Hub Structures Research Articles

ANCF-based modelling of rigid-flexible coupling of helicopter flexible rotor blades

Abstract A high-precision rotor structure dynamic model applicable to a variety of helicopter blade hub structures is established considering rotor blade flexible deformation. The absolute nodal coordinate method is applied to the traditional rotor blade rigid-flexible coupling dynamics model, making the new model applicable to both fully articulated rotor blades and other advanced blade hub structures. No small-volume assumptions were made in the modelling process. Both Green’s strain expression under finite deformation and the geometrical relation of elastic deformation motion based on the absolute nodal coordinate method are used to derive the blade strain energy, kinetic energy, and the external load virtual work. The rotation angle at the articulation constraint at the root of the blade coupled with the elastic deformation degrees of freedom of the blade in three independent rigid body degrees of freedom, respectively. The rotor blade rigid-flexible coupling dynamics equations were established according to the Hamilton action principle. The time-domain simulation is carried out under a free single pendulum and fixed rotational speed/fixed torque for the established flexible blade model, which verifies the validity and high accuracy of the rigid-flexible coupling articulation model of the flexible rotor blade and the blade hub.

Instability and Evolution of Shocked Clouds Formed by Orthogonal Collisions between Magnetized Filamentary Molecular Clouds

Filamentary molecular clouds are recognized as primary sites for the formation of stars. Specifically, regions characterized by the overlapping point of multiple filaments, known as hub regions, are often associated with active star formation. However, the formation mechanism of this hub structure is not well understood. Therefore, to understand the formation mechanism and star formation in hub structures, as a first step, we investigate the orthogonal collisions between two filaments using three-dimensional ideal magnetohydrodynamical simulations. As a model of initial filaments, we use an infinitely long filament in magnetohydrostatic equilibrium under a global magnetic field running perpendicular to the filament axis. Two identical equilibrium filaments, sharing the same magnetic flux, are arranged with their long axes perpendicular to each other and given an initial velocity perpendicular to their long axes to replicate an orthogonal collision. We find three types of evolution after the shocked cloud is formed: collapse, stable, and expansion modes. The energy balance just after the filaments completely collide explains the future evolution of the shocked cloud. If the magnitude of gravitational energy is larger than the sum of the kinetic, thermal, and magnetic energies, the shocked cloud evolves in collapse mode. If the magnitude of gravitational energy is less than the sum of these energies, the cloud evolves in stable mode when the kinetic energy is relatively small and in expansion mode when the kinetic energy is sufficiently large.

A Bayesian incorporated linear non-Gaussian acyclic model for multiple directed graph estimation to study brain emotion circuit development in adolescence.

Emotion perception is essential to affective and cognitive development which involves distributed brain circuits. Emotion identification skills emerge in infancy and continue to develop throughout childhood and adolescence. Understanding the development of the brain's emotion circuitry may help us explain the emotional changes during adolescence. In this work, we aim to deepen our understanding of emotion-related functional connectivity (FC) from association to causation. We proposed a Bayesian incorporated linear non-Gaussian acyclic model (BiLiNGAM), which incorporated association model into the estimation pipeline. Simulation results indicated stable and accurate performance over various settings, especially when the sample size was small. We used fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) to validate the approach. It included 855 individuals aged 8-22 years who were divided into five different adolescent stages. Our network analysis revealed the development of emotion-related intra- and intermodular connectivity and pinpointed several emotion-related hubs. We further categorized the hubs into two types: in-hubs and out-hubs, as the center of receiving and distributing information, respectively. In addition, several unique developmental hub structures and group-specific patterns were discovered. Our findings help provide a directed FC template of brain network organization underlying emotion processing during adolescence.

Impact of blade shape on the aerodynamic performance and turbulent jet dynamics produced by a ducted rotor

A ducted rotor system was used to produce turbulent jets with a Reynolds number up to 5.97 × 105 and Mach number of 0.222 based on mean streamwise velocity. Three rotors with a diameter of 11.8 cm were manufactured and tested inside a duct with a 1 mm tip clearance at a speed up to 30 000 revolutions per minute (rpm). All rotor blades contain the same aspect ratio of 2.2, a National Advisory Committee for Aeronautics 2410 airfoil, and ideal pitch distribution. However, three different blade planform shapes were used including a rectangular shape with constant chord, trapezoidal shape with a taper ratio of 0.5, and elliptical shape where the trailing edge of the blade is expressed with an elliptical function. The rotor thrust and electric power were measured, and the thrust coefficient and figure of merit was computed. The flow-field produced by the ducted rotors was measured in the near-field using laser Doppler velocimetry techniques. The inflow velocity approximately 3 mm upstream of the rotor blade leading edge was acquired and its significance on blade aerodynamics and performance is analyzed. Time-averaged contours of cross-stream vorticity reveal intense hub and blade tip vortex structures, which are impacted by the shape of the blade, particularly in the blade tip region. Tip vorticity as well as streamwise turbulence intensity and turbulent kinetic energy in this region were mitigated for the rotors with trapezoidal and elliptical blades. However, the turbulent structure of the jet produced by all three rotor blade shapes showed similarity at a mere 2.8 rotor diameters downstream of the rotor. This finding emphasizes the importance of blade design on the near-field dynamics of ducted rotor flows for aircraft propulsion.

Behavior evolution and deformation prediction of RC beams with double openings under fatigue loading

In the design of complex high-speed railway platforms, bridges, and other transport hub structures, openings need to be made in the reinforced concrete (RC) beam web due to the laying of pipeline lines. The performance degradation caused by the openings is a concern for the RC beam under fatigue load. To study the fatigue performance of RC beams with double openings, six RC beams with double openings were designed, with the load level ratio, location of loading points, and number of fatigue cycles as changing parameters. Fatigue tests were carried out under repeated loads. The stress state and failure mode of RC beams with double openings under fatigue load were studied, and the influence of various parameters on the fatigue performance of beams with double openings was discussed. The deformation compatibility model of opening and mid-span deformation was proposed, and the deformation evolution law of double-opening RC beam is analyzed based on the model. The results indicate that under cyclic loading, the fatigue failure modes of double opening RC beams are inclined cracks in the upper and lower beams of the opening and cracks in the corner of the opening. The specimen with a maximum load limit of 0.7Pu and a load position acting on the middle of the opening undergoes fatigue failure. The double opening RC beam has a fatigue life of at least 410000 cycles under a load level of 0.5Pu. After the fatigue test, the remaining ultimate bearing capacity, flexural stiffness, and ductility of the RC beams deteriorate with the increase of load level. The residual performance of the specimen after fatigue is even worse when the load is located in the middle of the opening. Increasing the fatigue cycle to 410000 cycles has an impact on the remaining ultimate bending moment of about 7% and the remaining flexural stiffness and ductility degrade by about 25%. Based on existing research results, a fatigue deformation prediction method for RC beams with double openings was proposed, with an error of less than 7%.

Static and Modal Models of Automotive Wheel Hubs Based on Lightweight Design and Cost-Effectiveness

This study investigated the optimization design methods for automotive wheel hub structures based on lightweight and economic considerations through static and modal analyses. A comprehensive static analysis and modal analysis of the automotive wheel hub were conducted using finite element analysis to understand its response characteristics under load. In order to optimize the structure of the wheel hub, the lightweight design is also considered on the premise of ensuring the reliability of its stiffness and strength. By appropriately setting the initial conditions and boundary conditions, the deformation and stress distributions of the optimized wheel hub were obtained for various loading positions. The modal analysis results showed that the vibration frequencies of the optimized wheel hub avoided external excitation frequencies, meeting the design requirements. Further analysis of the lightweight effect of the wheel hub was conducted, and a lightweight material replacement scheme was proposed. Ultimately, the rationality of the optimized wheel hub’s strength and vibration performance was validated through comprehensive static and modal analyses.

Impaired expression of metallothioneins contributes to allergen-induced inflammation in patients with atopic dermatitis

Regulation of cutaneous immunity is severely compromised in inflammatory skin disease. To investigate the molecular crosstalk underpinning tolerance versus inflammation in atopic dermatitis, we utilise a human in vivo allergen challenge study, exposing atopic dermatitis patients to house dust mite. Here we analyse transcriptional programmes at the population and single cell levels in parallel with immunophenotyping of cutaneous immunocytes revealed a distinct dichotomy in atopic dermatitis patient responsiveness to house dust mite challenge. Our study shows that reactivity to house dust mite was associated with high basal levels of TNF-expressing cutaneous Th17 T cells, and documents the presence of hub structures where Langerhans cells and T cells co-localised. Mechanistically, we identify expression of metallothioneins and transcriptional programmes encoding antioxidant defences across all skin cell types, that appear to protect against allergen-induced inflammation. Furthermore, single nucleotide polymorphisms in the MTIX gene are associated with patients who did not react to house dust mite, opening up possibilities for therapeutic interventions modulating metallothionein expression in atopic dermatitis.

Semantic knowledge of social interactions is mediated by the hedonic evaluation system in the brain.

Attaching semantic meaning to sensory information received from both inside and outside our bodies is a fundamental function of the human brain. The theory of Controlled Semantic Cognition (CSC) proposes that the formation of semantic knowledge relies on connections between spatially distributed modality-specific spoke-nodes, and a modality-general hub in the anterior temporal lobes (ATLs). This theory can also be applied to social semantic knowledge, though certain domain-specific spoke-nodes may make a disproportionate contribution to the understanding of social concepts. The ATLs have strong connections with spoke-node structures such as the subgenual ACC (sgACC) and the orbitofrontal cortex (OFC) that play an important role in predicting the hedonic value of stimuli. We hypothesized that in addition to the ATL semantic hub, a social semantic task would also require input from hedonic evaluation structures. We used voxel based morphometry (VBM) to examine structural brain-behavior relationships in 152 patients with neurodegeneration (Alzheimer's disease [N=12], corticobasal syndrome (N=18], progressive supranuclear palsy [N=13], behavioral variant frontotemporal dementia [N=56], and primary progressive aphasia (PPA) [N=53]) using the Social Interaction Vocabulary Task (SIVT). This task measures the ability to correctly match a social term (e.g. "gossiping") with a visual depiction of that social interaction. As predicted, VBM showed that worse SIVT scores corresponded with volume loss in bilateral ATL semantic hub regions, but also in the sgACC, OFC, caudate and putamen (pFWE <0.05). These results support the CSC model of a hub-and-spoke organization of social semantic knowledge with the ATL as a domain-general semantic hub, and ventromedial and striatal structures as domain specific spoke-nodes. Importantly, these results suggest that correct comprehension of social semantic concepts requires emotional 'tagging' of a concept by the evaluation system, and that the social deficits observed in some neurodegenerative disease syndromes may be caused by the break-down of this mechanism.

Key aspects of establishing research, knowledge, and innovation‐based hubs as part of the local innovation ecosystem

The purpose of this research is to increase the understanding of the principles of forming different types of hubs as spaces for collaborative R&amp;D and innovation. This article offers valuable new knowledge regarding the establishment of hubs as part of the local innovation ecosystem. The paper will, through a qualitative multiple case study, shed light on the research issue of the establishment of hubs through an analysis of empirical data collected from three different types of hubs: a research hub, knowledge hub, and innovation hub. As a result, we introduce new building blocks of hubs to support managers and practitioners in forming collaborative hub structures.

Actuator fault tolerant offshore wind turbine load mitigation control

Offshore wind turbine (OWT) rotors have large diameters with flexible blade structures which are subject to asymmetrical loads caused by blade flapping and turbulent or unsteady wind flow. Rotor imbalance inevitably leads to enhanced fatigue of blade rotor hub and tower structures. Hence, to enhance the life of the OWT and maintain good power conversion the unbalanced loading requires a reliable mitigation strategy, typically using a combination of Individual Pitch Control (IPC) and Collective Pitch Control (CPC). Increased pitch motion resulting from IPC activity can increase the possibility of pitch actuator faults and the resulting load imbalance results in loss of power and enhanced fatigue. This has accelerated the emergence of new research areas combining IPC with the fault tolerant control (FTC)-based fault compensation, a so-called FTC and IPC “co-design” system. A related research challenge is the clear need to enhance the robustness of the FTC IPC “co-design” to some dynamic uncertainty and unwanted disturbance. In this work a Bayesian optimization-based pitch controller using Proportional–Integral (PI) control is proposed to improve pitch control robustness. This is achieved using a systematic search for optimal controller coefficients by evaluating a Gaussian process model between the designed objective function and the coefficients. The pitch actuator faults are estimated and compensated using a robust unknown input observer (UIO)-based FTC scheme. The robustness and effectiveness of this “co-design” scheme are verified using Monte Carlo simulations applied to the 5MW NREL FAST WT benchmark system. The results show clearly (a) the effectiveness of the load mitigation control for a wide range of wind loading conditions, (b) the effect of actuator faults on the load mitigation performance and (c) the recovery to normal load mitigation, subject to FTC action.

Fragility Induced by Interdependency of Complex Networks and Their Higher-Order Networks

The higher-order structure of networks is a hot research topic in complex networks. It has received much attention because it is closely related to the functionality of networks, such as network transportation and propagation. For instance, recent studies have revealed that studying higher-order networks can explore hub structures in transportation networks and information dissemination units in neuronal networks. Therefore, the destruction of the connectivity of higher-order networks will cause significant damage to network functionalities. Meanwhile, previous works pointed out that the function of a complex network depends on the giant component of the original(low-order) network. Therefore, the network functionality will be influenced by both the low-order and its corresponding higher-order network. To study this issue, we build a network model of the interdependence of low-order and higher-order networks (we call it ILH). When some low-order network nodes fail, the low-order network's giant component shrinks, leading to changes in the structure of the higher-order network, which further affects the low-order network. This process occurs iteratively; the propagation of the failure can lead to an eventual network crash. We conducted experiments on different networks based on the percolation theory, and our network percolation results demonstrated a first-order phase transition feature. In particular, we found that an ILH is more fragile than the low-order network alone, and an ILH is more likely to be corrupted in the event of a random node failure.

A New Hybrid Multi-Objective Scheduling Model for Hierarchical Hub and Flexible Flow Shop Problems

In general, customers are looking to receive their orders in the fastest time possible and to make purchases at a reasonable price. Consequently, the importance of having an optimal delivery time is increasingly evident these days. One of the structures that can meet the demand for large supply chains with numerous orders is the hierarchical integrated hub structure. Such a structure improves efficiency and reduces chain costs. To make logistics more cost-effective, hub-and-spoke networks are necessary as a means to achieve economies of scale. Many hub network design models only consider hub type but do not take into account the hub scale measured using freight volume. This paper proposes a multi-objective scheduling model for hierarchical hub structures (HHS), which is layered from top to bottom. In the third layer, the central hub takes factory products from decentralized hubs and sends them to other decentralized hubs to which customers are connected. In the second layer, non-central hubs are responsible for receiving products from the factory and transferring them to central hubs. These hubs are also responsible for receiving products from central hubs and sending them to customers. Lastly, the first layer contains factories responsible for producing products and providing for their customers. The factory uses the flexible flow-shop platform and structure to produce its products. The model’s objective is to minimize transportation and production costs as well as product arrival times. To validate and evaluate the model, small instances have been solved and analyzed in detail with the weighted sum and ε-constraint method. Consequently, based on the mean ideal distance (MID) metric, two methods were compared for the designed instances.

Turbulence and heat release rate network structure in hydrogen-enriched combustion

Complex network theory is used to analyze the spatiotemporal dynamics of the PRECCINSTA swirl burner, operating on hydrogen-methane fuel blends. At a power setting of 15 kW with equivalence ratio of 0.8 and hydrogen fuel fraction (HFF) ranging from 0% to 80%, period-1 and period-2 limit cycle oscillations as well as chaotic oscillations were observed. A turbulence network was constructed from the vorticity data obtained with particle image velocimetry. In addition, a heat release rate (HRR) correlation network was constructed from chemiluminescence images. Although the two networks concern a common thermoacoustic system, both exhibit significant differences: the turbulence network displays power law degree distribution, maintains small world property for all HFFs and is scale-free only in the absence of hydrogen enrichment. The HRR correlation network does not feature these properties, but hints at an asymmetric coupling between the heat release rate and the acoustic pressure for all HFFs. Furthermore, the HRR network is responsive to changes in HFF and the corresponding shifts in the dynamical state as well as in the root-mean-square of acoustic pressure. On the contrary, the turbulence network displays no such sensitivity and its properties are almost constant in the upper HFF range. It exhibits stationary hub structures for all the fuel blends tested, whereas the HRR correlation network is hub-free.

Detecting abnormal connectivity in schizophrenia via a joint directed acyclic graph estimation model

Functional connectivity (FC) between brain region has been widely studied and linked with cognition and behavior of an individual. FC is usually defined as the correlation or partial correlation of fMRI blood oxygen level-dependent (BOLD) signals between two brain regions. Although FC has been effective to understand brain organization, it cannot reveal the direction of interactions. Many directed acyclic graph (DAG) based methods have been applied to study the directed interactions but their performance was limited by the small sample size while high dimensionality of the available data. By enforcing group regularization and utilizing samples from both case and control groups, we propose a joint DAG model to estimate the directed FC. We first demonstrate that the proposed model is efficient and accurate through a series of simulation studies. We then apply it to the case-control study of schizophrenia (SZ) with data collected from the MIND Clinical Imaging Consortium (MCIC). We have successfully identified decreased functional integration, disrupted hub structures and characteristic edges (CtEs) in SZ patients. Those findings have been confirmed by previous studies with some identified to be potential markers for SZ patients. A comparison of the results between the directed FC and undirected FC showed substantial differences in the selected features. In addition, we used the identified features based on directed FC for the classification of SZ patients and achieved better accuracy than using undirected FC or raw features, demonstrating the advantage of using directed FC for brain network analysis.

Analyzing temporal graphs of malware distribution networks

This research provides temporal insight on network topological structures, as well as transitional properties and malware attribution of malware distribution networks. This is accomplished with a temporal-based data set created using a novel data fusion of publicly-available data sources. We developed and used a crawler, along with public APIs, to collect publicly-available data of malicious top-level domains and relevant hosted malware from Google Safe Browsing and VirusTotal for an eight-month period between 19 January and 25 September of 2017. We then combined these data sources, which revealed new insight on fully qualified domain name topological network structure properties and temporal transitions not appreciable from the individual data sources. We have provided the technical details of our novel data fusion approach to GSB and VT static data. The result of this data fusion was the creation of new observable knowledge, primarily temporal-based structural changes and malware attribution within the distribution network, which is not available by analyzing the static data of GSB and VT in isolation. Data revealing details of malware-hosting on a domain brought to light topological structures of fully-qualified domain names involved in the distribution of malicious files. Our insights include: 1) malware distribution networks form clusters that follow the Power law, 2) network structure components such as bridges and hubs (both concepts presented and defined in this paper), and URL shortening providers serve significant roles in malware distribution dynamics, 3) persistence of fully-qualified domain names in malware distribution is random and often used only once to host malware, 4) a large number of unique, downloaded malicious files hosted on various nodes in a malware distribution network were found to belong to a much smaller set of malware families. These observed insights revealed continued persistence of surrounding topological structures. These topological structures were streaming malicious data flows to fully-qualified domain names identified as actively hosting malware before and after the date of identification. The insights further suggest large topological structures with data flow distributing malware persist over time with small sub-structural changes. We have provided suggestions on preventing sustained malicious data flows based on our temporal observations of bridge and hub structures. Individual persistent fully-qualified domain names within these large structures repeatedly served as either a source or an intermediate node of malicious data flows. This implies that the continued monitoring of data flows can serve to alert early-stage malware distribution.

Exploring barriers and solutions to encouraging evidence‐into‐use within an embedded evaluation approach: Reflections from the field

AbstractDespite increasing pressure for policy and practice to adopt a more evidence‐based approach, transferring evidence into use remains a stubborn challenge. This is largely due to a number of researcher‐derived and user‐derived barriers at play within institutions, organisations and systems that constrain active engagement with evidence. This paper contributes to current debates on evidence‐use by suggesting that embedded evaluation approaches might overcome such barriers, through the creation of social capital that can be drawn upon by embedded evaluators to: (i) build trust, confidence and understanding around evaluation and evidence, on the part of local practitioners and policy makers; (ii) develop a co‐productive evidence infrastructure that might draw together diverse stakeholders, as well as encourage user‐engagement with varied forms of evaluation data (both numerical data and rich narrative accounts), to capture the richness of the unfolding story of complex educational initiatives. To illustrate this, this paper presents six reflective vignettes that ponder the barriers, and potential responses sought, within a two‐year exploratory case study that aimed to actively engage local education‐related stakeholders, leading a borough‐wide place‐based initiative, with evidence. Data reported within these vignettes were collected through a series of semi‐structured interviews, supplemented by an embedded evaluator's field notes and analysed thematically. Although more research is needed, this paper concludes that the embedded evaluation model might have potential to respond to the diverse challenges associated with evidence‐use, by positioning the evaluator in a relationally elastic way that might enable them to embed evidence‐into‐use pathways within policy and practice both actively and iteratively. Context and implicationsRationale for this studyTo present an empirical example of an embedded evaluation approach that has sought to overcome a range of barriers to evidence‐use.Why the new findings matterResponding to a lack of consensus around the most effective ways to implement evidence into educational practice, the findings suggest potentially significant solutions to established barriers to evidence‐use.Implications forThis paper has implications for programme / project leads of place‐based education improvement strategies—both operationally and strategically—in demonstrating how embedded evaluation might enable more institutionally relevant and longitudinal evaluation agenda and designs to be co‐produced with a range of education‐related stakeholders, including young people.This paper also has implications for university‐based researchers and evaluators. In particular, it demonstrates the possibilities that embedded research and evaluation might pose for bridging between the university as a world‐leading research hub and regional education systems and structures, in ways that place evidence at the heart of local policy and practice.

Partial stability criterion for a heterogeneous power grid with hub structures

One of the priority tasks in studying power grids is to find the conditions of their stable operation. The fundamental requirement is synchronizing all the elements (generators and consumers) of a power grid. However, various disturbances can destroy the synchronization. Moreover, desynchronization occurring in a small part of the grid can lead to severe large-scale power outages (or blackouts) due to numerous cascading failures. Here we consider the model of a heterogeneous power grid with hub structures (subgrids), taking into account arbitrary lengths and impedances of grid’s transmission lines and also their arbitrary amount. Using the auxiliary comparison systems approach, we analyze the dynamics of a hub subgrid. Based on the findings, we develop a novel criterion of partial stability of power grids featuring hub structures. The criterion makes it possible to identify the regions of safe operation of individual hub subgrid elements. First, the criterion allows obtaining parameters’ values that guarantee existence and local stability of either synchronous or quasi-synchronous modes in individual hub subgrid elements, i.e. steady-state stability. Second, the criterion allows obtaining the safe values of abrupt frequency and phase disturbances that eventually vanish, i.e. imply transient stability with respect to state disturbance. Third, the criterion permits determining the safe ranges of parameters’ disturbances that do not lead to catastrophic desynchronizing effects, i.e. imply transient stability with respect to parameters’ disturbance. Also, we discover typical dependences of the safe regions on the parameters of transmission lines. We demonstrate the applicability of the criterion on two test power grids with arbitrary distributions of powers as well as effective lengths and impedances of transmission lines. The results may help optimize stability and contribute to developing new real-time control schemes for smart grids that can automatically recover from failures.

A connectomic analysis of deep brain stimulation for treatment-resistant depression

ObjectiveDeep brain stimulation (DBS) has been used as a treatment of last resort for treatment-resistant depression (TRD) for more than a decade. Many DBS targets have been proposed and tested clinically, but the underlying circuit mechanisms remain unclear. Uncovering white matter tracts (WMT) activated by DBS targets may provide crucial information about the circuit substrates mediating DBS efficacy in ameliorating TRD. MethodsWe performed probabilistic tractography using diffusion magnetic resonance imaging datas from 100 healthy volunteers in Human Connectome Project datasets to analyze the structural connectivity patterns of stimulation targeting currently-used DBS target for TRD. We generated mean and binary fiber distribution maps and calculated the numbers of WMT streamlines in the dataset. ResultsProbabilistic tracking results revealed that activation of distinct DBS targets demonstrated modulation of overlapping but considerably distinct pathways. DBS targets were categorized into 4 groups: Cortical, Striatal, Thalamic, and Medial Forebrain Bundle according to their main modulated WMT and brain areas. Our data also revealed that Brodmann area 10 and amygdala are hub structures that are associated with all DBS targets. ConclusionsOur results together suggest that the distinct mechanism of DBS targets implies individualized target selection and formulation in the future of DBS treatment for TRD. The modulation of Brodmann area 10 and amygdala may be critical for the efficacy of DBS-mediated treatment of TRD.

Phase fMRI defines brain resting-state functional hubs within central and posterior regions.

From a brain functional connectivity (FC) matrix, we can identify the hub nodes by a new method of eigencentrality mapping, which not only counts for one node's centrality but also all other nodes' centrality values through correlation connections in an eigenvector of the FC matrix. For the resting-state functional MRI (fMRI) data (complex-valued EPI images in nature), both magnitude and phase images are useful for brain FC analysis. We herein report on brain functional hubness analysis by constructing the FC matrix from phase fMRI data and identifying the hub nodes by eigencentrality mapping. In our study, we collected a cohort of 160 complex-valued fMRI dataset (consisting of magnitude and phase in pairs), and performed independent component analysis (ICA), FC matrix calculation (in size of 50 × 50) and FC matrix eigen decomposition; thereby obtained the 50-node eigencentrality values in the eigenvector associated with the largest eigenvalue. We also compared the hub structures inferred from FC matrices under different thresholding. Alternatively, we obtained the geometric hubs among p value the 50 nodes involved in the FC matrix through the use of harmonic centrality metric. Our results showed that phase fMRI data analysis defines the resting-state brain functional hubs primarily in the central region (subcortex) and the posterior region (parieto-occipital lobes and cerebella). The brain central hubness was supported by the geometric central hubness, which, however, is distinct from the magnitude-inferred hubness in brain superior region (frontal and parietal lobes). Our findings pose a new understanding of (or a debate over) brain functional connectivity architecture.

Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction

Real-time forwarding prediction for predicting online contents' popularity is beneficial to various social applications for enhancing interactive social behaviors. Cascade graphs, formed by online contents' propagation, play a vital role in real-time forwarding prediction. Existing cascade graph modeling methods are inadequate to embed cascade graphs that have hub structures and deep cascade paths, or they fail to handle the short-term outbreak of forwarding amount. To this end, we propose a novel real-time forwarding prediction method that includes an effective approach for cascade graph embedding and a short-term variation sensitive method for time-series modeling, making the best of cascade graph features. Using two real world datasets, we demonstrate the significant superiority of the proposed method compared with the state-of-the-art. Our experiments also reveal interesting implications hidden in the performance differences between cascade graph embedding and time-series modeling.