Changes In Network Configuration Research Articles

Multi-hierarchy Network Configuration Can Predict Brain States and Performance.

The brain is a hierarchical modular organization that varies across functional states. Network configuration can better reveal network organization patterns. However, the multi-hierarchy network configuration remains unknown. Here, we proposed an eigenmodal decomposition approach to detect modules at multi-hierarchy, which can identify higher-layer potential submodules, and is consistent with the brain hierarchical structure. We defined three metrics: node configuration matrix, combinability, and separability. Node configuration matrix represents network configuration changes between layers. Separability reflects network configuration from global to local, whereas combinability shows network configuration from local to global. First, we created a random network to verify the feasibility of the method. Results show that separability of real networks is larger than that of random networks, whereas combinability is smaller than random networks. Then, we analyzed a large data set incorporating fMRI data from resting and seven distinct tasking conditions. Experiment results demonstrates the high similarity in node configuration matrices for different task conditions, whereas the tasking states have less separability and greater combinability between modules compared with the resting state. Furthermore, the ability of brain network configuration can predict brain states and cognition performance. Crucially, derived from tasks are highlighted with greater power than resting, showing that task-induced attributes have a greater ability to reveal individual differences. Together, our study provides novel perspectives for analyzing the organization structure of complex brain networks at multi-hierarchy, gives new insights to further unravel the working mechanisms of the brain, and adds new evidence for tasking states to better characterize and predict behavioral traits.

ВЛИЯНИЕ ТРАНСПОРТНОЙ РЕФОРМЫ НА ВЫБОР СПОСОБА ПЕРЕМЕЩЕНИЯ ПО ГОРОДУ (НА ПРИМЕРЕ САНКТ-ПЕТЕРБУРГА)

More and more cities in Russia and around the world are following the path of reforming public transportation (PT) system to solve the problem of traffic congestion. Transport reforms imply significant changes in the transportation system, e. g., a change in the economic model of PT organization, changes in the route network configuration, and integration of various PT modes. Significant changes have taken place in the transport system of St. Petersburg under the sign of a transportation reform in 2022. The aim of this study is to analyze changes in the transport behavior of the St. Petersburg residents after the implementation of the transportation reform and compare them with the target indicators. The data for this study were collected in two stages: in March 2022, before the start of changes in the city’s transport system, and in April 2023, a year after the PT reform launch. Based on their analysis and benchmarking, possible further directions in the development of the St. Petersburg system are discussed.

Multiagent System-Based Adaptive Numerical Relay Design and Development: Part I - Firmware

Protection relays that incorporate advanced microprocessors are vital to electrical grids, providing fast and reliable responses to faulty conditions using efficient communication protocols. Instant detection and response to various faults are essential to minimise the risk of damage. Numerical relays can identify faulty conditions and trigger circuit breakers to open, thus preventing further damage to the system. Due to the lack of autonomous decision-making capabilities, existing numerical relays require manual reconfiguration in situations such as a change in network configuration and protection settings. These relays also do not have the ability to coordinate fault clearance when multiple sources supply power to the grid. A comprehensive overview of the research aimed at developing a multiagent system (MAS)-based adaptive protection relay will be provided by dividing it into separate articles such as firmware and hardware. This first part delves into the firmware aspects of this innovative relay, highlighting its adaptive capabilities and key considerations in its development. The second part will provide detailed design descriptions for the hardware features of the relay. An STM32MPU multicore advanced microprocessor is utilised to design and develop the adaptive numerical relay firmware. It incorporates several protection relay ANSI codes, communication protocols, and MAS-based adaptive protection schemes as part of the firmware.

Enhanced Combined Protection Scheme Using Dual Setting Directional Overcurrent and Distance Relays Considering Severe Line Contingencies

An electrical power system must have an optimal protection system for enhanced reliability. Numerous studies have attempted to address issues with protection schemes, such as coordination constraint violations due to change in network configuration. Furthermore, in a few articles, the N-1 line contingency is considered without considering the line contingency selection criterion, resulting in a large number of constraints to deal with in the optimization problem. This paper proposes a new relay coordination scheme for dual setting directional overcurrent relay (DOCR) and distance relay based on the severity index of line outages, which is calculated by using an active and reactive performance index. The proposed protection strategy is improved with the help of an intelligent selection of the relay characteristics coefficient for dual setting DOCR along with the other relay settings. Another significant contribution is to provide a comparative analysis of standard and user-defined relay characteristics using genetic algorithm and grey wolf optimization algorithms. The modified IEEE-14 bus test system and IEEE-39 bus test system is used to validate the efficacy of the proposed protection scheme.

Hybrid market offering in the medical technology sector and the role of network configuration: an exploratory assessment in both developed and emerging markets

PurposeApplying both the dynamic capability and configuration theoretical perspectives, the paper showcases the role of network configuration and dynamics of hybrid offerings in both developed and emerging markets by high-tech firms.Design/methodology/approachThe current paper uses an exploratory qualitative research methodology based on in-depth case studies of three Finnish high-tech firms operating in the medical technology industry globally.FindingsThe findings from the study showed that dynamic capabilities such as sensing and customer engagement along with internal coordination and adaptation capabilities are critical to the success of hybrid market offerings. Moreover, dynamic capabilities were found to be influential in those emerging and advanced international markets where case firms were less familiar with market dynamics. Moreover, the configuration of these capabilities within functional units and coordination of marketing and R&D activities can be effective for creating hybrid offerings in international markets. Ultimately, this was found to be the case even though target market selection for hybrid offerings was influenced by the level of convergence and fragmentation of the market.Originality/valueApplying the configuration theory, this is one of the first studies to specifically analyze the differences in organizational network configuration changes in relation to hybrid market offerings in both developed economies and emerging economies. The findings contribute to hybrid market offering literature by pointing out that not only internal capabilities are important for enacting hybrid offerings, but the roles of ecosystems and knowledge centers are also extremely important to develop hybrid offerings. This paper also highlights the criticality of under-studied dynamic capabilities such as market sensing and customer engagement in the context of hybrid offerings in international markets. This showcases the wider role of ecosystems in enabling technology firms to develop hybrid offerings.

Fast Verification of Network Configuration Updates

With the expansion of network services, large-scale networks have progressively become common. The network status changes rapidly in response to customer needs and configuration changes, so network configuration changes are also very frequent. However, no matter what changes, the network must ensure the correct conditions, such as isolating tenants from each other or guaranteeing essential services. Once changes occur, it is necessary to verify the after-changed network. Whereas, for the verification of large-scale network configuration changes, many current verifiers show poor efficiency. In order to solve the problem of multiple global verifications caused by frequent updates of local configurations in large networks, we present a fast configuration updates verification tool, FastCUV, for distributed control planes. FastCUV aims to enhance the efficiency of distributed control plane verification for medium and large networks while ensuring correctness. This paper presents a method to determine the network range affected by the configuration change. We present a flow model and graph structure to facilitate the design of verification algorithms and speed up verification. Our scheme verifies the network area affected by obtaining the change of the Forwarding Information Base (FIB) before and after. FastCUV supports rich network attributes, meanwhile, has high efficiency and correctness performance. After experimental verification and result analysis, our method outperforms the state-of-the-art method to a certain extent.

Cooperative Learning for Adversarial Multi-Armed Bandit on Open Multi-Agent Systems

This paper considers a cooperative decision-making method for an adversarial bandit problem on open multi-agent systems. In an open multi-agent system, the network configuration changes dynamically as agents freely enter and leave the network. We propose a distributed Exp3 policy in which a group of agents exchanges the estimation of the expected reward of each arm with active neighboring agents. Then, each agent updates the probability distribution of choosing arms by combining the estimated rewards of neighboring agents. We derive a sufficient condition for a sublinear bound of a pseudo regret. The numerical example shows that active agents can cooperatively find the optimal arm by the proposed Exp3 policy algorithm.

Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks.

In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection. To investigate the interaction effect between nigral iron deposition and PD status on brain networks. Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis. The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score. Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.

Network alterations in eating epilepsy during resting state and during eating using Magnetoencephalography

ObjectiveEating epilepsy presents various imaging and electrophysiological features along with various seizure triggers. As such, network changes in eating epilepsy have not been comprehensively explored. This study was conducted to illustrate resting state network changes in eating epilepsy and to study the changes in network configurations during eating. MethodsMagnetoencephalography recordings of nineteen patients with drug-resistant eating epilepsy were compared with healthy controls during resting state. A subgroup of nine patients and 12 controls had MEG recordings during eating. Network changes were analyzed using phase lag index across 5 frequency bands [delta, theta, alpha, beta, and gamma] using clustering coefficient (CC), betweenness centrality (BC), path length (PL), modularity (Q), and small worldness (SW). ResultsDuring the resting state, PL was decreased in patients with epilepsy in the delta, theta, and gamma band. Q was lower in patients with epilepsy in the beta and gamma bands. During eating, in patients with epilepsy, PL and SW were increased in all frequency bands, and Q was decreased in the beta band and increased in the rest of the frequency bands. Patients with mixed types of seizures showed higher PL in all bands except alpha, higher Q in all bands, and higher SW in the alpha and beta bands. Node-wise changes in CC and BC implicated changes in DMN and ‘eating’ networks. ConclusionReflex Eating epilepsy presents with a hyperconnected network that exacerbates during eating. The cause of seizure onset and loss of consciousness in eating epilepsy might be due to aberrant network interaction between the regions of the brain involved with eating, such as the sensorimotor cortex, lateral parietal cortex, and insula with the limbic cortex and default mode network across multiple frequency bands.

Eco-hydrological modelling of channel network dynamics-part 1: stochastic simulation of active stream expansion and retraction.

Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, mechanistically describing these processes at the relevant spatiotemporal scales without huge computational burdens remains challenging. Here, we present a novel stochastic framework for the effective simulation of channel network dynamics capitalizing on the concept of 'hierarchical structuring of temporary streams'-a general principle to identify the activation/deactivation order of network nodes. The framework allows the long-term description of event-based changes of the river network configuration starting from widely available climatic data (mainly rainfall and evapotranspiration). Our results indicate that climate strongly controls temporal variations of the active length, influencing not only the preferential configuration of the active channels but also the speed of network retraction during drying. Moreover, we observed that-while the statistics of wet length are mainly dictated by the underlying climatic conditions-the spatial patterns of active reaches and the size of the largest connected patch of the network are strongly controlled by the spatial correlation of local persistency. The proposed framework provides a robust mathematical set-up for analysing the multi-faceted ecological legacies of channel network dynamics, as discussed in a companion paper.

New insights into the Rhône–Simplon fault system (Swiss Alps) from a consistent earthquake catalogue covering 35 yr

SUMMARYSeismotectonic interpretations in regions characterized by low to moderate seismicity require consistent earthquake catalogues covering periods of several decades. Inevitable changes in network configuration and analysing procedures, however, introduce significant bias to the hypocentre parameters and uncertainty estimates reported in such catalogues. To overcome these limitations, we developed a procedure using coupled hypocentre-velocity inversions to compute consistent hypocentre locations covering time periods of several decades while accounting for changes in network geometry. We apply these procedures to 35 yr of instrumentally recorded seismicity along the Rhône–Simplon fault system in southwest Switzerland, which is at the transition between the Central and Western Alps. The entire catalogue is relocated using a probabilistic location algorithm in combination with the derived minimum 1-D velocity models. A combination of location parameters is used to define consistent location-quality classes allowing for reliable interpretation of epicentres and focal depths. The relocated catalogue is interpreted together with a recent 3-D P-wave tomographic model and available 2-D reflection seismic profiles. The relocated hypocentres indicate that the major band of seismicity north of the Rhône valley is associated with a 30–40 km long, steeply north-dipping shear zone, which roots in the crystalline basement of the Aar Massif and extends to the shallowest levels of the sedimentary cover of the Helvetic nappes in the Rawil Depression. Seismicity towards the southwest indicates the existence of a similar shear zone within the Aiguille Rouge Massif. This zone possibly extends to the northeast and joins the Rawil fault zone. To the south of the Rhône valley, seismicity is scattered within the Penninic nappes, but limited to the hanging wall of the Pennine Basal Thrust (PBT). The Penninic nappes are characterized by a relatively higher VP of about 5 per cent compared to the Aar Massif, indicating differences in composition or metamorphic grade across the PBT.

Modelling STEMI service delivery: a proof of concept study

BackgroundAccess to individual percutaneous coronary intervention (PCI) centres has traditionally been determined by historical referral patterns along arbitrarily defined geographic boundaries. We set out to produce predictive models of ST-elevation...

A Subspace Identification Technique for Real-Time Stability Assessment of Droop Based Microgrids

Real-time detection of the microgrid stability is crucial for determining and adjusting the power-sharing droop control's gains to maintain a sufficient stability margin. Maintaining minimum relative stability should be ensured at different operating conditions to accommodate sudden system changes. This article develops a novel subspace-based identification technique to assess microgrid stability in real-time without relying on offline analytical small or large-signal models. Unlike conventional system identification techniques that require the introduction of external excitation (signal probing, load switching, or contingency event), the proposed method employs a simple internal routine through small and short-duration perturbations in the active power droop gain of inverter-based distributed generation (IBDG). The use of subspace identification does not require pre-defining the system's order, avoids the exhaustive computations associated with iterative identification methods and can handle a change of microgrid's network configuration. The proposed stability assessment method has been tested on an IBDG microgrid considering different operating conditions in MATLAB/Simulink. The accuracy of the proposed real-time stability assessment tool is determined by comparing the results to the analytically-derived small-signal model as well as the Matrix pencil and Prony methods.

Analysis of Akangba Transmission substation protection system

The challenges of the Nigeria power system have deprived the yearning electricity consumer access to stable power supply. Apart from the incessant load shedding due to inadequate power generation capacities, poor power system protection design, lack of constant protection system review after several changes to the power network is one of the contributing factors for unstable power system and unplanned outages. The network engineers must devise method of reviewing the protection settings as changes are made to power system components. This motivates the adoption of power system symmetrical fault analysis model presented herein. The model was applied to Akangba Transmission substation using the network planning software (NEPLAN software). The results show the fault current flowing through every part of the network. The effect of network expansion, switching Scenarios /Component changes on the fault current and protection scheme was also verified. Protection engineers must therefore take note of changes in the power network configuration when planning protection coordination and relay setting. The method is interesting and can be used as a template for reviewing protection scheme when changes are made on the power network. This will ensure better power protection management, enhance stable power supply and reduce damage to expensive power components.

The Task Pre-Configuration Is Associated With Cognitive Performance Evidence From the Brain Synchrony.

Although many resting state and task state characteristics have been studied, it is still unclear how the brain network switches from the resting state during tasks. The current theory shows that the brain is a complex dynamic system and synchrony is defined to measure brain activity. The study compared the changes of synchrony between the resting state and different task states in healthy young participants (N = 954). It also examined the ability to switch from the resting state to the task-general architecture of synchrony. We found that the synchrony increased significantly during the tasks. And the results showed that the brain has a task-general architecture of synchrony during different tasks. The main feature of task-based reasoning is that the increase in synchrony of high-order cognitive networks is significant, while the increase in synchrony of sensorimotor networks is relatively low. In addition, the high synchrony of high-order cognitive networks in the resting state can promote task switching effectively and the pre-configured participants have better cognitive performance, which shows that spontaneous brain activity and cognitive ability are closely related. These results revealed changes in the brain network configuration for switching between the resting state and task state, highlighting the consistent changes in the brain network between different tasks. Also, there was an important relationship between the switching ability and the cognitive performance.

Conditional Generative Adversarial Networks for Dynamic Control-Parameter Selection in Power Systems

This paper describes the novel application of conditional Generative Adversarial Networks (cGANs) for real-time stability region determination (SRD) in power systems. As the network configuration changes during the course of operation, the availability of the stability region would enable the operator to suitably tune the control parameters to maintain stability while maximizing the dynamic performance. Here, the implementation of the cGANs-based SRD is described using transmission and microgrid case studies, where it is demonstrated to adaptively estimate the stability region for different network configurations with high accuracy. It is also shown that the cGANs approach has a significantly shorter execution time as compared to the conventional model-based method, demonstrating its value for real-time use in practical power systems.

Atmospheric observations consistent with reported decline in the UK's methane emissions (2013–2020)

Abstract. Atmospheric measurements can be used as a tool to evaluate national greenhouse gas inventories through inverse modelling. Using 8 years of continuous methane (CH4) concentration data, this work assesses the United Kingdom's (UK) CH4 emissions over the period 2013–2020. Using two different inversion methods, we find mean emissions of 2.10 ± 0.09 and 2.12 ± 0.26 Tg yr−1 between 2013 and 2020, an overall trend of −0.05 ± 0.01 and −0.06 ± 0.04 Tg yr−2 and a 2 %–3 % decrease each year. This compares with the mean emissions of 2.23 Tg yr−1 and the trend of −0.03 Tg yr−2 (1 % annual decrease) reported in the UK's 2021 inventory between 2013 and 2019. We examine how sensitive these estimates are to various components of the inversion set-up, such as the measurement network configuration, the prior emissions estimate, the inversion method and the atmospheric transport model used. We find the decreasing trend to be due, primarily, to a reduction in emissions from England, which accounts for 70 % of the UK CH4 emissions. Comparisons during 2015 demonstrate consistency when different atmospheric transport models are used to map the relationship between sources and atmospheric observations at the aggregation level of the UK. The posterior annual national means and negative trend are found to be consistent across changes in network configuration. We show, using only two monitoring sites, that the same conclusions on mean UK emissions and negative trend would be reached as using the full six-site network, albeit with larger posterior uncertainties. However, emissions estimates from Scotland fail to converge on the same posterior under different inversion set-ups, highlighting a shortcoming of the current observation network in monitoring all of the UK. Although CH4 emissions in 2020 are estimated to have declined relative to previous years, this decrease is in line with the longer-term emissions trend and is not necessarily a response to national lockdowns.

The Experimental Verification of Direct-Write Silver Conductive Grid and ARIMA Time Series Analysis for Crack Propagation.

The paper presents experimental verification of customized resistive crack propagation sensors as an alternative method for other common structural health monitoring (SHM) techniques. Most of these are sensitive to changes in the sensor network configuration and a baseline dataset must be collected for the analysis of the structure condition. Sensors investigated within the paper are manufactured by the direct-write process with electrically conductive, silver-microparticle-filled paint to prepare a tailored measuring grid on an epoxy or polyurethane coating as a driving/insulating layer. This method is designed to enhance the functionality and usability compared to commercially available crack gauges. By using paint with conductive metal particles, the shape of the sensor measuring grid can be more easily adapted to the structure, while, in the previous approach, only a few grid-fixed sensors are available. A fatigue test on the compact tension (CT) specimen is presented and discussed to evaluate the ability of the developed sensors to detect and monitor fatigue cracks. Additionally, the ARIMA time series algorithm is developed both for monitoring and predicting crack growth, based on the acquired data. The proposed sensors’ verification reveal their good performance to detect and monitor fatigue fractures with a relatively low measurement error and ARIMA estimated crack length compared with the crack opening displacement (COD) gauge.

Brain state-dependent dynamic functional connectivity patterns in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) patients have presented aberrant static brain networks, however identifying ADHD patients based on dynamic information in brain networks is not fully clear. Data were obtained from 32 boys with ADHD and 52 sex- and age-matched typically developing controls; a sliding-window method was used to assess dynamic functional connectivity (dFC), and two reoccurring dFC states (the hot and cool states) were then identified using a k-means clustering method. The results showed that ADHD patients had significant changes in occurrence, transitions times and dFC strength of the cingulo-opercular network (CON) and sensorimotor network (SMN) in the cool state. The severity of ADHD symptoms showed significant correlations with the regional amplitude of dFC fluctuations in the ventral medial prefrontal cortex (vmPFC), anterior medial prefrontal cortex (amPFC) and precuneus. These findings could provide insights on the state-dependent dynamic changes in large-scale brain connectivity and network configurations in ADHD.

A Network Analysis of Clinical Variables in Chronic Pain: A Study from the Swedish Quality Registry for Pain Rehabilitation (SQRP).

Efforts to identify specific variables that impact most on outcomes from interdisciplinary pain rehabilitation are challenged by the complexity of chronic pain. Methods to manage this complexity are needed. The purpose of the study was to determine the network structure entailed in a set of self-reported variables, examine change, and look at potential predictors of outcome, from a network perspective. In this study we apply network analysis to a large sample of people seeking interdisciplinary pain treatment (N = 2,241). Variables analyzed include pain intensity, pain interference, extent of pain, depression, anxiety, insomnia, and psychological variables from cognitive behavioral models of chronic pain. We found that Acceptance, Pain Interference, and Depression were key, "central," variables in the pretreatment network. Interestingly, there were few changes in the overall network configuration following treatment, specifically with respect to which variables appear most central relative to each other. On the other hand, Catastrophizing, Depression, Anxiety, and Pain Interference each became less central over time. Changes in Life Control, Acceptance, and Anxiety were most strongly related to changes in the remainder of the network as a whole. Finally, no network differences were found between treatment responders and non-responders. This study highlights potential future targets for pain treatment. Further application of a network approach to interdisciplinary pain rehabilitation data is recommended. Going forward, it may be better to next do this in a more comprehensive theoretically guided fashion, and ideographically, to detect unique individual differences in potential treatment processes.