Static and dynamic functional connectivity alterations in mice with LPS-induced depression: A 9.4T fMRI study using independent component and graph theory analyses.

Efficiently operating a single microgrid (MG) is increasingly challenging due to volatile electricity demand and intermittent renewable generation. Traditional static networks often fail to adapt to these fluctuations, compromising reliability. Incorporating these uncertainties into planning is essential for developing resilient optimization models that can withstand the stochastic nature of decentralized energy systems. This study proposes a dynamic reconfiguration strategy for interconnected microgrids that reroutes households based on real-time supply and demand. A stochastic nonlinear optimization model was developed to maximize load factors and flatten peaks while accounting for current-dependent power and distribution losses. The Sample Average Approximation (SAA) method was used to handle uncertainty, converting probabilistic variables into a robust deterministic equivalent that prioritizes electrical proximity during reconfiguration. The model was validated using a composite dataset spanning nearly two years of hourly load and renewable profiles. A total of 600 stochastic scenarios were considered and analyzed to represent an empirical distribution of real-world uncertainty while preserving key temporal correlations. Performance was tested under N-1 and N-2 contingency events, in which one or more microgrids are deactivated, to evaluate system resilience. Results indicate that while a single active MG improves the load factor, it also increases operational instability and objective function variance. Conversely, a three-MG configuration enhances system stability and predictability. Economically, the mesh architecture allows for temporary MG deactivation to reduce maintenance and fuel costs without compromising service. The proposed strategy achieves 100% resilience, ensuring uninterrupted service even under severe constraints.

Temporal dynamics in functional connectomes provide a physiologically grounded signature of ′hidden′ pathologies during preclinical stages of Alzheimer′s disease (AD). We evaluated the effect of beta-amyloid (Aβ) on dynamic functional connectomes in transgenic mice and human subjects. Functional magnetic resonance images (fMRI) were collected in two strains of Aβ mice. fMRI-derived connectomes were segmented into discrete states using a hidden Markov model, and network strength, efficiency, and transitivity were analyzed per state. Human fMRI-derived connectome measures were analyzed across 3 states. Static network measures were significantly different between Aβ mice and controls, the former having high values for strength, efficiency and clustering coefficient in anterior cingulate, hippocampus, and retrosplenium. Dynamic network measures were stable within-states in Aβ mice. Similarly, human subjects with high Aβ had high node strength in precuneus and temporoparietal areas compared to low Aβ. Conversely, high Aβ was associated with high switch rates, high fractional occupancy, and state dwell times. Also, global strength, efficiency, and transitivity were less stable within states in the high Aβ group. Our results indicate that static, but not dynamic, connectome strength, efficiency, and network integration are increased in Aβ mice, while dynamic network states appear less stable in human functional connectomes. This data supports a dissociable, species-specific impact of Aβ, with dynamic network alterations present in humans but not in Aβ mouse models, suggesting additional non-Aβ-driven influences on dynamic functional connectivity in preclinical AD.

Insomnia is common in major depressive disorder (MDD), and varying severity of insomnia may be associated with distinct neural alterations in MDD. Dynamic functional connectivity can capture time-varying brain network interactions and may help disentangle insomnia-related mechanisms in MDD. We recruited 203 drug-naïve adult MDD patients, divided into high- (HI-MDD, n=133) and low-insomnia (LI-MDD, n=70) groups using the Hamilton Depression Rating Scale insomnia subscale, along with 122 health controls (HCs). Independent component analysis and a sliding-window approach were applied to explore static and dynamic functional network connectivity (FNC). While static FNC revealed no significant group difference, dynamic analysis identified distinct connectivity states between two groups. Compared to HCs, HI-MDD displayed more frequent occurrence of state I and less of state IV, a pattern was absent in LI-MDD. Both MDD groups showed increased default mode network (DMN)-lateral ventral attention network (VAN) connectivity in states I and II, accompanied by decreased dorsal attention network-cerebellar/DMN connectivity in state I relative to HCs. Compared with LI-MDD, HI-MDD exhibited enhanced DMN-medial VAN connectivity in state II, along with increased DMN connectivity with visual and sensorimotor networks in state I, suggesting insomnia-related changes. In addition, we identified insomnia-related memory deficits and depression-related processing speed impairment in MDD. Insomnia significantly moderated the association between altered DMN-lateral VAN connectivity in state I and logical memory impairment in MDD. These findings suggest that insomnia severity in MDD is associated with distinct temporal patterns of brain network alterations beyond shared depression-related changes and moderate cognitive functions in MDD.

Repetitive Transcranial Magnetic Stimulation (rTMS) shows promise for treating Autism Spectrum Disorder (ASD), but its impact on the temporal dynamics of large-scale brain networks remains unclear. This study investigated the modulatory effects of rTMS on static and dynamic brain functional networks in children with ASD. Thirty-two children were randomized into an active rTMS group (1Hz over the dorsolateral prefrontal cortex) and a sham control group. Resting-state EEG and behavioral assessments were conducted before and after a 9-week intervention. We employed a multi-dimensional analysis approach, combining microstate temporal parameters, static functional connectivity based on the weighted Phase Lag Index (wPLI), and dynamic complexity measured by Fuzzy Entropy. Results indicated that intrinsic features of Microstate B were significantly correlated with social relating deficits. Although rTMS did not induce significant interaction effects in standard microstate temporal parameters, it significantly enhanced static functional connectivity strength and increased the dynamic complexity of brain networks across all microstates. These findings suggest that rTMS exerts its therapeutic effects by strengthening network integration and restoring neural flexibility rather than simply altering the duration of brain states. The study underscores the value of network-based EEG metrics in elucidating the neuroplastic changes induced by neuromodulation in ASD.

Understanding the evolution of cooperation in structured populations remains a central challenge in multidisciplinary areas. Although previous findings suggest that structural heterogeneity in static networks hinders cooperation, real-world interactions in most natural and social systems are dynamic and best represented as temporal networks. Here, we challenge this conventional wisdom and, by developing a systematic mathematical framework, we report that structural heterogeneity in temporal networks can instead promote collective cooperation. Importantly, we reveal that such advantages depend on an often-overlooked metric-fixation time-quantifying the time required for a single cooperator to drive the entire population to cooperation. Highly heterogeneous networks accelerate this process within each subnetwork, resulting in a quantitative enhancement of cooperation in temporal networks compared to their homogeneous counterparts. By validating our results on empirical datasets through theoretical analyses and simulations, we provide a consistent framework for analysing cooperative dynamics across static and temporal networked systems.

Abstract Converging evidence from studies on brain network “fingerprinting” and precision functional mapping suggest that brain networks are highly individualized in functionally meaningful ways. Concurrently with a growth in studies on this topic, there has been a rise in interest on dynamics (approximately second-to-second changes) in brain networks within scan sessions. While analyses of traditional static networks have increasingly grown towards emphasizing the importance of individual differences in brain network topology, studies of dynamic networks typically follow methodology that require brain states to be considered at a group level. Recent studies have begun to assess the individuality of recurring dynamic brain “states”. In this work, we extend this recent work by exploring the extent to which functional connectivity fingerprinting is feasible at single-frame temporal resolution. We estimate connectivity at individual volumes using phase coherence. We find that the identity of participants can be classified based on single volumes given sufficient database scan data and that having more highly parcellated atlases facilitates identification. Finally, we find that tasks can be identified more readily within subjects than between subjects. We conclude that participant identity may be an important driver of observed single-volume connectivity patterns. Further, the single-volume neural correlates of a task appear to be more consistent within subjects than between subjects. This highlights the importance of considering individual variability in studies of brain network dynamics.

This study divided the national land market into four submarkets—Seoul, Incheon-Gyeonggi, metropolitan cities, and local areas—from January 2005 to December 2025. Using the MST technique, the study compared and analyzed the network structure of the land market from both static and dynamic perspectives. The static network structure analysis revealed that the network structure of the four submarkets, unlike the stock market, exhibits a weaker degree of concentration. Based on the metropolitan land market network, Geumcheon-gu in Seoul was identified as the center of the network, while the three Gangnam districts in Seoul were located at the outermost edge. The metropolitan land market was further divided into seven submarkets. The dynamic network structure analysis revealed that the network structure of the submarkets constantly changes over time, but the pattern of change varies by region. Post-hoc analysis using the KW test and Dunn's test to determine whether there were differences in average hyper-distances revealed that Seoul exhibited statistically significant differences from all other regions. And across all four submarkets, the average initial distance has dramatically shortened since December 2008 and December 2022. This agglomeration of average hyperdistances suggests that the Black Monday effect in the stock market is also present in the land market, and that strong correlations between assets during market crashes lead to herding behavior.

Abstract The division of road network into sub-networks prior to the simulation (i.e., static division) is a common part of distributed or parallel road traffic simulation. Its quality has significant impact of the speed of the entire simulation, but the mutual comparison of the division methods is difficult, as their descriptions contain inconsistent tests. Hence, in this paper, a benchmark for static road network division methods is presented. It is based on performing divisions of road networks with various sizes and observing multiple parameters of the sub-networks and their computation time. These values are summarized into scores utilizable for direct comparison of the division methods. The description of a publicly available tool enabling actual performance of the benchmark—ROad Network DIvision BEnchmark Tool or RONDIBET—is also provided. The tool was also used to demonstrate the functioning of the benchmark in a case study on three different division methods.

INTRODUCTION: The increasing complexity of modern power grids, driven by the integration of distributed energy resources and dynamic operating conditions, presents significant challenges for stability assessment. Traditional stability analysis methods often fail to capture topological dependencies and nonlinear interactions among grid components, resulting in unreliable predictions. Furthermore, existing approaches such as static models and graph convolution networks lack effective node-level importance weighting, limiting their ability to distinguish between stable and unstable states. OBJECTIVES: This study aims to develop an advanced framework for power grid stability classification by integrating digital twin technology with Graph Attention Networks (GAT). The objective is to improve the modeling of inter-node relationships and enhance classification accuracy under complex grid conditions. METHODS: A digital twin-inspired graph model of the power grid is constructed, where nodes represent grid components and edges represent their interactions. A Graph Attention Network is employed to learn weighted inter-node dependencies using attention mechanisms, enabling effective differentiation between stable and unstable operating modes. The proposed framework is evaluated in an offline, simulation-based environment using the Smart Grid Stability dataset. RESULTS: Experimental results demonstrate the effectiveness of the proposed approach, achieving an accuracy of 0.9640, precision of 0.9411, recall of 0.9607, F1-score of 0.9508, and ROC-AUC of 0.9958. Comparative analysis indicates that the proposed model outperforms conventional methods, including Artificial Neural Networks (ANN), Deep Neural Networks (DNN), and Random Forest, in overall classification performance. CONCLUSION: The proposed digital twin-inspired GAT framework provides accurate and reliable offline stability classification, significantly improving upon existing methods. However, challenges related to scalability for larger grid systems and real-time cyber–physical synchronization remain, highlighting important directions for future research.

Large Language Models (LLMs) have shown immense human-like capabilities for reasoning and generating digital content. However, their ability to freely sense, interact, and actuate the physical domain remains significantly limited due to three fundamental challenges: (1) physical environments require specialized sensors for different tasks, yet deploying dedicated sensors for each application is impractical; (2) events and objects of interest are often localized to small areas within large spaces, making them difficult to detect with static sensor networks; and (3) foundation models need flexible actuation capabilities to meaningfully interact with the physical world. To bridge this gap, we introduce EmbodiedFly, an embodied LLM agent combining a foundation model pipeline with a reconfigurable drone platform to observe, understand, and interact with the physical world. Our co-design approach features (1) a FM orchestration framework connecting multiple LLMs, VLMs, and an open-set object detection model; (2) a novel image segmentation technique that identifies task-relevant areas; and (3) a custom drone platform that autonomously reconfigures with appropriate sensors and actuators based on commands from the FM orchestration framework. Through real-world deployments, we demonstrate that EmbodiedFly completes diverse physical tasks with up to \(85\%\) higher success rates compared to traditional approaches leveraging static deployments.

In current online economic dispatch problems of power systems, the cost function of each generation unit typically only includes time-varying generation costs, which does not account for the costs associated with current carbon emission market transactions and high-frequency communications. These factors need to be considered. Additionally, considering the lag in obtaining information about cost coefficients, feedback delay should be considered as a factor for the online optimization process of economic dispatch. This research addresses the challenge of online economic dispatch in the presence of delayed feedback and carbon emission expenses, proposing a decentralized, event-initiated algorithm for online optimization. To address these challenges, this paper proposes a decentralized, event-triggered online optimization algorithm tailored for Agentic AI in smart energy systems operating over an edge–cloud continuum. In each local optimization iteration, each node in the power system can access its local objective function with a delayed time sequence and updates its local decision-making behavior online based on information from multiple neighboring nodes, controlled by an event-triggering mechanism, to minimize the global cumulative generation cost and carbon emission cost. The research results show that, under the assumption that the time-varying balanced undirected communication topology remains connected, the designed online optimization algorithm ensures that the upper bound of static network regret grows sub-linearly, fundamentally related to feedback delays and event-triggering thresholds and scaled as O ( T ) . This work provides a foundational paradigm for Agentic AI in smart energy systems, bridging distributed optimization theory with practical constraints of modern edge-cloud infrastructures.

Advancing environmental monitoring through coordinated autonomous systems is central to sustainable smart region governance and data-driven territorial management. The article presents an engineering-oriented architecture and deployment methodology for an integrated wildfire monitoring and response system that combines unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), and stationary sensor networks (SNs). We formalise hub-and-spoke infrastructure placement as a mixed-integer optimisation problem that accounts for platform types, endurance, travel times and logistical constraints, and propose a practical pre-processing pipeline (confidence scoring, resampling, Kalman/median filtering, strategy fusion) for heterogeneous telemetry and imagery. The system couples multimodal neural network processing (image backbones, clustering and time-series models) with online resource-allocation and mission-planning mechanisms to prioritise UAV/UGV sorties and dynamically select launch sites. The article describes scenario-driven operational modes (early warning, alarm verification, autonomous local extinguishing, post-fire recovery, sensor-gap compensation, and inter-hub reinforcement), defines validation protocols (synthetic experiments, precision/recall/F1, and hardware-in-the-loop testing), and proposes KPIs to assess environmental, social, and economic impacts for smart regions. The contribution is a reproducible, deployment-focused blueprint that bridges conceptual UAV–UGV–SN research and practical implementation, highlighting trade-offs in reliability, communication redundancy, and sustainability, and outlining directions for simulation, field pilots and algorithmic refinement.

Vagus nerve stimulation (VNS) is the most common neuromodulation technique used to treat drug-resistant epilepsy (DRE) in children. Despite this, approximately half of those implanted do not realize a benefit and there are currently no means to preoperatively identify responders. Recent neuroimaging work has suggested that intrinsic differences in brain connectivity may explain some heterogeneity in VNS responsiveness. In the current work, we sought to study whether preimplantation functional network perturbations in relation to interictal epileptiform discharges (IEDs) are associated with VNS response in children with focal DRE. We retrospectively studied resting-state magnetoencephalography in children with focal DRE (n = 65), recorded before VNS implantation. Beamforming was used to reconstruct source-level estimations of neural activity within a parcellation of 52 cortical brain regions. Static functional connectivity was estimated using amplitude envelope correlation, followed by general linear modeling and network-based statistics to identify spatial networks associated with VNS response (>50% seizure reduction at 6 months). Perturbations in brain connectivity were estimated by inferring dynamic cortical microstates from amplitude envelopes and extracting event-related microstate probability time courses surrounding IEDs. Differences in microstate dynamics after IEDs were assessed using t tests at each time point, comparing responders and nonresponders, followed by temporal cluster-based correction for multiple comparisons. A total of 44 children were included in the final analysis (mean age 15 years, 57% male, 52% responders). No clinical variables, including IED topographies, were associated with VNS response. Significant static networks were identified in alpha-band connectivity relating to both VNS response (anterior-dominant, t = 4.52) and nonresponse (posterior-dominant, t = -4.98). From the dynamic microstate analysis, one microstate related to a frontotemporal network showed significantly greater perturbation in nonresponders compared with responders (temporal cluster p < 0.05), in the 500 milliseconds after IEDs. Our results provide evidence that connectivity of an intrinsic, anterior-dominant network is associated with response to VNS. Responders to VNS are characterized by stronger baseline connectivity of this network and greater resilience of this network to IED-related disruption.

Enterprise web systems support many organizational functions, including digital transactions, cloud services, data storage, and enterprise software operations. As these systems operate across distributed infrastructures, traditional security models based on static authentication and network boundaries face significant limitations. This study proposes an identity-centric security model that integrates identity authentication, identity profiling, behavioral monitoring, risk evaluation, and policy-based access control within a unified framework. The model evaluates identity activity continuously during active sessions instead of relying only on initial login verification. Identity profiles contain contextual information derived from authentication attributes, device information, location data, and historical usage patterns. Behavioral monitoring observes session activity and identifies deviations from established patterns. A risk evaluation mechanism combines authentication irregularities and behavioral deviations to calculate identity risk scores. These scores guide policy-based access decisions within enterprise applications. Experimental analysis using simulated enterprise session data indicates improved anomaly detection capability, faster response to suspicious activity, and higher accuracy in access decisions compared with traditional role-based access control systems. Continuous monitoring and adaptive policy evaluation allow enterprise platforms to react to changing identity conditions during system interaction. The findings indicate that identity-centric security frameworks provide a context-aware approach for protecting enterprise web systems.

Accurately quantifying carbon dioxide (CO2) emissions from heavy-duty diesel trucks (HDTs) is crucial for developing effective transportation emission reduction strategies. In this study, we adopted a bottom–up approach and, in conjunction with the “International Vehicle Emissions” (IVE) model, constructed a high-resolution 1 × 1 km CO2 emission inventory for the urban area of Kunming, China. Using data from 1.24 million track points collected from 5996 heavy-duty diesel trucks, we implemented a map matching algorithm based on a simplified hidden Markov model (HMM) to efficiently process large-scale GPS data. Furthermore, we improved upon traditional spatial allocation methods by dynamically integrating track point density with static road network density. The results indicate that although higher driving speeds correspond to lower CO2 emission rates, heavy-duty diesel trucks typically operate within an observed speed range of 40–60 km/h, with an average emission factor of approximately 500 g/km. Vehicles compliant with the “National III” emission standards remain the primary source of CO2 emissions in this region. Correlation analysis reveals a significant positive relationship (p &lt; 0.01) between emissions from heavy-duty diesel trucks and both traffic volume and mileage. Notably, daytime vehicle restriction policies led to a temporal redistribution of emissions rather than a net reduction in emissions; this resulted in increased activity levels of heavy-duty diesel trucks at night, leading to a surge in nighttime emissions. In terms of spatial distribution, the “dual-density” allocation method proposed in this study more accurately captured emission hotspots, revealing that CO2 emissions are primarily concentrated in the southeastern part of the city—a distribution pattern largely influenced by the city’s industrial layout.

Hydrated imidazolium hemimelitate with helical hydrogen bonding network is the first amphidynamic organic crystal observed in a group of imidazolium and carboxylic acid compounds. The sublattice of acid ions forms a static network, while the dynamic part comprises imidazole ions and water molecules. A transition from positional to orientational disorder of water molecules is observed as the temperature closes to room temperature and the spatial arrangement of cations leads to an order-disorder phase transition at a temperature of 150 K, which we analyzed in a wide spectral range using THz, FIR, MIR, and Raman spectroscopies. Furthermore, DFT calculations were employed to understand the molecular dynamics and the phase transition mechanism of the studied compound. The temperature-dependent spectra also revealed proton-phonon coupling to occur below 100 K. Our findings provide valuable information, such as temperature behavior of hydrogen bonds, anharmonicity, and coupling effects for the targeted design of amphidynamic materials.

Cerebellar transcranial direct current stimulation (tDCS) has emerged as a promising adjunct therapy for motor recovery after stroke. This study aimed to investigate whether cerebellar tDCS can modulate the topologic properties of static and dynamic functional networks in patients with ischemic stroke. In this randomized controlled trial, 26 patients with stroke were allocated to receive either active or sham cerebellar tDCS alongside conventional physical therapy for three weeks. Resting-state functional magnetic resonance imaging and clinical assessments using the Fugl-Meyer Assessment (FMA) and the Barthel Index (BI) were conducted before and after the intervention. Both static and dynamic functional networks were constructed, and graph theory was used to quantify global and nodal topologic properties. The active tDCS group showed significantly greater improvement in FMA and BI scores compared with the sham group. Although static functional network analysis revealed no significant changes in global or nodal metrics after the intervention, dynamic network analysis showed a significant decrease in the temporal variability of several global and nodal metrics. Notably, a significant time-by-group interaction was observed for the variability of local efficiency, which decreased significantly only in the active tDCS group. This reduction in local efficiency variability correlated with improvements in BI scores. Cerebellar tDCS enhanced motor recovery and activities of daily living in patients with stroke and stabilized dynamic brain network configuration without altering static network properties. The correlation between reduced temporal variability and functional improvement suggests that stabilizing the dynamic functional configurations represents a key mechanism through which cerebellar tDCS promotes neuroplasticity and functional recovery after stroke.

Multidimensional reorganization of static and dynamic brain network architecture following rTMS in young children with autism spectrum disorder and co-occurring intellectual disability.

Rate-dependent Plasticity and Fracture of Five DP-Steels: Static and Dynamic High-throughput Experiments and Neural Network Modeling