Large Time Behavior on the Linear Self-Interacting Diffusion Driven by Sub-Fractional Brownian Motion With Hurst Index Large Than 0.5 I: Self-Repelling Case

Let SH be a sub-fractional Brownian motion with index 12<H<1. In this paper, we consider the linear self-interacting diffusion driven by SH, which is the solution to the equationdXtH=dStH−θ(∫0tXtH−XsHds)dt+νdt,X0H=0,where θ &lt; 0 and ν∈R are two parameters. Such process XH is called self-repelling and it is an analogue of the linear self-attracting diffusion [Cranston and Le Jan, Math. Ann. 303 (1995), 87–93]. Our main aim is to study the large time behaviors. We show the solution XH diverges to infinity, as t tends to infinity, and obtain the speed at which the process XH diverges to infinity as t tends to infinity.

Modeling Complex Networks Based on Deep Reinforcement Learning

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

Compaction Density Evaluation Model of Sand-Gravel Dam Based on Elman Neural Network With Modified Particle Swarm Optimization

The compaction density of sand-gravel materials has a strong gradation correlation, mainly affected by some material source parameters such as P5 content (material proportion with particle size greater than 5 mm), maximum particle size and curvature coefficient. When evaluating the compaction density of sand-gravel materials, the existing compaction density evaluation models have poor robustness and adaptability because they do not take into full consideration the impact of material source parameters. To overcome the shortcomings of existing compaction density models, this study comprehensively considers the impact of material source parameters and compaction parameters on compaction density. Firstly, asymmetric data were fused and a multi-source heterogeneous dataset was established for compaction density analysis. Then, the Elman neural network optimized by the adaptive simulated annealing particle swarm optimization algorithm was proposed to establish the compaction density evaluation model. Finally, a case study of the Dashimen water conservancy project in China is employed to demonstrate the effectiveness and feasibility of the proposed method. The results show that this model performs high-precision evaluation of the compaction density at any position of the entire working area which can timely correct the weak area of compaction density on the spot, and reduce the number of test pit tests.

Path-dependent Vortex Switching in Ferroelectric Nanoplate Junctions Toward a Memory Device Concept

Ferroelectric vortex has attracted much attention as a promising candidate for memories with high density and high stability. It is a crucial problem to precisely manipulate the vortex chirality in order to utilize it to store information. Nevertheless, so far, a practical and direct strategy for vortex switching is still lacking. Moreover, the strong coupling of chirality between neighboring vortices in continuous systems like superlattices limits the application of ferroelectric-vortex-based memories. Here, we design a ferroelectric nanoplate junction to break the strong coupling between neighboring vortices. Phase-field simulation results demonstrate that the vortex chirality of the nanoplates could be efficiently tuned by sweeping local electric and thermal fields in the nanoplate junction. More importantly, the weak coupling between two neighboring nanoplates through the intermediate junction brings a deterministic vortex switching behavior. Based on this, we propose a concept of vortex memory devices. Our study provides an effective way to control the vortex chirality and suggests an opportunity for designing new memory devices based on ferroelectric vortex.

Simulation of Deformation Process Failure of Jointed Rock Masses Based on the Numerical Manifold Method

On the basis of the numerical manifold method, this work introduces the concept of stress intensity factor at the crack tip in fracture mechanics and proposes the utilisation of artificial joint technology to ensure the accuracy of joint geometric dimensions in the element generation of the numerical manifold method. The contour integral method is used to solve the stress intensity factor at the joint tip, and the failure criterion and direction of crack propagation at the joint tip are determined. Element reconstruction and crack tracking are implemented in crack propagation, and a simulation programme of the entire process of deformation, failure, propagation and coalescence of jointed rock masses is developed. The rationality of the proposed method is verified by performing the typical uniaxial compression test and direct shear test.

Fluorescence Record Diagnostics of 3D Rough-Surface Landscapes With Nano-Scale Inhomogeneities

The paper proposes a new approach that enables the structure analysis and reconstruction of a rough surface where the height of inhomogeneities (from the depression to the upper point) varies within the spread about 20 nm. For the surface diagnostics, carbon nanoparticles are used, which serve as sensitive probes of the local surface height. A single nanoparticle can be positioned at a desirable point of the studied surface with the help of an optical tweezer employing the He-Ne laser radiation. Then the particle is illuminated by the strongly focused exciting beam of 405 nm wavelength, with the waist plane precisely fixed at a certain distance from the surface base plane. The particle’s luminescence response (in the yellow-green spectral range) strongly depends on the distance between the exciting beam waist and the particle, thus indicating the local height of the surface. After scanning the surface area and the consecutive interpolation, the surface “vertical” landscape can be reconstructed with a high accuracy: the numerical simulation shows that the RMS surface roughness is restored with an accuracy of 6.9% while the landscape itself is reconstructed with the mean error 7.7%.

Mechanical Properties of Rock–Coal–Rock Composites at Different Inclined Coal Seam Thicknesses

A comprehensive understanding of the mechanical properties of coal and rock sections is necessary for interpreting the deformation and failure modes of such underground sections and for evaluating the potential dynamic hazards. However, most studies have focused on horizontal coal–rock composites and the mechanical properties of inclined coal–rock composites have not been considered. To explore the influence of different confining pressures and inclined coal seam thicknesses on the mechanical properties and failure characteristics of rock–coal–rock (RCR) composites, a numerical model based on the particle flow code was used to perform simulations on five inclined RCR composites at different confining pressures. The results show that the mechanical properties and failure characteristics of the RCR composites are affected considerably by the inclined coal seam thickness and the confining pressure. (1) When the inclined coal seam thickness is constant, the elasticity modulus of the inclined RCR composite increases nonlinearly with the confining pressure at first, and then remains constant. At the same confining pressure, the elasticity modulus of the inclined RCR composite decreases nonlinearly with the inclined coal seam thickness. (2) When the confining pressure is constant, the peak stress of the inclined RCR composite decreases with the increase of the inclined coal seam thickness. When the inclined coal seam thickness is constant, the peak stress increases with the confining pressure. (3) As the inclined coal seam thickness increases, the peak strain of the inclined RCR composite first decreases rapidly, and then remains constant when there is no confining pressure. When the confining pressure is between 5 and 20 MPa, the peak strain of the inclined RCR composite gradually increases. (4) In the absence of confining pressure, there are few microcracks in the rock at an inclined coal seam thickness of 10 mm, whereas all the other cracks are in the coal section. When the confining pressure ranges between 5 and 20 MPa, the failure modes of the RCR composite can be divided into Y- and X-types.

Status and Extension of the Geant4-DNA Dielectric Models for Application to Electron Transport

The development of accurate physics models that enable track structure simulations of electrons in liquid water medium over a wide energy range, from the eV to the MeV scale, is a subject of continuous efforts due to its importance (among other things) in theoretical studies of radiation quality for application in radiotherapy and radiation protection. A few years ago, the Geant4-DNA very low-energy extension of the Geant4 Monte Carlo code had offered to users an improved set of physics models for discrete electron transport below 10 keV. In this work we present refinements to this model set and its extension to energies up to 1 MeV. Preliminary comparisons against the existing Geant4-DNA physics models with respect to total and differential ionization cross sections of electrons in liquid water are reported and discussed.

Deep Learning-Aided Automatic Contouring of Clinical Target Volumes for Radiotherapy in Breast Cancer After Modified Radical Mastectomy

Purpose: The aim of this study is to develop a practicable automatic clinical target volume (CTV) delineation method for radiotherapy of breast cancer after modified radical mastectomy.Methods: Unlike breast conserving surgery, the radiotherapy CTV for modified radical mastectomy involves several regions, including CTV in the chest wall (CTVcw), supra- and infra-clavicular region (CTVsc), and internal mammary lymphatic region (CTVim). For accurate and efficient segmentation of the CTVs in radiotherapy of breast cancer after modified radical mastectomy, a multi-scale convolutional neural network with an orientation attention mechanism is proposed to capture the corresponding features in different perception fields. A channel-specific local Dice loss, alongside several data augmentation methods, is also designed specifically to stabilize the model training and improve the generalization performance of the model. The segmentation performance is quantitatively evaluated by statistical metrics and qualitatively evaluated by clinicians in terms of consistency and time efficiency.Results: The proposed method is trained and evaluated on the self-collected dataset, which contains 110 computed tomography scans from patients with breast cancer who underwent modified mastectomy. The experimental results show that the proposed segmentation method achieved superior performance in terms of Dice similarity coefficient (DSC), Hausdorff distance (HD) and Average symmetric surface distance (ASSD) compared with baseline approaches.Conclusion: Both quantitative and qualitative evaluation results demonstrated that the specifically designed method is practical and effective in automatic contouring of CTVs for radiotherapy of breast cancer after modified radical mastectomy. Clinicians can significantly save time on manual delineation while obtaining contouring results with high consistency by employing this method.

Spacetimes Admitting Concircular Curvaure Tensor in f(R) Gravity

The main object of this paper is to investigate spacetimes admitting concircular curvature tensor in f(R) gravity theory. At first, concircularly flat and concircularly flat perfect fluid spacetimes in fR gravity are studied. In this case, the forms of the isotropic pressure p and the energy density σ are obtained. Next, some energy conditions are considered. Finally, perfect fluid spacetimes with divergence free concircular curvature tensor in f(R) gravity are studied; amongst many results, it is proved that if the energy-momentum tensor of such spacetimes is recurrent or bi-recurrent, then the Ricci tensor is semi-symmetric and hence these spacetimes either represent inflation or their isotropic pressure and energy density are constants.