Physical Transport Properties of Porous Rock with Computed Tomography

In this chapter, three-dimensional digital rock models can be constructed by the micron X-ray computed tomography (CT). Then, lattice gas automata was applied to simulate the flow of electrical current in the saturated digital rocks to reveal the non-Archie relation of resistivity index and water saturation (I-Sw). The flow of single-phase Newtonian fluid in pore space had been studied with LBM for calculating the absolute permeability. Moreover, we have developed a model based on digital rock to simulate thermal neutrons transporting for imaging the anisotropy of pore structure. The advantages of the model over traditional methods indicate that it can simultaneously consider both the separation of matrix and pore and the distribution of mineral components. The results of numerical simulation with Monte Carlo are in good agreement with the pore distribution from X-ray CT, which can further verify the validity of the new model. In contrast to the conventional conclusion, we find that the porosity calculated with neutron data can be affected by the anisotropy. Therefore, a new formula to relate the resolution of array detectors to the quality of imaging, had been proposed to analyze the critical resolution and to optimize the number of neutrons in each simulation

Graph Neural Network Based Method for Path Planning Problem

Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of the path planning, collision detection is the most time-consuming operation. In this paper, we propose a learning-based path planning method that aims to reduce the number of collision detection. We develop an efficient neural network model based on Graph Neural Networks (GNN) and use the environment map as input. The model outputs weights for each neighbor based on the input and current vertex information, which are used to guide the planner in avoiding obstacles. We evaluate the proposed method's efficiency through simulated random worlds and real-world experiments, respectively. The results demonstrate that the proposed method significantly reduces the number of collision detection and improves the path planning speed in high-dimensional environments

Numerical Simulation of the Ion Transport Behavior in Concrete under Coupled Axial Loading and Sulfate Attack

During a sulfate attack on concrete, ions, which are transported to the interior of concrete through pores, react with the concrete components. The transport characteristics are affected by various factors. A chemical-mechanical coupling method for accurately evaluating the transport behavior of sulfate ions in concrete under stress conditions was proposed in this study to investigate the transport characteristics of these ions. The diffusion-reaction equations of sulfate ions were obtained based on the diffusion-reaction approach in combination with the mechanism of volume expansion under a sulfate attack and the influence of load on the concrete voidage. The constitutive response and crack density of the matrix were calculated according to the volumetric strain caused by external load and ettringite growth. Then, the diffusion coefficient of the equation was dynamically corrected. T\u27his phenomenon was a strongly coupled moving boundary problem, and the equations were solved using numerical method. A case study was conducted to analyze the distribution law of ionic concentration and volumetric strain obtained using the proposed method. Results demonstrate that the crack damage due to volumetric strain plays a major role in the diffusion of sulfate ions. The load has minimal effect on the transport behavior under a low stress level, and the water-cement ratio is negatively correlated with ion transport capacity. The proposed method serves as a reference for evaluating the durability of an underground structure in a sulfate formation

Monthly Change of Nutrients impact on Phytoplankton in Kuroshio of East China Sea

AbstractUsing the Nutrients data from World Ocean Atlas 2009 issued by NOAA in 2010 to analyze the monthly change of nutrient content, nutrient proportion and nutrient limitation in Kuroshio East China Sea, the results show that: (1)The ratios of N/P, Si/N, Si/P in shallow waters of 250m with a significant spatial differences. The spatial differences of Si/N and N/P are most obvious in March to April and August to September, the smaller differences occurs in October to December. The spatial distribution of Si/P is different from N/P and Si/N, Strong regional differences appears in September, the relatively uniform spatial distribution appears in May to June. (2)The major nutrient concentrations limit in Kuroshio of East China Sea are N and P, which impact in shallow of 300m. The nutrient concentrations are higher than threshold concentration in 300m to deeper area, so it could not appear the phenomenon of nutrient limitation. Phytoplankton growth mainly occurs in the range of 100–200m in April to November

HumanGen: Generating Human Radiance Fields with Explicit Priors

Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and $\text{360}^{\circ}$ realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of "anchor image". We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D

Online unicasting and multicasting in software-defined networks

Software-Defined Networking (SDN) has emerged as the paradigm of the next-generation networking through separating the control plane from the data plane. In a software-defined network, the forwarding table at each switch node usually is implemented by expensive and power-hungry Ternary Content Addressable Memory (TCAM) that only has limited numbers of entries. In addition, the bandwidth capacity at each link is limited as well. Provisioning quality services to users by admitting their requests subject to such critical network resource constraints is a fundamental problem, and very little attention has been paid. In this paper, we study online unicasting and multicasting in SDNs with an objective of maximizing the network throughput under network resource constraints, for which we first propose a novel cost model to accurately capture the usages of network resources at switch nodes and links. We then devise two online algorithms with competitive ratios O(log n) and O(Kϵlog n) for online unicasting and multicasting, respectively, where n is the network size, K is the maximum number of destinations in any multicast request, and ϵ is a constant with 0 < ϵ ≤ 1. We finally evaluate the proposed algorithms empirically through simulations. The simulation results demonstrate that the proposed algorithms are very promising

NARRATE: A Normal Assisted Free-View Portrait Stylizer

In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical face model, NARRATE leverages complementary benefits of geometry-aware generative approaches and normal-assisted physical face models. In a nutshell, NARRATE first inverts the input portrait to a coarse geometry and employs neural rendering to generate images resembling the input, as well as producing convincing pose changes. However, inversion step introduces mismatch, bringing low-quality images with less facial details. As such, we further estimate portrait normal to enhance the coarse geometry, creating a high-fidelity physical face model. In particular, we fuse the neural and physical renderings to compensate for the imperfect inversion, resulting in both realistic and view-consistent novel perspective images. In relighting stage, previous works focus on single view portrait relighting but ignoring consistency between different perspectives as well, leading unstable and inconsistent lighting effects for view changes. We extend Total Relighting to fix this problem by unifying its multi-view input normal maps with the physical face model. NARRATE conducts relighting with consistent normal maps, imposing cross-view constraints and exhibiting stable and coherent illumination effects. We experimentally demonstrate that NARRATE achieves more photorealistic, reliable results over prior works. We further bridge NARRATE with animation and style transfer tools, supporting pose change, light change, facial animation, and style transfer, either separately or in combination, all at a photographic quality. We showcase vivid free-view facial animations as well as 3D-aware relightable stylization, which help facilitate various AR/VR applications like virtual cinematography, 3D video conferencing, and post-production.Comment: 14 pages,13 figures https://youtu.be/mP4FV3evmy

Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype

IntroductionAnxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder. Current studies have revealed abnormal amygdala function in patients with anxiety compared with healthy people. However, the diagnosis of anxiety disorder and its subtypes still lack specific features of amygdala from T1-weighted structural magnetic resonance (MR) imaging. The purpose of our study was to investigate the feasibility of using radiomics approach to distinguish anxiety disorder and its subtype from healthy controls on T1-weighted images of the amygdala, and provide a basis for the clinical diagnosis of anxiety disorder.MethodsT1-weighted MR images of 200 patients with anxiety disorder (including 103 GAD patients) as well as 138 healthy controls were obtained in the Healthy Brain Network (HBN) dataset. We extracted 107 radiomics features for the left and right amygdala, respectively, and then performed feature selection using the 10-fold LASSO regression algorithm. For the selected features, we performed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls.ResultsFor the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and the area under receiver operating characteristic curve (AUC) of linear kernel SVM in cross-validation experiments was 0.6739±0.0708 for the left amygdala features and 0.6403±0.0519 for the right amygdala features; for classification task for GAD patients vs. healthy controls, 7 and 3 features were selected from left and right amygdala, respectively, and the cross-validation AUCs were 0.6755±0.0615 for the left amygdala features and 0.6966±0.0854 for the right amygdala features. In both classification tasks, the selected amygdala radiomics features had higher discriminatory significance and effect sizes compared with the amygdala volume.DiscussionOur study suggest that radiomics features of bilateral amygdala potentially could serve as a basis for the clinical diagnosis of anxiety disorder