Operation Of Complex Systems Research Articles

Advances in the application of network analysis methods in traditional Chinese medicine research

ObjectiveThis review aims at evaluating the role and potential applications of network analysis methods in the medicinal substances of traditional Chinese medicine (TCM), theories of TCM compatibility, properties of herbs, and TCM syndromes. MethodsLiterature was retrieved from databases, such as CNKI, PubMed, and Web of Science, using keywords, including "network analysis," "network biology," "network pharmacology," and "network medicine." The extracted literature included the biological network construction (including ingredient-target and target-disease relations), analysis of network topology characteristics (including node degree, clustering coefficient, and path length), network modularization analysis, functional annotation and so on. These studies were categorized and organized based on their research methods, application domains, and other relevant characteristics. ResultsNetwork analysis algorithms, such as network distance, random walk, matrix factorization, graph embedding, and graph neural networks, are widely applied in fields related to the properties, compatibility, and mechanisms of TCM. They effectively reflect the interactive relations within the complex systems of TCM and elucidate and clarify theories, such as the effective substances, the principles of TCM compatibility, the TCM syndromes, and the properties of TCM. ConclusionThe network analysis method is a powerful mathematical and computational tool that reveals the structure, dynamics, and functions of complex systems by analyzing the elements and their relations. This approach has effectively promoted the modernization of TCM, providing essential theoretical and practical tools for personalized treatment and scientific research on TCM. It also offers a significant methodological framework for the modernization and internationalization of TCM.

Weak integration allows novel fin shapes and spurs locomotor diversity in reef fishes.

In complex functional systems composed of many traits, selection for specialized function can induce trait evolution by acting directly on individual components within the system, or indirectly through networks of trait integration. However, strong integration can also hinder diversification into regions of trait space that are not aligned with axes of covariation among traits. As a result, non-independence among traits may limit capacity for functional expansion. We explore this dynamic in the evolution of fin shapes in 106 species from 38 families of coral reef fishes, a polyphyletic assemblage that shows exceptional diversity in locomotor function. Despite strong shared developmental pathways and expectations of a strong match between form and function, we find that species that share swimming mode show substantial disparity in fin shape, and preferred swimming mode is a poor predictor of fin shape. The evolution of fin shape is weakly integrated across the four functionally dominant fins in swimming (the pectoral, caudal, dorsal, and anal fins) and integration is weakened as derived swimming modes evolve. The weak integration among fins in the ancestral locomotor condition provides a primary axis of diversification while allowing for substantial off-axis diversification via independent trait responses to selection. However, the evolution of novel locomotor modes coincides with a loss of integrated axes of covariation among fins. Our study highlights the need for additional work on the functional consequences of fin shape in fishes and impact of evolutionary integration on functions other than locomotion.

Environment induced dynamical quantum phase transitions in two-qubit Rabi model

Quantum states beyond thermodynamic equilibrium represent fascinating and cutting-edge research. However, the behavior of dynamical quantum phase transitions in complex open quantum systems remains poorly understood. Here, using state-of-the-art numerical approaches, we show that by quenching the qubits-oscillator coupling in a dissipative two-qubit Rabi model, the system undergoes dynamical quantum phase transitions. These transitions are characterized by kinks in the Loschmidt echo rate function at parameter values close to a thermodynamic quantum phase transition and are associated with distinct entanglement features. The two classes of critical phenomena depend on qubit interactions and entanglement, revealing different behaviors of the critical exponent of the first kink of the Loschmidt echo for interacting versus non-interacting qubits. This research enhances our understanding of non-equilibrium quantum systems and offers potential applications in quantum sensing and metrology, as it examines how dynamical transitions can enhance the sensitivity of the Loschmidt echo to the quench parameters.

New horizons in systems engineering and thinking to improve health and social care for older people.

Existing models for the safe, timely and effective delivery of health and social care are challenged by an ageing population. Services and care pathways are often optimised for single-disease management, while many older people are presenting with multiple long-term conditions and frailty. Systems engineering describes a holistic, interdisciplinary approach to change that is focused on people, system understanding, design and risk management. These principles are the basis of many established quality improvement (QI) tools in health and social care, but implementation has often been limited to single services or condition areas. Newer engineering techniques may help reshape more complex systems. Systems thinking is an essential component of this mindset to understand the underlying relationships and characteristics of a working system. It promotes the use of tools that map, measure and interrogate the dynamics of complex systems. In this New Horizons piece, we describe the evolution of systems approaches while noting the challenges of small-scale QI efforts that fail to address whole-system problems. The opportunities for novel soft-systems approaches are described, along with a recent update to the Systems Engineering Initiative for Patient Safety model, which includes human-centred design. Systems modelling and simulation techniques harness routine data to understand the functioning of complex health and social care systems. These tools could support better-informed system change by allowing comparison of simulated approaches before implementation, but better effectiveness evidence is required. Modern systems engineering and systems thinking techniques have potential to inform the redesign of services appropriate for the complex needs of older people.

A research of higher-order network emotional phase transitions

This paper explores the law of emotion transmission on the social Internet. By applying higher-order network theory, ER random networks are reconstructed and higher-order network structures with multiple interaction points are generated. The I-state of the SI model is extended to set up the propagation rules of emotions based on higher-order networks. Concepts such as sentiment recession rate are introduced to make sentiment propagate through different simplexes, so as to obtain the propagation rules of higher-order networks. Qualitative analysis of emotional changes during the propagation process verifies the phase transition phenomenon of emotions in the network. By comparing simulated results with actual data, we demonstrate the accuracy of our higher-order network-based emotion propagation model in reflecting real-world trends and outcomes. The study also explores the effects of factors such as the initial proportion of extreme emotions on emotion propagation. This study provides valuable insights into understanding the operation of complex systems and offers important implications for government predictions of opinion development.

Numerical methods for optimizing logistics systems in the process of inventory management

Scientists have always faced the task of evaluating the effectiveness of the functioning of complex systems, which unambiguously include logistics systems of transport (water, air, rail, automobile and their combinations) and stocks of products (goods), which (stocks of goods) are in static (warehouses) and dynamic (transport) states over time. A huge amount of theoretical material has been accumulated in this field of knowledge, which has been repeatedly tested and successfully applied, especially in relation to logistics systems. However, as is known, on the basis of an efficiency assessment, similar systems are compared, the operation of the systems is promptly evaluated, the system's compliance with its purpose is checked, the presence or absence of targeted targeting is found out, etc. Hence, the relevance of the mathematical apparatus (numerical methods) for optimizing stocks of products (goods) is determined as a calculation for reasoned decision-making on effective inventory management using transport and storage capacities on an economic basis: the objective function is to minimize costs. Cost reduction in the processes of functioning of logistics systems due to the action of two basic laws – the law of saving time and the law of conservation of energy is one of the main modern directions for increasing their efficiency as complex systems [1-5]. Therefore, the optimization of logistics systems according to the criterion of costs (losses) in the processes of their functioning is defined as the formulation of the research task. The aim of the work is to develop the application of numerical methods for optimizing logistics systems in the process of inventory management. The novelty consists in the application of numerical methods and the calculated ratios obtained, which can be used in the practice of the logistics system of water, air, rail and road transport for inventory management by optimizing the determined losses (costs). The result consists in the developed method of studying theoretically calculated losses occurring in the processes of functioning of logistics systems of all types of transport and their combinations (hereinafter transport). The theoretical significance of the results obtained is the development of the application of numerical methods for optimizing logistics systems in the process of inventory management, including: a set of factors and criteria for optimizing logistics processes; a method for modeling and optimizing the processes of functioning of logistics transport systems; a method for analyzing losses in the processes of functioning of logistics transport systems. These theoretical provisions make it possible to optimize the processes of functioning of logistics transport systems and, ultimately, to increase the efficiency of providing consumers, which is of important socio-economic importance.

Anomaly Detection for Power Quality Analysis Using Smart Metering Systems.

The problem of Power Quality analysis is becoming crucial to ensuring the proper functioning of complex systems and big plants. In this regard, it is essential to rapidly detect anomalies in voltage and current signals to ensure a prompt response and maximize the system's availability with the minimum maintenance cost. In this paper, anomaly detection algorithms based on machine learning, such as One Class Support Vector Machine (OCSVM), Isolation Forest (IF), and Angle-Based Outlier Detection (ABOD), are used as a first tool for rapid and effective clustering of the measured voltage and current signals directly on-line on the sensing unit. If the proposed anomaly detection algorithm detects an anomaly, further investigations using suitable classification algorithms are required. The main advantage of the proposed solution is the ability to rapidly and efficiently detect different types of anomalies with low computational complexity, allowing the implementation of the algorithm directly on the sensor node used for signal acquisition. A suitable experimental platform has been established to evaluate the advantages of the proposed method. All the different models were tested using a consistent set of hyperparameters and an output dataset generated from the principal component analysis technique. The best results achieved included models reaching 100% recall and a 92% F1 score.

Designing Retirement Strategies for Coal-Fired Power Plants To Mitigate Air Pollution and Health Impacts.

Retiring coal power plants can reduce air pollution and health damages. However, the spatial distribution of those impacts remains unclear due to complex power system operations and pollution chemistry and transport. Focusing on coal retirements in Pennsylvania (PA), we analyze six counterfactual scenarios for 2019 that differ in retirement targets (e.g., reducing 50% of coal-based installed capacity vs generation) and priorities (e.g., closing plants with higher cost, closer to Environmental Justice Areas, or with higher CO2 emissions). Using a power system model of the PJM Interconnection, we find that coal retirements in PA shift power generation across PA and Rest of PJM, leading to scenario-varying changes in the plant-level release of air pollutants. Considering pollution transport and the size of the exposed population, these emissions changes, in turn, give rise to a reduction of 6-136 PM2.5-attributable deaths in PJM across the six scenarios, with most reductions occurring in PA. Among our designed scenarios, those that reduce more coal power generation yield greater aggregate health benefits due to air quality improvements in PA and adjacent downwind regions. In addition, comparing across the six scenarios evaluated in this study, vulnerable populations─in both PA and Rest of PJM─benefit most in scenarios that prioritize plant closures near Environmental Justice Areas in PA. These results demonstrate the importance of considering cross-regional linkages and sociodemographics in designing equitable retirement strategies.

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

Watt’s Next? Leveraging Process Flexibility for Power Cost Optimization

The transition from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent power supply and demand. Although demand-side energy flexibility, offered particularly by industrial companies, is seen as a promising and necessary approach to address these challenges and realize benefits for companies, its implementation is not yet common practice. Often facing highly complex process landscapes and operational systems, process mining provides significant potential to increase transparency of actual process flows and to discover or reflect existing dependencies and interrelationships of activities, instances or resources. It facilitates the implementation of energy flexibility measures and enables the realization of monetary benefits associated with flexible process operation. This paper contributes to the successful integration of energy flexibility into process operations by presenting a design science research artifact called PM4Flex. This is a prescriptive process monitoring approach that uses linear programming to generate recommendations for pending process flows optimized under fluctuating power prices by utilizing established energy flexibility measures. Thereby, event logs and corresponding company- as well as process-specific constraints are considered. PM4Flex is demonstrated and evaluated based on its implementation as a software prototype, its application to exemplary data from two real-world processes exhibiting power cost savings of up to 75% compared to the original execution, and based on semi-structured expert interviews. PM4Flex provides new design knowledge at the interface of prescriptive process monitoring and the energy domain providing decision support to optimize industrial energy procurement costs.

New Random Walk Algorithm Based on Different Seed Nodes for Community Detection

A complex network is an abstract modeling of complex systems in the real world, which plays an important role in analyzing the function of complex systems. Community detection is an important tool for analyzing network structure. In this paper, we propose a new community detection algorithm (RWBS) based on different seed nodes which aims to understand the community structure of the network, which provides a new idea for the allocation of resources in the network. RWBS provides a new centrality metric (MC) to calculate node importance, which calculates the ranking of nodes as seed nodes. Furthermore, two algorithms are proposed for determining seed nodes on networks with and without ground truth, respectively. We set the number of steps for the random walk to six according to the six degrees of separation theory to reduce the running time of the algorithm. Since some traditional community detection algorithms may detect smaller communities, e.g., two nodes become one community, this may make the resource allocation unreasonable. Therefore, modularity (Q) is chosen as the optimization function to combine communities, which can improve the quality of detected communities. Final experimental results on real-world and synthetic networks show that the RWBS algorithm can effectively detect communities.

Multilevel assessment method for post-earthquake functionality of medical buildings considering component–department–floor interdependencies

The assessment of the post-earthquake functionality of medical buildings (PEFMB) is critical for the post-earthquake emergency response, recovery, and resilience-based seismic design of medical buildings. Hospitals are complex functional systems that exhibit multilevel functional coupling from components to departments to floors. Existing methods have challenges, including the difficulty of considering the impact of different departments located on different floors on medical functions, and a lack of multilevel performance improvement strategies for enhancing post-earthquake medical functionality. A multilevel assessment method for the PEFMB that considers component–department–floor interdependencies (CDFI) was developed in this study. This method establishes a functional logical relationship between components and departments using fault tree models of typical departments. A multilevel functional coupling relationship among the components, departments, and floors was established by incorporating floor nodes to reflect the impact of departments located on different floors. This enabled the assessment of the PEFMB considering the CDFI. Furthermore, methods for identifying critical failure paths in the PEFMB were developed, and multilevel performance improvement strategies by adjusting medical equipment and departments along the critical failure path were recommended. The proposed method is demonstrated using an outpatient building. The main conclusions are as follows. (1) The proposed multilevel assessment method enables quantitative assessment of the PEFMB and exhibits good scalability. (2) This method provides a clear and intuitive visualization of the critical failure path in healthcare functionality under seismic conditions. The proposed multilevel improvement strategies can enhance the post-earthquake functionality of medical buildings at multiple levels, which provides a valuable reference for the seismic design of medical buildings.

Формирование навыков командной деятельности и их объективная количественная оценка на основе квантовых представлений

<p>Presented are mathematical and software tools providing the formation and objective quantitative assessment of teamwork skills based on quantum representations. These tools utilize the formal representations of subjects' activities through probability matrices of transitions between the performed elementary operations. Analysis of the subjects' activities is based on comparisons of these matrices for a pattern of actions under study with relevant matrices for patterns of empirical data representing various variants of the subjects' work. When evaluating the teamwork skills, these comparisons are performed according to the "Triangle Scheme", which involves comparing the coordinated actions of the subjects performed in pairs and individually. For this purpose, the quantum spectral analysis is used, which provides effective quantitative comparisons of the hidden periodicities of performing typical elementary operations and their quantities in the spectral metric. The tools for the formation and assessment of teamwork skills are implemented in the form of both a flight simulator with the separation of control channels between pilots and a modified computer game "Sea Battle", which allows to evaluate the dynamics of partners’ communication and cooperation by analyzing their moves as well as tips transmitted to each other. The presented approach to the formation and assessment of teamwork skills is quite universal and can be used in various applications areas, including the assessment and training of flight crews, operators of complex technical systems, students of various levels and other target groups.</p>

Анализ исследований роли компьютерных тренажеров в формировании, измерении и совершенствовании навыков командной работы

<p>The article considers the influence of training on computer simulators on the development of teamwork skills of operators of complex systems, in particular, in aviation. The article substantiates the effectiveness of work on computer simulators for the formation of interaction skills in people with disabilities. A review of experimental studies demonstrating the positive impact of using computer simulators on the formation of skills necessary for teamwork: coordinated actions, communication, decision-making and situational awareness is presented. The analysis of methods for assessing teamwork skills is given and the realization of a hardware and software complex in the form of a simulator for training these skills and their quantitative measurement is described.</p>

Emergent scale-free networks.

Many complex systems-from the Internet to social, biological, and communication networks-are thought to exhibit scale-free structure. However, prevailing explanations require that networks grow over time, an assumption that fails in some real-world settings. Here, we explain how scale-free structure can emerge without growth through network self-organization. Beginning with an arbitrary network, we allow connections to detach from random nodes and then reconnect under a mixture of preferential and random attachment. While the numbers of nodes and edges remain fixed, the degree distribution evolves toward a power-law with an exponent that depends only on the proportion p of preferential (rather than random) attachment. Applying our model to several real networks, we infer p directly from data and predict the relationship between network size and degree heterogeneity. Together, these results establish how scale-free structure can arise in networks of constant size and density, with broad implications for the structure and function of complex systems.

An Overview of Digital Engineering Methods for Platform Integration of Power and Energy Systems

US Navy ships, and combatant ships in particular, have requirements for integrated systems that are designed and configured for operational efficiency, redundancy, and survivability. Mission systems today and in the future will not always come with their own energy and many may, at times, require extreme pulse power loads. In addition, the migration away from fossil fuels to hybrid systems with energy storage, or the requirements for autonomous platforms, is challenging our ability to design platforms for these systems. Understanding the interdependencies between components, the systems they support, and the energy domains they are member of is a critical ontological design requirement. This paper will address how we applied digital engineering principles and computer science to the design of ontologies that allow for the modeling of complex operational systems riding on the same shipboard energy network. This system network must support varying levels of detail needed during design while at the same time understanding the impacts of that design on ship system operations and their energy loads over time.

Can We Really Control Giant Open Complex Systems?

Human society and nature interact through complex interconnections and interdependencies. Using Chinese scientist Qian Xuesen’s concept of Giant Open Complex Systems, this paper argues that if human beings are themselves such open complex systems, their interaction with other human beings and nature create co-relative, co-creative interactions that cannot be reduced to simple reductionist principles that enable prediction and control. We should therefore have few illusions that we can fully control human destinies under current theories and policies.

Identification of key nodes in complex networks by using a joint technique of nonnegative matrix factorization and regularization

Identifying key nodes in complex networks is essential to deeply understand and fully utilize the properties and functions of complex systems. Currently, existing traditional methods perform critical nodes identification by manually selecting important attribute features of nodes, but there are limitations in this approach. Manual selection of attribute features may overlook non-obvious features related to nodes criticality and correlations between attribute features. To compensate for the shortcomings of traditional methods, a Joint Technique for identifying critical nodes, called JTNMFR, is presented based on Nonnegative Matrix Factorization and Regularization. Factorization of weighted adjacency matrix is performed to obtain potential attribute features of nodes, and communicability network matrix and similarity matrix are introduced as regularization terms to control sparsity of the decomposition results. Ultimately, the importance of nodes is assessed by constructing an objective function that integrates these two aspects and utilizing alternative iteration to obtain the attribute matrix. To validate the accuracy and reliability of JTNMFR, we compare it with nine other identification approaches on eight real networks. Experimental results show that JTNMFR not only significantly outperforms the other algorithms in terms of accuracy of node importance, monotonicity, and node spreading ability but also provides a more accurate means of assessing node importance.

Dynamic human error risk assessment of group decision-making in extreme cooperative scenario

For groups in extreme environments, their reliability requirements in complex operational systems are high due to isolation from society, physical constraints for extended periods of time, and exposure to significant hazards. This study attempts to solve the problem of dynamic quantitative risk analysis of decision-making process for extreme group during complex cooperative scenarios. Based on an extended IDA team cognitive model, typical failure modes and performance quantification mechanisms of team communication were analyzed. The performance curve was utilized to distinctively model the cognitive behaviors of operators within the team, driving the cognitive transmission paths of group decision-making in a hybrid event tree. Error calibration of human error due to time uncertainty was conducted. The steps and techniques for dynamic risk analysis of decision-making of extreme groups under complex, dynamic, and ambiguous scenarios were summarized. The proposed method was validated through simulation in a typical powered diving task of manned submarine.

OHCA-GCN: A novel graph convolutional network-based fault diagnosis method for complex systems via supervised graph construction and optimization

Fault diagnosis is crucial for ensuring the safe and stable operation of complex systems. Recently, graph convolutional network (GCN)-based fault diagnosis method has emerged as a hot research topic due to its ability to effectively handle the association information inherent in the collected dataset. Regarding the method, a fundamental challenge is the construction of the association graph, conventional construction methods struggle to capture the intricate relationships between nodes with complex coupling. Besides, they operate in an unsupervised learning mode, lacking the ability to evaluate and adjust the constructed graph. To handle this challenge, a novel graph convolutional network-based fault diagnosis method via supervised graph construction and optimization (OHCA-GCN) is proposed. Specifically, the supervised orthogonalized-autoencoder is proposed to construct the association graph. Then, a high-order correlation analysis-based graph optimization strategy is proposed to ensure graph sparsity and alleviate the over-smoothing issue. To validate the effectiveness of the proposed method, experiments are carried out on a pulse rectifier dataset from a hardware-in-the-loop platform. The results show that the proposed method exhibits the excellent diagnostic performance compared with the existing methods.