Search

• Both maximum Lyapunov exponents and correlation dimensions were significantly greater during heigher sevoflurane anaesthesia. • Approximate entropy conversely decrease along with deepening of sevoflurane anesthesia. • Chaotic nature of EEG might increase as anaesthesia deepens, despite the decrease in randomness as reflected in entropy. In conscious states, the electrodynamics of the cortex are reported to work near a critical point or phase transition of chaotic dynamics, known as the edge-of-chaos, representing a boundary between stability and chaos. Transitions away from this boundary disrupt cortical information processing and induce a loss of consciousness. The entropy of the electroencephalogram (EEG) is known to decrease as the level of anesthesia deepens. However, whether the chaotic dynamics of electroencephalographic activity shift from this boundary to the side of stability or the side of chaotic enhancement during anesthesia-induced loss of consciousness remains poorly understood. We investigated the chaotic properties of EEGs at two different depths of clinical anesthesia using the maximum Lyapunov exponent, which is mathematically regarded as a formal measure of chaotic nature, using the Rosenstein algorithm. In 14 adult patients, 12 s of electroencephalographic signals were selected during two depths of clinical anesthesia (sevoflurane concentration 2% as relatively deep anesthesia, sevoflurane concentration 0.6% as relatively shallow anesthesia). Lyapunov exponents, correlation dimensions and approximate entropy were calculated from these electroencephalographic signals. As a result, maximum Lyapunov exponent was generally positive during sevoflurane anesthesia, and both maximum Lyapunov exponents and correlation dimensions were significantly greater during deep anesthesia than during shallow anesthesia despite reductions in approximate entropy. The chaotic nature of the EEG might be increased at clinically deeper inhalational anesthesia, despite the decrease in randomness as reflected in the decreased entropy, suggesting a shift to the side of chaotic enhancement under anesthesia. [ABSTRACT FROM AUTHOR]

Background Aging is characterized by loss of resilience, the ability to resist or recover from stressors. Network analysis has shown promise in investigating dynamic relationships underlying resilience. We aimed to use network analysis to measure resilience in a longitudinal cohort of older adults and quantify whole-system vulnerabilities associated with frailty. Methods We used data from the Rugao Longitudinal Ageing Study, including 71 biomarkers from participants classified as robust, prefrail, or frail. We quantified biomarker correlations and topological parameters. Additionally, we proposed propagation models to simulate damage and recovery dynamics, investigating network resilience under various conditions. Results We classified 1 754 individuals into robust (n = 369), prefrail (n = 1 103), and frail (n = 282) groups with 71 biomarkers. Several biomarkers were linked to frailty, including those related to blood pressure, electrocardiogram (ECG), kidney function, platelets, and white blood cells. Each frailty stage was associated with increased network correlations. The frail network showed increased average degree and connectance, decreased average path length and diameter, and reduced modularity compared to robust and prefrail networks. Hub biomarkers, particularly β2-microglobulin and platelet count, played a significant role, potentially propagating dysfunction across physiological systems. Simulations revealed that damage to critical hubs led to longer recovery times in the frail network than robust and prefrail networks. Conclusions Network analysis could serve as a valuable tool for quantifying resilience and identifying vulnerabilities in older adults with frailty. Our findings contribute to understanding frailty-related physiological disturbances and offer potential for personalized healthcare interventions targeting resilience in older populations. [ABSTRACT FROM AUTHOR]

There are increased sector-wide efforts within health and social care systems to engage those with lived experience in service design, delivery, and monitoring – aiming to secure more equitable health outcomes. However, critical knowledge gaps persist around how national whole-system engagement strategies can account for the challenges experienced by populations that encounter exclusion within complex multi-layered systems. This includes a failure to delineate shared challenges across groups, and to develop transferable cross-group frameworks to assist sector-wide change. There is, therefore, a danger that those groups already least heard will be collectively left behind. With a view to informing a more inclusive engagement strategy in Ireland, this national study aims to investigate multi-level (policy and strategic, operational, on-the-ground services, individual) shared challenges impacting engagement for five populations who have been identified as underserved groups in a complex health and social care system, including: (1) those who misuse drugs and alcohol, (2) those who are experiencing homelessness, (3) those experiencing mental health, (4) migrants and those of minority ethnicies, and (5) Irish Travellers. Adopting a mixed-methods approach which draws on an evidence-informed multistakeholder perspective, this study employs data from: focus groups and life-course interviews with lived-experience populations (n=136), five focus groups (n=39) and a national on-line survey (n=320) with population-specific services providers; and national-level stakeholder interviews (n=9). Two cross-group participatory consultative forums with lived-experience and provider participants (n=28) were used to co-produce priority action areas based on study findings. This article presents findings on shared challenges in engaging these groups around leadership and commitment, implementation and action, population capacities, trust, and representation, stigma, and discrimination. Derived from these challenges, six development areas are presented to advance an inclusive equitable engagement approach in Ireland. These comprise: 1) balancing top-down prioritisation, and bottom-up direction; 2) sustaining multi-level, multi-form implementation; 3) measuring effectiveness and action; 4) embedding inclusive equitable engagement; 5) trust as a prerequisite, and outcome; and 6) an equalising, agency empowering agenda. [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]

Systemic risk has certain properties that make it behave as a complex system, one of which is its nonlinearity. The objective of this paper is to empirically address the systemic risk-taking channel of monetary policy from this perspective using the U.S. case and the TVPVAR model with Structural Factor Augmented (SFA-TVP-VAR). In order to obtain robust results, I estimate this model with two different metrics of the monetary policy stance, with different parameters and priors in each case. Considering the remaining system variables as latent variables, I document evidence of a complex channel: I find that the relationship between monetary policy stance and systemic risk-taking is non-linear and adaptive to economic conditions. Specifically, the posterior estimation shows that the monetary policy maintains an inverse relationship with systemic risk-taking in the long run, indicating that a loose stance increases systemic risk; however, in the short run, in earlier periods and during a financial crisis, a restrictive shock of monetary policy, instead of reducing systemic risk-taking, increases it. [ABSTRACT FROM AUTHOR]

Modern production and technological systems and objects of the agroindustrial complex are characterized by complex dependences between systemic and external factors, incompleteness and uncertainty of information about their condition and functioning. As a basis for the analysis and modeling of such systems, in order to support effective management decisions, the article develops a fuzzy situational approach, focused on the flexible construction and adaptive structural-parametric adjustment of fuzzy situational models (networks), taking into account the generalization and updating of information about precedent situations, taking place in the process of functioning and development of such objects and systems. [ABSTRACT FROM AUTHOR]

Road traffic accidents result in numerous fatalities and injuries annually. Advanced driving assistance systems (ADASs) have garnered significant attention to mitigate these harms. An accurate safety assessment can significantly improve the effectiveness and credibility of ADASs. However, a real-time safety assessment remains a key challenge due to the complex interactions among humans, vehicles, and the road environment. Traditional safety assessment methods, relying on crash data and surrogate safety measures (SSMs), face limitations in real-time applicability and scenario coverage, especially in freeway ramp areas with frequent merging and lane changing. To address these gaps, this paper develops a driving safety field based on the uncertainty of vehicle movements, which integrates the characteristics of driving behaviors, vehicles, and the road environment. The proposed method is validated with a simulation of driving scenarios and ROC curves obtained from the NGSIM dataset. The results demonstrate that our proposed driving safety field effectively quantifies the real-time risk in ramp influence areas and outperforms Time to Collision (TTC), making it suitable for integration into collision warning systems of ADASs. [ABSTRACT FROM AUTHOR]

The high-quality development of the Yellow River Basin is still facing the issue of imbalance and inadequacy, and it urgently requires the backing and assistance of a well-coordinated and efficient comprehensive transportation network system. Based on this purpose, this study uses a composite system synergy degree model incorporating "mode dimension + regional dimension" to analyze the evolution of this network. The results indicate the following: (1) Railway and air transportation surpass highway transportation in growth and synergy, though overall system synergy is still low. (2) Downstream areas are gradually taking the lead, and the orderliness of each subsystem is gradually becoming consistent. The complexity and variability of the composite system's synergy are high, with an overall low level of synergy. Overall, despite improvements in service capacity and quality, better integration of different transport modes and enhanced cross-regional cooperation are needed. This study reveals and analyzes in depth the complexity of the comprehensive transportation network system in the Yellow River Basin and its spatiotemporal characteristics of synergistic evolution. We propose suggestions for high-quality development at national, regional, and industry levels, fostering the continuous improvement of the system structure, which aids in overcoming the tangible obstacles faced by the high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

We consider a method for solving the problem of optimal control of a composite dynamic system with stochastic resource constraints. The method is based on the method of hierarchical optimization, which includes iterative solution of local problems and the coordination problem. To solve local problems, we apply the model predictive control. Taking into account stochastic resource constraints leads to the solution of one-stage problems of stochastic programming. [ABSTRACT FROM AUTHOR]

Swarmalator model attracts the attention of numerous scholars because of its rich dynamical behaviors. We investigate the reason for the phase transition that occurs in a 2D swarmalator model, and conclude that the limit cycle, a reflection of the coexistence of the forces of maintaining and disrupting orders, results in the various clustering phenomena. Through novel mean-field approximation and using the self-consistency argument method, we prove the clustering conditions and the influence of the number of clusters on the existence of clusters, and provide estimates of the cluster size of the splintered phase wave state and the phase transition threshold between the splintered phase wave state and active phase wave state. Due to the widespread presence of nonlinearity, our study is essential to the analysis of clustering phenomena in real physical models. [ABSTRACT FROM AUTHOR]

Based on the complex network, the relationship in the real complex system can be modeled, and the bipartite network is a special complex network, which can describe the complex system containing two kinds of objects. Although existing bipartite networks can model complex systems, conventional methods are restricted to a couple of limitations. (1) The dynamic interaction between nodes cannot be described over time. (2) The implicit features of nodes in the network cannot be effectively mined. Based on these, this paper proposes a dynamic bipartite network model (DBN) to model the dynamic interaction between two types of objects in real complex systems, and mine the structure features and preference features of nodes in the network. First, the dynamic interaction between two types of objects in a complex system is modeled as a dynamic bipartite network, which can reflect the interaction between objects in each time slice. Then, the structure features and preference features of each time slice are mined based on the dynamic bipartite network, where the structure features reflect the dynamic structural changes of the nodes, and the preference features reflect the potential preferences of the nodes. Finally, the features of each time slice are fused and input into the gate recurrent unit model to predict the interaction between nodes. Extensive experiments are performed on a large-scale real complex system. The results show that DBN significantly outperforms state-of-the-art prediction methods in terms of multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

Abstract There are increased sector-wide efforts within health and social care systems to engage those with lived experience in service design, delivery, and monitoring – aiming to secure more equitable health outcomes. However, critical knowledge gaps persist around how national whole-system engagement strategies can account for the challenges experienced by populations that encounter exclusion within complex multi-layered systems. This includes a failure to delineate shared challenges across groups, and to develop transferable cross-group frameworks to assist sector-wide change. There is, therefore, a danger that those groups already least heard will be collectively left behind. With a view to informing a more inclusive engagement strategy in Ireland, this national study aims to investigate multi-level (policy and strategic, operational, on-the-ground services, individual) shared challenges impacting engagement for five populations who have been identified as underserved groups in a complex health and social care system, including: (1) those who misuse drugs and alcohol, (2) those who are experiencing homelessness, (3) those experiencing mental health, (4) migrants and those of minority ethnicies, and (5) Irish Travellers. Adopting a mixed-methods approach which draws on an evidence-informed multistakeholder perspective, this study employs data from: focus groups and life-course interviews with lived-experience populations (n=136), five focus groups (n=39) and a national on-line survey (n=320) with population-specific services providers; and national-level stakeholder interviews (n=9). Two cross-group participatory consultative forums with lived-experience and provider participants (n=28) were used to co-produce priority action areas based on study findings. This article presents findings on shared challenges in engaging these groups around leadership and commitment, implementation and action, population capacities, trust, and representation, stigma, and discrimination. Derived from these challenges, six development areas are presented to advance an inclusive equitable engagement approach in Ireland. These comprise: 1) balancing top-down prioritisation, and bottom-up direction; 2) sustaining multi-level, multi-form implementation; 3) measuring effectiveness and action; 4) embedding inclusive equitable engagement; 5) trust as a prerequisite, and outcome; and 6) an equalising, agency empowering agenda.

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.

In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.

Nuclear energy plays a crucial role in energy supply in the 21st century, and more and more Nuclear Power Plants (NPPs) will be in operation to contribute to the development of human society. However, as a typical complex system engineering, the operation and development of NPPs require efficient and stable control methods to ensure the safety and efficiency of nuclear power generation. Reinforcement learning (RL) aims at learning optimal control policies via maximizing discounted long-term rewards. The reward-oriented learning paradigm has witnessed remarkable success in many complex systems, such as wind power systems, electric power systems, coal fire power plants, robotics, etc. In this work, we try to present a systematic review of the applications of RL on these complex systems, from which we believe NPPs can borrow experience and insights. We then conduct a block-by-block investigation on the application scenarios of specific tasks in NPPs and carried out algorithmic research for different situations such as power startup, collaborative control, and emergency handling. Moreover, we discuss the possibilities of further application of RL methods on NPPs and detail the challenges when applying RL methods on NPPs. We hope this work can boost the realization of intelligent NPPs, and contribute to more and more research on how to better integrate RL algorithms into NPPs.

Dementia is a highly prevalent condition with devastating clinical and socioeconomic sequela. It is expected to triple in prevalence by 2050. No treatment is currently known to be effective. Symptomatic late-onset dementia and predementia (SLODP) affects 95% of patients with the syndrome. In contrast to trials of pharmacological prevention, no treatment is suggested to remediate or cure these symptomatic patients. SLODP but not young onset dementia is intensely associated with multimorbidity (MUM), including brain-perturbating conditions (BPCs). Recent studies showed that MUM/BPCs have a major role in the pathogenesis of SLODP. Fortunately, most MUM/BPCs are medically treatable, and thus, their treatment may modify and improve SLODP, relieving suffering and reducing its clinical and socioeconomic threats. Regrettably, the complex system features of SLODP impede the diagnosis and treatment of the potentially remediable conditions (PRCs) associated with them, mainly due to failure of pattern recognition and a flawed diagnostic workup. We suggest incorporating two SLODP-specific conceptual themes into the diagnostic workup: MUM/BPC and multilevel phenomenological themes. By doing so, we were able to improve the diagnostic accuracy of SLODP components and optimize detecting and favorably treating PRCs. These revolutionary concepts and their implications for remediability and other parameters are discussed in the paper. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) advances and the rapid adoption of generative AI tools, like ChatGPT, present new opportunities and challenges for higher education. While substantial literature discusses AI in higher education, there is a lack of a systems approach that captures a holistic view of the structure and dynamics of the AI transformation of higher education institutions (HEIs). To fill this gap, this article develops a causal loop diagram (CLD) to map the causal feedback mechanisms of AI transformation in a typical HEI. We identify important variables and their relationships and map multiple reinforcing and balancing feedback loops accounting for the forces that drive the AI transformation and its impact on value creation in a typical HEI. The model shows how, motivated by AI technology advances, the HEI can invest in AI to improve student learning, research, and administration while dealing with academic integrity problems and adapting to job market changes by emphasizing AI-complementary student skills. We explore model insights, scenarios, and policy interventions and recommend that HEI leaders become systems thinkers to manage the complexity of the AI transformation and benefit from the AI feedback loops while avoiding policy traps that may lead to decline. We also discuss the notion of HEIs influencing the direction of AI and directions for future research on AI transformation and the sustainability of HEIs. [ABSTRACT FROM AUTHOR]

The functional building blocks of single neurons can be conceptualized as network motifs that span the molecular and cellular scales of neuronal physiology. Single-neuron functions and neuronal plasticity emerge from interactions across several network motifs that span different neuronal compartments. Network motifs in cellular neurophysiology display various forms of degeneracy: disparate molecular components can yield the same network motif, representing component degeneracy; similar neural function can be achieved by network motifs with the same architecture but variable edge strengths, representing edge degeneracy; and the same neuronal function can be achieved through different network motifs, representing motif degeneracy. The functional modularity of network motifs allows targeted manipulation of specific motifs towards achieving physiological goals or devising cures for pathology. Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease. [ABSTRACT FROM AUTHOR]

Copyright of Integrated Intelligent Energy is the property of Editorial Department of Integrated Intelligent Energy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Superspreading events underscore the uneven distribution of COVID-19 transmission among individuals and locations. These heterogenous transmission patterns could stem from human mobility, yet the underlying mechanisms are still not fully understood. Here, we employ an agent-based model incorporating urban scaling structure to simulate fine-grained mobility and the human-to-human transmission process. Our results reveal that not only the quantity but also the scaling structure of mobility profoundly influences local transmission risk. Urban scaling structure is characterized by a widely found power-law scaling distribution of mobility volumes across different locations. By integrating this structure, our model fits reasonably well with empirical Omicron data at various spatial scales in Hong Kong. Further analyses show a positive association between the scaling index, representing the location's importance within the structure, and local transmission risks among urban areas as well as the likelihood of becoming superspreaders among local visitors. This implies that urban scaling structure could offer the first-mover advantage to a minority of places and individuals to infect earlier and thus infect more. This study brings important insights for the transmission dynamics of COVID-19 and similar diseases, highlighting the role of urban scaling structure in influencing local transmission risks and superspreading events. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs), as a member of the unmanned intelligent ocean vehicle group, can replace human beings to complete dangerous tasks in the ocean. It is of great significance to apply reinforcement learning (RL) to AUVs to realize intelligent control. This paper proposes an AUV obstacle avoidance framework based on event-triggered reinforcement learning. Firstly, an environment perception model is designed to judge the relative position relationship between the AUV and all unknown obstacles and known targets. Secondly, considering that the detection range of AUVs is limited, and the proposed method needs to deal with unknown static obstacles and unknown dynamic obstacles at the same time, two different event-triggered mechanisms are designed. Soft actor–critic (SAC) with a non-policy sampling method is used. Then, improved reinforcement learning and the event-triggered mechanism are combined in this paper. Finally, a simulation experiment of the obstacle avoidance task is carried out on the Gazebo simulation platform. Results show that the proposed method can obtain higher rewards and complete tasks successfully. At the same time, the trajectory and the distance between each obstacle confirm that the AUV can reach the target well while maintaining a safe distance from static and dynamic obstacles. [ABSTRACT FROM AUTHOR]

This study investigated the application of machine learning to optimise the pumping load shift of a complex dewatering system in a deep-level mine, aiming to reduce energy costs associated with the dewatering process, which consumes an average of 14% of the mine's electricity. Traditional practices, reliant on human control and simulations, often lead to inconsistent savings and occasional losses. The study employed multivariate linear regression (MLR) and extreme gradient boosting (XGBoost) on a mine dewatering system, to identify important parameters influencing the pumping load shift performance. Critical parameters significantly impacting the energy consumption of the dewatering system were identified by the best-performing model, XGBoost. Implementing a pumping schedule based on XGBoost insights resulted in consistent load shifting and enhanced energy cost savings. These findings highlight the potential of machine learning in comprehending and optimising complex systems in deep-level mines, with the case study approach proving effective in quantifying and validating real-world impacts. This approach could offer substantial energy savings through data-driven decision-making. [ABSTRACT FROM AUTHOR]

The city represents a complex adaptive system consisting of a set of interconnected subsystems within self-organization and flexible structures that give it the ability to adapt to continue operating over time. This research discusses the adaptive flexibility of the complex urban system by measuring the degree of complexity and adaptation of secondary systems within the general system depending on its kinetic and visual system, making it self-organized and within a coherent and harmonious unit. Thus, the research problem arises from "the lack of knowledge on how to determine the appropriate degree of complexity and adaptation for urban systems, providing them with adaptive flexibility that allows them to maintain their energy and ensure their survival and continuity over time". This research aims to determine the adaptive flexibility of the complex urban system, which keeps it self-organized and able to survive depending on its kinetic and visual system that links the secondary systems of the general system based on the degree of complexity and adaptation ideal. The research adopted quantitative analysis, which includes calculating the adaptive dimensions of the complex system based on the kinetic and visual system, as well as the nodes connecting the parts of the secondary system, in addition to determining both the area and the perimeter of the study area. The historic Karkh area in Baghdad was selected for implementation because it includes a model of the complex system that originated and developed within different time stages. The system's adaptive flexibility was calculated to determine its ability to survive and continue. This research found that it is possible to determine the strength of the urban format and its ability to survive or not, as well as the possibility of developing it, by understanding the ideal degree of adaptive flexibility for the format. This ideal degree is 1, achieved through visual and kinetic coherence among the nodes that make up the parts of the format. The farther the format is from this degree, the less adaptive flexibility it has, which affects its ability to survive and continue. Therefore, it is necessary to maintain the strength of the interconnections between the parts of the kinetic and visual system and between the parts of the complex format, which directly affects its ability to self-regulate and survive. [ABSTRACT FROM AUTHOR]

In order to evaluate the simulation effect of the model on the dynamic characteristics of the climate system, the approximate entropy method was used to analyze the complexity of surface air temperature (SAT) based on observational data (CN05.1) and the model-simulated data by the Beijing Climate Central Climate Model (BCC-CSM2-MR) in this study. We investigated whether the simulated climate model data exhibited complexity characteristics similar to real-world meteorological elements. In this scenario, we could assess the climate model's performance by comparing the complexity of observed and simulated data. The results indicate that the BCC-CSM2-MR model can accurately reflect the complexity of SAT in China. When considering the entire year, the model exhibited greater complexity in the simulated SAT than the observations in most regions of China, indicating an overestimation of complexity by the model. When examining different seasons, the model's complexity was similar to the observations in spring, summer, and winter, indicating a better performance during these seasons. In contrast, the simulated performance is poor during autumn, mainly due to an overestimation of complexity in Qinghai, south-central Xinjiang, and western Xizang. [ABSTRACT FROM AUTHOR]

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics. [ABSTRACT FROM AUTHOR]

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

This study investigated the application of machine learning to optimise the pumping load shift of a complex dewatering system in a deep-level mine, aiming to reduce energy costs associated with the dewatering process, which consumes an average of 14% of the mine’s electricity. Traditional practices, reliant on human control and simulations, often lead to inconsistent savings and occasional losses. The study employed multivariate linear regression (MLR) and extreme gradient boosting (XGBoost) on a mine dewatering system, to identify important parameters influencing the pumping load shift performance. Critical parameters significantly impacting the energy consumption of the dewatering system were identified by the best-performing model, XGBoost. Implementing a pumping schedule based on XGBoost insights resulted in consistent load shifting and enhanced energy cost savings. These findings highlight the potential of machine learning in comprehending and optimising complex systems in deep-level mines, with the case study approach proving effective in quantifying and validating real-world impacts. This approach could offer substantial energy savings through data-driven decision-making.

Abstract Background The aim of this systematic review was to examine the relationship between strategies to improve care delivery for older adults in ED and evaluation measures of patient outcomes, patient experience, staff experience, and system performance. Methods A systematic review of English language studies published since inception to December 2022, available from CINAHL, Embase, Medline, and Scopus was conducted. Studies were reviewed by pairs of independent reviewers and included if they met the following criteria: participant mean age of ≥ 65 years; ED setting or directly influenced provision of care in the ED; reported on improvement interventions and strategies; reported patient outcomes, patient experience, staff experience, or system performance. The methodological quality of the studies was assessed by pairs of independent reviewers using The Joanna Briggs Institute critical appraisal tools. Data were synthesised using a hermeneutic approach. Results Seventy-six studies were included in the review, incorporating strategies for comprehensive assessment and multi-faceted care (n = 32), targeted care such as management of falls risk, functional decline, or pain management (n = 27), medication safety (n = 5), and trauma care (n = 12). We found a misalignment between comprehensive care delivered in ED for older adults and ED performance measures oriented to rapid assessment and referral. Eight (10.4%) studies reported patient experience and five (6.5%) reported staff experience. Conclusion It is crucial that future strategies to improve care delivery in ED align the needs of older adults with the purpose of the ED system to ensure sustainable improvement effort and critical functioning of the ED as an interdependent component of the health system. Staff and patient input at the design stage may advance prioritisation of higher-impact interventions aligned with the pace of change and illuminate experience measures. More consistent reporting of interventions would inform important contextual factors and allow for replication.

Researchers have long been working on interpreting and predicting the dynamics of complex systems in various fields. Conventional methods including full-scale simulations and reduced-order models are used to solve related problems, but often yield unsatisfactory results in terms of precision and computational efficiency. This is primarily due to the challenges posed by simulating small-scale dynamics. An alternative consideration is to leverage well-represented and readily accessible large-scale dynamics to assist small-scale modelling. This paper proposes a novel methodology, named as Neural Downscaling (ND), that effectively captures small-scale dynamics within a complementary subspace from corresponding large-scale dynamics well-represented in a low-dimensional space. The framework of ND integrates neural operator techniques with the principles of inertial manifold and nonlinear Galerkin theory. The effectiveness and efficiency of ND are demonstrated in the complex systems governed by Kuramoto–Sivashinsky and Navier–Stokes equations. Being an unprecedentedly deterministic model targeted at general small-scale dynamics, ND sheds light on the intricate spatiotemporal nonlinear dynamics of complex systems and reveals how small-scale dynamics are interacting with large-scale dynamics.

In this paper, a reliability evaluation method based on the hidden Markov model (HMM) considers the influence of degradation trajectory fluctuation. To address the problem in which the performance degradation trajectory modelling of complex systems is limited by sampling time points, this method constructs an approximate degradation trajectory model based on effective discrete data points through a radial basis function to realize the degradation data expansion of small samples. Then, the average degradation rate is defined, and the fluctuation degree of the degradation trajectory is quantitatively evaluated to obtain the evaluation function of the fluctuation degree of the degradation trajectory. In addition, to determine the relationship between the fluctuation degree of the degradation trajectory and the operating state of the system, the method combines HMM with the evaluation function of the fluctuation degree of the degradation trajectory and obtains the transition probability matrix of different operating states of the system affected by the fluctuation degree of the degradation trajectory. On the basis of the probability matrix of the failure state, the reliability change curve of the system is derived. Finally, the effectiveness of the proposed method is verified via an example of a computer numerical control machine tool. [ABSTRACT FROM AUTHOR]

Aiming at the problems of long computation time, error-prone results, and difficulty in verification associated with the traditional determinant feedback loop calculation method in system dynamics, a rapid calculation method for system dynamics feedback loops was proposed.This method utilizes the Loops function of Vensim software, the feedback loop set of the system was generated by taking each level bit variable as the starting point, followed by the removal of duplicate feedback loops to obtain a set of all non-repetitive feedback loops in the system. Taking the "Base+Farmers" supply chain complex system of Mingxin Farm in Pingxiang as an example, 11 strongly simplified flow rate basic in-tree models that have been constructed, and both the determinant calculation method and this new method were used to compute all feedback loops in the system.The research showed that both methods yielded the same results, but the fast calculation method of the feedback loop significantly reduced the calculation time and improved the calculation accuracy.Based on this, the system structure can be deeply analyzed to put forward corresponding management countermeasures. [ABSTRACT FROM AUTHOR]

Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains. [ABSTRACT FROM AUTHOR]

Complex systems contain numerous interacting components, thus deep learning methods with powerful performance and complex structure are often used to achieve condition monitoring. However, the deep learning methods are always too time-consuming and hardware-demanding to be loaded into complex systems for online training and updates. To achieve accurate and timely monitoring of complex system state, based on broad learning system (BLS), an online condition monitoring method is proposed in this paper. General BLSs are based on a randomly generated hidden-layer, usually perform poorly in high-dimensional data classification tasks. In this work, based on correlation and causality, two modified BLSs are proposed and mixed to establish the online monitoring system. Specifically, logistic regression (LR) and structural causal model (SCM) are considered to form rough predictions of the system state, thus to replace the randomly generated ones with no practical significance. The effectiveness of the proposed online monitoring method is verified by both simulation data and real data. [ABSTRACT FROM AUTHOR]

Analyzing and obtaining useful information is challenging when facing a new complex system. Traditional methods often focus on specific structural aspects, such as communities, which may overlook the important features and result in biased conclusions. To address this, this article suggests an adaptive algorithm for exploring complex system structures using a generative model. This method calculates and optimizes node parameters, which can reflect the latent structural characteristics of the complex system. The effectiveness and stability of this method have been demonstrated in comparative experiments on 10 sets of benchmark networks using our model parameter configuration scheme. To enhance adaptability, algorithm fusion strategies were also proposed and tested on two real-world networks. The results indicate that the algorithm can uncover multiple structural features, including clustering, overlapping, and local chaining. This adaptive algorithm provides a promising approach for exploring complex system structures. [ABSTRACT FROM AUTHOR]

Background: The aim of this systematic review was to examine the relationship between strategies to improve care delivery for older adults in ED and evaluation measures of patient outcomes, patient experience, staff experience, and system performance. Methods: A systematic review of English language studies published since inception to December 2022, available from CINAHL, Embase, Medline, and Scopus was conducted. Studies were reviewed by pairs of independent reviewers and included if they met the following criteria: participant mean age of ≥ 65 years; ED setting or directly influenced provision of care in the ED; reported on improvement interventions and strategies; reported patient outcomes, patient experience, staff experience, or system performance. The methodological quality of the studies was assessed by pairs of independent reviewers using The Joanna Briggs Institute critical appraisal tools. Data were synthesised using a hermeneutic approach. Results: Seventy-six studies were included in the review, incorporating strategies for comprehensive assessment and multi-faceted care (n = 32), targeted care such as management of falls risk, functional decline, or pain management (n = 27), medication safety (n = 5), and trauma care (n = 12). We found a misalignment between comprehensive care delivered in ED for older adults and ED performance measures oriented to rapid assessment and referral. Eight (10.4%) studies reported patient experience and five (6.5%) reported staff experience. Conclusion: It is crucial that future strategies to improve care delivery in ED align the needs of older adults with the purpose of the ED system to ensure sustainable improvement effort and critical functioning of the ED as an interdependent component of the health system. Staff and patient input at the design stage may advance prioritisation of higher-impact interventions aligned with the pace of change and illuminate experience measures. More consistent reporting of interventions would inform important contextual factors and allow for replication. [ABSTRACT FROM AUTHOR]

The 21st century has given rise to a growing class of challenges that are difficult – perhaps impossible – to address effectively within framework of the existing global order. While numerous factors play a role in causing this problem, this essay focuses on difficulties arising from the influence of the paradigm of "relative gains maximization" as a determinant of the course of interactions among actors on a global scale. Following an account of the nature and impact of this paradigm, the essay explores prospects both, for reforming the paradigm to ameliorate its effects and for more transformative changes featuring the development of a new paradigm. The way forward is to develop perspectives that highlight the need for cooperative measures to address common concerns arising in a world of complex systems. [ABSTRACT FROM AUTHOR]

The development of social networks provides a broad platform for the dissemination of information and also leads to the proliferation of fake news and false information, which we collectively refer to as rumors. The spread of rumors causes unnecessary panic and loss to individuals and society. To reduce the negative impacts of rumors, an appropriate rumor control strategy is necessary. To come up with some reasonable strategies, we need to have a clearer understanding of the spread of rumors. In this paper, we analyze crowd attitudes during the spreading of rumors by setting the misinformation prevalent progress on the social network as a dynamic system. Considering that most people do not have a clear supportive or opposing attitude when exposed to rumor information, we introduce a new group, stiflers who remain neutral, based on the infectious disease model scheme. By deriving the mean-field equation describing the rumor propagation process, we judge the stability of the constructed model. Finally, we use the model to fit the real-world data related to COVID-19, and based on this, we discuss the properties of the model and propose related strategies. [ABSTRACT FROM AUTHOR]

In the architecture, engineering and construction (AEC) industry, many efforts have provided remarkable contributions to construction planning and control processes during work execution. Nevertheless, frequent coordination issues among stakeholders and difficulties in dealing with unexpected events can be explained by the complexity featuring the construction sector. Several approaches to deal with this issue were investigated in the manufacturing area, among which this paper looks at the holonic approach as one of the most promising strategies. This study first analyzes the more fragmented and dynamic nature of the construction industry as compared with the manufacturing one. Secondly, it suggests developing a process-based holonic construction management system based on building information modeling (BIM) and a conceptual architecture for manufacturing control called Product Resource Order Staff Architecture (PROSA). The process-based paradigm ensures exploiting the benefits of BIM towards the development of sustainable and efficient regeneration methods of the built environment. Subsequently, a first management system prototype was developed and tested for the purpose of renovation works management. For the first time, results from an actual implementation of PROSA were applied to a real construction site, and its feasibility was assessed using the data on the field. Key performance indicators (KPIs) evaluated during the onsite demonstration confirmed a good performance of PROSA and the presented holonic approach, which contributed to the overall success of the energy efficient refurbishment project. [ABSTRACT FROM AUTHOR]

Considering a class of complex nonlinear systems whose dynamics are mostly governed by statistical regulations, the pattern-moving theory was developed to characterise such systems and successfully estimate the outputs or states. However, since the pattern class variable is not computable directly, this study establishes a clustered generalized cell mapping (C-GCM) to reveal system characteristics. C-GCM is a two-stage approach consisting of a pattern-moving-based description and analysis method. First, a density algorithm, named density-based spatial clustering of applications with noise (DBSCAN), is designed to obtain cell space Ω and the corresponding classification guidelines; this algorithm is initiated after the initial pre-image cells, and the total number of entity cells amounts to N s . Then, the GCM provides several image cells based on a cell mapping function that refers to the multivariate ARMAX model. The global dynamic analysis employing both searching and storing algorithms depend on the attractor, domain of attraction, and periodic cell groups. At last, simulation results of two examples emphasise the practicality as well as efficacy of the technique suggested. The chief aim of this study was to offer a new perspective for a class of complex systems that could inspire research into nonmechanistic principles modelling and application to nonlinear systems. [ABSTRACT FROM AUTHOR]

Wastewater-based epidemiology (WBE) has been used for monitoring infectious diseases like polio, hepatitis, etc. since the 1940s. It is also being used for tracking the SARS-CoV-2 at the population level. This article aims to compile and assess the information for the qualitative and quantitative detection of the SARS-CoV-2 in wastewater. Based on the globally published studies, we highlight the importance of monitoring SARS-CoV-2 presence/ detection in the wastewater and concurrently emphasize the development of early surveillance techniques. SARS-CoV-2 RNA sheds in the human feces, saliva, sputum and mucus that ultimately reaches to the wastewater and brings viral RNA into it. For the detection of the virus in the wastewater, different detection techniques have been optimized and are in use. These are based on serological, biosensor, targeted PCR, and next generation sequencing for whole genome sequencing or targeted amplicon sequencing. The presence of the SARS-CoV-2 RNA in wastewater could be used as a potential tool for early detection and devising the strategies for eradication of the virus before it is spread in the community. Additionally, with the right and timely understanding of viral behavior in the environment, an accurate and instructive model that leverages WBE-derived data may be created. This might help with the creation of technological tools and doable plans of action to lessen the negative effects of current viral epidemics or future potential outbreaks on public health and the economy. Further work toward whether presence of viral load correlates with its ability to induce infection, still needs evidence. The current increasing incidences of JN.1 variant is a case in point for continued early detection and surveillance, including wastewater. [ABSTRACT FROM AUTHOR]

In order to solve the problems of vague and inconsistent requirement descriptions and understanding during the development of electronic control systems, and taking into account the experience of actual research and development projects and the CMMI3 level certification process, a requirement development method based on a visual analysis model was proposed. Firstly, the complex automotive electronic control system was hierarchically divided, and the customer's top-level functional requirements of the vehicle were analyzed. The use case scenario modeling was used to define the system and the external environment boundary and interaction relationship to capture the top-level system requirements. Then, based on the visual analysis method of activity diagram and sequence diagram, the static and dynamic behaviors of the system were analyzed in detail, and the ideas and methods of requirement mining at different levels were given. The requirement diagram was used to show the details of requirement development and the traceability relationship of different levels of requirement. Finally, taking trigger diesel particulate filter DPF regeneration as a case study, the application process of the requirement method of visual analysis model was systematically expounded, and the feasibility and effectiveness of the proposed method were verified. Results provide technical means and guidance for the forward development of automobile electronic control system. [ABSTRACT FROM AUTHOR]

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures. [ABSTRACT FROM AUTHOR]

Massively multiplayer online games (MMOGs) played on the Web provide a new form of social, computer-mediated interactions that allow the connection of millions of players worldwide. The rules governing team-based MMOGs are typically complex and nondeterministic giving rise to an intricate dynamical behavior. However, due to the novelty and complexity of MMOGs, their behavior is understudied. In this article, we investigate the MMOG World of Tanks Blitz by using a combined approach based on data science and complex adaptive systems. We analyze data on the population level to get insights into organizational principles of the game and its game mechanics. For this reason, we study the scaling behavior and the predictability of system variables. As a result, we find a power-law behavior on the population level revealing long-range interactions between system variables. Furthermore, we identify and quantify the predictability of summary statistics of the game and its decomposition into explanatory variables. This reveals a heterogeneous progression through the tiers and identifies only a single system variable as key driver for the win rate. [ABSTRACT FROM AUTHOR]

Quasilattices are algebraic structures that comprise a semilattice-ordered system of lattices. In this paper, certain quasilattices (that are characterized abstractly by a local completeness property) provide an extension of Wille's concept analysis to the study of complex systems that function on a number of distinct levels. In an important special case, a chain semilattice serves to represent a time series governing the evolution of a single system. Natural set representations of locally complete quasilattices have opposed set inclusions describing order relations within a complete lattice, and parallel set inclusions tracking homomorphisms that connect distinct lattice fibers. In the time series model, the sets that appear within the set representation accumulate successive layers at each time point, establishing a mathematical model for historical phenomena. [ABSTRACT FROM AUTHOR]

In aerospace, to ensure the safe operation of aircraft, as well as efficiency and maintenance costs. It is necessary to perform reliable safety checks on complex systems such as aircraft engines. The Belief Rule Base (BRB) system is an intelligent decision support system for dealing with uncertain information and simulating human reasoning processes, which combines evidential reasoning and fuzzy logic and introduces a belief framework structure with strong interpretability, and is commonly used in areas such as evaluation and diagnosis. However, the rule base of BRBs often faces the risk of combinatorial rule explosion, and the traditional hierarchical structure often lacks a common construction standard. In this paper, we introduce a BRB model (MTS-BRB) based on a multi-layer tree structure that can self-organize to construct a tree structure, thus providing a guiding scheme for modeling hierarchical BRBs and solving the combinatorial rule explosion problem. In addition, an interpretable optimization algorithm applicable to the MTS-BRB model is proposed, which ensures the post-hoc interpretability of the model to a certain extent, and the reliability of the model and the effectiveness of the optimization algorithm are demonstrated by the turbofan engine case study and the milling case study.

The improvement of fault diagnosis for complex equipment is an important step towards intelligent systems. Unlike component-level fault detection, system-level fault diagnosis presents new challenges, such as the integration of multiple sensors, the handling of massive datasets, and the necessity for robust system-level decision-making. To address these challenges, this paper proposes a novel Physics-embedded Recurrent Graph Neural Network (Pe-RGNN). By embedding physical knowledge about the interdependencies among sensors, the graph constructed of sensors are built through physical knowledge obtaining from high-fidelity model. With the sensory graph, the parameters are modeled through recurrent graph neural network to extract the temporal information. The proposed model has the advantage of simultaneously considering both spatial and temporal information. Experimental results from both simulated and real-world tests demonstrate the superior performance of the Pe-RGNN in early fault detection and fault identification accuracy when compared to traditional graph neural networks. These findings highlight the importance of incorporating both physical knowledge and time-series information in fault diagnosis models.

With the rapid technological advancements and the ever-evolving complex systems, the identification and integration of the components and resources for the functioning of multi-agent systems (MAS) are crucial tasks. However, difficulties arise due to the complexity of not having reference frameworks that normalize their implementation. Therefore, in this survey, we propose the FC-MAS (Framework-Components in Multi-Agent System) model as a conceptual framework designed to simplify comprehension and standardization in incorporating the required functions and components for the deployment and operation of MAS in engineering applications. This model comprises five abstract layers, each of which serves a specific purpose and encompasses the details and resources required to operate MAS. Furthermore, we propose a structured workflow for centralized and distributed MAS schemes with a set of related activities that integrate the fundamental steps and stages for the successful implementation of MAS. Finally, this work discusses potential directions for future research, including a deeper exploration of essential components, the establishment of terminology standards across various domains, and the refinement of the proposed model to enhance its applicability and relevance across a broader spectrum of contexts.

The Entropy-based Categorical Exploratory Data Analysis (CEDA) paradigm is elaborately refined to algorithmically explore the intricate high-order directional associative relational patterns within the heterogeneous chronical disease dynamics captured by Behavioral Risk Factor Surveillance System (BRFSS) database. Operating on this imbalanced categorical dataset represented fully by its metric-free high-dimensional histogram, our algorithms conduct data-driven computations to investigate chronic disease mechanisms across four sub-populations along the age-axis, culminating in comprehensive systemic understandings. Upon this categorical data-world, CEDA first recognizes the category-oriented 1D histogram as the simplest form of a piece of explainable information. Then, utilizing Kolmogorov’s randomness-proper-based reliability check, CEDA identifies and confirms collectives of 1D histograms as major feature-categories of varying orders within each sub-population. These confirmed major feature-categories’ binary memberships are then arranged into a subject-vs-feature-category bipartite network heatmap, revealing serial horizontal and vertical blocks framed by clusters of similar subjects characterized by individual-risk-landscapes (IRL) against clusters of structurally dependent major feature-categories. Based on such block-series, sub-population-specific disease mechanisms emerge as collective high-order interacting effects, elucidating directional associative relationships from study subjects’ topological neighborhoods to response-categories. Notably, the topological individual-risk-landscape offers profound insights into complex system dynamics and simultaneously exposes atypical subjects as explainable errors across all Machine Learning classifiers.