Spatial Couplings Research Articles

Complex patterning in jerky flow from time series analysis and numerical simulation

The paper is a tribute to Ladislas P Kubin’s long-standing work on the collective behavior of dislocations in jerky flow. In a first part, it reviews his contributions to the statistical, dynamical and multifractal analyses carried out on stress-time series recorded from both single crystals and polycrystalline samples of dilute alloys subjected to tensile tests at constant strain rate. Various spatio-temporal dynamical regimes were found as the applied strain rate was varied. Type C static bands were associated with quasi-random collective behavior, the hopping type B and propagating type A bands could be shown to correspond to chaotic and self-organized critical dynamics, respectively. The crossover between the A and B regimes was characterized by a large spread in the multifractal spectrum of stress drops, associated with heterogeneity of the dynamics. In a second part, the paper reviews the nonlocal models Ladislas inspired to interpret these results from numerical solutions of the boundary value problem, on the basis of dynamic strain aging, the incompatibility stresses associated with dislocations, their plastic relaxation and the spatial couplings they inherently involve. Eventual developments of this research, rooted in the same ideas, on the statistical and multifractal analyses of the accompanying acoustic emission are reviewed and discussed in terms of the synchronization of small-scale plastic events.

Identifying baseflow source areas using remotely sensed and ground‐based hydrologic data

AbstractUnderstanding how rainfall and snowmelt influence baseflow, the groundwater‐fed component of streamflow, is essential for sound water resources management. Current approaches to understand the spatial couplings between these processes and baseflow are limited. The most commonly used methods include geochemical tracers and hydrologic models. A key limitation of the first is cost, while the second is limited by the need for simplifying assumptions. This study developed a data‐driven approach which leverages satellite Earth observation data and ground‐based data to assess the degree to which baseflow is influenced by upstream rainfall and snowmelt in California's Sierra Nevada. The procedure involved: (1) separation of baseflow from streamflow time series using a low‐pass filtering technique, (2) quantification of aquifer drainage timescales through baseflow recession analysis, (3) application of time series and information theory methods to identify the areas which have the greatest impacts on baseflow through both rainfall and snowmelt, and (4) characterization of the elevation zones which have a prevailing influence on baseflow. Results suggest that areas which have the strongest impact on baseflow through rainfall and snowmelt are not necessarily the areas which experience the highest annual rates of snowmelt or rainfall; snowmelt occurring in the 3000–3700 m elevation range was found to be the most important driver of baseflow.

Robust H∞ Pinning Synchronization for Multiweighted Coupled Reaction-Diffusion Neural Networks.

This article focuses on the robust H∞ synchronization of two types of coupled reaction-diffusion neural networks with multiple state and spatial diffusion couplings by utilizing pinning adaptive control strategies. First, based on the Lyapunov functional combined with inequality techniques, several sufficient conditions are formulated to ensure H∞ synchronization for these two networks with parameter uncertainties. Moreover, node-based pinning adaptive control strategies are devised to address the robust H∞ synchronization problem. In addition, some criteria of H∞ synchronization for these two networks under parameter uncertainties are developed via edge-based pinning adaptive controllers. Finally, two numerical examples are presented to verify our results.

Altered metabolic-functional coupling in the epileptogenic network could predict surgical outcomes of mesial temporal lobe epilepsy.

To investigate the relationship between glucose metabolism and functional activity in the epileptogenic network of patients with mesial temporal lobe epilepsy (MTLE) and to determine whether this relationship is associated with surgical outcomes. 18F-FDG PET and resting-state functional MRI (rs-fMRI) scans were performed on a hybrid PET/MR scanner in 38 MTLE patients with hippocampal sclerosis (MR-HS), 35 MR-negative patients and 34 healthy controls (HC). Glucose metabolism was measured using 18F-FDG PET standardized uptake value ratio (SUVR) relative to cerebellum; Functional activity was obtained by fractional amplitude of low-frequency fluctuation (fALFF). The betweenness centrality (BC) of metabolic covariance network and functional network were calculated using graph theoretical analysis. Differences in SUVR, fALFF, BC and the spatial voxel-wise SUVR-fALFF couplings of the epileptogenic network, consisting of default mode network (DMN) and thalamus, were evaluated by Mann-Whitney U test (using the false discovery rate [FDR] for multiple comparison correction). The top ten SUVR-fALFF couplings were selected by Fisher score to predict surgical outcomes using logistic regression model. The results showed decreased SUVR-fALFF coupling in the bilateral middle frontal gyrus (PFDR = 0.0230, PFDR = 0.0296) in MR-HS patients compared to healthy controls. Coupling in the ipsilateral hippocampus was marginally increased (PFDR = 0.0802) in MR-HS patients along with decreased BC of metabolic covariance network and functional network (PFDR = 0.0152; PFDR = 0.0429). With Fisher score ranking, the top ten SUVR-fALFF couplings in regions from DMN and thalamic subnuclei could predict surgical outcomes with the best performance being a combination of ten SUVR-fALFF couplings with an AUC of 0.914. These findings suggest that the altered neuroenergetic coupling in the epileptogenic network is associated with surgical outcomes of MTLE patients, which may provide insight into their pathogenesis and help with preoperative evaluation.

Distributed Graph-Based Optimization of Multicast Data Dissemination for Internet of Vehicles

The Internet of Vehicles (IoV) is a promising paradigm for autonomous driving, where the sensing data from the onboard sensors can be disseminated and processed cooperatively via vehicle-to-vehicle links. Autonomous vehicles can share their local views for cooperative, reliable, and robust driving decisions. However, the limited wireless resources may become the bottleneck with the increasing number of vehicles. The technical challenges also arise from the decentralized control, the spatial couplings of decisions, and the complexity of combinatorial optimization. This paper proposes a novel fully distributed graph-based approach to jointly optimize multicast link establishment with data dissemination and processing decisions by only exchanging partial information among neighboring vehicles. The mixed-integer programming problem aims to maximize system energy efficiency while achieving maximum data throughput. We prove that maximizing data processing throughput is submodular optimization to find the local optimum efficiently. The optimization of data dissemination and processing is reformulated to a minimum-cost maximum-flow problem in a three-layer graph, and efficiently solved by exploiting the graphical interdependence. Both simulation-generated and trace-based datasets are evaluated to validate the effectiveness of the proposed approach in terms of data throughput and energy efficiency.

Robust load restoration optimization of power system considering the Spatial-Temporal correlation of wind power

Wind power can be incorporated with load restoration of power systems, and the robust method is usually utilized to maintain feasibility considering the uncertainty of wind power. However, traditional robust load restoration always ignores the inherent time and spatial couplings of wind generation variability, which may increase the conservativeness of the solution. Therefore, this paper proposes a robust load restoration optimization considering the spatial and temporal correlation of wind power to reduce conservativeness. To eliminate unlikely-to-appear cases in time and space, the spatial–temporal correlation budget is introduced to construct a budget-constrained uncertainty set. Based on the constructed uncertainty set, the load restoration method is established as a two-stage robust model, which aims to maximize the restored load while guaranteeing the voltage and frequency security constraints. To make the proposed model computationally tractable, the binary expansion method is utilized to linearize the two-stage robust model with quadratic terms. In addition, due to inner-level binary variables in the subproblem, the extended column-and-constraint generation (EC&CG) algorithm is employed to iteratively solve the proposed model. Finally, the proposed robust method is tested on the modified IEEE 39-bus test system, and simulation results show that the proposed method can effectively reduce conservativeness while maintaining the feasibility of a robust solution.

Synchronization and adaptive control for coupled fractional-order reaction–diffusion neural networks with multiple couplings

In this paper, two kinds of coupled fractional-order reaction–diffusion neural networks (CFORDNNs) with multiple state couplings or spatial diffusion couplings are proposed. By resorting to the Laplace transform and the properties of Mittag-Leffler functions, sufficient synchronization conditions are derived for the concerned network models. In addition, to guarantee the synchronization of these two networks, several appropriate adaptive control schemes are also developed. Ultimately, the validity of the devised adaptive strategies are verified by adopting some numerical examples with simulation results.

Boundary Coupling for Consensus of Nonlinear Leaderless Stochastic Multi-Agent Systems Based on PDE-ODEs

This paper studies the leaderless consensus of the stochastic multi-agent systems based on partial differential equations–ordinary differential equations (PDE-ODEs). Compared with the traditional state coupling, the most significant difference between this paper is that the space state coupling is designed. Two boundary couplings are investigated in this article, respectively, collocated boundary measurement and distributed boundary measurement. Using the Lyapunov directed method, sufficient conditions for the stochastic multi-agent system to achieve consensus can be obtained. Finally, two simulation examples show the feasibility of the proposed spatial boundary couplings.

Spatio-Temporal-Spectral Hierarchical Graph Convolutional Network With Semisupervised Active Learning for Patient-Specific Seizure Prediction.

Graph theory analysis using electroencephalogram (EEG) signals is currently an advanced technique for seizure prediction. Recent deep learning approaches, which fail to fully explore both the characterizations in EEGs themselves and correlations among different electrodes simultaneously, generally neglect the spatial or temporal dependencies in an epileptic brain and, thus, produce suboptimal seizure prediction performance consequently. To tackle this issue, in this article, a patient-specific EEG seizure predictor is proposed by using a novel spatio-temporal-spectral hierarchical graph convolutional network with an active preictal interval learning scheme (STS-HGCN-AL). Specifically, since the epileptic activities in different brain regions may be of different frequencies, the proposed STS-HGCN-AL framework first infers a hierarchical graph to concurrently characterize an epileptic cortex under different rhythms, whose temporal dependencies and spatial couplings are extracted by a spectral-temporal convolutional neural network and a variant self-gating mechanism, respectively. Critical intrarhythm spatiotemporal properties are then captured and integrated jointly and further mapped to the final recognition results by using a hierarchical graph convolutional network. Particularly, since the preictal transition may be diverse from seconds to hours prior to a seizure onset among different patients, our STS-HGCN-AL scheme estimates an optimal preictal interval patient dependently via a semisupervised active learning strategy, which further enhances the robustness of the proposed patient-specific EEG seizure predictor. Competitive experimental results validate the efficacy of the proposed method in extracting critical preictal biomarkers, indicating its promising abilities in automatic seizure prediction.

Learning Multiaspect Traffic Couplings by Multirelational Graph Attention Networks for Traffic Prediction

Temporal traffic prediction is critical for ITS yet remains challenging in handling complex spatio-temporal dynamics of traffic systems. The continuous traffic data (e.g., traffic flow, and speed) from various channels and nodes in a traffic network are coupled with each other over the time points of each channel, spatially between traffic nodes, and jointly in both spatial and temporal dimensions. Such multi-aspect traffic data couplings reflect the conditions of a real-life traffic system and evolve over traffic movement and network dynamics. The recent studies formulate traffic prediction by high-profile graph neural networks. However, they mainly focus on hidden relations captured by neural graph mechanisms while overlooking or simplifying the above multi-aspect traffic data couplings. By modeling a traffic system as a coupled traffic network, we learn the multi-aspect traffic data couplings by a Multi-relational Synchronous Graph Attention Network (MS-GAT). Specifically, MS-GAT learns three embeddings to respectively but synchronously represent the traffic data-based channel, temporal, and spatial relations between nodes by specific graph attention designs. The embeddings are further adaptively coupled according to their respective importance to prediction. Tested on five real-world datasets, MS-GAT outperforms six SOTA graph networks-based traffic predictors. MS-GAT captures not only spatial and temporal couplings but also traffic data-based channel interactions over traffic evolution.

PD control for passivity of coupled reaction-diffusion neural networks with multiple state couplings or spatial diffusion couplings

This paper studies the passivity for coupled reaction-diffusion neural networks with multiple state couplings (CRDNNMSCs) or spatial diffusion couplings (CRDNNMSDCs) by employing the proportional-derivative (PD) control method. Firstly, a PD control strategy is presented for the sake of ensuring the passivity of CRDNNMSCs, and a synchronization condition is also given by utilizing the output-strict passivity of CRDNNMSCs. In addition, some corresponding conditions for guaranteeing the passivity and synchronization of CRDNNMSDCs are also developed in virtue of PD controller. Lastly, the correctness of the obtained passivity and synchronization criteria for the proposed models is verified by two numerical examples.

Distributed Multistage Optimization of Large-Scale Microgrids Under Stochasticity

Microgrids are recognized as a relevant tool to absorb decentralized renewable energies in the energy mix. However, the sequential handling of multiple stochastic productions and demands, and of storage, make their management a delicate issue. We add another layer of complexity by considering microgrids where different buildings stand at the nodes of a network and are connected by the arcs; some buildings host local production and storage capabilities, and can exchange with others their energy surplus. We formulate the problem as a multistage stochastic optimization problem, corresponding to the minimization of the expected temporal sum of operational costs, while satisfying the energy demand at each node, for all time. The resulting mathematical problem has a large-scale nature, exhibiting both spatial and temporal couplings. However, the problem displays a network structure that makes it amenable to a mix of spatial decomposition-coordination with temporal decomposition methods. We conduct numerical simulations on microgrids of different sizes and topologies, with up to 48 nodes and 64 state variables. Decomposition methods are faster and provide more efficient policies than a state-of-the-art Stochastic Dual Dynamic Programming algorithm. Moreover, they scale almost linearly with the state dimension, making them a promising tool to address more complex microgrid optimal management problems.

Failure of the tail rotor of the Mi-24 helicopter as a result of a long-term impact of spatial resonance coupling

Abstract The most important element that has an impact on the safety of flight includes the helicopter power plant and the transmission shaft assembly. Monitoring with the use of the traditional vibroacoustic techniques does not provide satisfactory results. It is especially difficult to detect spatial resonance couplings, which may result in dangerous and rapid breaking of elements. This article focuses on a specific mechanism for formation and deepening of resonance and effects of resonance couplings of two subassemblies. For their monitoring, the FAM-C method was applied. It uses an on-board generator as a sensor. This is a synchronous method, which enables one to detect the onset of spatial resonance.

Quantum-controlled cluster states

Quantum optical cluster states have been increasingly explored, in the light of their importance for measurement-based quantum computing. Here we set forth a new method for generating quantum controlled cluster states: pumping an optical parametric oscillator with spatially structured light. We show that state-of-the-art techniques for producing clusters in the spectral and temporal domains are improved by a structured light pump, which manipulates the spatial mode couplings in the parametric interaction. We illustrate the method considering a second-order pump structure, and show that a simple mode rotation yields different cluster states, including, but not limited to the two-dimensional square and hexagonal lattices. We also introduce a novel technique for generating non-uniform cluster states, and propose a simple setup for its implementation.

A self-exciting point process to study multicellular spatial signaling patterns

Multicellular organisms rely on spatial signaling among cells to drive their organization, development, and response to stimuli. Several models have been proposed to capture the behavior of spatial signaling in multicellular systems, but existing approaches fail to capture both the autonomous behavior of single cells and the interactions of a cell with its neighbors simultaneously. We propose a spatiotemporal model of dynamic cell signaling based on Hawkes processes-self-exciting point processes-that model the signaling processes within a cell and spatial couplings between cells. With this cellular point process (CPP), we capture both the single-cell pathway activation rate and the magnitude and duration of signaling between cells relative to their spatial location. Furthermore, our model captures tissues composed of heterogeneous cell types with different bursting rates and signaling behaviors across multiple signaling proteins. We apply our model to epithelial cell systems that exhibit a range of autonomous and spatial signaling behaviors basally and under pharmacological exposure. Our model identifies known drug-induced signaling deficits, characterizes signaling changes across a wound front, and generalizes to multichannel observations.

Computation of Solid-State Vibrational Circular Dichroism in the Periodic Gauge.

We introduce a new theoretical formalism to compute solid-state vibrational circular dichroism (VCD) spectra from molecular dynamics simulations. Having solved the origin-dependence problem of the periodic magnetic gauge, we present IR and VCD spectra of (1S,2S)-trans-1,2-cyclohexanediol obtained from first-principles molecular dynamics calculations and nuclear velocity perturbation theory, along with the experimental results. Because the structure model imposes periodic boundary conditions, the common origin of the rotational strength has hitherto been ill-defined and was approximated by means of averaging multiple origins. The new formalism reconnects the periodic model with the finite physical system and restores gauge freedom. It nevertheless fully accounts for nonlocal spatial couplings from the gauge transport term. We show that even for small simulation cells the rich nature of solid-state VCD spectra found in experiments can be reproduced to a very satisfactory level.

Price-region bids in electricity markets

Current bid formats in pool-based electricity markets are ill-equipped to accommodate the broad range of non-conventional sources of flexibility, such as demand response and interconnected heating, natural gas and water infrastructure networks. To address this issue, this paper introduces the novel price-region bid format to be used in both forward electricity markets and financial right auctions. We show that price-region bids are able to accommodate a broad range of techno-economic characteristics, including complex spatial and temporal couplings, and facilitate market access to non-conventional flexibility providers. We then show that this new bid format is compatible with existing market structures, and satisfies desirable market properties under common assumptions. Three numerical studies are provided: two motivating examples based on a district heating utility and a cascaded hydro power plant, and a case study based on an integrated power and heat system. These studies illustrate the inability of existing bid formats to accommodate flexible resources, and show how price-region bids overcome this shortcoming.

Distributed Control of Multizone HVAC Systems Considering Indoor Air Quality

This article studies a scalable control method for multizone heating, ventilation, and air-conditioning (HVAC) systems to optimize the energy cost for maintaining thermal comfort (TC) and indoor air quality (IAQ) (represented by CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) simultaneously. This problem is computationally challenging due to the complex system dynamics, various spatial and temporal couplings, as well as multiple control variables to be coordinated. To address the challenges, we propose a two-level distributed method (TLDM) with an upper level and lower level control integrated. The upper level computes zone mass flow rates for maintaining zone TC with minimal energy cost, and then, the lower level strategically regulates zone mass flow rates and the ventilation rate to achieve IAQ while preserving the near energy-saving performance of upper level. As both the upper and the lower level computation are deployed in a distributed manner, the proposed method is scalable and computationally efficient. The near-optimal performance of the method in energy cost saving is demonstrated through comparison with the centralized method. In addition, the comparisons with the existing distributed method show that our method can provide IAQ with only little increase of energy cost, while the latter fails. Moreover, we demonstrate that our method outperforms the demand-controlled ventilation (DCVs) strategies for IAQ management with about 8%–10% energy cost reduction. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners:</i> The high portion of building energy consumption has motivated the energy saving for heating, ventilation, and air-conditioning (HVAC) systems. Concurrently, the living standards for indoor environment are rising among the occupants. Nevertheless, the status quo on improving building energy efficiency has mostly focused on maintaining thermal comfort (such as temperature), and the indoor air quality (IAQ) (usually represented by CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> level) has been seldom incorporated. In our previous work with the similar setting, we observed that the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> levels will surge beyond tolerance during the high occupancy periods if only thermal comfort (TC) is considered for HVAC control. This deduces the IAQ and TC should be jointly considered while pursuing the energy cost saving target and thus studied in this article. This task is computationally cumbersome due to the complex system dynamics (thermal and CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) and tight correlations among the different control components (variable air volume and fresh air damper). To cope with these challenges, this work develops a two-level distributed computation paradigm for HVAC systems based on problem structures. Specifically, the upper level control (ULC) first calculates zone mass flow rates for maintaining comfortable zone temperature with minimal energy cost, and then, the lower level strategically regulates the computed zone mass flow rates as well as ventilation rate to satisfy IAQ while preserving the near energy-saving performance of the ULC. As both the upper and lower level calculations can be implemented in a distributed manner, the proposed method is scalable to large multizone deployment. The method’s performance both in maintaining comfort (i.e., TC and IAQ) and energy cost saving is demonstrated via simulations in comparisons with the centralized method, the distributed token-based scheduling strategy, and the demand-controlled ventilation strategies.

Synchronization for hybrid coupled reaction-diffusion neural networks with stochastic disturbances via spatial sampled-data control strategy

The synchronization of reaction-diffusion neural networks with state and spatial couplings is investigated in this article, and the time-varying delay and stochastic disturbances are considered in the proposed systems. Due to the development and merits of digital controllers, sampled-data control is a natural choice to establish synchronization in continuous-time systems. Here, we suggest a spatial sampled-data controller design, where the sampled-data (in space) measurements of the state are taken in a finite number of fixed sampling points in the spatial domain. It is assumed that the sampling intervals in space are bounded. Based on the Lyapunov stability theory, Young’s and Wirtinger’s inequalities techniques, some sufficient conditions are presented to synchronize the hybrid coupling reaction-diffusion neural networks with stochastic disturbances. Finally, the efficiency of the derived criteria will be demonstrated by resorting to two numerical examples.

Transient enhanced cell division by blocking DNA synthesis in Escherichia coli.

Duplication of the bacterial nucleoid is necessary for cell division hence specific arrest of DNA replication inhibits divisions culminating in filamentation, nucleoid dispersion and appearance of a-nucleated cells. It is demonstrated here that during the first 10 min however, Escherichia coli enhanced residual divisions: the proportion of constricted cells doubled (to 40%), nucleoids contracted and cells remodelled dimensions: length decreased and width increased. The preliminary data provides further support to the existence of temporal and spatial couplings between the nucleoid/replisome and the sacculus/divisome, and is consistent with the idea that bacillary bacteria modulate width during the division process exclusively.