Connection Topology Research Articles

An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks

Opportunistic networks are characterized by intermittent connectivity and dynamic topologies, which pose significant challenges for efficient message delivery, resource management, and routing decision-making. This paper introduces the Fuzzy Control Routing Protocol, a novel approach designed to address these challenges by leveraging fuzzy logic to enhance routing decisions and improve overall network performance. The protocol considers buffer occupancy, angle to destination, and the number of unique connections of the target nodes to make context-aware routing decisions. It was implemented and evaluated using the FuzzyC framework for simulations and the opportunistic network environment simulator for realistic network scenarios. Simulation results demonstrate that the Fuzzy Control Routing Protocol achieves competitive delivery probability, efficient resource utilization, and low overhead compared to the Epidemic and MaxProp protocols. Notably, it consistently outperformed the Epidemic protocol across all metrics and exhibited comparable delivery probability to MaxProp while maintaining significantly lower overhead, particularly in low-density scenarios. The results demonstrate the protocol’s ability to adapt to varying network conditions, effectively balance forwarding and resource management, and maintain robust performance in dynamic vehicular environments.

Dissection of the long-range circuit of the mouse intermediate retrosplenial cortex

The retrosplenial cortex (RSP) is a complex brain region with multiple interconnected subregions that plays crucial roles in various cognitive functions, including memory, spatial navigation, and emotion. Understanding the afferent and efferent connectivity of the RSP is essential for comprehending the underlying mechanisms of its functions. Here, via viral tracing and fluorescence micro-optical sectioning tomography (fMOST), we systematically investigated the anatomical organisation of the upstream and downstream circuits of glutamatergic and GABAergic neurons in the dorsal and ventral RSP. The cortical connections of the RSP show laminar organisation in which the input neurons are distributed more in the deeper layers of the upstream cortex. Although different types of neurons have similar upstream circuits, GABAergic neurons show bidirectional connections with the hippocampus, whereas glutamatergic neurons only show unidirectional connections. Moreover, GABAergic neurons receive more inputs from the primary sensory cortex than from the prefrontal cortex and association cortex. The dorsal and ventral subregions have preferred circuits such that the dorsal RSP exhibits spatially topological connections with the dorsal visual cortex and lateral thalamus. The systematic study on long-range connections across RSP subregions and cell types may provide useful information for future revealing of RSP working mechanisms.

A Semi-Supervised Fracture-Attention Model for Segmenting Tubular Objects with Improved Topological Connectivity.

Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets. Additionally, current evaluation metrics are insufficient for effectively capturing segmentation fractures. To address these challenges, we propose a semi-supervised fracture-attention model (SSFA) for tubular object segmentation. SSFA enhances connectivity, reduces fractures, and maintains volumetric accuracy. It outperforms state-of-the-art models in topological performance. Extensive experiments on four public datasets validate the effectiveness of SSFA. Furthermore, we introduce a novel evaluation metric, the Fracture Rate (FR), which provides an intuitive and quantitative assessment of segmentation fractures. Our source code is available at http://github.com/Yanfeng-Zhou/SSFA. Supplementary data are available at Bioinformatics online.

Rendezvous analysis and control design for multiple mobile agents with preserved network connectivity on hyperboloid

This paper studies the rendezvous problem for a second-order multi-agent system on the hyperboloid under a proximity-based communication topology. Using the Riemannian geometrical structure on the hyperboloid, we design a nonlinear feedback control law in which each agent has a limited sensing range. By analyzing the switching behavior of the communication topology, we show that the switching time of the resulting closed-loop system is finite, and the connectivity of the communication topology is preserved if the initial network is connected. In addition, we also provide an analysis of the asymptotic behavior of the second-order closed-loop system on the hyperboloid and prove that the proposed feedback control law enables all agents to converge to the same position.

Topological functional network analysis of cortical blood flow in hyperacute ischemic rats

Acute cerebral ischemia alters brain network connectivity, leading to notable increases in both anatomical and functional connectivity while observing a reduction in metabolic connectivity. However, alterations of the cerebral blood flow (CBF) based functional connectivity remain unclear. We collected continuous CBF images using laser speckle contrast imaging (LSCI) technology to monitor ischemic occlusion-reperfusion progression through occlusion of the left carotid artery. We also used a dense cortical grid atlas to construct CBF-based functional connectivity networks for hyperacute ischemic rodents. Graph theoretical analysis was used to measure network topological characteristics and construct topological connection graphs. Coactivation pattern (CAP) analysis was utilized to examine the spatiotemporal characteristics of the global network. Additionally, we measured evoked functional hyperemia and correlated it with network topologies. Network analysis indicated a significant increase in functional connectivity, global efficiency, local efficiency, small-worldness, clustering coefficient, and regional degree centrality primarily within the left ischemic intra-hemisphere, accompanied by weaker changes in the right intra-hemisphere. Inter-hemisphere networks exhibited reduced homologous connections, global efficiency, and small-worldness. CAP analysis revealed increased strength of the left negative activation brain network's state fraction of time and transition probability from equilibrium-to-imbalance states. Left network metrics declined following blood flow reperfusion. Furthermore, positive/negative correlations between barrel-evoked intensity and regional network topologies were reversed as negative/positive correlations after cerebral ischemia. These findings suggest a damaged CBF functional network mechanism following acute cerebral ischemia and a disrupted association between resting state and evoked hyperemia.

OPTIMIZATION OF THE SCATTERING MATRIX OF FREQUENCY -DETUNED ADD/DROP FILTERS FOR MULTIPLEXERS BUILT ON SYSTEMS OF OPTICAL DIELECTRIC RESONATORS WITH WHISPERING GALLERY MODES

Background. A significant increase in the speed of information transmission in fiber-optic communication networks is determined by the strict requirements imposed on the elemental base of receiving and transmitting devices. One of the important components of such devices is diplexers built on different notch and bandpass filters, which are often performed on dielectric resonators (DR) with whispering gallery mode (WGM) oscillations. Calculation and optimization of the parameters of multilink filters and diplexers built on DR is impossible without further development of the theory of their design. The development of the theory of diplexers today is often based on electrodynamic modelling, which is built on preliminary calculations of filter scattering parameters in various transmission lines with a complex topology of connections. Objective. The aim of this study is to construct electrodynamics' models of wave scattering on complex multi-connected DR structures with degenerate types of WGM natural oscillations, which contain several frequency-detuned bandpass or notch filters located in one or several transmission lines. To solve the scattering problem, we proposed a system of equations derived from perturbation theory for Maxwell's equations [24], modified to describe the DR fields with whispering gallery oscillations in transmission lines. The construction of such solutions is complicated by the fact that each of the partial optical resonators of the filters, in the case of excitation of azimuthally inhomogeneous WGM in it, has, as a rule, two degenerate types of natural oscillations. Moreover, each such type of oscillation is characterized by different complex values of the coupling coefficients with the line, open space, and also with other resonators. The latter circumstance leads to the fact that the systems of equations for the amplitudes of natural and forced oscillations of resonators are doubled. The signs of the coefficients of mutual coupling are usually different, this leads to the fact that the behaviour of DRs in the system becomes more difficult to predict, therefore the second objective of this work is to study the patterns of the scattering characteristics of line waves on systems of frequency-detuned DRs with degenerate types of oscillations with the possibility of constructing multiplexers for modern optical communication systems. Methods. The methods of technical electrodynamics are used for calculating and analysing scattering matrices. The end result is obtaining new analytical equations and formulas for new complex structures of coupled dielectric resonators with whispering gallery oscillations in the different transmission lines. Results. Frequency dependences of scattering matrices on complex structures of frequency-detuned filters built on coupled optical DR with whispering gallery oscillations located in one or more transmission lines are considered. Electromagnetic models of bandstop and add/drop filters are built, consisting of various optical resonators with degenerate types of natural oscillations. General analytical expressions of vector coefficients and matrices for building systems of equations describing coupled oscillations, as well as forced oscillations of resonators in cases of their use in optical filters with serial and parallel arrangement, are given. General solutions for the scattering field on frequency-detuned resonators located in different optical transmission lines have been found. Examples of calculating frequency dependences of the scattering matrix for the most interesting structures consisting of two different frequency-detuned filters are given. The frequency scattering characteristics of several types of devices are calculated, which consist of two notch filters with different blocking bands, made on detuned DRs in one transmission line. The possibilities of the earlier proposed method are demonstrated in the example of calculating the scattering characteristics of known types of diplexers built on the basis of the use of two add/drop filters with different frequency bandwidths. The frequency dependences of the scattering matrices of the two most common types of devices, located in parallel between two or four regular transmission lines of add/drop filters with different numbers of resonators; laterally coupled add/drop filters; parallel-coupled add/drop filters; twisted double-channel side-coupled integrated space sequence of resonators (SCISSORs), were studied. New scattering models of diplexers consisting of optical resonators of different connection topologies were built: serial, parallel with the use of laterally coupled add/drop filters; parallel-coupled add/drop filters; twisted double-channel SCISSORs. The frequency dependences scattering matrix of the diplexers were also calculated. The characteristics of the designed diplexers obtained from the examined filters of different types were compared. Conclusions. The theory of diplexer construction, which takes place on the simultaneous optimization of the scattering matrix of several filters built on complex systems of dielectric resonators with degenerate types of whispering gallery oscillations is expanded. A calculation method was developed and new analytical relations were found for the scattering matrix coefficients of optical diplexers of various types.

Roughhousing with Ions: Surface-Induced Dissociation and Electron Capture Dissociation as Diagnostics of Q-Cyclic IMS-TOF Instrument Tuning Gentleness.

Native mass spectrometry can characterize a range of biomolecular features pertinent to structural biology, including intact mass, stoichiometry, ligand-bound states, and topology. However, when an instrument's ionization source is tuned to maximize signal intensity or adduct removal, it is possible that the biomolecular complex's tertiary and quaternary structures can be rearranged in a way that no longer reflect its native-like conformation. This could affect downstream ion activation experiments, leading to erroneous conclusions about the native-like structure. One activation strategy is surface-induced dissociation (SID), which generally causes native-like protein complexes to dissociate along the weakest subunit interfaces, revealing critical information about the complex's native-like topology and subunit connectivity. If the quaternary structure has been disturbed, then the SID fingerprint will shift as well. Thus, SID was used to diagnose source-induced quaternary structure rearrangement and help tune an instrument's source and other upstream transmission regions to strike the balance between signal intensity, adduct removal, and conserving the native-like structure. Complementary to SID, electron-capture dissociation (ECD) can also diagnose rearranged quaternary structures and was used after in-source activation to confirm that the subunit interfaces were rearranged, opening the structure to electron capture and subsequent dissociation. These results provide a valuable guide for new practitioners of native mass spectrometry and highlight the importance of using standard protein complexes when tuning new instrument platforms for optimal native mass spectrometry performance.

A high resistance grounding fault identification method for distribution network considering topological connection

Abstract Traditional high resistance grounding fault identification in distribution networks fails to meet load demands, demanding optimization for reliability and economics. A novel approach considers topological connections, applies the Hilbert-Huang transform for signal decomposition, refines components, and utilizes particle swarm optimization to enhance feature vectors. This defines an association matrix and leverages zero sequence current features, accurately pinpointing suspected high resistance ground faults through filtered second derivative concavity and zero crossing point analysis. Experimental data underscores the design method’s exceptional calculation efficiency, capping at 40 milliseconds, and maintaining over 90% fault recognition accuracy. Recognition time notably diminishes, shrinking from 0.91 seconds for 10 iterations to 0.28 seconds for 50 iterations.

DeepHoloBrain: From Geometric Deep Model to Computational Neuroscience

AbstractBackgroundThe human brain is a complex inter‐wired system that emerges spontaneous functional fluctuations. In spite of tremendous success in the experimental neuroscience field, a system‐level understanding of how brain anatomy supports various neural activities remains elusive.MethodCapitalizing on the unprecedented amount of neuroimaging data, we present a physics‐informed deep model to uncover the coupling mechanism between brain structure and function through the lens of data geometry that is rooted in the widespread wiring topology of connections between distant brain regions. Since deciphering the puzzle of self‐organized patterns in functional fluctuations is the gateway to understanding the emergence of cognition and behavior, we devise a geometric deep model to uncover manifold mapping functions that characterize the intrinsic feature representations of evolving functional fluctuations on the Riemannian manifold. In lieu of learning unconstrained mapping functions, we introduce a set of graph‐harmonic scattering transforms to impose the brain‐wide geometry on top of manifold mapping functions, which allows us to cast the manifold‐based deep learning into a reminiscent of MLP‐Mixer architecture (in computer vision) for Riemannian manifold.ResultAs a proof‐of‐concept approach, we explore a neural‐manifold perspective to understand the relationship between (static) brain structure and (dynamic) function, challenging the prevailing notion in cognitive neuroscience by proposing that neural activities are essentially excited by brain‐wide oscillation waves living on the geometry of human connectomes, instead of being confined to focal areas.ConclusionIn practice, we evaluate the clinical value of our mixer model in (1) disease early diagnosis and (2) generality of adapting the pre‐trained model to a new dataset. Our extensive experimental results affirm the model’s effectiveness and practicality, marking a significant advancement in the field of neuroscience.

Analysis of pre-El Niño and La Niña events using climate network approach

El Niño Southern Oscillation is a climate mode in which sea surface temperature anomalies are warmer (cooler) in the central-eastern (western) Pacific during an El Niño (La Niña) phase. It is one of the most important climatic phenomena and has a substantial influence on global climate patterns due to its ability to change the global atmospheric circulation induced by ocean–air coupling. We analyze the space-embedded climate networks’ connectivity pattern to study the preconditions of El Niño and La Niña phases. We observe that during the March–April–May period, the spatially embedded climate network connectivity pattern exhibits the alteration of atmospheric warming activity preceding El Niño and La Niña phases. These distinct preconditions of both phases are observed during the spring season prior to the event’s onset. We study the distribution of teleconnection along with the pattern of connectivity of climate networks to understand the change in the connection topology. Before El Niño events, we observe the dominance of only the neighboring links, and preceding the La Niña events, a higher number of neighboring and long-range links appeared in the networks during March–April–May, simultaneously with the Tropical/ Northern hemisphere warming up. Finally, we observe that before La Niña events, the climate network is densely connected.

Mutual Synchronization of Spintronic Nano-Oscillator Ensembles

Introduction. The use of spintronic components significantly enhances the performance, reduces the size, and lowers the power consumption of modern electronic devices. The spintronic oscillator (SO) is an integral part of spintronic devices. Connecting several SOs (> 100) into ensembles with subsequent synchronization mitigates such SO drawbacks as low output power and high phase noise. These drawbacks appear as a result of an increase in the output power of an SO ensemble compared to a single oscillator under a simultaneous decrease in the spectral linewidth of the ensemble.Aim. To investigate the impact of connection topologies, synchronization mechanisms, and oscillator failures on the synchronization of oscillator ensembles.Materials and methods. The Kuramoto phase model was used to simplify the numerical modeling of synchronization of SOs connected into an ensemble.Results. A Kuramoto equation for phases of SOs connected in an ensemble was derived, and the influence of connection topologies and oscillator failures on the synchronization parameters of an ensemble of N connected oscillators was demonstrated.Conclusion. In order to ensure the shortest transition time of an SO ensemble to the synchronous mode, topologies with a higher number of connections between oscillators (e.g., "all-to-all") are preferable. The results obtained confirm the advantages of local connection of an SO ensemble by a common current, thus forming an "all-to-all" topology. This makes the transition time of the SO ensemble to the synchronous mode less dependent on both oscillator failures and the number of synchronized SOs.

A three-classification model for identifying migraine with right-to-left shunt using lateralization of functional connectivity and brain network topology: a resting-state fMRI study.

Right-to-left shunting has been significantly associated with migraine, although the neural mechanisms remain complex and not fully elucidated. The aim of this study was to investigate the variability of brain asymmetry in individuals with migraine with right-to-left shunting, migraine without right-to-left shunting and normal controls using resting-state fMRI technology and to construct a three-classification model. Firstly, asymmetries in functional connectivity and brain network topology were quantified to laterality indices. Secondly, the laterality indices were employed to construct a three-classification model using decision tree and random forest algorithms. Ultimately, through a feature score analysis, the key brain regions that contributed significantly to the classification were extracted, and the associations between these brain regions and clinical features were investigated. Our experimental results showed that the initial classification accuracy reached 0.8961. Subsequently, validation using an independent sample set resulted in a classification accuracy of 0.8874. Further, after expanding the samples by the segmentation strategy, the classification accuracies were improved to 0.9103 and 0.9099. Additionally, the third sample set yielded a classification accuracy of 0.8745. Finally, 9 pivotal brain regions were identified and distributed in the default network, the control network, the visual network, the limbic network, the somatomotor network and the salience/ventral attention network. The results revealed distinct lateralization features in the brains of the three groups, which were closely linked to migraine and right-to-left shunting symptoms and could serve as potential imaging biomarkers for clinical diagnosis. Our findings enhanced our understanding of migraine and right-to-left shunting mechanisms and offered insights into assisting clinical diagnosis.

Structural brain connectivity does not associate with childhood trauma in individuals with schizophrenia

BackgroundSchizophrenia is a brain dysconnectivity disorder. However, it is not well understood whether the experience of childhood trauma (CT) affects dysconnectivity in individuals with schizophrenia (SZ). Using a network-based approach, we examined whether self-reported CT would explain additional variance compared to whole-brain topology and structural connectivity changes in SZ versus healthy controls (HC). Material and methodsCT was assessed in 51 SZ (mean age ± standard deviation 44 ± 11 years) and 140 HC (34.0 ± 12 years). Structural brain networks were constructed from T1-weighted MR and diffusion-MRI scans using non-tensor based tractography. Group differences in whole-brain topology and permutation-based statistics were examined and corrected for age and sex. ResultsSZ showed reductions in efficiency, strength, clustering and density (p < 0.01) as well as increases in path length (F(range) = 4.71–18.1, p < 0.03) when compared to HC. We also observed hypoconnectivity in a subnetwork of frontotemporal, frontoparietal and occipital regions in SZ relative to HC (T > 4.0, p < 0.001). However, we did not find that high CT levels were related to structural network differences or structural connectivity changes in SZ. ConclusionsCT did not impact on topology or subnetwork connectivity changes in SZ. High CT levels were also not associated with any differences in network organisation irrespective of diagnosis. However, our findings confirm that SZ showed both network-level reductions and increases in a subnetwork. These findings suggest that the patterns of neuroanatomical dysconnectivity in established schizophrenia may not be influenced by CT. Future studies are needed to investigate the association between CT and structural dysconnectivity in schizophrenia.

Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation.

Accurately segmenting tubular structures, such as blood vessels or nerves, holds significant clinical implications across various medical applications. However, existing methods often exhibit limitations in achieving satisfactory topological performance, particularly in terms of preserving connectivity. To address this challenge, we propose a novel deep-learning approach, termed Deep Closing, inspired by the well-established classic closing operation. Deep Closing first leverages an AutoEncoder trained in the Masked Image Modeling (MIM) paradigm, enhanced with digital topology knowledge, to effectively learn the inherent shape prior of tubular structures and indicate potential disconnected regions. Subsequently, a Simple Components Erosion module is employed to generate topology-focused outcomes, which refines the preceding segmentation results, ensuring all the generated regions are topologically significant. To evaluate the efficacy of Deep Closing, we conduct comprehensive experiments on 4 datasets: DRIVE, CHASE DB1, DCA1, and CREMI. The results demonstrate that our approach yields considerable improvements in topological performance compared with existing methods. Furthermore, Deep Closing exhibits the ability to generalize and transfer knowledge from external datasets, showcasing its robustness and adaptability. The code for this paper has been available at: https://github.com/5k5000/DeepClosing.

Identification of asymmetrical abnormalities in functional connectivity and brain network topology for migraine sufferers: A preliminary study based on resting-state fMRI data

Research on the neural mechanisms underlying brain asymmetry in patients with migraine patients using fMRI is insufficient. This study proposed using lateralized algorithms for functional connectivity and brain network topology and investigated changes in their lateralization in patients with migraine. In study 1, laterality indices of functional connectivity (LFunctionCorr) and brain network topological properties (LBetweennessCentrality, LDegree, and LStrength) were defined. Differences between migraineurs and normal subjects were compared at whole-brain, half-brain, and region levels. In study 2, laterality indices were used to classify migraine and were validated using independent samples and the segment method for repeatability. In study 3, abnormal brain regions related to migraine were extracted based on the classification results and differences analysis. Study 1 found no significant differences related to in for migraine at the whole-brain level; however, significant differences were identified at the half-brain level for the hemispheric lateralization of the LFunctionCorr, while 11 significantly different brain regions were also identified at the brain region level. Furthermore, the classification accuracy in study 2 was 0.9366. With repeated validation, the accuracy reached 0.8561. Furthermore, after extending the samples according to the segmentation strategy, the classification accuracies were improved to 0.9408 and 0.8585. Study 3 identified 10 crucial brain regions with asymmetrical specificity based on laterality indices distributed across the visual network, the frontoparietal control network, the default mode network, the salience/ventral attention network and the limbic system. The results revealed novel insights and avenues for research into the mechanisms of migraine asymmetry and showed that the laterality indices could be used as a potential diagnostic imaging marker for migraine.

Integrating Geospatial and Real Time Technologies for Risk zone monitoring in Periyar Tiger Reserve, India.

There are numerous monitoring technologies available today, owing to the rapid advancements in technology and the increasing demand for safety and security in forests. Real-time monitoring with AI cameras, which are commonly utilized for creating and updating real-time features through surveillance, stands out as one of the most effective monitoring solutions. The objective of this current research is to monitor various risk zones within the Periyar Tiger Reserve by integrating real-time AI camera with geographic data. AI cameras were strategically placed using spatial analysis techniques. Leveraging Geographic Information System (GIS) technology, the system facilitates the spatiotemporal management of multiple cameras and their associated data. The spatial distribution and monitoring range of the AI cameras are depicted on the GIS map, along with the layout densities of the cameras and other pertinent information stored in a geospatial database. Additionally, merging various risk areas identified from past incidents with the camera locations enhances the system's capability to establish accurate topological connections between cameras and other points of interest. The results revealed that only 13% of the risk zone was observable from the nine available Real Time Monitoring towers. However, with the addition of 51 more towers, the visibility of the risk zone would increase to 40%. The remaining 15% of the risk zones were not visible through the existing infrastructure. To address this visibility gap, if permitted by the Wildlife Protection Act, wired communication may need to be implemented instead of wireless for monitoring these areas.

Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning

Deep and ultra-deep hydrocarbon reservoirs are regarded as an attractive target for onshore oil and gas exploration and development in China. The multi-scale fractures are widely distributed in this type of reservoir. The fluid-solid coupling effect is strong, which can greatly affect the fluid flow, resulting in a low hydrocarbon recovery with the average value less than 15%. To explore the underlying flow dynamics during efficient development of deep and ultra-deep hydrocarbon reservoirs, it is of great importance to characterize the multi-scale fracture-pore structure evolution of rock affected by strong geo-stress field variation. To tackle the above issues, an actual rock drilled from a typical ultra-deep reservoir in Tarim Basin are selected to conduct in-situ stress-loading computed tomography (CT) scanning experiments, and the CT gray images of rock microstructure under different dynamic loading conditions are obtained. A fully convolutional neural network (U-Net) deep learning segmentation algorithm is introduced to accurately distinguish the rock skeleton, pore space and fractures in deep rock. The three-dimensional (3-D) digital rock model of deformed multi-scale fracture-porous media under different dynamic loading conditions is ultimately established to investigate the evolution of fracture morphology and deep rock microstructure as the in-situ stress is gradually loaded. It indicates that, the U-Net deep learning semantic segmentation algorithm can accurately segment CT images of deep rocks, and the established 3D digital rock model of fractured porous media can accurately represent the pore-throat distribution, fracture morphology, and pore-scale topological connectivity. At the initial stage of stress loading, there are few micro-fractures inside the rock, and the fracture connectivity is poor. Due to effect of rock compaction, the average pore throat radius increases while pore throat ratio, coordination number and tortuosity greatly decrease. As the effective stress is gradually loaded, the micro-fractures begin to propagate, leading to stronger heterogeneity of micro-fractures’ distribution and better topological connectivity, and both the coordination number and tortuosity gradually increase. After the deep rock is fractured, micro-fractures run through a fracture network and all the above topological parameters increase greatly.

A variable threshold weight approach to dynamic event-triggered consensus control for leader-following multi-agent systems under asynchronous DoS attacks

This paper is concerned with the event-triggered consensus problem for leader-following multi-agent systems under asynchronous denial-of-service (DoS) attacks. Since the asynchronous attacks on different edges may enlarge the defense burden, the topology connectivity is employed to reveal the effectiveness of attacks and determine the unhealthy time periods. Relying on the internal system state, a dynamic event-triggered mechanism is built to regulate the information transmission between agents. In this mechanism, a variable threshold weight composed of the deviations of the relative neighboring errors and historical state errors is proposed to adaptively schedule the triggering thresholds with systems running stages. To guarantee secure consensus performance even during the triggering intervals, the local estimation of the neighboring information is integrated into the controller such that sufficient conditions to obtain the control parameters and the tolerable attack parameters are established without any Zeno behavior. The proposed method is validated by a simulation study.

Disrupted gray matter connectome in vestibular migraine: a combined machine learning and individual-level morphological brain network analysis

BackgroundAlthough gray matter (GM) volume alterations have been extensively documented in previous voxel-based morphometry studies on vestibular migraine (VM), little is known about the impact of this disease on the topological organization of GM morphological networks. This study investigated the altered network patterns of the GM connectome in patients with VM.MethodsIn this study, 55 patients with VM and 57 healthy controls (HCs) underwent structural T1-weighted MRI. GM morphological networks were constructed by estimating interregional similarity in the distributions of regional GM volume based on the Kullback–Leibler divergence measure. Graph-theoretical metrics and interregional morphological connectivity were computed and compared between the two groups. Partial correlation analyses were performed between significant GM connectome features and clinical parameters. Logistic regression (LR), support vector machine (SVM), and random forest (RF) classifiers were used to examine the performance of significant GM connectome features in distinguishing patients with VM from HCs.ResultsCompared with HCs, patients with VM exhibited increased clustering coefficient and local efficiency, as well as reduced nodal degree and nodal efficiency in the left superior temporal gyrus (STG). Furthermore, we identified one connected component with decreased morphological connectivity strength, and the involved regions were mainly located in the STG, temporal pole, prefrontal cortex, supplementary motor area, cingulum, fusiform gyrus, and cerebellum. In the VM group, several connections in the identified connected component were correlated with clinical measures (i.e., symptoms and emotional scales); however, these correlations did not survive multiple comparison corrections. A combination of significant graph- and connectivity-based features allowed single-subject classification of VM versus HC with significant accuracy of 77.68%, 77.68%, and 72.32% for the LR, SVM, and RF models, respectively.ConclusionPatients with VM had aberrant GM connectomes in terms of topological properties and network connections, reflecting potential dizziness, pain, and emotional dysfunctions. The identified features could serve as individualized neuroimaging markers of VM.

Intensive study, tuning and modification of reactive routing approach to improve flat FANET performance in data collection scenario.

The Flying Ad-hoc Network (FANET) can be defined as the Ad-hoc network that connects unmanned aerial vehicles flying in the space with each other and with a ground base station. However, the 3D movement of these drones with higher speeds results in a network of highly dynamic topology and intermittent connections, making the standard Ad Hoc routing protocols are not suitable for FANET. The approaches followed to address this issue include designing from scratch a routing protocol specific to FANET or modifying the existing protocols. From the view point of reliability, accuracy, and time, it is preferable to base the work on a protocol standard. But before amending the standard, tuning its performance and applying it under suitable conditions may be satisfactory for the new use. Therefore, this work considers flat FANET of fully mission-controlled drones and performs an extensive parametric simulation study to determine the best conditions and parameters' values for applying the popular Ad Hoc On-demand Distance Vector (AODV) to it. After deducing the recommended operating environment (FAODVN-OE), some examples of amendments were suggested to further improve the performance. It was found that the modified FAODVN-OE achieves high performance compared to the default standard in terms of jitter and delay. It helped reduce jitter and delay by an average of 93.2% and 83.8%, respectively, while exhausting less energy; however the network experiences a 24.5% reduction in packet delivery ratio.