The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficultie...The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.展开更多
The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wid...The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.展开更多
The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphas...The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphasis on systems, IBsE builds upon the interfaces between systems biology, bioprocessing, and systems engineering. This paper discussed the background, the suggested definition, the theoretical framework and methodologies of this new discipline as well as its challenges and future development.展开更多
With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experien...With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experiences in the history of civil aviation safety management.Therefore,a new safety concept based on a system-of-systems(SoS)perspective is proposed for the next-generation aviation.This article begins by elucidating the complexity of existing aviation risks and emphasizing the necessity for an updated safety concept.It then presents the challenges of current safety management and potential solutions from the new SoS perspective.To address future risks,the concept of SoS safety is introduced with the inspiration of the human immune system in terms of capability,logic,and architecture,which can serve as a guiding framework and methodology for safety engineering in complex large-scale CATS.This concept indicates the transition from“process and outcome-oriented”to“capability-oriented”intelligent safety management.Our research highlights the development directions and potential technological areas that need to be addressed at different stages of SoS safety.The integration of SoS design and operation through rapid iterations enabled by artificial intelligence(AI)will ultimately achieve endogenous SoS safety.展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate...Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.展开更多
This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as ru...This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.展开更多
This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-sta...This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.展开更多
With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resourc...With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints(the total resource consumption of the attacks is not greater than a given initial resource of the attacker)to mislead a discrete event system under supervisory control to reach unsafe states.We consider that the attacker can implement two types of attacks:One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor.Each attack has its corresponding resource consumption and remains covert.To solve this problem,we first introduce a notion of combined-attackability to determine whether a closedloop system may reach an unsafe state after receiving attacks with resource constraints.We develop an algorithm to construct a corrupted supervisor under attacks,provide a verification method for combined-attackability in polynomial time based on a plant,a corrupted supervisor,and an attacker's initial resource,and propose a corresponding attack synthesis algorithm.The effectiveness of the proposed method is illustrated by an example.展开更多
A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nit...A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nitrogen doping.This achievement notably overcomes the inherent constraints of conventional metal-based absorbers,including low resonance frequency,high conductivity,and elevated density,for the synergistic advantages provided by multimetallic alloys and foams.Microstructural analysis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism,electronic structure,and magnetic moment of MEANFs.Furthermore,deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and absorption.The results show that the MEANFs exhibit a minimum reflection loss(RL_(min))value of-60.32 dB and a maximum effective absorption bandwidth(EAB_(max))of 5.28 GHz at 1.69 mm.This augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure,interfacial polarization,defect-induced polarization,and magnetic resonance.This study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys,emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties.展开更多
The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal de...The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent s...This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.展开更多
1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,trans...1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,transportation,and logistics,have become increasingly complex,generating vast amounts of data[1–3].These big data encompass a wide range of data sources,including sensor data,production logs,and maintenance records,which hold valuable insights[4–6].Moreover,machine learning-based AI techniques can be applied to extract meaningful insights from this big data[7].展开更多
The inherent complexities of excitable cardiac,nervous,and skeletal muscle tissues pose great challenges in constructing artificial counterparts that closely resemble their natural bioelectrical,structural,and mechani...The inherent complexities of excitable cardiac,nervous,and skeletal muscle tissues pose great challenges in constructing artificial counterparts that closely resemble their natural bioelectrical,structural,and mechanical properties.Recent advances have increasingly revealed the beneficial impact of bioelectrical microenvironments on cellular behaviors,tissue regeneration,and therapeutic efficacy for excitable tissues.This review aims to unveil the mechanisms by which electrical microenvironments enhance the regeneration and functionality of excitable cells and tissues,considering both endogenous electrical cues from electroactive biomaterials and exogenous electrical stimuli from external electronic systems.We explore the synergistic effects of these electrical microenvironments,combined with structural and mechanical guidance,on the regeneration of excitable tissues using tissue engineering scaffolds.Additionally,the emergence of micro/nanoscale bioelectronics has significantly broadened this field,facilitating intimate interactions between implantable bioelectronics and excitable tissues across cellular,tissue,and organ levels.These interactions enable precise data acquisition and localized modulation of cell and tissue functionalities through intricately designed electronic components according to physiological needs.The integration of tissue engineering and bioelectronics promises optimal outcomes,highlighting a growing trend in developing living tissue construct-bioelectronic hybrids for restoring and monitoring damaged excitable tissues.Furthermore,we envision critical challenges in engineering the next-generation hybrids,focusing on integrated fabrication strategies,the development of ionic conductive biomaterials,and their convergence with biosensors.展开更多
Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the la...Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.展开更多
In this paper, we propose selected research topics that are believed central to progress and growth in the application of systems engineering （SE）. As a professional activity, and as an intellectual activity, system...In this paper, we propose selected research topics that are believed central to progress and growth in the application of systems engineering （SE）. As a professional activity, and as an intellectual activity, systems engineering has strong links to such associated disciplines as decision analysis, operation research, project management, quality management, and systems design. When focussing on systems engineering research, we should distinguish between subjects that are of systems engineering essence and others that more closely correspond to those that are more relevant for related disciplines.展开更多
Since its inception,Systems Engineering(SE)has developed as a distinctive discipline,and there has been significant progress in this field in the past two decades.Compared to other engineering disciplines,SE is not af...Since its inception,Systems Engineering(SE)has developed as a distinctive discipline,and there has been significant progress in this field in the past two decades.Compared to other engineering disciplines,SE is not affirmed by a set of underlying fundamental propositions,instead it has emerged as a set of best practices to deal with intricacies stemming from the stochastic nature of engineering complex systems and addressing their problems.Since the existing methodologies and paradigms(dominant patterns of thought and concepts)of SE are very diverse and somewhat fragmented.This appears to create some confusion regarding the design,deployment,operation,and application of SE.The purpose of this paper is 1)to delineate the development of SE from 1926-2017 based on insights derived from a histogram analysis,2)to discuss the different paradigms and school of thoughts related to SE,3)to derive a set of fundamental attributes of SE using advanced coding techniques and analysis,and 4)to present a newly developed instrument that could assess the performance of systems engineers.More than Two hundred and fifty different sources have been reviewed in this research in order to demonstrate the development trajectory of the SE discipline based on the frequency of publication.展开更多
As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of...As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of multiscale process modeling is to connect the molecular level with the phase level,where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level information.Typically,quantum chemical(QC)computation,molecular simulation,and equations of state are used to provide such predictions.Recently,due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools,data-driven descriptor models have been developed and widely used for property predictions[2].展开更多
文摘The challenge of transitioning from temporary humanitarian settlements to more sustainable human settlements is due to a significant increase in the number of forcibly displaced people over recent decades, difficulties in providing social services that meet the required standards, and the prolongation of emergencies. Despite this challenging context, short-term considerations continue to guide their planning and management rather than more integrated, longer-term perspectives, thus preventing viable, sustainable development. Over the years, the design of humanitarian settlements has not been adapted to local contexts and perspectives, nor to the dynamics of urbanization and population growth and data. In addition, the current approach to temporary settlement harms the environment and can strain limited resources. Inefficient land use and ad hoc development models have compounded difficulties and generated new challenges. As a result, living conditions in settlements have deteriorated over the last few decades and continue to pose new challenges. The stakes are such that major shortcomings have emerged along the way, leading to disruption, budget overruns in a context marked by a steady decline in funding. However, some attempts have been made to shift towards more sustainable approaches, but these have mainly focused on vague, sector-oriented themes, failing to consider systematic and integration views. This study is a contribution in addressing these shortcomings by designing a model-driving solution, emphasizing an integrated system conceptualized as a system of systems. This paper proposes a new methodology for designing an integrated and sustainable human settlement model, based on Model-Based Systems Engineering and a Systems Modeling Language to provide valuable insights toward sustainable solutions for displaced populations aligning with the United Nations 2030 agenda for sustainable development.
文摘The challenges posed by smart manufacturing for the process industries and for process systems engineering（PSE） researchers are discussed in this article. Much progress has been made in achieving plant- and site-wide optimization, hut benchmarking would give greater confidence. Technical challenges confrontingprocess systems engineers in developing enabling tools and techniques are discussed regarding flexibilityand uncertainty, responsiveness and agility, robustness and security, the prediction of mixture propertiesand function, and new modeling and mathematics paradigms. Exploiting intelligence from big data to driveagility will require tackling new challenges, such as how to ensure the consistency and confidentiality ofdata through long and complex supply chains. Modeling challenges also exist, and involve ensuring that allkey aspects are properly modeled, particularly where health, safety, and environmental concerns requireaccurate predictions of small but critical amounts at specific locations. Environmental concerns will requireus to keep a closer track on all molecular species so that they are optimally used to create sustainablesolutions. Disruptive business models may result, particularly from new personalized products, but that isdifficult to predict.
文摘The concept of Industrial Biosystems Engineering (IBsE) was suggested as a new engineering branch to be developed for meeting the needs for science, technology and professionals by the upcoming bioeconomy. With emphasis on systems, IBsE builds upon the interfaces between systems biology, bioprocessing, and systems engineering. This paper discussed the background, the suggested definition, the theoretical framework and methodologies of this new discipline as well as its challenges and future development.
基金supported by the National Natural Science Foundation of China(72225012)the National Key Research and Development Program of China(2023YFB4302901)+1 种基金the National Natural Science Foundation of China(72288101,71822101,and 62201577)the Safety Capability Building Fund of the Civil Aviation Administration of China(ASSA2023/19).
文摘With the anticipated growth in air traffic complexity in the coming years,future civil aviation transportation system(CATS)is transforming into a complex cyber–physical–social system,surpassing all previous experiences in the history of civil aviation safety management.Therefore,a new safety concept based on a system-of-systems(SoS)perspective is proposed for the next-generation aviation.This article begins by elucidating the complexity of existing aviation risks and emphasizing the necessity for an updated safety concept.It then presents the challenges of current safety management and potential solutions from the new SoS perspective.To address future risks,the concept of SoS safety is introduced with the inspiration of the human immune system in terms of capability,logic,and architecture,which can serve as a guiding framework and methodology for safety engineering in complex large-scale CATS.This concept indicates the transition from“process and outcome-oriented”to“capability-oriented”intelligent safety management.Our research highlights the development directions and potential technological areas that need to be addressed at different stages of SoS safety.The integration of SoS design and operation through rapid iterations enabled by artificial intelligence(AI)will ultimately achieve endogenous SoS safety.
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
文摘Visual question answering(VQA)is a multimodal task,involving a deep understanding of the image scene and the question’s meaning and capturing the relevant correlations between both modalities to infer the appropriate answer.In this paper,we propose a VQA system intended to answer yes/no questions about real-world images,in Arabic.To support a robust VQA system,we work in two directions:(1)Using deep neural networks to semantically represent the given image and question in a fine-grainedmanner,namely ResNet-152 and Gated Recurrent Units(GRU).(2)Studying the role of the utilizedmultimodal bilinear pooling fusion technique in the trade-o.between the model complexity and the overall model performance.Some fusion techniques could significantly increase the model complexity,which seriously limits their applicability for VQA models.So far,there is no evidence of how efficient these multimodal bilinear pooling fusion techniques are for VQA systems dedicated to yes/no questions.Hence,a comparative analysis is conducted between eight bilinear pooling fusion techniques,in terms of their ability to reduce themodel complexity and improve themodel performance in this case of VQA systems.Experiments indicate that these multimodal bilinear pooling fusion techniques have improved the VQA model’s performance,until reaching the best performance of 89.25%.Further,experiments have proven that the number of answers in the developed VQA system is a critical factor that a.ects the effectiveness of these multimodal bilinear pooling techniques in achieving their main objective of reducing the model complexity.The Multimodal Local Perception Bilinear Pooling(MLPB)technique has shown the best balance between the model complexity and its performance,for VQA systems designed to answer yes/no questions.
文摘This research aims to address the challenges of fault detection and isolation(FDI)in digital grids,focusing on improving the reliability and stability of power systems.Traditional fault detection techniques,such as rule-based fuzzy systems and conventional FDI methods,often struggle with the dynamic nature of modern grids,resulting in delays and inaccuracies in fault classification.To overcome these limitations,this study introduces a Hybrid NeuroFuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic.The model’s performance was evaluated through extensive simulations on the IEEE 33-bus test system,considering various fault scenarios,including line-to-ground faults(LGF),three-phase short circuits(3PSC),and harmonic distortions(HD).The quantitative results show that the model achieves 97.2%accuracy,a false negative rate(FNR)of 1.9%,and a false positive rate(FPR)of 2.3%,demonstrating its high precision in fault diagnosis.The qualitative analysis further highlights the model’s adaptability and its potential for seamless integration into smart grids,micro grids,and renewable energy systems.By dynamically refining fuzzy inference rules,the model enhances fault detection efficiency without compromising computational feasibility.These findings contribute to the development of more resilient and adaptive fault management systems,paving the way for advanced smart grid technologies.
基金supported by the Special Fund for Scientific and Technological Innovation Strategy of Guangdong Province(2022A0505030025)the Science and Technology Fund,FDCT,Macao SAR(0064/2021/A2)
文摘This paper addresses the verification of strong currentstate opacity with respect to real-time observations generated from a discrete-event system that is modeled with time labeled Petri nets. The standard current-state opacity cannot completely characterize higher-level security. To ensure the higher-level security requirements of a time-dependent system, we propose a strong version of opacity known as strong current-state opacity. For any path(state-event sequence with time information)π derived from a real-time observation that ends at a secret state, the strong current-state opacity of the real-time observation signifies that there is a non-secret path with the same real-time observation as π. We propose general and non-secret state class graphs, which characterize the general and non-secret states of time-dependent systems, respectively. To capture the observable behavior of non-secret states, a non-secret observer is proposed.Finally, we develop a structure called a real-time concurrent verifier to verify the strong current-state opacity of time labeled Petri nets. This approach is efficient since the real-time concurrent verifier can be constructed by solving a certain number of linear programming problems.
基金partially supported by the Science Technology Development Fund,Macao Special Administrative Region(0029/2023/RIA1)the National Research Foundation Singapore under its AI Singapore Programme(AISG2-GC-2023-007)
文摘With the development of cyber-physical systems,system security faces more risks from cyber-attacks.In this work,we study the problem that an external attacker implements covert sensor and actuator attacks with resource constraints(the total resource consumption of the attacks is not greater than a given initial resource of the attacker)to mislead a discrete event system under supervisory control to reach unsafe states.We consider that the attacker can implement two types of attacks:One by modifying the sensor readings observed by a supervisor and the other by enabling the actuator commands disabled by the supervisor.Each attack has its corresponding resource consumption and remains covert.To solve this problem,we first introduce a notion of combined-attackability to determine whether a closedloop system may reach an unsafe state after receiving attacks with resource constraints.We develop an algorithm to construct a corrupted supervisor under attacks,provide a verification method for combined-attackability in polynomial time based on a plant,a corrupted supervisor,and an attacker's initial resource,and propose a corresponding attack synthesis algorithm.The effectiveness of the proposed method is illustrated by an example.
基金supported by the National Natural Science Foundation of China(Grant No.52071294)the National Key Research and Development Program(Grant No.2022YFE0109800)the Natural Science Foundation of Zhejiang Province(Grant No.LY20E020015).
文摘A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice expansion induced by nitrogen doping.This achievement notably overcomes the inherent constraints of conventional metal-based absorbers,including low resonance frequency,high conductivity,and elevated density,for the synergistic advantages provided by multimetallic alloys and foams.Microstructural analysis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism,electronic structure,and magnetic moment of MEANFs.Furthermore,deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and absorption.The results show that the MEANFs exhibit a minimum reflection loss(RL_(min))value of-60.32 dB and a maximum effective absorption bandwidth(EAB_(max))of 5.28 GHz at 1.69 mm.This augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure,interfacial polarization,defect-induced polarization,and magnetic resonance.This study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys,emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties.
文摘The optimization of civil engineering structures is critical for enhancing structural performance and material efficiency in engineering applications.Structural optimization approaches seek to determine the optimal design,by considering material performance,cost,and structural safety.The design approaches aim to reduce the built environment’s energy use and carbon emissions.This comprehensive review examines optimization techniques,including size,shape,topology,and multi-objective approaches,by integrating these methodologies.The trends and advancements that contribute to developing more efficient,cost-effective,and reliable structural designs were identified.The review also discusses emerging technologies,such as machine learning applications with different optimization techniques.Optimization of truss,frame,tensegrity,reinforced concrete,origami,pantographic,and adaptive structures are covered and discussed.Optimization techniques are explained,including metaheuristics,genetic algorithm,particle swarm,ant-colony,harmony search algorithm,and their applications with mentioned structure types.Linear and non-linear structures,including geometric and material nonlinearity,are distinguished.The role of optimization in active structures,structural design,seismic design,form-finding,and structural control is taken into account,and the most recent techniques and advancements are mentioned.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金supported in part by the National Key Research and Development Program of China(2021YFE0206100)the National Natural Science Foundation of China(62425310,62073321)+2 种基金the National Defense Basic Scientific Research Program(JCKY2019203C029,JCKY2020130C025)the Science and Technology Development FundMacao SAR(FDCT-22-009-MISE,0060/2021/A2,0015/2020/AMJ)
文摘This paper highlights the utilization of parallel control and adaptive dynamic programming(ADP) for event-triggered robust parallel optimal consensus control(ETRPOC) of uncertain nonlinear continuous-time multiagent systems(MASs).First, the parallel control system, which consists of a virtual control variable and a specific auxiliary variable obtained from the coupled Hamiltonian, allows general systems to be transformed into affine systems. Of interest is the fact that the parallel control technique's introduction provides an unprecedented perspective on eliminating the negative effects of disturbance. Then, an eventtriggered mechanism is adopted to save communication resources while ensuring the system's stability. The coupled HamiltonJacobi(HJ) equation's solution is approximated using a critic neural network(NN), whose weights are updated in response to events. Furthermore, theoretical analysis reveals that the weight estimation error is uniformly ultimately bounded(UUB). Finally,numerical simulations demonstrate the effectiveness of the developed ETRPOC method.
基金supported by the Science and Technology Innovation Program of Hunan Province(No.2023RC3097)in part the National Natural Science Foundation of China(No.52105108)in part Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘1 Introduction In recent years,the rapid development of industrial big data and artificial intelligence(AI)technologies has revolutionized the industrial landscape.Industrial systems,such as manufacturing,energy,transportation,and logistics,have become increasingly complex,generating vast amounts of data[1–3].These big data encompass a wide range of data sources,including sensor data,production logs,and maintenance records,which hold valuable insights[4–6].Moreover,machine learning-based AI techniques can be applied to extract meaningful insights from this big data[7].
基金financially supported by the National Natural Science Foundation of China(Nos.52125501,52405325)the Key Research Project of Shaanxi Province(Nos.2021LLRH-08,2024SF2-GJHX-34)+5 种基金the Program for Innovation Team of Shaanxi Province(No.2023-CX-TD17)the Postdoctoral Fellowship Program of CPSF(No.GZB20230573)the Postdoctoral Project of Shaanxi Province(No.2023BSHYDZZ30)the Basic Research Program of Natural Science in Shaanxi Province(No.2021JQ-906)the China Postdoctoral Science Foundationthe Fundamental Research Funds for the Central Universities。
文摘The inherent complexities of excitable cardiac,nervous,and skeletal muscle tissues pose great challenges in constructing artificial counterparts that closely resemble their natural bioelectrical,structural,and mechanical properties.Recent advances have increasingly revealed the beneficial impact of bioelectrical microenvironments on cellular behaviors,tissue regeneration,and therapeutic efficacy for excitable tissues.This review aims to unveil the mechanisms by which electrical microenvironments enhance the regeneration and functionality of excitable cells and tissues,considering both endogenous electrical cues from electroactive biomaterials and exogenous electrical stimuli from external electronic systems.We explore the synergistic effects of these electrical microenvironments,combined with structural and mechanical guidance,on the regeneration of excitable tissues using tissue engineering scaffolds.Additionally,the emergence of micro/nanoscale bioelectronics has significantly broadened this field,facilitating intimate interactions between implantable bioelectronics and excitable tissues across cellular,tissue,and organ levels.These interactions enable precise data acquisition and localized modulation of cell and tissue functionalities through intricately designed electronic components according to physiological needs.The integration of tissue engineering and bioelectronics promises optimal outcomes,highlighting a growing trend in developing living tissue construct-bioelectronic hybrids for restoring and monitoring damaged excitable tissues.Furthermore,we envision critical challenges in engineering the next-generation hybrids,focusing on integrated fabrication strategies,the development of ionic conductive biomaterials,and their convergence with biosensors.
文摘Refugee settlements face several challenges in transitioning from a temporary planning approach to more sustainable settlements. This is mainly due to an increase in the number of forcibly displaced people over the last few decades, and the difficulties of sustainably providing social services that meet the required standards. The development of refugee settlements assumed that forcibly displaced people would return to their places or countries of origin. Unfortunately, displacement situations are prolonged indefinitely, forcing these people to spend most of their lives in conditions that are often deplorable and substandard, and therefore unsustainable. In most cases, the establishment of refugee settlements is triggered by an emergency caused by an influx of forcibly displaced people, who need to be accommodated urgently and provided with some form of international assistance and protection. This leaves little or no time for proper planning for long-term development as required. In addition, the current approach to temporary settlement harms the environment and can strain limited resources with ad hoc development models that have exacerbated difficulties. As a result, living conditions in refugee settlements have deteriorated over the last few decades and continue to pose challenges as to how best to design, plan, and sustain settlements over time. To contribute to addressing these challenges, this study proposes a new methodology supported by Model-Based Systems Engineering (MBSE) and a Systems Modeling Language (SysML) to develop a typical sustainable human settlement system model, which has functionally and operationally executed using a Systems Engineering (SE) approach. To assess the sustainability capacity of the proposed system, this work applies a matrix of crossed impact multiplication through a case study by conducting a system capacity interdependence analysis (SCIA) using the MICMAC methodology (Cross-impact matrix multiplication applied to classification) to assess the interdependency that exist between the sub-systems categories to deliver services at the system level. The sustainability analysis results based on capacity variables influence and dependency models shows that development activities in the settlement are unstable and, therefore, unsustainable since there is no apparent difference between the influential and dependent data used for the assessment. These results illustrate that an integrated system could improve human settlements’ sustainability and that capacity building in service delivery is beneficial and necessary.
文摘In this paper, we propose selected research topics that are believed central to progress and growth in the application of systems engineering （SE）. As a professional activity, and as an intellectual activity, systems engineering has strong links to such associated disciplines as decision analysis, operation research, project management, quality management, and systems design. When focussing on systems engineering research, we should distinguish between subjects that are of systems engineering essence and others that more closely correspond to those that are more relevant for related disciplines.
基金We would like to personally thank anonymous reviewers for raising such questions that have transformed this manuscript into a muchimproved paper.
文摘Since its inception,Systems Engineering(SE)has developed as a distinctive discipline,and there has been significant progress in this field in the past two decades.Compared to other engineering disciplines,SE is not affirmed by a set of underlying fundamental propositions,instead it has emerged as a set of best practices to deal with intricacies stemming from the stochastic nature of engineering complex systems and addressing their problems.Since the existing methodologies and paradigms(dominant patterns of thought and concepts)of SE are very diverse and somewhat fragmented.This appears to create some confusion regarding the design,deployment,operation,and application of SE.The purpose of this paper is 1)to delineate the development of SE from 1926-2017 based on insights derived from a histogram analysis,2)to discuss the different paradigms and school of thoughts related to SE,3)to derive a set of fundamental attributes of SE using advanced coding techniques and analysis,and 4)to present a newly developed instrument that could assess the performance of systems engineers.More than Two hundred and fifty different sources have been reviewed in this research in order to demonstrate the development trajectory of the SE discipline based on the frequency of publication.
文摘As indicated by Grossmann and Westerberg[1],a process system can be generally decomposed into hierarchical levels or scales at which different physical and/or chemical phenomena take place(see Fig.1).The first step of multiscale process modeling is to connect the molecular level with the phase level,where the main task is to model and predict the properties of fluid mixtures based on the atomic-or molecular-level information.Typically,quantum chemical(QC)computation,molecular simulation,and equations of state are used to provide such predictions.Recently,due to the ever-increasing number of available data and fast development of cheminformatics and machine learning tools,data-driven descriptor models have been developed and widely used for property predictions[2].
