Dynamic Connectivity in Cislunar Communication Networking Based on Geosynchronous Orbit Relay Satellites

The architecture of cislunar multi-hop communication networks, which focuses on the requirements of lunar full-coverage and continuous cislunar communications, is presented on the basis of Geosynchronous Orbit (GEO) satellite network relays. According to the geographical distribution of the forthcoming Chinese Deep Space Measuring and Controlling Network (DSMCN), two networking schemes are proposed and two elevation angle optimization models are established for locating GEO relay satellites. To analyze the dynamic connectivity, a dynamic network model is constructed with respect to the time-varying characteristics of cislunar trunk links. The advantages of the two proposed schemes, in terms of the Connectivity Rate (CR), Interruption Frequency (IF), and Average Length of Connecting Duration (ALCD), are corroborated by several simulations. In the case of the lunar polar orbit constellation case, the gains in the performance of scheme I are observed to be 134.55%, 117.03%, and 217.47% compared with DSMCN for three evaluation indicators, and the gains in the performance of scheme II are observed to be 238. 22%, 240.40%, and 572.71%. The results validate that the connectivity of GEO satellites outperforms that of earth facilities significantly and schemes based on GEO satellite relays are promising options for cislunar multi-hop communication networking.

Learning-Based Dynamic Connectivity Maintenance for UAV-Assisted D2D Multicast Communication

Unmanned aerial vehicles(UAVs) enable flexible networking functions in emergency scenarios.However,due to the movement characteristic of ground users(GUs),it is challenging to capture the interactions among GUs.Thus,we propose a learningbased dynamic connectivity maintenance architecture to reduce the delay for the UAV-assisted device-todevice(D2D) multicast communication.In this paper,each UAV transmits information to a selected GU,and then other GUs receive the information in a multi-hop manner.To minimize the total delay while ensuring that all GUs receive the information,we decouple it into three subproblems according to the time division on the topology:For the cluster-head selection,we adopt the Whale Optimization Algorithm(WOA) to imitate the hunting behavior of whales by abstracting the UAVs and cluster-heads into whales and preys,respectively;For the D2D multi-hop link establishment,we make the best of social relationships between GUs,and propose a node mapping algorithm based on the balanced spanning tree(BST) with reconfiguration to minimize the number of hops;For the dynamic connectivity maintenance,Restricted Q-learning(RQL) is utilized to learn the optimal multicast timeslot.Finally,the simulation results show that our proposed algorithms perfor better than other benchmark algorithms in the dynamic scenario.

Dynamic interwell connectivity analysis of multi-layer waterflooding reservoirs based on an improved graph neural network

The analysis of interwell connectivity plays an important role in the formulation of oilfield development plans and the description of residual oil distribution. In fact, sandstone reservoirs in China's onshore oilfields generally have the characteristics of thin and many layers, so multi-layer joint production is usually adopted. It remains a challenge to ensure the accuracy of splitting and dynamic connectivity in each layer of the injection-production wells with limited field data. The three-dimensional well pattern of multi-layer reservoir and the relationship between injection-production wells can be equivalent to a directional heterogeneous graph. In this paper, an improved graph neural network is proposed to construct an interacting process mimics the real interwell flow regularity. In detail, this method is used to split injection and production rates by combining permeability, porosity and effective thickness, and to invert the dynamic connectivity in each layer of the injection-production wells by attention mechanism.Based on the material balance and physical information, the overall connectivity from the injection wells,through the water injection layers to the production layers and the output of final production wells is established. Meanwhile, the change of well pattern caused by perforation, plugging and switching of wells at different times is achieved by updated graph structure in spatial and temporal ways. The effectiveness of the method is verified by a combination of reservoir numerical simulation examples and field example. The method corresponds to the actual situation of the reservoir, has wide adaptability and low cost, has good practical value, and provides a reference for adjusting the injection-production relationship of the reservoir and the development of the remaining oil.

Abnormal characterization of dynamic functional connectivity in Alzheimer’s disease

Numerous studies have shown abnormal brain functional connectivity in individuals with Alzheimer’s disease(AD)or amnestic mild cognitive impairment(aMCI).However,most studies examined traditional resting state functional connections,ignoring the instantaneous connection mode of the whole brain.In this case-control study,we used a new method called dynamic functional connectivity(DFC)to look for abnormalities in patients with AD and aMCI.We calculated dynamic functional connectivity strength from functional magnetic resonance imaging data for each participant,and then used a support vector machine to classify AD patients and normal controls.Finally,we highlighted brain regions and brain networks that made the largest contributions to the classification.We found differences in dynamic function connectivity strength in the left precuneus,default mode network,and dorsal attention network among normal controls,aMCI patients,and AD patients.These abnormalities are potential imaging markers for the early diagnosis of AD.

The Dynamic Relationship of Brain Networks Across Time Windows During Product-Based Creative Thinking

Consensus of creativity research suggests that the measurement of both originality and valuableness is necessary when designing creativity tasks.However,few studies have emphasized valuableness when exploring underlying neural substrates of creative thinking.The present study employs product-based creativity tasks that measure both originality and valuableness in an exploration of the dynamic relationship between the default mode(DMN),executive control(ECN),and salience(SN)networks through time windows.This methodology highlights relevance,or valuableness,in creativity evaluation as opposed to divergent thinking tasks solely measuring originality.The researchers identified seven brain regions belonging to the ECN,DMN,and SN as regions of interest(ROIs),as well as four representative seeds to analyze functional connectivity in 25 college student participants.Results showed that all of the identified ROIs were involved during the creative task.The insula,precuneus,and ventrolateral prefrontal cortex(vlPFC)remained active across all stages of product-based creative thinking.Moreover,the connectivity analyses revealed varied interaction patterns of DMN,ECN,and SN at different thinking stages.The integrated findings of the whole brain,ROI,and connectivity analyses suggest a trend that the DMN and SN(which relate to bottom-up thinking)attenuate as time proceeds,whereas the vlPFC(which relates to top-down thinking)gets stronger at later stages;these findings reflect the nature of our creativity tasks and decision-making of valuableness in later stages.Based on brain region activation throughout execution of the task,we propose that product-based creative process may include three stages:exploration and association,incubation and insight,and finally,evaluation and decision making.This model provides a thinking frame for further research and classroom instruction.

Parallel Molecular Dynamics on the Connection Machines

Abstract Abstract:We have demonstrated using vectorized parallel Lennard-Jones fluid program that vectorizing general-purpose parallel molecular package for simulating biomolecules which currently runs on the Connection Machine CM-5 using CMMD message passing would offer a significant improvement over 4 non-vectorized version. Our results indicate that the Lennard-Jones fluid program written in C*/CMNID is five times faster than the same program written in C/CMMD.

Aberrant dynamic functional connectivity of thalamocortical circuitry in major depressive disorder

Thalamocortical circuitry has a substantial impact on emotion and cognition.Previous studies have demonstrated alterations in thalamocortical functional connectivity(FC),characterized by region-dependent hypo-or hyper-connectivity,among individuals with major depressive disorder(MDD).However,the dynamical reconfiguration of the thalamocortical system over time and potential abnormalities in dynamic thalamocortical connectivity associated with MDD remain unclear.Hence,we analyzed dynamic FC(dFC)between ten thalamic subregions and seven cortical subnetworks from resting-state functional magnetic resonance images of 48 patients with MDD and 57 healthy controls(HCs)to investigate time-varying changes in thalamocortical FC in patients with MDD.Moreover,dynamic laterality analysis was conducted to examine the changes in functional lateralization of the thalamocortical system over time.Correlations between the dynamic measures of thalamocortical FC and clinical assessment were also calculated.We identified four dynamic states of thalamocortical circuitry wherein patients with MDD exhibited decreased fractional time and reduced transitions within a negative connectivity state that showed strong correlations with primary cortical networks,compared with the HCs.In addition,MDD patients also exhibited increased fluctuations in functional laterality in the thalamocortical system across the scan duration.The thalamo-subnetwork analysis unveiled abnormal dFC variability involving higher-order cortical networks in the MDD cohort.Significant correlations were found between increased dFC variability with dorsal attention and default mode networks and the severity of symptoms.Our study comprehensively investigated the pattern of alteration of the thalamocortical dFC in MDD patients.The heterogeneous alterations of dFC between the thalamus and both primary and higher-order cortical networks may help characterize the deficits of sensory and cognitive processing in MDD.

Epidemic Spreading Model Based on Social Active Degree in Social Networks

In this paper,an improved Susceptible-Infected-Susceptible(SIS) epidemic spreading model is proposed in order to provide a theoretical method to analyze and predict the spreading of diseases.This model is based on the following ideas:in social networks,the contact probability between nodes is decided by their social distances and their active degrees.The contact probability of two indirectly connected nodes is decided by the shortest path between them.Theoretical analysis and simulation experiment were conducted to evaluate the performance of this improved model.Because the proposed model is independent of the network structure,simulation experiments were done in several kinds of networks,namely the ER network,the random regular network,the WS small world network,and the BA scale-free network,in order to study the influences of certain factors have on the epidemic spreading,such as the social contact active degree,the network structure,the average degree,etc.This improved model provides an idea for studying the spreading rule of computer virus,attitudes,fashion styles and public opinions in social networks.

Abnormal Effective Connectivity in the Brain is Involved in Auditory Verbal Hallucinations in Schizophrenia

Information flow among auditory and language processing-related regions implicated in the pathophysiology of auditory verbal hallucinations（AVHs） in schizophrenia（SZ） remains unclear. In this study, we used stochastic dynamic causal modeling（s DCM） to quantify connections among the left dorsolateral prefrontal cortex（inner speech monitoring）, auditory cortex（auditory processing）, hippocampus（memory retrieval）, thalamus（information filtering）, and Broca＇s area（language production） in 17 first-episode drug-na？¨ve SZ patients with AVHs, 15 without AVHs, and 19 healthy controls using resting-state functional magnetic resonance imaging.Finally, we performed receiver operating characteristic（ROC） analysis and correlation analysis between image measures and symptoms. s DCM revealed an increasedsensitivity of auditory cortex to its thalamic afferents and a decrease in hippocampal sensitivity to auditory inputs in SZ patients with AVHs. The area under the ROC curve showed the diagnostic value of these two connections to distinguish SZ patients with AVHs from those without AVHs. Furthermore, we found a positive correlation between the strength of the connectivity from Broca＇s area to the auditory cortex and the severity of AVHs. These findings demonstrate, for the first time, augmented AVHspecific excitatory afferents from the thalamus to the auditory cortex in SZ patients, resulting in auditory perception without external auditory stimuli. Our results provide insights into the neural mechanisms underlying AVHs in SZ. This thalamic-auditory cortical-hippocampal dysconnectivity may also serve as a diagnostic biomarker of AVHs in SZ and a therapeutic target based on direct in vivo evidence.

Dynamically Optimized Sensor Deployment Based on Game Theory

Sensor network deployment is the key for sensors to play an important performance. Based on game theory, first, the authors propose a multi-type sensor target allocation method for the autonomous deployment of sensors, considering exploration cost, target detection value, exploration ability and other factors. Then, aiming at the unfavorable environment, e.g., obstacles and enemy interference, the authors design a method to maintain the connectivity of sensor network, under the conditions of effective detection of the targets. Simulation result shows that the proposed deployment strategy can achieve the dynamic optimization deployment under complex conditions.

Hamiltonian Analysis of 3-Dimensional Connection Dynamics in Bondi-like Coordinates

The Hamiltonian analysis for a 3-dimensional connection dynamics of o（1, 2）, spanned by {L-＋, L-2, L＋2） instead of {Lol, L02, L12}, is first conducted in a Bondi-like coordinate system. The symmetry of the system is clearly presented. A null coframe with 3 independent variables and 9 connection coefficients are treated as basic configuration variables. All constraints and their consistency conditions, the solutions of Lagrange multipliers as well as the equations of motion are presented. There is no physical degree of freedom in the system. The Bafiados-Teitelboim-Zanelli （BTZ） spaeetime is discussed as an example to check the analysis. Unlike the ADM formalism, where only non-degenerate geometries on slices are dealt with and the Ashtekar formalism, where non-degenerate geometries on slices are mainly concerned though the degenerate geometries may be studied as well, in the present formalism the geometries on the slices are always degenerate though the geometries for the spacetime are not degenerate.

Application of Connection in Molecular Dynamics

The evolution of electronic states in molecule has two origins: dynamical one produced by Schr¨odinger equation and kinematical one caused by base transformation due to nuclear motion.In current theories,the former gets analytic expression; the latter depends on heavy numerical calculation,which contains uncertainty.By using connection of fiber bundles,this paper establishes an analytic formula for the latter,and the numerical work is simplified.It shows the mathematical structure of molecule is fiber bundle.

Dynamical parton distributions from DGLAP equations with nonlinear corrections

Determination of proton parton distribution functions is presented under the dynamical parton model assumption by applying DGLAP equations with GLR-MQ-ZRS corrections.We provide two data sets,referred to as IMParton16,which are from two different nonperturbative inputs.One is the naive input of three valence quarks and the other is the input of three valence quarks with flavor-asymmetric sea components.Basically,both data sets are compatible with the experimental measurements at high scale（Q^2〉 2 GeV^2）.Furthermore,our analysis shows that the input with flavor-asymmetric sea components better reproduces the structure functions at high Q^2.Generally,the parton distribution functions obtained,especially the gluon distribution function,are good options for inputs to simulations of high energy scattering processes.The analysis is performed under the fixed-flavor number scheme for nf = 3,4,5.Both data sets start from very low scales,around 0.07 GeV^2,where the nonperturbative input is directly connected to the simple picture of the quark model.These results may shed some lights on the origin of the parton distributions observed at high Q^2.

Influence of the connecting condition on the dynamic buckling of longitudinal impact for an elastic rod

The stress wave propagation law and dynamic buckling critical velocity are formulated and solved by considering a general axial connecting boundary for a slender elastic straight rod impacted by a rigid body. The influence of connecting stiffness on the critical velocity is investigated with varied impactor mass and buckling time. The influences of rod length and rod mass on the critical velocity are also discussed. It is found that greater connecting stiffness leads to larger stress amplitude, and further results in lower critical velocity. It is particularly noteworthy that when the connecting stiffness is less than a certain value, dynamic buckling only occurs before stress wave reflects off the connecting end. It is also shown that longer rod with larger slenderness ratio is easier to buckle, and the critical velocity for a larger-mass rod is higher than that for a lighter rod with the same geometry.