Complex Interconnected System Research Articles

Distributed Finite-Time ADP-Based Optimal Secure Control for Complex Interconnected Systems Under Topology Attacks

Distributed Finite-Time ADP-Based Optimal Secure Control for Complex Interconnected Systems Under Topology Attacks

Entirely Coupled Recurrent Neural Network-Based Backstepping Control for Global Stability of Power System Networks

In an interconnected power system network, global performance is affected by various unknown power system parameters subjected to system uncertainties. These unknown parameters in the control expression deteriorate the performance of the power system network. Therefore, these terms associated with the control expression must be estimated precisely. This paper proposes an adaptive backstepping scheme using an entirely coupled recurrent neural network (ECRNN) to estimate these control terms. Three continuous differentiable functions are used to design control input, virtual signals, and adaptive control laws to achieve global performance. The objective of ECRNN is to reduce the control complexity by estimating the associated nonlinear term in the control expression. It will facilitate the complex formulation of the controller and improve system performance. The robust functional estimation ability of ECRNN will make the system immune to uncertainties that may appear due to external disturbances. In ECRNN, feedback loops are added to each neuron layer to achieve additional estimation accuracy. The proposed adaptive law enhances the online weight update speed and accuracy. Verification of the proposed scheme is carried out in MATLAB/Simulink. It is also verified in the real-time digital simulator (RTDS) platform using the IEEE standard New England 39-bus, 10-machine power system model. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the existing limitations in the global performance of a multimachine power system network due to uncertainty in the system parameters. Uncertainties in the power system network primarily appeared due to penetration of renewable power sources, load uncertainty, or occurrence of an uncertain fault in the network. As power system networks are complex nonlinear interconnected systems, these perturbations in the generator states may cause economic losses, load demand uncertainty, or electric scarcity. Thus, a control scheme is required to address the global performance of such networks. This article proposes uniform ultimate bounded stability of synchronous generators in a multimachine power system network with a robust ECRNN-based backstepping control design. The other available technique has not adequately addressed the global performance under uncertainties. The efficacy of the proposed scheme is verified in a real-time environment via a real-time digital simulator which may be a feasible solution for designing excitation control for the synchronous machine in an extensive industrial network.

Unraveling the crosstalk between renin-angiotensin system receptors.

The renin-angiotensin system (RAS) plays a key role in blood pressure regulation. The RAS is a complex interconnected system composed of two axes with opposite effects. The pressor arm, represented by angiotensin (Ang) II and the AT1 receptor (AT1R), mediates the vasoconstrictor, proliferative, hypertensive, oxidative, and pro-inflammatory effects of the RAS, while the depressor/protective arm, represented by Ang-(1-7), its Mas receptor (MasR) and the AT2 receptor (AT2R), opposes the actions elicited by the pressor arm. The AT1R, AT2R, and MasR belong to the G-protein-coupled receptor (GPCR) family. GPCRs operate not only as monomers, but they can also function in dimeric (homo and hetero) or higher-order oligomeric states. Due to the interaction with other receptors, GPCR properties may change: receptor affinity, trafficking, signaling, and its biological function may be altered. Thus, heteromerization provides a newly recognized means of modulation of receptor function, as well as crosstalk between GPCRs. This review is focused on angiotensin receptors, and how their properties are influenced by crosstalk with other receptors, adding more complexity to an already complex system and potentially opening up new therapeutic approaches.

An Experiment in Transdisciplinary Systems Mapping: Architecture and the Water–Energy–Sanitation Nexus in Brazil

Urban environments contain and are part of a wide range of interconnected complex systems, including infrastructures and services. Rapid and often uncontrolled urbanization triggers distributive inequities and environmental injustices, posing urgent and interconnected challenges that demand inter- and transdisciplinary solutions. Despite architecture’s commitment to ‘sustainability’, its central role in urban systems and their dynamics as well as the discipline’s intersections with other disciplines remain relatively little explored. In this contribution, we focus on the water–energy–sanitation (WES) nexus in Brazil, drawing from transdisciplinary workshops, scoping reviews, and systems mapping. We propose a framework for the analysis of urban nexuses. This framework builds on transdisciplinary systems mapping for the identification of nexus components, nodes, and their interconnections. Our findings indicate that a nexus perspective allows us to identify challenges in urban nexuses, productive intersections with the knowledge and approaches from other disciplines, and possible solutions in collaboration with non-academic stakeholders. We advocate for an expanded professional field and a redefined sense of responsibility within the discipline.

Chattering-Free Second Order Robustness Sliding Mode Controller for Complex Interconnected Systems: A Moore-Penrose Inverse Technique

In this paper, a novel second order sliding mode control approach is extended toa class of complex interconnected systems with mismatched interconnections and unknown perturbations. The key contribution of this paper is to cancel two common restrictions clogging the application of the variable structure control to complex interconnected systems: 1) all of state variables must be accessible; 2) the control input is affected by chattering problems. First, a novel reduced-order sliding mode estimator (ROSME) is proposed to estimate the unmeasurable variables. Second, based on Moore-Penrose inverse technique and ROSME tool, a new decentralized chattering-free second order robustness sliding mode controller (CSORSMC) is developed to force the system states to stay in a sliding surface from the initial period instance and attenuate the chattering phenomenon in the control signal. Besides, a novel appropriate linear matrix inequality (LMI) requirement is proved such that the plant in sliding mode is asymptotically stable. Lastly, a mathematical model including two subsystems is simulated which confirms the usefulness and advantages of proposed technique.

Analyses on standards and regulations for connected and automated vehicles: Identifying the certifications roadmap

Protecting Connected and Automated Vehicle (CAV) from cyber attacks and data breaches is a major challenge facing the deployment of driverless vehicles. The CAV is a complex interconnected system consisting of sensors, Artificial Intelligence (AI) processors and external units to assure the automated driving without human interaction. Such complexity increases the attack surfaces and makes the CAV highly vulnerable to cyber assaults. It also entangles security audits and certifications procedures. Our work lays out a novel approach towards CAV’s certification focused on cybersecurity and data privacy aspects. Stipulated by the analysis on existing standards’ limitations, we propose a Standards Coverage Map (SCM) outlining the CAV’s entire ecosystem and linking organisational and technical aspects to the latest standards and regulations from the cyber perspective.

Stability Analysis: Two-Area Power System with Wind Power Integration

This paper focuses on a comprehensive stability study of a two-area power system with wind power integration and synthetic inertia control in each area, considering the effects of varying the interconnection link. Normally, synthetic inertia proposals are analyzed in one-area systems, in which stability is tested without considering transmission system phenomena, such as coherency. As modern power systems are progressively becoming interconnected, the possibility of forming two or more non-coherent areas is likely, which poses a challenge to synthetic inertia control techniques that use system frequency as a main feedback signal. In this context, this work addresses a crucial gap in the existing literature and provides a valuable starting point for studying more complex interconnected power systems with wind power integration. Simulations were performed in Matlab-Simulink considering a data-driven frequency dynamics model of the Chilean Electric System, and a wind power model with synthetic inertia control H2 norm minimization in each area. The results showed that it is possible to find local optimal feedback gains, preserving the stability of the global system under significant variations in the interconnection link. RoCoF and Nadir indicators are provided, highlighting the benefits of synthetic inertia control, particularly in low-inertia situations.

Maternal Supplementation with N-Acetylcysteine Modulates the Microbiota-Gut-Brain Axis in Offspring of the Poly I:C Rat Model of Schizophrenia.

The microbiota-gut-brain axis is a complex interconnected system altered in schizophrenia. The antioxidant N-acetylcysteine (NAC) has been proposed as an adjunctive therapy to antipsychotics in clinical trials, but its role in the microbiota-gut-brain axis has not been sufficiently explored. We aimed to describe the effect of NAC administration during pregnancy on the gut-brain axis in the offspring from the maternal immune stimulation (MIS) animal model of schizophrenia. Pregnant Wistar rats were treated with PolyI:C/Saline. Six groups of animals were studied according to the study factors: phenotype (Saline, MIS) and treatment (no NAC, NAC 7 days, NAC 21 days). Offspring were subjected to the novel object recognition test and were scanned using MRI. Caecum contents were used for metagenomics 16S rRNA sequencing. NAC treatment prevented hippocampal volume reduction and long-term memory deficits in MIS-offspring. In addition, MIS-animals showed lower bacterial richness, which was prevented by NAC. Moreover, NAC7/NAC21 treatments resulted in a reduction of proinflammatory taxons in MIS-animals and an increase in taxa known to produce anti-inflammatory metabolites. Early approaches, like this one, with anti-inflammatory/anti-oxidative compounds, especially in neurodevelopmental disorders with an inflammatory/oxidative basis, may be useful in modulating bacterial microbiota, hippocampal size, as well as hippocampal-based memory impairments.

Comparing Self-Report Assessments and Scenario-Based Assessments of Systems Thinking Competence

AbstractSelf-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents’ judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students’ performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.

Comparing Societal Impact Planning and Evaluation Approaches across Four Urban Living Labs (in Food-Energy-Water Systems)

Achieving societal impact, as opposed to academic impact, is a growing area of focus for the research community globally. Central to this changing mission is the focus on multiple interconnected complex systems and the need for research that is not just interdisciplinary, but also transdisciplinary and grounded in stakeholder co-production. This document compares multiple approaches to impact planning and evaluation across four newly formed urban living labs in Sao Paolo (Brazil), Western Cape (South Africa), Bristol (UK) and Rotterdam (Netherlands), each of which sought to address societal issues linked to the food-energy-water nexus. A comparison matrix and a disaggregated impact table are derived from a comprehensive review of key definitions. These new tools were completed by each ULL alongside a post hoc pathway to impact statements. Comparisons are presented and discussed, the strengths and weaknesses of this approach are considered and opportunities for improvement in societal impact planning and evaluation are provided. Our main findings include the importance of establishing clear shared definitions while accepting plural understandings, the need to acknowledge resource as a critical factor in impact delivery and the headline need for far greater focus in this area from both funders and research groups.

Geographic characteristics and environmental variables determine the diversities and assembly of the algal communities in interconnected river-lake system

Algal blooms in lakes are a major hazard worldwide. Although various geographical and environmental patterns affect algal communities during river-lake transit, a thorough understanding of what patterns shape the algal communities is still rarely researched, particularly in complex interconnected river-lake systems. In this study, focusing on the most typical interconnected river-lake system in China, the Dongting Lake, we collected paired water and sediment samples in summer, when algal biomass and growth rate are at high levels. Based on 23S rRNA gene sequencing, we investigated the heterogeneity and the differences in assembly mechanisms of planktonic and benthic algae in Dongting Lake. Planktonic algae contained more Cyanobacteria and Cryptophyta, while sediment harbored higher proportions of Bacillariophyta and Chlorophyta. For planktonic algae, stochastic dispersal dominated the assembly of the communities. Upstream rivers and confluences were important sources of planktonic algae in lakes. Meanwhile, for benthic algae, deterministic environmental filtering shaped the communities, and the proportion of benthic algae exploded with increasing N:P ratio and Cu concentration until reaching thresholds of 1.5 and 0.013 g/kg respectively, and then started falling, showing non-linear responses. This study revealed the variability of different aspects of algal communities in different habitats, traced the main sources of planktonic algae, and identified the thresholds for benthic algal shifts in response to environmental filters. Hence, upstream and downstream monitoring as well as thresholds of environmental factors should be considered in further aquatic ecological monitoring or regulatory programs of harmful algal blooms in these complex systems.

Review of Discrete Fourier Transform During Dynamic Phasor Estimation and the Design of Synchrophasor Units

In a large and complex interconnected power system, the measurement of synchronized bus voltage and line current plays a vital role in the monitoring and precise control of various sophisticated electrical equipment for secure and reliable operation. Phasor measurement units (PMUs) are incorporated into a wide area of the power system to extract the different signals of synchronized phasors. In this paper, the capacity of the PMU phasor estimation algorithm is explored based on discrete Fourier transform (DFT) under different sampling frequency rates during various dynamic scenarios in accordance with the IEEE C37.118.1a-2014 standard. Furthermore, the performance of the DFT algorithm varies according to the phase angle and dynamic parameters such as frequency, frequency ramp rates, modulation frequency, harmonic levels, step change, decaying dc, and noise levels. The simulation results reveal that accuracy of the phasor estimation algorithm based on DFT can be achieved at high sampling frequency rates. Furthermore, the results of DFT-based phasor estimation are compared with Shank’s estimation method (SEM) and the least-squares estimation method (LEM). The presented method is best suited to PMU algorithms development based on DFT for better visualization of the smart electric grid.

Functioning of the insurance market of Ukraine: new challenges and threats

The domestic insurance market can be described as a complex system of interconnections between insurance entities. Modern events give rise to new challenges and threats to the functioning and development of the insurance market of Ukraine. Therefore, despite the existence of a number of publications in the sphere of functioning of the insurance market, the study of this issue should be systematic, since the balanced operation of the insurance market has a significant impact on the entire economic system of the state as a whole. In the research process, the authors considered the interpretation of scientists and practitioners to determine the essence of the concept of «insurance market». The authors systematized the definition of scientists and formed scientific approaches. The insurance market category was also proposed. In order to understand the current state of the insurance market of Ukraine, statistics were analyzed, which best characterize the functioning of the market. The analysis showed that against the background of reducing the number of insurance companies, and fluctuations in the dynamics of gross insurance premiums and payments for the analyzed period, the insurers were able to increase assets and increase the volumes of the formed insurance reserves. Despite the crisis caused by the coronavirus pandemic, as well as the extreme conditions of martial law, the insurers were able to resist the challenges and continue to continue their activities. The level of insurance payments in the Ukrainian market is lower than the generally accepted world practice by more than 17%. This indicator indicates a low level of insurance culture in Ukraine. The structure of the insurance portfolio of the Ukrainian market is represented in general CICL, life insurance, continuous health insurance, accident insurance and property insurance. In recent years, the proportion of continuous health insurance has been constantly reduced due to low solvency as well as the emigration of the population abroad due to hostilities. One of the main indicators that illustrates the level of insurance market development, the level of insurance penetration is extremely low compared to countries such as the United States, Italy, France, the United Kingdom, and the Netherlands. Modern insurance products designed to protect against military risks are considered. The authors propose a few measures, the implementation of which will improve the functioning of the insurance market of Ukraine and increase its level of development.

Cybersecurity and Data Privacy Certification Gaps of Connected and Automated Vehicles

Protecting Connected and Automated Vehicles (CAVs) from cyber attacks and data breaches is a major challenge facing the deployment of driverless vehicles. The CAV is a complex interconnected system consisting of sensors, Artificial Intelligence (AI) processors and external units to assure the automated driving without human interaction. Such complexity increases the surface attacks and makes the CAV highly vulnerable to cyber assaults. It also entangles security audits and certifications procedures. Our work lays out a novel approach towards CAV's certification focused on cybersecurity and data privacy aspects. Stipulated by the analysis on existing standards’ limitations, we propose a Standards Coverage Map (SCM) outlining the CAV's entire ecosystem and linking organisational and technical aspects to the latest standards and regulations from the cyber perspective

Cascaded heat merit order for industrial energy systems to evaluate district heating potential

Industrial companies are undergoing a transformation to decrease energy costs and reduce emissions. The integration of renewables, sector coupling technologies, and industrial waste heat lead to complex interconnected industrial energy systems. As district heating systems play a decisive role for the integration of industrial waste heat in the building sector, barriers must be analyzed to overcome the gap between waste heat potential and waste heat use. Although data from production systems and their on-site energy supply are becoming available in the ongoing process of digitization, information deficits can be identified as one of the main barriers to couple industrial energy systems with district heating. We present a data-based methodology to evaluate the potential of industrial energy systems for connecting to district heating systems. Data from production systems, energy converters, thermal networks and necessary parameters of district heating systems are merged into a data model to determine a cascaded heat merit order and indicators for the energetic, economic and ecological potential. To set up the cascaded heat merit order, an algorithm for balancing complex industrial energy systems is integrated within the data model. In a case study, we apply the methodology to data of an industrial site. Besides increasing transparency through visualization of the cascaded heat merit order and corresponding indicators, the results show a base load potential of up to 0.8 MW over a year.

Insight into calcium signalling in salt stress response

• SOS pathway is a major pathway triggered by Ca 2+ under salt stress. • Silicon has a role in the improved functioning of the SOS pathway under salt stress. • Calcium signalling and ABA synthesis results in stomatal closure. • High cytosolic Ca 2+ results in upregulating ROS production. Owing to the sessile nature of plants, they are at the mercy of environmental conditions and are often subject to severe abiotic stresses. Amongst these abiotic stresses, high salinity stress is one of the most common and severe stresses affecting one-third of irrigated agricultural lands worldwide. Calcium ion is well known for its essential role as a mediator of signals and in regulating and specifying the cellular responses to abiotic stresses. To adapt to salt stress, one of the main strategies developed by plants is calcium signalling responses. The excess extracellular salt is detected by specific root receptors and resulted in immediate rise of cytosolic free Ca 2+ . Exhaustive research has been conducted to identify the genetic identities of Ca 2+ channels and signalling factors, developing novel findings on such tolerance mechanisms in plants. However, the complex interconnected system of the salinity stress response by Ca 2+ signalling in plants has not been adequately justified. Plants employ a combination of Ca 2+ integrated proteins, phytohormones, osmolytes, receptors and other signalling factors in response to salinity stress, making this a complex calcium response network. In this review, we summarise and discuss the role of calcium signalling in response to salinity stress. In contrast to other studies, we try to provide a holistic overview of both chemical and mechanical processes involved in salt stress response while expanding on gaps filled by recent research. Furthermore, based on existing knowledge, we evaluate the different types of mechanisms and pathways through which calcium signalling works.

Sparsistent filtering of comovement networks from high-dimensional data

Network filtering is a technique to isolate core subnetworks of large and complex interconnected systems, which has recently found many applications in financial, biological, physical and technological networks among others. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory of high-dimensional covariance matrix estimation, we show that the proposed filter can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity via thresholding, while having the least spectral distance from a consistent (non-)linear shrinkage estimator. We demonstrate the application of our proposed filter by applying it to covariance networks constructed from financial data, to extract core subnetworks embedded in full networks.

Social cost of carbon: A revisit from a systems analysis perspective

The social cost of carbon (SCC) is estimated by integrated assessment models (IAMs) and is widely used by government agencies to value climate policy impacts. Although there is an ongoing debate about obtained numerical estimates and related uncertainties, little attention has been paid so far to the SCC calculation method itself. This work attempts to fill the gap by providing the theoretical background and economic interpretation of the SCC calculation approach implemented in the DICE (Dynamic Integrated Climate-Economy) IAM. Our analysis indicates that the present calculation method is unable to reflect the linkages between two key IAM components—complex interconnected systems—climate and economy, both influenced by emission abatement policies. Within the modeling framework of DICE, the presently estimated SCC valuates emissions, which are beyond policy control, against consumption of products, which cannot be produced by the economy. This makes the SCC irrelevant for application in climate-economic policies and, therefore, calls for a replacement by a more appropriate indicator. An apparent SCC alternative, which can be considered for policy formulation, is the direct output of the DICE model, the socially optimal marginal abatement cost (SMAC), which corresponds to technological possibilities at the optimal level of carbon emissions abatement. In policymaking, because of the revealed SCC deficiency, great attention needs to be paid to the use of estimates obtained earlier.

An overview of the operation architectures and energy management system for multiple microgrid clusters

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges.

Colloquium: Multiscale modeling of brain network organization

A complete understanding of the brain requires an integrated description of the numerous scales of neural organization. It means studying the interplay of genes, synapses, and even whole brain regions which ultimately leads to different types of behavior, from perception to action, while asleep or awake. Yet, multiscale brain modeling is challenging, in part because of the difficulty to access simultaneously to information from multiple spatiotemporal scales. While some insights have been gained on the role of specific microcircuits (e.g., thalamocortical), a comprehensive characterization of how changes occurring at one scale can have an impact on other ones, remains poorly understood. Recent efforts to address this gap include the development of new analytical tools mostly adapted from network science and dynamical systems theory. These theoretical contributions provide a powerful framework to analyze and model interconnected complex systems exhibiting interactions within and between different scales, or layers. Here, we present recent advances for the characterization of the multiscale brain organization in terms of structure-function, oscillation frequencies and temporal evolution. Efforts are reviewed on the multilayer network properties underlying higher-order organization of neuronal assemblies, as well as on the identification of multimodal network-based biomarkers of brain pathologies, such as Alzheimer's disease. We conclude this Colloquium with a perspective discussion on how recent results from multilayer network theory, involving generative modeling, controllability and machine learning, could be adopted to address new questions in modern neuroscience.