Changes In Topological Structure Research Articles

A deep learning tissue classifier based on differential Co-expression genes predicts the pregnancy outcomes of cattle.

Economic losses in cattle farms are frequently associated with failed pregnancies. Some studies found that the transcriptomic profiles of blood and endometrial tissues in cattle with varying pregnancy outcomes display discrepancies even before artificial insemination (AI) or embryo transfer (ET). In the study, 330 samples from seven distinct sources and two tissue types were integrated and divided into two groups based on the ability to establish and maintain pregnancy after AI or ET: P (pregnant) and NP (nonpregnant). By analyzing gene co-variation and employing machine learning algorithms, the objective was to identify genes that could predict pregnancy outcomes in cattle. Initially, within each tissue type, the top 100 differentially co-expressed genes (DCEG) were identified based on the analysis of changes in correlation coefficients and network topological structure. Subsequently, these genes were used in models trained by seven different machine learning algorithms. Overall, models trained on DCEGs exhibited superior predictive accuracy compared to those trained on an equivalent number of differential expression genes (DEGs). Among them, the deep learning models based on differential co-expression genes in blood and endometrial tissue achieved prediction accuracies of 91.7% and 82.6%, respectively. Finally, the importance of DCEGs was ranked using SHapley Additive exPlanations (SHAP) and enrichment analysis, identifying key signaling pathways that influence pregnancy. In summary, this study identified a set of genes potentially affecting pregnancy by analyzing the overall co-variation of gene connections between multiple sources. These key genes facilitated the development of interpretable machine learning models that accurately predict pregnancy outcomes in cattle.

An adaptive level-set/square-root-conformation representation/discontinuous Galerkin method for simulating viscoelastic two-phase flow systems

A new numerical method, which is based on the coupling of adaptive mesh technique, level set (LS) method, square-root-conformation representation (SRCR) approach, and discontinuous Galerkin (DG) method within the dual splitting framework, is developed for viscoelastic two-phase flow problems. This combination has been more effective than expected. The LS method is performed to capture the moving interface due to its efficiency and simplicity when dealing with the significant interface deformations. The dual splitting scheme is applied to decouple the whole system into subequations, which circumvent the limitation of the Ladyzhenskaya-Babuška-Brezzi condition. The SRCR approach is employed to reconstruct the Oldroyd-B constitutive equation to solve the high Weissenberg number problem. The high-order DG method is performed for the spatial discretizations of equations to deal with the convection-dominated problems. In addition, the reinitialization method of the LS function and a simple mass correction technique are applied to guarantee the mass conservation in calculation. In this coupled method, there is no need to require reinitialization within every time step but after suitable time steps. Meanwhile, the adaptive mesh technique is implemented in the coupling procedure, which greatly improves the computational efficiency. The coupled algorithm is performed to simulate the swirling deformation flow, Rayleigh–Taylor instability and bubble rising problems. And the influences of the parameters on the rising speed and shape of bubble in viscoelastic liquid are analyzed in detail. The numerical results indicate that the coupled algorithm is effective and accurate for simulating the interface evolution problems with complex topological structure changes, and can guarantee the mass conservation property.

A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects

EEG-based emotion recognition uses high-level information from neural activities to predict emotional responses in subjects. However, this information is sparsely distributed in frequency, time, and spatial domains and varied across subjects. To address these challenges in emotion recognition, we propose a novel neural network model named Temporal-Spectral Graph Convolutional Network (TSGCN). To capture high-level information distributed in time, spatial, and frequency domains, TSGCN considers both neural oscillation changes in different time windows and topological structures between different brain regions. Specifically, a Minimum Category Confusion (MCC) loss is used in TSGCN to reduce the inconsistencies between subjective ratings and predefined labels. In addition, to improve the generalization of TSGCN on cross-subject variation, we propose Deep and Shallow feature Dynamic Adversarial Learning (DSDAL) to calculate the distance between the source domain and the target domain. Extensive experiments were conducted on public datasets to demonstrate that TSGCN outperforms state-of-the-art methods in EEG-based emotion recognition. Ablation studies show that the mixed neural networks and our proposed methods in TSGCN significantly contribute to its high performance and robustness. Detailed investigations further provide the effectiveness of TSGCN in addressing the challenges in emotion recognition.

Hydrogen behavior and microstructural evolution in flexible IGZO thin films under stress

In this study, we investigated the effect of mechanical stress on hydrogen diffusion in flexible amorphous InGaZnO (a-IGZO) thin films and the resulting microstructural changes. The cyclic bending test under different curvature radii (R) revealed significant morphological evolution and bond state changes in IGZO thin films. As the curvature radius decreases from 20 mm to 5 mm, the surface of the sample gradually becomes rough and cracks appear. Simultaneously, changes in nanoscale topological structure and chemical composition exhibit stronger hydrogen diffusion and structural relaxation: The oxygen-hydrogen (O-H) bond content increased from 19 % to 55 %, while the metal-oxygen (M − O) bond content decreased from 50 % to 28 %. The M − H content increased, and In-H related structures underwent transformation. The radius of gyration (Rg) increasing from 1.652 nm to 1.812 nm. These results provide quantitative insights into the stability and performance of IGZO-based flexible electronic devices under mechanical deformation.

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In the present work, an efficient dynamic graph embedding approach is proposed, called dynamic GCN (DyGCN), which is an extension of the GCN-based methods. The embedding propagation scheme of GCN is naturally generalized to a dynamic setting in an efficient manner, which propagates the change in topological structure and neighborhood embeddings along the graph to update the node embeddings. The most affected nodes are updated first, and then their changes are propagated to further nodes, which in turn are updated. Extensive experiments on various dynamic graphs showed that the proposed model can update the node embeddings in a time-saving and performance-preserving way.

An improved micro–macro model of multicomponent reactive transport for multimineral systems

The multiscale coupled reactive transport model has attracted increasing attention in recent years because it can reveal the different characteristics of the spatial structure evolution of porous media. A micro–macro model of multiscale coupled reaction transport has been derived from detailed pore-scale models by using the upscaling technique based on homogenization method. Because of its clear upscaling theory, it has been widely used. However, the current micro–macro model still has obvious defects in dealing with the geochemical reaction process in multimineral systems. To solve this problem, we coupled the model with existing geochemical models that have been widely verified so that the model has the ability to deal with multimineral reactions under complex hydrochemical conditions. The newly developed coupling model and the current model are used to simulate the same problem, which proves the superiority of the new model for geochemical reaction prediction. We extend the new model to a two-dimensional scanning electron microscope image, where a challenging medium geometry setup is handled well during simulation. At the same time, the new model also accurately captures the abrupt changes in macroscopic effective parameters caused by the change in geometric topological structure. Finally, we use the principle of mass conservation to quantify numerical errors caused by geometric evolution, ensuring the accuracy and validity of simulation results, which verifies the numerical scheme of the model to a certain extent.

Heat transfer performance enhancement and mechanism analysis of thermal energy storage unit designed by using a modified transient topology optimization model

The spatial layout of the highly conductive fin and phase change materials (PCM) and the thermophysical properties of PCM are important factors restricting the heat transfer rate of the latent heat thermal energy storage (LHTES). The current fin design concentrates on the limited design space, and rarely adopts the optimizing form of unconstrained structure evolution in the process. So this study proposes a modified transient topology optimization (TO) model to seek the optimal distribution of the fins embedded into PCM subject to the constraints of fin area and specified time. This topology optimization problem is formulated using the density-based material design variable in governing equations, physical constraints, etc. The design freedom and procedural iterative solving of TO model allow for the free evolution of innovative fin structures and elucidate the interaction mechanism between the fin design and physical occurring processes. The double interpolation function, variable regularization, adjoint-based sensitivity analysis and GCMMA algorithm are established to further improve TO model to obtain high-precision solutions. Using above methods, the regular evolution mechanism of fins and physical performance parameters and the trade-off between performance and structure are analyzed. Then, the effects and reasons of pure paraffin (PW) and the adding carbon nanotubes (nPW) on changes in topology structure and performance parameters are also comparatively studied. Finally, by comparing TO-fins with conventional rectangular fins, the superiority of the established method is verified, and the performance improvement mechanism and extent of TO-fins are revealed. Results show that TO-fin LHTES with nPW can achieve better performance than TO-fin LHTES with PW under fewer branches and shorter branch length. The uniform distribution and multi-branch characteristics of TO-fins greatly shorten the heat conduction path, and its local segmentation of whole PCM enhances the local convection effect.

Cell maximum life cycle based reliability analysis method for wireless mobile networks

The nodes of wireless mobile networks have mobility, which leads to the change of network topology and signal strength. Connectivity, a classical reliability index, calculated by existing methods based on graph theory often needs to find out all paths in fixed topology, which inevitably have combination explosion problems. With the changes of topological structure over time, a heavier calculation amount arises when the reliability of wireless mobile networks is calculated by existing methods to characterize the whole network as components of nodes and edges at every moment of a working phase. To solve the above problem, a new type of reliability analysis index for wireless mobile networks – the maximum life cycle of network cells, and its assessment model and algorithm are proposed, based on the concept of network cells and considered the network cells as components of wireless mobile networks. Through network cells, the reliability can be represented by cell maximum life cycle when it is relevant to the reliability of the whole network, and the focus is narrowed from whole network to the local part. Finally, the analysis and verification of a UAV(unmanned aerial vehicle) group network show that the proposed index has greatly improved the algorithm efficiency compared with the existing reliability analysis index.

Temporal Network Embedding for Link Prediction via VAE Joint Attention Mechanism.

Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over time. Compared with methods on static networks, temporal network embedding (TNE) methods are facing three challenges: 1) it cannot describe the temporal dependence across network snapshots; 2) the node embedding in the latent space fails to indicate changes in the network topology; and 3) it cannot avoid a lot of redundant computation via parameter inheritance on a series of snapshots. To overcome these problems, we propose a novel TNE method named temporal network embedding method based on the VAE framework (TVAE), which is based on a variational autoencoder (VAE) to capture the evolution of temporal networks for link prediction. It not only generates low-dimensional embedding vectors for nodes but also preserves the dynamic nonlinear features of temporal networks. Through the combination of a self-attention mechanism and recurrent neural networks, TVAE can update node representations and keep the temporal dependence of vectors over time. We utilize parameter inheritance to keep the new embedding close to the previous one, rather than explicitly using regularization, and thus, it is effective for large-scale networks. We evaluate our model and several baselines on synthetic data sets and real-world networks. The experimental results demonstrate that TVAE has superior performance and lower time cost compared with the baselines.

Altered functional connectivity and topology structures in default mode network induced by inflammatory exposure in aged rat: A resting-state functional magnetic resonance imaging study.

Inflammatory stress in anesthesia management and surgical process has been reported to induce long-term cognitive dysfunction in vulnerable aged brain, while few studies focused on the network mechanism. The default mode network (DMN) plays a significant role in spontaneous cognitive function. Changes in topology structure and functional connectivity (FC) of DMN in vulnerable aged brain following inflammatory stress-induced long-term cognitive dysfunction are rarely studied. Eighty-eight aged male rats received intraperitoneal injection of lipopolysaccharide (LPS) as treatment or equal amount of normal saline (NS) as control. Morris Water Maze (MWM) was performed to assess short- (<7 days) and long-term (>30 days) learning and spatial working memory. Enzyme-linked immunosorbent assay (ELISA) was used to measure systemic and hippocampus inflammatory cytokines. Real-time polymerase chain reaction (RT-PCR) was used to measure the changes in gene level. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to exam brain function prior to MWM on days 3, 7, and 31 after LPS exposure. Graph theory analysis was used to analyze FC and topology structures in aged rat DMN. Aged rats treated with LPS showed short- and long-term impairment in learning and spatial working memory in MWM test. Graph theory analysis showed temporary DMN intrinsic connectivity increased on day 3 followed with subsequent DMN intrinsic connectivity significantly altered on day 7 and day 31 in LPS-exposed rats as compared with controls. Short- and long-term alterations were observed in FC, while alterations in topology structures were only observed on day 3. Rats with inflammatory stress exposure may cause short- and long-term alterations in intrinsic connectivity in aged rat's DMN while the changes in topology structures only lasted for 3 days. Inflammatory stress has prolonged effects on FC, but not topology structures in venerable aged brain.

Internet of Things Based Korean Cross-Cultural Communication Interactive Talent Training Model under Curriculum, Ideology, and Politics

With the development of society, the exchanges between various countries have become increasingly close, and this open communication mode also affects and changes the communication culture of each country. In the context of globalization, cross-cultural communication is increasing day by day, and cross-cultural communication ability has become a necessary quality for modern talents, so it is imperative to cultivate cross-cultural communication talents. However, the development level of informatization is insufficient, especially since the development of Internet of Things technology is lagging behind, the application level is low, and there are fracture characteristics in the application of each link. Information exchange cannot be effectively realized, information is incomplete, and asymmetry is obvious, and there is no effective information exchange platform. Following the trend of internationalization, scholars are paying more and more attention to the combination of cross-cultural communication and IoT technology. The combination of the two is to realize the real-time monitoring of personnel training. The acquisition and transmission of personnel training information is the primary problem to be solved in the realization of Korean cross-cultural communication. The wireless sensor network (WSN) has the characteristics of wide distribution of nodes, strong self-organization ability of routing, and good adaptability to dynamic changes in topology structure. WSN is used to solve this problem and is a good choice. Therefore, this paper used random forest algorithm and GBDT algorithm to apply WSN technology to the process of interactive talent training and established a network information service (NIS) system for interactive talent training. The experimental results have shown that the random forest algorithm and the GBDT algorithm can further simplify the input feature quantity and both can achieve good prediction results, while the GBDT model of the two models has relatively better prediction performance. The models obtained by the two methods meet the needs of detection parameter optimization, which realizes the real-time development of interactive talent training and realizes the intelligence and high efficiency of interactive talent training.

Event-Triggered Security Consensus for Multi-Agent Systems with Markov Switching Topologies under DoS Attacks

This paper studies secure consensus control for multi-agent systems subject to denial-of-service (DoS) attacks. The DoS attacks cause changes in topologies, which will destroy the channels of communication and result in network paralysis. Unlike the existing publications with Markov switching, this paper mainly studies the topological structure changes of the subsystem models after DoS attacks. To ensure the consensus of systems, this paper designs an event triggered to reduce the use of the controller and decrease the influence of channel breaks off caused by DoS attacks. On this basis, different Lyapunov functions are established in each period of attack. Then, stochastic and Lyapunov stable theory is used to form the consensus criteria. Moreover, Zeno behavior is excluded by theoretical analysis. Finally, the simulation experiment proves the effectiveness of the proposed protocol.

Impact of the non-axisymmetric SOL current driven by a biased electrode on the diverted J-TEXT plasma

It is observed in EAST that the non-axisymmetric helical currents induced by lower hybrid waves (LHWs) in the scrape-off layer (SOL) can produce resonant magnetic perturbations (RMPs), hence mitigate edge-localized modes (ELMs) significantly and cause the strike points splitting. In this work, the non-axisymmetric SOL current driven by an alternative method, i.e. biased electrode, is shown to influence the plasma boundary, especially the strike points, in the divertor configuration on J-TEXT. With positive bias to the electrode, the upper strike points on the divertor target plate is observed to split as captured by the visible camera. Such splitting is usually observed with the RMPs. In addition, the SOL plasma parameters measured by the reciprocating probe, such as floating potential (V f), electron temperature (T e), and ion saturation current (I s), are significantly altered by applying modulation bias, which is closely related to the SOL current. A SOL current model has been extended from the limiter configuration version to the poloidal divertor configuration, and the magnetic field generated by SOL current and the change of magnetic topological structure of plasma boundary can be simulated. The simulation results shows that the magnetic perturbation generated by the SOL current can induce the lobes near the X-point, which is three-dimensional asymmetric structure, and shows good agreement with the experimental observation. The active control of the boundary magnetic topology indicates that the SOL current is capable of generating strong magnetic perturbations, and hence might be sufficient for the control of ELMs.

Enhancing traffic capacity of multilayer networks with two logical layers by link deletion

Improving the traffic capacity of multilayer networks has been the subject of extensive interest and research. This paper proposes an effective strategy to improve traffic capacity of three-layer networks via the process of removing links linking to nodes with large degrees. In order to improve the traffic capacity, two different link deletion rules are proposed to remove a portion of logical layer links without the change of physical layer topology structure. It is found that the proposed strategy can improve traffic capacity; furthermore, the link deletion rule that two logical layers delete the same number of links at the same time is better than the link deletion rule that only the links of one logical layer are deleted. This work contributes to the design and optimization of realistic multilayer networks. This paper proposes an effective strategy to improve traffic capacity of three-layer networks via the process of removing links linking to nodes with large degrees. Under the premise that the structure of physical layer in three-layer networks does not change, some logical layer links between the nodes with large degrees are removed to improve the traffic capacity, and two different link deletion rules are proposed.

NONLINEAR ROLLING STABILITY AND CHAOS RESEARCH OF TRIMARAN VESSEL WITH VARIABLE LAY-OUTS IN REGULAR AND IRREGULAR WAVES UNDER WIND LOAD

The trimaran vessel rolls strongly at low forward speed and may capsize in high sea conditions due to chaos and loss of stability, which is not usually considered in conventional limit-based criteria. In order to perfect the method of measuring roll performance of trimaran, a set of nonlinear roll motion stability analysis method based on Lyapunov and Melnikov theory was established. The nonlinear roll motion equation was constructed by CFD and high-order polynomial fitting method. The wave force threshold of rolling chaos in regular waves is calculated by Gauss-Legendre numerical integration method. The limited significant wave height of rolling chaos in random sea conditions is deduced by the phase space transfer rate, and the complex effect of wind load is superposed in the calculation. The influence of trimaran configuration on the roll system is analyzed through the state differentiation of homoclinic and heteroclinic orbit in phase portrait. The calculation of the maximum Lyapunov exponent further verified the applicability of Melnikov method, and the topological structure change of gradual failure of the rolling system is analyzed by the erosion of safe basin. The complex changes of the nonlinear damping coefficient and the nonlinear restoring moment coefficient caused by the change of the transverse lay-outs between the main hull and side hull have a significant influence on chaos and stability, and the existence of wind load has a certain weakening effect on the stability and symmetry of the system. The conclusion also further indicates the importance of the lay-outs to the dynamic stability of the trimaran vessel, which is significant for its seakeeping design.

Adaptive Routing Strategy Based on Improved Double Q-Learning for Satellite Internet of Things

Satellite Internet of Things (S-IoT), which integrates satellite networks with IoT, is a new mobile Internet to provide services for social networks. However, affected by the dynamic changes of topology structure and node status, the efficient and secure forwarding of data packets in S-IoT is challenging. In view of the abovementioned problem, this paper proposes an adaptive routing strategy based on improved double Q-learning for S-IoT. First, the whole S-IoT is regarded as a reinforcement learning environment, and satellite nodes and ground nodes in S-IoT are both regarded as intelligent agents. Each node in the S-IoT maintains two Q tables, which are used for selecting the forwarding node and for evaluating the forwarding value, respectively. In addition, the next hop node of data packets is determined depending on the mixed Q value. Second, in order to optimize the Q value, this paper makes improvements on the mixed Q value, the reward value, and the discount factor, respectively, based on the congestion degree, the hop count, and the node status. Finally, we perform extensive simulations to evaluate the performance of this adaptive routing strategy in terms of delivery rate, average delay, and overhead ratio. Evaluation results demonstrate that the proposed strategy can achieve more efficient and secure routing in the highly dynamic environment compared with the state-of-the-art strategies.

Enabling Superior Thermo-Oxidative Resistance Elastomers Based on a Structure Recovery Strategy.

Thermo-oxidative process leads to the structure damage of elastomers, such as the scission of main chains and destruction of crosslinks. The problem that damaged structure brings about the deterioration of mechanical properties has not been solved by the conventional anti-aging methods. Inspired by self-healing process, a structure recovery strategy for recovering the damaged structure induced by thermo-oxidative process is proposed, which endows elastomers with superior thermo-oxidative resistance. The high reactivity between 1,3-diisopropenylbenzene and free radicals realizes high recovery efficiency (from 83% to 118%); the changes in topology structure during recovery process make much more rubber chains bear external stress and improve mechanical properties significantly (from 18.5 to 29.6MPa). This work paves the way for the development of elastomers with superior thermo-oxidative resistance, meanwhile this work is helpful to push the theoretical research of self-healing to practical application.

Persistent Homology: Application To Monitoring Hydraulic Fracturing

Persistent homology is a topological data analysis tool which is reflecting changes in topological structure of data along its scale. Application of persistent homology to monitoring hydraulic fracturing which is allowing researchers to consider prior information in a natural way is given in the article

Exploring node importance evolution of weighted complex networks in urban rail transit

With the development of complex networks in urban rail transit (URT), the topological structure changes accordingly and node importance also redistributes dynamically. However, many deficiencies exist in the single measure or unweighted network or static network when ranking node importance. Most importantly, the evolution mechanism of node importance with the network development is seldom studied. In view of this, in this paper, six unweighted and weighted complex networks are firstly modeled in the evolution of URT networks. One of Multiple Attribute Decision Making (MADM) methods is proposed, that is WTOPSIS (The Weighted Technique for Order of Preference by Similarity to Ideal Solution) algorithm combining Coefficient of Variation method and TOPSIS. Then four local and global centralities are aggregated and utilized in WTOPSIS to rank the node importance in those six networks. On the basis, the intersection degrees among the ranking sets are calculated to evaluate the similarities of ranking results. Furthermore, the factors contributing to the evolution of node importance are discussed quantitatively and qualitatively with examples. Finally, the feasibility of the method is verified by the Shenzhen Metro system in 2016. Results show that WTOPSIS algorithm outperforms the single attribute in ranking node importance, which makes up for the shortcomings in existing studies. Besides, for different stations in URT network development, node importance evolution is affected differently by the changes of topological structure and passenger flow. It is necessary to combine with the actual situations for the specific analysis. This study reveals the evolution mechanism of the node importance in the development of URT networks and it also has great theoretical and practical significance.

Cognitive routing optimization protocol based on multiple channels in wireless sensor networks

With the development of modern communication, available spectrum resources are becoming increasingly scarce, which reduce network throughput. Moreover, the mobility of nodes results in the changes of network topological structure. Hence, a considerable amount of control information is consumed, which causes a corresponding increase in network power consumption and exerts a substantial impact on network lifetime. To solve the real-time transmission problem in large-scale wireless mobile sensor networks, opportunistic spectrum access is applied to adjust the transmission power of sensor nodes and the transmission velocity of data. A cognitive routing and optimization protocol based on multiple channels with a cross-layer design is proposed to study joint optimal cognitive routing with maximizing network throughput and network lifetime. Experimental results show that the cognitive routing and optimization protocol based on multiple channels achieves low computational complexity, which maximizes network throughput and network lifetime. This protocol can be also effectively applied to large-scale wireless mobile sensor networks.