Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Change of stream network connectivity and its impact on flood control

AbstUrbanization can alter the hydrogeomorphology of streams and rivers,change stream network structures,and reduce stream network connectivity,which leads to a decrease in the storage capacity of stream networks and aggravates flood damage.Therefore,investigation of the ways in which stream network connectivity impacts flood storage capacity and flood control in urbanized watersheds can provide significant benefits.This study developed a framework to assess stream network connectivity and its impact on flood control.First,a few connectivity indices were adopted to assess longitudinal stream network connectivity.Afterward,the static and dynamic storage capacities of stream networks were evaluated using storage capacity indices and a one-dimensional hydrodynamic model.Finally,the impact of stream network connectivity change on flood control was assessed by investigating the changes in stream network connectivity and storage capacity.This framework was applied to the Qinhuai River Basin,China,where intensive urbanization has occurred in the last few decades.The results show that stream network storage capacity is affected by stream network connectivity.Increasing stream network connectivity enhances stream network storage capacity.©2020 Hohai University.Production and hosting by Elsevier B.V.This is an open access article under the CC BY-NC-ND license(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Global Functional Network Connectivity Disturbances in Parkinson’s Disease with Mild Cognitive Impairment by Resting-State Functional MRI

Examining the spontaneous BOLD activity to understand the neural mechanism of Parkinson’s disease(PD)with mild cognitive impairment(MCI)is a focus in resting-state functional MRI(rs-fMRI)studies.This study aimed to investigate the alteration of brain functional connectivity in PD with MCI in a systematical way at two levels:functional connectivity analysis within resting state networks(RSNs)and functional network connectivity(FNC)analysis.Using group independent component analysis(ICA)on rs-fMRI data acquired from 30 participants(14 healthy controls and 16 PD patients with MCI),16 RSNs were identified,and functional connectivity analysis within the RSNs and FNC analysis were carried out between groups.Compared to controls,patients with PD showed decreased functional connectivity within putamen network,thalamus network,cerebellar network,attention network,and self-referential network,and increased functional connectivity within execution network.Globally disturbed,mostly increased functional connectivity of FNC was observed in PD group,and insular network and execution network were the dominant network with extensively increased functional connectivity with other RSNs.Cerebellar network showed decreased functional connectivity with caudate network,insular network,and self-referential network.In general,decreased functional connectivity within RSNs and globally disturbed,mostly increased functional connectivity of FNC may be characteristics of PD.Increased functional connectivity within execution network may be an early marker of PD.The multi-perspective study based on RSNs may be a valuable means to assess functional changes corresponding to specific RSN,contributing to the understanding of the neural mechanism of PD.

Acquisition of Network Connection Status Information from Physical Memory on Windows Vista Operating System

A method to extract information of network connection status information from physical memory on Windows Vista operating system is proposed. Using this method, a forensic examiner can extract accurately the information of current TCP/ IP network connection information, including IDs of processes which established connections, establishing time, local address, local port, remote address, remote port, etc., from a physical memory on Windows Xflsta operating system. This method is reliable and efficient. It is verified on Windows Vista, Windows Vista SP1, Windows Vista SP2.

Network Connectivity Probability of Linear Vehicular Ad Hoc Networks on Two-Way Street

In this paper, we present an analytical model to determine the network connectivity probability of a linear vehicular ad hoc network (VANET) formed by communication equipped vehicles on a two-way street scenario. We consider the highway to be consisting of two lanes with vehicles moving in both directions on these lanes and focus on the probability of being able to convey messages from a source vehicle to a destination vehicle, which may be multiple hops away. Closed form analytical expression is obtained for the network connectivity probability in the presence of Nakagami fading channel. In our model, the transmission range of each vehicle is modeled as a random variable due to channel fading. The analytical results are validated by extensive simulations.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the“dual carbon”strategy,intelligent networked new energy vehicle technology plays a crucial role.This type of vehicle combines the advantages of new energy technology and intelligent network technology,effectively reduces carbon emissions in the transportation sector,improves energy utilization efficiency,and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles,making significant contributions.This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.

MSADCN:Multi-Scale Attentional Densely Connected Network for Automated Bone Age Assessment

Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.

Antidepressant Effects of Electroconvulsive Therapy Unrelated to the Brain＇s Functional Network Connectivity alterations at an Individual Level

Background： Electroconvulsive therapy （ECT） can alleviate the symptoms of treatment-resistant depression （TRD）. Functional network connectivity （FNC） is a newly developed method to investigate the brain＇s functional connectivity patterns. The first aim of this study was to investigate FNC alterations between TRD patients and healthy controls. The second aim was to explore the relationship between the ECT treatment response and pre-ECT treatment FNC alterations in individual TRD patients. Methods： This study included 82 TRD patients and 41 controls. Patients were screened at baseline and after 2 weeks of treatment with a combination of ECT and antidepressants. Group information guided-independent component analysis （G1G-ICA） was used to compute subject-specific functional networks （FNs）. Grassmann maniibld and step-wise forward component selection using support vector machines were adopted to perform the FNC measure and extract the functional networks＇ connectivity patterns （FCP）. Pearson＇s correlation analysis was used to calculate the correlations between the FCP and ECT response. Results： A total of 82 TRD patients in the ECT group were successfully treated. On an average, 8.50 ~ 2.00 ECT sessions were conducted. After ECT treatment, only 42 TRD patients had an improved response to ECT （the Hamilton scores reduction rate was more than 50%）, response rate 51%. 8 FNs （anterior and posterior default mode network, bilateral frontoparietal network, audio network, visual network, dorsal attention network, and sensorimotor network） were obtained using GIG-ICA. We did not found that FCPs were significantly different between TRD patients and healthy controls. Moreover, the baseline FCP was unrelated to the ECT treatment response. Conclusions： The FNC was not significantly different between the TRD patients and healthy controls, and the baseline FCP was unrelated to the ECT treatment response. These findings will necessitate that we modify the experimental scheme to explore the mechanisms underlying ECT＇s effects on depression and explore the specific predictors of the effects of ECT based on the pre-ECT treatment magnetic resonance imaging.

The g-Component Connectivity of Some Networks

In 2012, Hsu et al. generalized the classical connectivity of graph G and introduced the concept of g-component connectivity CKg (G) to measure the fault tolerance of networks. In this paper, we determine the g-component connectivity of some graphs, such as fan graph, helm graph, crown graph, Gear graph and the Mycielskian graph of star graph and complete bipartite graph.

Relay movement control for maintaining connectivity in aeronautical ad hoc networks

As a new sort of mobile ad hoc network(MANET), aeronautical ad hoc network(AANET) has fleet-moving airborne nodes(ANs) and suffers from frequent network partitioning due to the rapid-changing topology. In this work, the additional relay nodes(RNs) is employed to repair the network and maintain connectivity in AANET. As ANs move, RNs need to move as well in order to re-establish the topology as quickly as possible. The network model and problem definition are firstly given, and then an online approach for RNs' movement control is presented to make ANs achieve certain connectivity requirement during run time. By defining the minimum cost feasible moving matrix(MCFM), a fast algorithm is proposed for RNs' movement control problem. Simulations demonstrate that the proposed algorithm outperforms other control approaches in the highly-dynamic environment and is of great potential to be applied in AANET.

Second-Order Consensus of Multiple Agents with Bounded Control Inputs and Preserved Network Connectivity

This paper investigates second-order consensus of multi-agent systems with a virtual leader of varying velocity while preserving network connectivity.We propose a novel second-order consensus algorithm with bounded control inputs.Under the condition that the initial network is connected,the network will be connected all the time and all agents and the virtual leader can attain the same position and move with the same velocity.A simulation example is proposed to illustrate the effective of the proposed algorithm.

Minimizing Maximum Risk for Fair Network Connection with Interval Data

In this paper we introduce a minimax model for network connection problems with interval parameters. We consider how to connect given nodes in a network with a path or a spanning tree under a given budget, where each link is associated with an interval and can be established at a cost of any value in the interval. The quality of an individual link （or the risk of link failure, etc.） depends on its construction cost and associated interval. To achieve fairness of the network connection, our model aims at the minimization of the maximum risk over all links used. We propose two algorithms that find optimal paths and spanning trees in polynomial time, respectively. The polynomial solvability indicates salient difference between our minimax model and the model of robust deviation criterion for network connection with interval data, which gives rise to NP-hard optimization problems.

An Efficient Connectivity Restoration Technique(ECRT)for Wireless Sensor Network

Node failure in Wireless Sensor Networks(WSNs)is a fundamental problem because WSNs operate in hostile environments.The failure of nodes leads to network partitioning that may compromise the basic operation of the sensor network.To deal with such situations,a rapid recovery mechanism is required for restoring inter-node connectivity.Due to the immense importance and need for a recovery mechanism,several different approaches are proposed in the literature.However,the proposed approaches have shortcomings because they do not focus on energy-efficient operation and coverage-aware mechanisms while performing connectivity restoration.Moreover,most of these approaches rely on the excessive mobility of nodes for restoration connectivity that affects both coverage and energy consumption.This paper proposes a novel technique called ECRT(Efficient Connectivity Restoration Technique).This technique is capable of restoring connectivity due to single and multiple node failures.ECRT achieves energy efficiency by transmitting a minimal number of control packets.It is also coverage-aware as it relocates minimal nodes while trying to restore connectivity.With the help of extensive simulations,it is proven that ECRT is effective in connectivity restoration for single and multiple node failures.Results also show that ECRT exchanges a much smaller number of packets than other techniques.Moreover,it also yields the least reduction in field coverage,proving its versatility for connectivity restoration.

Analysis of Mobile and Internet Network Coverage: Propagation of Electromagnetic Waves and Concept of Digital Divide in Burundi

Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.

Numerical evaluation of the effect of fracture network connectivity in naturally fractured shale based on FSD model

In this paper, the effect of pre-existing discrete fracture network(DFN) connectivity on hydraulic fracturing is numerically investigated in a rock mass subjected to in-situ stress. The simulation results show that DFN connectivity has a significant influence on the hydraulic fracture(HF) & DFN interaction and hydraulic fracturing effectiveness, which can be characterized by the total interaction area, stimulated DFN length, stimulated HF length, leak-off ratio, and stimulated total length. In addition, even at the same fluid injection rate, simulation models exhibit different responses that are strongly affected by the DFN connectivity. At a low injection rate, total interaction area decreases with increasing DFN connectivity; at a high injection rate, total interaction area increases with the increase of DFN connectivity. However, for any injection rate, the stimulated DFN length increases and stimulated HF length decreases with the increase of connectivity. Generally, this work shows that the DFN connectivity plays a crucial role in the interaction between hydraulic fractures, the pre-existing natural fractures and hydraulic fracturing effectiveness; in return, these three factors affect treating pressure, created microseismicity and corresponding stimulated volume. This work strongly relates to the production technology and the evaluation of hydraulic fracturing effectiveness. It is helpful for the optimization of hydraulic fracturing simulations in naturally fractured formations.

3D reconstruction method and connectivity rules of fracture networks generated under different mining layouts

In current research, a series of triaxial tests, which were employed to simulate three typical mining lay-outs (i.e., top-coal caving, non-pillar mining and protected coal seam mining), were conducted on coal by using MTS815 Flex Test GT rock mechanics test system, and the fracture networks in the broken coal samples were qualitatively and quantitatively investigated by employing CT scanning and 3D reconstruc-tion techniques. This work aimed at providing a detail description on the micro-structure and fracture-connectivity characteristics of rupture coal samples under different mining layouts. The results show that: (i) for protected coal seam mining layout, the coal specimens failure is in a compression-shear manner and oppositely, (ii) the tension-shear failure phenomenon is observed for top-coal caving and non-pillar mining layouts. By investigating the connectivity features of the generated fractures in the direction of r1 under different mining layouts, it is found that the connectivity level of the fractures of the samples corresponding to non-pillar mining layout was the highest.

Robust Virtual Network Embedding Based on Component Connectivity in Large-Scale Network

Virtual network embedding problem which is NP-hard is a key issue for implementing software-defined network which is brought about by network virtualization. Compared with other studies which focus on designing heuristic algorithms to reduce the hardness of the NP-hard problem we propose a robust VNE algorithm based on component connectivity in large-scale network. We distinguish the different components and embed VN requests onto them respectively. And k-core is applied to identify different VN topologies so that the VN request can be embedded onto its corresponding component. On the other hand, load balancing is also considered in this paper. It could avoid blocked or bottlenecked area of substrate network. Simulation experiments show that compared with other algorithms in large-scale network, acceptance ratio, average revenue and robustness can be obviously improved by our algorithm and average cost can be reduced. It also shows the relationship between the component connectivity including giant component and small components and the performance metrics.

Chaos in complex motor networks induced by Newman-Watts small-world connections

We investigate how dynamical behaviours of complex motor networks depend on the Newman-Watts small-world （NWSW） connections. Network elements are described by the permanent magnet synchronous motor （PMSM） with the values of parameters at which each individual PMSM is stable. It is found that with the increase of connection probability p, the motor in networks becomes periodic and falls into chaotic motion as p further increases. These phenomena imply that NWSW connections can induce and enhance chaos in motor networks. The possible mechanism behind the action of NWSW connections is addressed based on stability theory.