Search

Understanding the tectonic evolution of the Proto-Tethys Ocean is important for exploring the initial assembly of the Gondwana supercontinent. The closure of the Proto-Tethys Ocean represents the end of convergence along the northern edge of Eastern Gondwana. However, the timing and processes of the closing of the ocean have different interpretations. This work focuses on the Early Paleozoic granitic rocks in the Baoshan Block, SW China to constrain the tectonic evolution of the Proto-Tethys Ocean. Zircons from the Pinghe and Zhen'an granitic plutons yield concordant U–Pb ages from 489.8 to 467.7 Ma. The bulk-rock geochemical features suggest that these samples are high-K calc-alkaline, S-type granites enriched in light rare earth elements and depleted in heavy rare earth elements. With obvious negative Eu anomalies and high K2O/Na2O ratios (1.01–2.57), they are enriched in large ion lithophile elements and depleted in high field strength elements. These rocks were therefore derived from partial crustal melting associated with subduction of the converging plate. Previous studies on the Changning–Menglian suture zone have suggested that the Early Paleozoic magmatic activity in the Baoshan Block was related to the westward subduction of the Proto-Tethys Ocean. Combined with the Early Paleozoic subduction-related magmatic activity to the east, it is therefore suggested that the Changning–Menglian Ocean experienced bidirectional subduction. [ABSTRACT FROM AUTHOR]

• Using machine learning to recommend appropriate parameters and operators for differential evolution algorithm. • The fitness landscape features are used as a basis for recommending parameters for the optimization problems. • A mutation operator selector based on AdaBoost & decision tree, and a parameter selector based on BP neural network are established for the differential evolution algorithm. Due to the problems of low accuracy and increasing control parameters in the existing parameter adaptive methods of differential evolution (DE) algorithm, in this paper a mutation operator selector and a parameter selector are proposed through Fitness Landscape (FL) analysing. At first, the performance differences of the two categories of mutation operators named DE / b e s t / 1 and DE / c u r r e n t - t o - r a n d / 1 were analyzed on many test problems. Secondly, the relationship between the FL and mutation operator is founded by using ensemble learning and decision tree, and achieved a classifier named mutation operator selector. Thirdly, the relationship between the FL and algorithm parameters is founded by using a neural network, and then a classifier named parameter selector is achieved. Finally, the improved DE algorithm equip with the two selectors is tested on the CEC2017 benchmark set. The results show that the proposed improved DE algorithm is outperforms both the basis DE algorithm and other three state-of-the-arts algorithms. [ABSTRACT FROM AUTHOR]

Image enhancement techniques are commonly used to improve problems such as lack of brightness, high noise, and low contrast in low-light images. Notably, deep learning-based approaches have recently achieved substantial advancements in this domain. However, learning-based methods often require many parameters and multi-layer network structures to achieve high-quality enhancement effects, which limits their application in real-time image processing. To solve this problem, a lightweight Feature Activation Guided Multi-Receptive Field Attention Network (FAMANet) is designed in this paper. The Wavelet Feature Activation Block (WFAB) introduced in the network utilizes the discrete wavelet transform and residual connection to achieve selective activation of image features, thus reducing the redundant information in the feature map and improving the computational efficiency. In addition, the Multi-Receptive Field Attention (MRFA) introduced in this paper addresses the issue of inadequate pixel information and feature map loss stemming from a single input image by concentrating on the image structure, spanning from intricate details to the overall composition. By better-utilizing image information and distinguishing between global and local features, MRFA can improve the speed and efficiency of real-time image processing. After sufficient experimental validation, FAMANet significantly outperforms state-of-the-art methods in low-light image enhancement and exposure correction tasks. [ABSTRACT FROM AUTHOR]

We studied the zircon U–Pb ages, whole-rock geochemistry and Sr–Nd isotopic compositions of a Paleozoic gabbro–diabase–granite association in the Shangdan suture zone to constrain the petrogenesis of these intrusive rocks and identify their tectonic implications. These rocks have distinctive crystallization ages: the Guchenggou gabbro yielded zircon U–Pb ages of 424–423 Ma, the average age of the Yueritan diabase was 411 Ma and the Danfeng granites yielded zircon U–Pb ages of 414–411 Ma. The Guchenggou gabbro was formed by partial melting of a phlogopite-bearing garnet lherzolite modified by subduction fluids in a continental arc setting. The Yueritan diabase was generated by mixing a phlogopite-bearing garnet lherzolite modified by subduction melts with continental crustal rocks in a post-collision setting. The Danfeng granites were weakly peraluminous I-type granites formed by mixing juvenile crust with metagreywackes at high temperatures and pressures in a tectonic environment transitioning from compression to extension. We suggest that the subduction of the Shangdan Ocean induced the formation of the gabbro. The diabases and granites are related to relamination of the continental crust in response to the collision of the North Qinling Belt with the South Qinling Belt, suggesting that closure of the Shangdan Ocean may have occurred at c. 410 Ma. [ABSTRACT FROM AUTHOR]

The dynamic behaviors of the well-known multi-phase magneto-electro-elastic (MEE) structures usually receive much attention in designing various intelligent devices, and the finite element method (FEM) is an effective numerical procedure for MEE structural dynamics. However, the relatively high mesh quality is necessary for the FEM to generate reliable results because of the overestimation of stiffness matrices. For this reason, other powerful numerical approaches are still pursued. In present work, a coupling MEE edge-based smoothed finite element method (ES-FEM) model is established to evaluate the dynamic performance of the MEE solids. With the edge-based gradient smoothing operation, the smoothed strains, electric fields and magnetic fields are obtained. As a result, the close-to-exact system stiffness matrices are constructed. The solution precision, convergence rate and computation efficiency of the ES-FEM are carefully compared with those of the traditional FEM through several typical numerical examples, and the outstanding behaviors of the ES-FEM in free vibration analyses and harmonic response analyses of MEE structures are examined. The numerical results from the ES-FEM are still reliable even with relatively coarse spatial discretization. [ABSTRACT FROM AUTHOR]

The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image–spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the ${l}_{2,1}$ -norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods. [ABSTRACT FROM AUTHOR]

Data analysis based on information from different sources, typically known as the data fusion problem, is common in economic and biomedical studies. An interesting question concerns the regression of an outcome variable on certain covariates when combining two distinct datasets. These datasets consist of a primary sample containing the outcome and a subset of the covariates, and a supplemental sample comprising information only on the full set of covariates. Previous methods have proposed doubly robust estimation procedures that employ a single propensity score model for the data fusion process and a single imputation model for the covariates available only in the supplemental dataset. However, it may be questionable to assume that either model is correctly specified due to an unknown data generating process. To address this issue, an empirical likelihood based approach that calibrates multiple propensity scores and imputation models is introduced. The resulting estimator is consistent when any one of the models is correctly specified and is robust against extreme values of the fitted propensity scores. The asymptotic normality property and the estimation efficiency are also discussed. Simulation studies show that the proposed estimator has substantial advantages over existing estimators, and an assembled U.S. household expenditure data example is used for illustration. [ABSTRACT FROM AUTHOR]

The meshless numerical manifold method (MNMM) inherits two covers of the numerical manifold method. A mathematical cover is composed of nodes' influence domains and a physical cover consists of physical patches, which are produced through cutting mathematical cover by physical boundaries. Because two covers are adopted, MNMM can naturally and uniquely solve both the continuous and discontinuous problems under Galerkin's variational framework. However, Galerkin's meshless method needs background integration grids to realize solving, which often does not match the nodes' influence domains, so the accuracy of numerical integration is reduced. Consider that MNMM allows even distribution of nodes and the physical cover contains the characteristics of the boundary and nodes' influence domains, the study presents a new numerical integration strategy to ensure that the background integration grids match the nodes' influence domains. The method can be applied to continuous and discontinuous problems, and is proved to be equivalent to the influence domain integration. At the same time, a reasonable arrangement of mathematical nodes is made to assure the background integration grid that it is accordant and straightforward. In this way, the number of physical patches of each integral point is the same, which improves the accuracy of interpolation calculation. The effectiveness of the proposed method is verified by numerical examples of both continuous and discontinuous problems. [ABSTRACT FROM AUTHOR]

Three parallel east–west-trending shear zones are components of the polyphase shear zones in the eastern Jiangnan belt. The meso- and microstructural characteristics and the estimates of deformation temperatures, vorticities and geochronology of these shear zones provide significant information regarding the tectonic evolution of the South China Block (SCB) during the early Paleozoic. Most shear sense indicators (e.g. asymmetric folds, S-C fabrics and rotating porphyroclasts) reveal that the kinematics were characterized by east–west dextral shear. Finite strain measurements indicate an oblate strain ellipsoid shape with S-type and SL-type tectonites. The rotated rigid porphyroclasts and Mohr circle methods yield vorticity (Wk) values of 0.58–0.80 and 0.66–0.89, respectively, indicating that the flow was generally shear-dominated with almost equal proportions of simple and pure shear. Both mineral deformation thermometers and quartz c-axis fabrics indicate that the deformation occurred at medium temperature conditions of 400–550°C. Combined with our new age and regional geological data, the east–west-trending shear zones might represent an increment of deformation during the last period of the early Paleozoic orogeny in the SCB. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is applied in complex model solving problems due to its fast convergence rate and concise algorithm structure. However, the balancing ability of PSO between exploration and exploitation still faces significant challenges. Therefore, a cooperative-based difference learning particle swarm optimization (CDLPSO) is proposed. In CDLPSO, dual swarm time-varying adjustment (DST) based on the subswarm division method is introduced on different periods, while using an appropriate subswarm division method to promote the cooperative learning of particles. Furthermore, a pros-cons coevolution mechanism (PCC) is designed in cooperative learning based on swarms. The elite subswarm can adaptively select a group of proper particles as the swarm updates, thereby obtaining a better exploration performance. Meanwhile, a stage-based cross-learning strategy (SBC) which is adopted to utilize common subswarm rationally adjusts different learning strategies on different stages. To further enhance the convergence accuracy, the common subswarm is added in more superior particles for learning. Ultimately, CDLPSO is used to solve the complex function problems on the CEC2013, CEC2022 test suite and a practical application. The experimental results demonstrate that CDLPSO can significantly balance exploitation and exploration abilities. • Applies a cooperative particle swarm optimization with difference learning. • Designing dual swarm time-varying adjustment balance exploration and exploitation of the algorithm. • Controlling by pros-cons coevolution mechanism adaptive determine more promising solutions. • Utilizing stage-based cross-learning strategy to alleviate the problem of premature convergence. • Experimental results demonstrate the superiority of the proposed algorithm with other state-of-the-art PSO variants. [ABSTRACT FROM AUTHOR]

In this paper, a new Logistic Basis Function Neural Network (LBFNN) algorithm is proposed and applied to solve Fokker–Planck–Kolmogorov (FPK) equations for stochastic dynamical systems under random excitation. Our attention is focused on the transient solution of the systems under Non-Gaussian excitation. Firstly, we built a neural network by using Logistic probability functions as the basis functions, in which the weighted coefficients are unknown and to be determined. After that, considering the constraint from FPK equation and the normalization condition from the weighted parameters, we construct the loss function to be comprised by these two parts. The innovation of our algorithm is that unknown weighted parameters in LBFNN can be obtained by solving a set of algebraic iteration formulas instead of testify by sample data. Four examples with different dimensions and different excitations are discussed to identify the availability of the LBFNN algorithm. All results show that LBFNN algorithm is not only capable to get transient solutions of the systems under Gaussian white-noise, but also enable us to get the transient solutions in the case of Non-Gaussian excitation. Other techniques including Exact expressions, Gaussian radial basis function neural network (RBFNN) and Monte-carlo simulation are utilized in order to examine the accuracy of the LBFNN algorithm. The good agreements in different comparisons demonstrate the validity and the strength of the LBFNN algorithm. • A LBFNN method is proposed to solve the transient response of the systems. • The method is able to study the systems with Gaussian or non-Gaussian excitation. • Only an algebra iteration formula is needed to obtain the optimal parameters. [ABSTRACT FROM AUTHOR]

We consider identification and inference about a counterfactual outcome mean when there is unmeasured confounding using tools from proximal causal inference (Miao et al., 2018, Tchetgen Tchetgen et al., 2020). Proximal causal inference requires existence of solutions to at least one of two integral equations. We motivate the existence of solutions to the integral equations from proximal causal inference by demonstrating that, assuming the existence of a solution to one of the integral equations, n -estimability of a linear functional (such as its mean) of that solution requires the existence of a solution to the other integral equation. Solutions to the integral equations may not be unique, which complicates estimation and inference. We construct a consistent estimator for the solution set for one of the integral equations and then adapt the theory of extremum estimators to find from the estimated set a consistent estimator for a uniquely defined solution. A debiased estimator for the counterfactual mean is shown to be root- n consistent, regular, and asymptotically semiparametrically locally efficient under additional regularity conditions. [ABSTRACT FROM AUTHOR]

In this paper, we use stochastic averaging method and data-driven artificial neural network to study the response and reliability of a fractional stochastic dynamical system. Firstly, a generalized Harmonic transformation is introduced to get an approximate expression for the fractional derivative. Based on this procedure, stochastic averaging method is utilized to get Fokker–Planck Kolmogorov equation for the system response and Backward Kolmogorov equation for the system reliability. An exact stationary solution of amplitude response is obtained by solving Fokker–Planck Kolmogorov equation, in the meanwhile, numerical results regarding to the reliability by Crank–Nicolson difference method are derived too. We propose an algorithm of data-driven artificial neural network and apply it for solving these two equations simulatively, in order to verify the correction and accuracy of the analytical methods. All results illustrate that data-driven artificial neural network is highly effective to achieve the system response and reliability. The advantage of this deep learning algorithm mainly includes the meshless structure, boundary sample data collection, small amount of training data and unconstrained optimization. In addition, the increase of the fractional order can enhance the system response and leads to stochastic bifurcation. • Response and reliability are analyzed by stochastic averaging and DDANN. • Proposed DDANN is mesh free and high effectiveness. • All results obtained by analytical methods and DDANN are agreement. • Higher order of fractional derivative is helpful for improving the reliability. [ABSTRACT FROM AUTHOR]

The present study investigates the accurate inference of Reynolds-averaged Navier–Stokes solutions for the two-dimensional compressible flow over aerofoils with a deep neural network. Our approach yields networks that learn to generate precise flow fields for varying body-fitted, structured grids by providing them with an encoding of the corresponding mapping to a canonical space for the solutions. We apply the deep neural network model to a benchmark case of incompressible flow at randomly given angles of attack and Reynolds numbers and achieve an improvement of more than an order of magnitude compared to previous work. Further, for transonic flow cases, the deep neural network model accurately predicts complex flow behavior at high Reynolds numbers, such as shock wave/boundary layer interaction, and quantitative distributions like pressure coefficient, skin friction coefficient as well as wake total pressure profiles downstream of aerofoils. The proposed deep learning method significantly speeds up the predictions of flow fields and shows promise for enabling fast aerodynamic designs. [Display omitted] • Complex flows are inferred by encoding the mapping from physical to canonical space. • The accuracy and robustness are analyzed for new shapes and free-stream conditions. • The model shows orders of magnitude faster compared to the traditional PDE solver. [ABSTRACT FROM AUTHOR]

Fuzzy rough set (FRS) has a great effect on data mining processes and the fuzzy logical operators play a key role in the development of FRS theory. In order to generalize the FRS theory to more complicated data environments, we firstly propose four types of fuzzy neighborhood operators based on fuzzy covering by overlap functions and their implicators in this paper. Meanwhile, the derived fuzzy coverings from an original fuzzy covering are defined and the equalities among overlap function-based fuzzy neighborhood operators on a finite fuzzy covering are also investigated. Secondly, we prove that new operators can be divided into seventeen groups according to equivalence relations, and the partial order relations among these seventeen classes of operators are discussed, as well. And then, two types of neighborhood-related fuzzy covering-based rough set models are proposed via the overlap function-based fuzzy neighborhood operators and the t -norm-based fuzzy neighborhood operators. Furthermore, the groups and partially order relations are also discussed. Finally, a novel fuzzy TOPSIS methodology is put forward to solve a biosynthetic nanomaterials select issue, and the rationality and enforceability of our new approach is verified by comparing its results with nine different methods. [ABSTRACT FROM AUTHOR]

Underwater image processing has been shown to exhibit significant potential for exploring underwater environments. It has been applied to a wide variety of fields, such as underwater terrain scanning and autonomous underwater vehicles (AUVs)-driven applications, such as image-based underwater object detection. However, underwater images often suffer from degeneration due to attenuation, color distortion, and noise from artificial lighting sources as well as the effects of possibly low-end optical imaging devices. Thus, object detection performance would be degraded accordingly. To tackle this problem, in this article, a lightweight deep underwater object detection network is proposed. The key is to present a deep model for jointly learning color conversion and object detection for underwater images. The image color conversion module aims at transforming color images to the corresponding grayscale images to solve the problem of underwater color absorption to enhance the object detection performance with lower computational complexity. The presented experimental results with our implementation on the Raspberry pi platform have justified the effectiveness of the proposed lightweight jointly learning model for underwater object detection compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this paper, the theoretic analysis by stochastic dynamics and numerical simulation by ANN algorithm for tumor-immune models driven by random noises are studied. Firstly, a mathematical model about the competition between tumor cells and immune system is established based on the Michaelis-Menten enzyme kinetics. Secondly, the microenvironmental fluctuations are modeled as Gaussian white noises and Gaussian coloured noises, respectively. Then the corresponding Fokker-Planck equation (FPE) and the approximated Fokker-Planck equation (AFPE) are obtained to explore the stationary probability density (SPD) of the tumor cells. The innovation of this paper is that artificial neural network (ANN) algorithm is introduced to solve the SPD based on FPE or AFPE, which has higher robustness and accuracy. The SPDs of tumor cells show that the greater the intensity of Gaussian white noises, the more beneficial the prevention of tumor growth. In other words, the microenvironmental fluctuations could accelerate the extinction of tumor to a certain extent. For the case of Gaussian coloured noises, the existence of the cross-correlation time between multiplicative and additive Gaussian coloured noises lead the tumor to be large concentrations with probability of almost 1. All the theoretical results are examined by ANN algorithm and they are all in good agreement. In addition, we discuss the mean first passage time (MFPT) from the metastable state to the state of extinction of tumor cells, and discover the phenomena of the noise-enhanced stability (NES) as well as stochastic resonant activation (SRA). At last, the best parameters including penalty factors, the number of layers and nodes in ANN algorithm are also discussed in order to get the optimal accuracy. • The tumor-immune model is characterized by Michaelis-Menten enzyme reaction. • The environment fluctuations are modeled as Gaussian white and coloured noises. • The biological explanations of SPD and MFPT under noise situations are given. • Theoretical analysis and ANN algorithm are compared to examine our methods. [ABSTRACT FROM AUTHOR]

In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts. [ABSTRACT FROM AUTHOR]

• Existing capacity models neglect the effects of flow sediment-storage variation. • Its effects on dam-break flooding and swash flows are numerically studied. • Simplification may lead to under-estimation of erosion or over-estimation of deposition, which considerably affects hydrodynamics. The capacity model based on the Exner equation has been widely used to simulate bed load sediment transport. However, most of their governing equations neglect a sediment storage term (∂ h C e / ∂ t), of which the impact on bed load transport simulation is unknown and needs to be better comprehended. This paper has split the capacity model into two sub-models: one with the sediment storage term and the other without the sediment storage term. The two sub-models are applied to simulate a series of typical bed load transport processes, such as dam-break flooding and swash flows. A comparison of their results is used to shed light on the effect of sediment storage term on flows and morphological evolutions. It is shown that for both dam-break flooding and swash flows, neglection of the inflow sediment storage may lead to under-estimation of bed erosion or over-estimation of bed deposition, which further affects the computed hydrodynamics greatly. Moreover, it is shown that including this factor can improve quantitative accuracy of sediment transport estimation. These indicate an important role of this factor and accordingly the present work provide a numerical justification for using sediment storage terms in capacity models. [ABSTRACT FROM AUTHOR]

This paper proposes a cooperative target allocation method for efficiently utilizing air-sea heterogeneous unmanned vehicles to counter saturation attacks by low-cost UAVs. Specifically, a bilevel model that combines the advantages of heuristic algorithms and the coalition formation game (CFG) is constructed by integrating unmanned vehicles' motion and attack attributes. The upper layer model aims to utilize the advantages of the heuristic algorithm to generate a feasible target allocation scheme in a shorter time. A genetic algorithm constrained by inheritance conditions (GAC) is proposed to solve the problem of the low efficiency of traditional genetic algorithms in solving the upper model. The lower layer model is established based on a coalition formation game, which aims to continuously and stably optimize the target allocation scheme. A search method based on mobile players is designed to form a stable coalition structure faster when the number of unmanned vehicles is enormous. Simulation results show the feasibility and superiority of the algorithm proposed in this paper in dealing with allocation tasks of different complexity. [ABSTRACT FROM AUTHOR]

The problem of effectively exploiting the information multiple data sources has become a relevant but challenging research topic in remote sensing. In this article, we propose a new approach to exploit the complementarity of two data sources: hyperspectral images (HSIs) and light detection and ranging (LiDAR) data. Specifically, we develop a new dual-channel spatial, spectral and multiscale attention convolutional long short-term memory neural network (called dual-channel $A^{3}$ CLNN) for feature extraction and classification of multisource remote sensing data. Spatial, spectral, and multiscale attention mechanisms are first designed for HSI and LiDAR data in order to learn spectral- and spatial-enhanced feature representations and to represent multiscale information for different classes. In the designed fusion network, a novel composite attention learning mechanism (combined with a three-level fusion strategy) is used to fully integrate the features in these two data sources. Finally, inspired by the idea of transfer learning, a novel stepwise training strategy is designed to yield a final classification result. Our experimental results, conducted on several multisource remote sensing data sets, demonstrate that the newly proposed dual-channel $A^{\,3}$ CLNN exhibits better feature representation ability (leading to more competitive classification performance) than other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

• A novel concept Cycle Nodes Ratio (CNR) is proposed to define the complex networks tend to cycle or tree. • Give the analytical solutions and simulation results of CNR in Erdős–Rényi (ER) networks, which appear critical transition point. • CNO is one most prominent feature in the networks' classification. Cycles, which can be found in many different kinds of networks, make the problems more intractable, especially when dealing with dynamical processes on networks. On the contrary, tree networks in which no cycle exists, are simplifications and usually allow for analyticity. There lacks a quantity, however, to tell the ratio of cycles which determines the extent of network being close to tree networks. Therefore we introduce the term Cycle Nodes Ratio (CNR) to describe the ratio of number of nodes belonging to cycles to the number of total nodes, and provide an algorithm to calculate CNR. CNR is studied in both network models and real networks. The CNR remains unchanged in different sized Erdős-–Rényi (ER) networks with the same average degree, and increases with the average degree, which yields a critical turning point. The approximate analytical solutions of CNR in ER networks are given, which fits the simulations well. Furthermore, the difference between CNR and two-core ratio (TCR) is analyzed. The critical phenomenon is explored by analysing the giant component of networks. We compare the CNR in network models and real networks, and find the latter is generally smaller. Combining the coarse-graining method can distinguish the CNR structure of networks with high average degree. The CNR is also applied to four different kinds of transportation networks and fungal networks, which give rise to different zones of effect. It is interesting to see that CNR is very useful in network recognition of machine learning. [ABSTRACT FROM AUTHOR]

Bundle Adjustment (BA) plays a crucial role in 3D reconstruction methods and Structure from Motion (SFM) algorithms, as it involves nonlinear minimization of reprojection error. The efficiency and accuracy of BA solutions are of paramount importance for achieving high-quality 3D reconstructions. The solution of the normal equations derived from nonlinear minimization is typically the most time-consuming step in BA and directly impacts the reconstruction accuracy. In this paper, we propose a Preconditioned Landweber Iteration solver for the normal equations. This solver utilizes a preconditioner and a corresponding relaxation strategy to improve the conditioning of the normal equations, leading to improved convergence speed and accuracy of the iteration. Compared to traditional direct and iterative algorithms, our approach significantly enhances the accuracy and solution time of BA. Experimental results demonstrate that our proposed solver outperforms the Schur complement solver in terms of both speed and accuracy when solving the normal equations. This improvement contributes to further acceleration and accuracy in large-scale 3D reconstruction, particularly for challenging large-scale intensive scenarios. • Proposed Preconditioned Landweber Iteration for efficient Bundle Adjustment (BA). • Enhanced conditioning of normal equations for quicker convergence and accuracy. • Improved BA solution time and accuracy compared to traditional solvers. • Particularly beneficial for large-scale 3D reconstruction and Structure from Motion. • Outperformed Schur complement solver, especially in challenging large-scale problem. [ABSTRACT FROM AUTHOR]

In the analyses of transient wave propagations, a numerical method with the monotonic convergence property is favored by analysts. However, the classic finite element method does not possess this attractive property due to pronounced numerical dispersions and can generate severe spurious oscillations by using a small time step. In this work, the novel quadrilateral overlapping element is extended into the investigation of transient wave propagations by using the standard implicit Bathe time integration method. Since the local field approximation in overlapping elements can be a function, the performances of different basis functions in transient wave propagation problems are studied. Detailed analyses regarding the numerical dispersion error caused by spatial and temporal discretization are conducted. Then several numerical experiments with different types of excitation loading are investigated to show the performance of the quadrilateral overlapping elements. It is shown that the overlapping element with the trigonometric functions and the bilinear Lagrange polynomials has the monotonic convergence property and can perform well in handling various transient wave propagation problems. [ABSTRACT FROM AUTHOR]

• A method for representing node performance based on multi-attribute expression. • A TOPSIS-based algorithm for evaluating the node performance. • A replica placement algorithm based on the entropy weight TOPSIS method. Performance optimization based on node attributes is of profound significance in the replica placement algorithm used in Hadoop distributed file system (HDFS). Currently, most researchers studying replica placement algorithms consider only a single attribute or a few multiple attributes. However, a single attribute cannot accurately express the performance of a node. Therefore, this paper proposes a replica placement algorithm based on the entropy weight TOPSIS (technique for order preference by similarity to ideal solution) method, called TS-REPLICA. First, a multi-attribute matrix that comprehensively reflects the performance and load of nodes is defined. Then, a TOPSIS-based algorithm is proposed to calculate the performance score of each data node. In addition, the entropy weight method is introduced to derive the weights of attributes for balancing the influence of weights of multiple attributes. Next, the comprehensive load score of each data node in the Spark cluster, the average comprehensive load score of each rack, and the average comprehensive load score of the entire cluster are calculated, and replica placement is performed based on the obtained scores. Finally, the effectiveness of the proposed algorithm is verified on various datasets and test cases. The experimental results show that the TS-REPLICA algorithm outperforms the better comparison algorithm in execution number in Spark cluster. [ABSTRACT FROM AUTHOR]

The moving least squares (MLS) interpolation gets rid of the shackles of meshes, while the numerical manifold method (NMM) can solve both continuity and discontinuity problems in a unified way. The MLS-based NMM (abbreviated as MLS-NMM) inherits the individual merits of MLS and NMM. In the present work, the MLS-NMM is extended to solve groundwater flow problems in heterogeneous porous media. To accurately simulate the flow velocity field at and in the vicinity of the material interfaces, the refraction law is introduced into the proposed model as a posteriori condition. Several examples are employed to verify the correctness and accuracy of the proposed model. The results indicate that the MLS-NMM can model the Darcy flow in heterogeneous porous media with high accuracy, as well as satisfy the refraction law rigorously. In addition, the smooth flow velocity field can be obtained directly without the need for extra post-processing. [ABSTRACT FROM AUTHOR]

A family of new generalized multi-scroll Hamiltonian conservative chaotic systems(HCCSs) are firstly found by investigating Hamiltonian energy of a Hamiltonian conservative chaotic system. And the novelty of the family of new generalized multi-scroll HCCSs is that they are on some invariant hypersurfaces depending on Hamiltonian energy. Subsequently, some numerical analysis are given to show multi-scroll chaotic dynamics and complex characteristics of the family of new generalized multi-scroll HCCSs. Finally, FPGA circuits are employed to implement the family of new generalized multi-scroll HCCSs, which not only physically show the existence of multi-scroll dynamics, but also provide a family of new pseudo-random signal generators for engineering applications. • Finds a family of new multi-scroll Hamiltonian conservative chaotic systems (HCCSs). • The family of new HCCSs are on some invariant hypersurfaces. • Both numerical analysis and FPGA implementation are given to show multi-scroll dynamics. [ABSTRACT FROM AUTHOR]

In this paper, the complexification-averaging method is firstly used to derive the theoretical solutions and analyze the dynamical performance of the energy harvesters under galloping and base excitations. Based on the distributed parameter model of the energy harvesters, the model equations can be turned into a set of high-order ordinary differential equations by solving the ordinary differential equations with coupled terms, then the equation of the amplitude frequency and the analytical relationship to determine the displacement amplitude and the voltage amplitude are derived. In addition, the influence of parameters such as the wind speed, the electromechanical coupling parameter and the base excitation frequency on the nonlinear behaviors of the energy harvesters are explored. It is found that there exists very good consistency between the theoretical results and the numerical results. The response properties are verified by means of the phase portraits and time histories. Additionally, according to the implicit function derivative theorem, the critical condition for identifying the unstable region is obtained. And with the different system parameters, the critical unstable boundary can be identified. In order to improve the efficiency of energy harvesting, the parameters of the energy harvesters are optimized based on the purpose of maximum voltage amplitude. Finally, the results are predicted by means of the multilayer feedforward neural network, and the excellent prediction results can be found. • The response amplitudes are derived by using the complexification-averaging method. • The critical condition for identifying the unstable region is obtained. • Optimizing the parameters for the purpose of maximum voltage amplitude is considered. • Predicting the responses by means of the multilayer feedforward neural network is presented. [ABSTRACT FROM AUTHOR]

In acoustic analyses the standard radial point interpolation meshless method (RPIM) is able to outperform the classic finite element method (FEM) because the numerical dispersion can be significantly mitigated by increasing the size of the interpolation domain. However, this operation usually leads to a surge in computational cost. In this work an enriched RPIM (e-RPIM) is developed by using the general multiple wave patterns but no extra degrees of freedom are needed. In this enriched scheme the traditional radial basis function interpolation is augmented by a set of plane wave enrichment functions which helps to reflect the oscillation characteristics of the wave propagation. This e-RPIM can obviously alleviate the numerical dispersion and yield satisfactory solution accuracy by using a relatively small size interpolation domain. Although extra computational cost is required in constructing the enriched interpolation shape functions, the computational efficiency of the original RPIM is evidently improved by the proposed e-RPIM if the computation accuracy is taken into consideration. Several numerical experiments validate that the proposed e-RPIM is a promising competitor to the standard RPIM and FEM in acoustic analyses. [ABSTRACT FROM AUTHOR]

With the advancement of information technology, the concept of local-edge-cloud computing has gained prominence. Operating on a collaborative model, heterogeneous computing nodes converge to deliver a spectrum of multi-type services, including calculation-intensive, latency-sensitive, and privacy-requiring services. This collaborative approach fosters high-quality development in power economy. However, the proliferation of heterogeneous computing nodes, while beneficial, introduces challenges. The intricate connections and limited energy supply may lead to interruptions in the service processes of nodes. In this study, we present an energy-efficient resource allocation scheme designed for low-latency multi-type service provision within a local-edge-cloud collaboration. Our methodology focuses on optimizing the performance of multi-type service provision in a local-edge-cloud network, taking into account considerations such as latency, resource allocation, and energy consumption. To accomplish this, we employ the Alopex-based Differential Evolution algorithm. Initially, we construct three sub-models to analyze latency and energy aspects across various computing modes. Subsequently, we formulate a constrained optimization problem aimed at minimizing both latency and energy consumption in multi-type service provisioning. These models seek to derive optimal resource allocation decisions for the given scenario. To address this optimization problem, we introduce the hybrid differential evolution algorithm, Alopex-DE. A formal analysis is conducted to showcase its near-optimal performance in comparison to three state-of-the-art algorithms. Additionally, extensive simulations are carried out to validate the superior effectiveness of our proposed approach. • Designed three-layer architecture for efficient interconnection and info sharing among local devices, edge servers, and Cloud. • Proposed energy-efficient resource allocation scheme for local-edge-cloud computing service provision. • Developed sub-models to analyze latency, energy consumption, and classify service types under different computing modes. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification (HSIC) is essential in remote sensing image analysis. Applying a graph neural network (GNN) to hyperspectral image (HSI) classification has attracted increasing attention. However, the available GNNs for HSIC only adopt a kind of graph filter and an aggregator, which cannot well deal with the problems of land cover discrimination, noise impaction, and spatial feature learning. To overcome these problems, a graph convolution with adaptive filters and aggregator fusion (AF2GNN) is developed for HSIC. To reduce the number of graph nodes, a superpixel segment algorithm is employed to refine the local spatial features of the HSI. A two-layer 1D CNN is proposed to transform the spectral features of superpixels. In addition, a linear function is designed to combine the different graph filters, with which the graph filter can be adaptively determined by training different weight matrices. Moreover, degree-scalers are defined to combine the multiple filters and present the graph structure. Finally, the AF2GNN is proposed to realize the adaptive filters and aggregator fusion mechanism within a single network. In the proposed network, a softMax function is utilized for graph feature interpretation and pixel-label prediction. Compared with state-of-the-art methods, the proposed method achieves superior experimental results. [ABSTRACT FROM AUTHOR]

The multi-peak solitons and their stability are investigated for the nonlocal nonlinear system with the sine-oscillation response, including both the cases of positive and negative Kerr coefficients. The Hermite–Gaussian-type multi-peak solitons and the ranges of the degree of nonlocality within which the solitons exist are analytically obtained by the variational approach. This is the first time, to our knowledge at least, to discuss the solution existence range of the multi-peak solitons analytically, although approximately. The variational analytical results are confirmed by the numerical ones. The stability of the multi-peak solitons are addressed by the linear stability analysis. It is found that the upper thresholds of the peak-number of the stable solitons are five and four for the system with negative and positive Kerr coefficients, respectively. • We first obtain analytically the existence range of the multi-peak solitons. • We provide the multi-peak solitons by both the analytical and numerical methods. • The results got by the variational approach agree well with the numerical results. • The existence range of the stable solitons is got by the linear stability analysing. [ABSTRACT FROM AUTHOR]

We present the ElasTool package, an automated toolkit for calculating the second-order elastic constants (SOECs) of any two- (2D) and three-dimensional (3D) crystal systems. ElasTool uses three kinds of strain-matrix sets, i.e., the high-efficiency strain-matrix sets (OHESS), the universal linear-independent coupling strains (ULICS), and the all-single-element strain-matrix sets (ASESS), to calculate the SOECs automatically. ElasTool can efficiently compute both zero- and high-temperature elastic constants. We describe in detail the theoretical background and computational method of elastic constants, the package structure, the installation, and run, the input/output files, the controlling parameters, and two representative examples of how to use the ElasTool package. ElasTool is useful for either the exploration of materials' elastic properties or high-throughput new materials screening and design. ElasTool is freely available on GitHub: https://github.com/elastool Program Title: ElasTool CPC Library link to program files: https://doi.org/10.17632/ktvmxrdhpz.1 Code Ocean capsule: https://codeocean.com/capsule/1893813 Licensing provisions: GNU General Public License, version 3 Programming language: Python 3 External routines: NumPy [1], Spglib [2], ASE [3], Pandas [4] Nature of problem: The stress-strain method of elastic constants calculation depends on accurate stresses calculated with first-principles methods, such as the density functional theory (DFT). Compared to the energy-strain method, the stress-strain approach needs a smaller number of strain sets to solve the equation sets needed to deduce the elastic constants; it is also more straightforward to implement. However, accurate stresses take a lot of time to compute within DFT. Thus, a smaller number of strain sets and more efficient strain sets are urgently needed to improve the computational efficiency of elastic constants. An automated solution coupled with DFT is necessary for the exploration of materials' elastic properties and high-throughput new materials screening and design. Solution method: The solution to improve the computational efficiency of the stress-strain method is to decrease the number of strain-matrix sets and optimize the strain-matrix sets. We coupled our previously proposed high-efficiency strain-matrix sets (OHESS) with DFT and automated the processes of calculating the elastic tensor using the stress-strain method in the ElasTool package. ElasTool can also adopt the all-single-element strain-matrix sets (ASESS) and the universal linear-independent coupling strains (ULICS) approaches. It can deal with both zero- and high-temperature elastic constants of any crystal systems belonging to 2D or 3D. Having obtained the elastic moduli, ElasTool also gives other essential mechanical and elastic properties of materials such as Young's modulus, bulk modulus, elastic anisotropy, Debye temperature, and the sound velocities. Additional comments including restrictions and unusual features: Currently, this package interfaces with Vienna Ab initio Simulation Package (VASP) code as the stress tensors calculator. [5-7] Extension to other electronic structures is straightforward. [1] https://numpy.org/ [2] https://atztogo.github.io/spglib/ [3] https://wiki.fysik.dtu.dk/ase/ [4] https://pandas.pydata.org/ [5] https://www.vasp.at/ [6] G. Kresse, J. Furthmüller, Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set, Phys. Rev. B 54 (1996) 11169. [7] G. Kresse, D. Joubert, From ultrasoft pseudopotentials to the projector augmented-wave method, Phys. Rev. B 59 (1999) 1758. [ABSTRACT FROM AUTHOR]

• A hybrid first-order and WENO scheme is proposed for pollutant transport modeling. • The new model achieves comparable quantitative accuracy as compared to the WENO method but has significantly reduced computational cost. • A threshold value for switching between first-order and WENO schemes is recommended. To improve the modeling quality of pollutant transport in shallow waters, different reconstruction schemes have been proposed to better link the edge values to the centroid values of a pollutant concentration in finite-volume shallow water models: a scheme of higher (lower) order generally has a better (poorer) quantitative accuracy but lower (higher) computational efficiency. Here, a numerical comparative study of several classical schemes is first conducted under a variety of pollutant distribution conditions. The results reveal that, for the condition of relatively uniform pollutant distribution, the numerical accuracy of a lower-order scheme (such as the first-order scheme or the MUSCL scheme) may be similar to that of a higher-order scheme (such as the WENO scheme). The second-order derivative of the concentration, here termed the nonlinear indicator (NI), correlates well with the discrepancies between the numerical solutions and analytical solutions. A threshold value of approximately 10 − 7 ∼ 10 − 6 m-2 for the NI is identified, above which a higher-order scheme may be required. Based on this understanding, a hybrid first-order and WENO scheme is proposed. Numerical case studies show that the hybrid scheme can successfully combine the efficiency of the first-order scheme with the high accuracy of the WENO scheme for pollutant modeling. [ABSTRACT FROM AUTHOR]

Cyber hierarchy and interactional network (CHAIN) concept control of autonomous plug-in hybrid electric vehicles is presented to fully release the potential of the vehicle components while guaranteeing the driving safety and stability, which was divided into cyber intelligent driving system (cyber-IDS) on the cyber-end and vehicle intelligent driving system (vehicle-IDS) on the vehicle-end. On the cyber-end, the state information of surrounding vehicles and infrastructures about traffic conditions are uploaded via vehicle-to-everything (V2X) and processed in the cloud to obtain future driving behavior via car following theory, achieving automatic following control. On the vehicle-end, the vehicle-IDS determines the set of its future control variables, and simultaneously provides an optimized control sequence in the prediction horizon by performing approximate global optimization instead of local optimization. The system global optimization needs the high coupling between the macro physical process(cyber-IDS) and the micro physical process(vehicle-IDS), and only the macro planning cannot be directly applied to the micro process control. The nonlinear system at the vehicle-end is decomposed into linear systems around each working point, and the dynamic characteristics between nodes are connected by neural network based on radial basis function of heterogeneous distribution coefficients. By introducing the genetic algorithm for time series, the CHAIN autonomous driving strategy capable of macro and micro coupling can realize the accurate optimal control under the actual driving conditions, which not only eliminates random fluctuations, but also enables a full-lifespan fuel saving of autonomous hybrid vehicles as well as no damage to battery health. • Connected vehicles system encounters physical deviation of superimposed objective. • Lightweight sparse kernel neural network for generalization. • Local control set extended to improve multi-scale physical coupling. • Genetic algorithms for time-series smoothing of dynamic systems. [ABSTRACT FROM AUTHOR]

Autocovariance least-squares (ALS) is an off-line noise covariance estimation method that uses steady-state gain to establish a linear model of noise covariance; however, it is unable to correct the noise covariance in real time. The gain will reconverges after the correction, and the varying gain destroys the original estimation model. This study presents a real-time autocovariance least-squares (RT-ALS) algorithm that continuously estimates and corrects the noise covariance while filtering. First, a noise covariance estimation model was established separately for each window. A forgetting factor is then combined to suppress the effect of gain reconvergence on innovation. Finally, the historical model was coupled in a sequential manner to improve real-time estimation accuracy and computational efficiency. Numerical simulations and real-world examples demonstrate that the RT-ALS algorithm can estimate accurate and reasonable noise covariance in real-time, as well as improve the filtering accuracy. [ABSTRACT FROM AUTHOR]