements that should be addressed and satisfied during the design, implementation and operation. In this context, we introduce the MADTwin (Multi-Agent Digital Twin) framework driven by a Multi-agent Systems (MAS) paradigm and supported by flexible architecture and extendible upper ontology for modelling agent-based digital twins. A comprehensive case study of a smart warehouse supported by multi-robots has been presented to show the feasibility and applicability of this framework. The introduced framework powered by intelligent agents integrated with enabler technologies enabled us to cope with parts of the challenges imposed by modelling and integrating Cyber-Physical Systems (CPS) with digital twins for multi-robots of the smart warehouse. In this framework, different components of CPS (robots) are represented as autonomous physical agents with their digital twin agents in the digital twin environment. Agents act autonomously and cooperatively to achieve their local goals and the objectives of the whole system. Eventually, we discuss the framework's strengths and identify areas of improvement and plans for future work. [ABSTRACT FROM AUTHOR]

It is difficult for students to learn the concepts of chemical laboratory safety education from actual experiences because the traditional teaching model prohibits students from performing dangerous operations. Therefore, this study develops a novel mixed reality-based chemistry experiment learning system (MRCELS) to allow learners to conduct inquiry-based experimental operations in an environment integrating virtual and physical space by using a free and safe way so that chemical laboratory safety problems that usually occur in actual chemical laboratories that may hurt learners can be avoided. A total of 36 Grade 11 students from a girl's high school in Taipei City, Taiwan, were recruited to participate in the experiment. A total of 17 students were randomly assigned to the experimental group using the MRCELS to conduct chemical laboratory safety education, while the remaining 19 students were randomly assigned to the control group taught by a teacher in a physical-chemical laboratory. The research results show that the learning effectiveness of experimental group learners in chemical laboratory safety education is significantly superior to that of the control group. Besides, experimental and control groups significantly improved their attitudes toward laboratory safety after the instruction experiment, but no significant differences existed between them. Both groups of learners showed high satisfaction with the learning model they used, but no significant differences existed between them. Encouragingly, according to the interview results, learners preferred to adopt the MRCELS to support learning. They stated that the immersion experience in operating the chemical experiments through the MRCELS could enhance their awareness of hazardous operations and the development of laboratory safety concepts. The developed MRCELS contributes an effective and innovative learning model to chemical laboratory safety education. [ABSTRACT FROM AUTHOR]

In this article, a quality of service (QoS) dependent variable sampling dynamic event-triggered control method is designed for a cyber–physical system (CPS) with delays and packets dropout to cope with non-ideal network environments, maintain the desired control performance and improve the communication efficiency. To achieve the variable period sampling, a sampler is designed based on the QoS of the wireless network by using the delta operator discretization method. Then, a variable period sampling scheme for the delta operator system converted from the CPS is designed. Furthermore, a dynamic event-triggered mechanism (DETM) is proposed using the variable period sampling signal, which can reduce event triggered data calculations and increase event triggered intervals. By utilizing the average dwell time (ADT) approach, sufficient conditions contains the explicit variable sampling period are derived for the derived switched CPS. Finally, the effectiveness of the designed method is verified by numerical examples. • A novel QoS-based variable period sampling strategy is proposed to realize real-time adjustment to ensure the desired control performance of the CPS. • A DETM is derived using the variable period sampling signal for the CPS to improve the communication efficiency. • A switched delta operator system converted from the CPS is designed, and the sufficient conditions contain the explicit sampling period for system stability are obtained. [ABSTRACT FROM AUTHOR]

Technology development in the modern era encourages the utilization of efficient technologies, such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS). This study focuses on developing a cyber-physical system using smartphones to control and monitor refrigeration systems in fishing vessels. Through performance testing, IoT and CPS technologies have been successfully implemented to monitor refrigeration system temperature in real-time, optimize performance, and take preventive actions as needed. The testing included the temperature control and monitoring of the primary sensor of the refrigeration machine. The accuracy of the application was evaluated by adding a reference sensor using the standard deviation as a measure between the primary and reference sensors. The test results consist of three data points. First, the test on data 1 shows the room temperature before activating the refrigeration machine, with an average difference of 2.6 and a standard deviation of 0.985. The second aspect involves a sudden pipe leakage, resulting in a difference of approximately 0.9 with a standard deviation exceeding 1.1. The third aspect includes the average difference in the measurements after activating the refrigeration machine, which indicates a higher consistency between the primary and reference sensors. The research findings indicate a minimal standard deviation, indicating a high level of measurement accuracy. Implementing this system is expected to enable the control and monitoring of refrigeration systems, assist fishermen in minimizing losses, and improve the quality of fishery products. [ABSTRACT FROM AUTHOR]

The digitalisation of production has a positive impact on manufacturing processes in terms of resource efficiency and environmental impact, particularly in the form of increased efficiency as well as cost and resource savings. However, the use of digitalisation technologies is also associated with efforts such as costs, CO2 emissions, and raw material consumption. When planning or deciding on the digitalisation of manufacturing systems, it is therefore necessary to assess whether these technologies pay off in terms of sustainability over their life cycle. This literature review (based on the PRISMA guidelines) analyses the relevance of sustainability assessment and its methods for the digitalisation of production in research. The review reveals that research focuses on the benefits of digitalisation technologies in manufacturing, while the assessment of efforts and their benefits is in its infancy. There is a need for further research on holistic assessment methods for digitalisation technologies. In particular, there is a lack of assessment methods that consistently link the economic and environmental dimensions of sustainability, and there is also a lack of guidance for the application of assessment methods in production. [ABSTRACT FROM AUTHOR]

Background: With socio-economic development, an increase in waste generation is observed. In view of its negative impact on the environment and human health, it is becoming increasingly important to follow the waste hierarchy and perform research on reverse logistics. It is crucial to maximize the efficiency of the separation of the waste mixture into fractions which can be returned to the material cycle in the economy. The separation process is carried out in systems such as waste sorting plants, which are characterized by complexity and variable input waste stream composition. Therefore, research in this field is usually based on simplifying assumptions and static input-output data and limited in scope. There is a lack of studies dedicated to the real-time control of these systems. Methods: We propose a Digital Twin framework for waste sorting systems to address the limitations of solutions available in the literature and to standardize the development of cyber-physical waste stream control systems. The proposed approach includes one of the key technologies of Industry 4.0, so it is in line with the Reverse Logistics 4.0 concept. Results: The main components of the Digital Twin considered were a sensor-based waste stream monitoring system, a physical model of the sorting system, a simulation model and its integration with the physical model. The deployment of a Digital Twin in line with the presented framework is demonstrated using the example of a selected plastic waste sorting plant. Conclusions: Based on the proposed approach, it is possible to include waste stream features (heterogeneity, composition uncertainty and high variability) in logistics decision-making in detail and in real time. The implementation of a Digital Twin can reduce the amount of waste sent to landfill and significantly increase the achieved recovery rates, thus increasing the amount of waste being reused and thereby contributing to the transformation to a circular economy. [ABSTRACT FROM AUTHOR]