Multi-scale simulation of capillary pores and gel pores in Portland cement paste

The microstructures of Portland cement paste (water to cement ratio is 0.4, curing time is from 1 day to 28 days) are simulated based on the numerical cement hydration model, HUMOSTRUC3D (van Breugel, 1991; Koenders, 1997; Ye, 2003). The nanostructures of inner and outer C-S-H are simulated by the packing of monosized (5 nm) spheres. The pore structures (capillary pores and gel pores) of Portland cement paste are established by upgrading the simulated nanostructures of C-S-H to the simulated microstructures of Portland cement paste. The pore size distribution of Portland cement paste is simulated by using the image segmentation method (Shapiro and Stockman, 2001) to analyse the simulated pore structures of Portland cement paste. The simulation results indicate that the pore size distribution of the simulated capillary pores of Portland cement paste at the age of 1 day to 28 days is in a good agreement with the pore size distribution determined by scanning electron microscopy (SEM). The pore size distribution of the simulated gel pores of Portland cement paste (interlayer gel pores of outer C-S-H and gel pores of inner C-S-H are not included) is validated by the pore size distribution obtained by mercury intrusion porosimetry (MIP). The pores with pore size of 20 nm to 100 nm occupy very small volume fraction in the simulated Portland cement paste at each curing time (0.69% to 1.38%). This is consistent with the experimental results obtained by nuclear magnetic resonance (NMR)

SNE-RoadSegV2: Advancing Heterogeneous Feature Fusion and Fallibility Awareness for Freespace Detection

Feature-fusion networks with duplex encoders have proven to be an effective technique to solve the freespace detection problem. However, despite the compelling results achieved by previous research efforts, the exploration of adequate and discriminative heterogeneous feature fusion, as well as the development of fallibility-aware loss functions remains relatively scarce. This paper makes several significant contributions to address these limitations: (1) It presents a novel heterogeneous feature fusion block, comprising a holistic attention module, a heterogeneous feature contrast descriptor, and an affinity-weighted feature recalibrator, enabling a more in-depth exploitation of the inherent characteristics of the extracted features, (2) it incorporates both inter-scale and intra-scale skip connections into the decoder architecture while eliminating redundant ones, leading to both improved accuracy and computational efficiency, and (3) it introduces two fallibility-aware loss functions that separately focus on semantic-transition and depth-inconsistent regions, collectively contributing to greater supervision during model training. Our proposed heterogeneous feature fusion network (SNE-RoadSegV2), which incorporates all these innovative components, demonstrates superior performance in comparison to all other freespace detection algorithms across multiple public datasets. Notably, it ranks the 1st on the official KITTI Road benchmark

3D Semantic Subspace Traverser: Empowering 3D Generative Model with Shape Editing Capability

Shape generation is the practice of producing 3D shapes as various representations for 3D content creation. Previous studies on 3D shape generation have focused on shape quality and structure, without or less considering the importance of semantic information. Consequently, such generative models often fail to preserve the semantic consistency of shape structure or enable manipulation of the semantic attributes of shapes during generation. In this paper, we proposed a novel semantic generative model named 3D Semantic Subspace Traverser that utilizes semantic attributes for category-specific 3D shape generation and editing. Our method utilizes implicit functions as the 3D shape representation and combines a novel latent-space GAN with a linear subspace model to discover semantic dimensions in the local latent space of 3D shapes. Each dimension of the subspace corresponds to a particular semantic attribute, and we can edit the attributes of generated shapes by traversing the coefficients of those dimensions. Experimental results demonstrate that our method can produce plausible shapes with complex structures and enable the editing of semantic attributes. The code and trained models are available at https://github.com/TrepangCat/3D_Semantic_Subspace_TraverserComment: Published in ICCV 2023. Code: https://github.com/TrepangCat/3D_Semantic_Subspace_Traverse

S3^3M-Net: Joint Learning of Semantic Segmentation and Stereo Matching for Autonomous Driving

Semantic segmentation and stereo matching are two essential components of 3D environmental perception systems for autonomous driving. Nevertheless, conventional approaches often address these two problems independently, employing separate models for each task. This approach poses practical limitations in real-world scenarios, particularly when computational resources are scarce or real-time performance is imperative. Hence, in this article, we introduce S$^3$M-Net, a novel joint learning framework developed to perform semantic segmentation and stereo matching simultaneously. Specifically, S$^3$M-Net shares the features extracted from RGB images between both tasks, resulting in an improved overall scene understanding capability. This feature sharing process is realized using a feature fusion adaption (FFA) module, which effectively transforms the shared features into semantic space and subsequently fuses them with the encoded disparity features. The entire joint learning framework is trained by minimizing a novel semantic consistency-guided (SCG) loss, which places emphasis on the structural consistency in both tasks. Extensive experimental results conducted on the vKITTI2 and KITTI datasets demonstrate the effectiveness of our proposed joint learning framework and its superior performance compared to other state-of-the-art single-task networks. Our project webpage is accessible at mias.group/S3M-Net.Comment: accepted to IEEE Trans. on Intelligent Vehicles (T-IV

Test-negative designs with various reasons for testing: statistical bias and solution

Test-negative designs are widely used for post-market evaluation of vaccine effectiveness, particularly in cases where randomization is not feasible. Differing from classical test-negative designs where only healthcare-seekers with symptoms are included, recent test-negative designs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they introduce bias into the current framework of test-negative designs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical simulations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not accounted for. To eliminate this bias, we identify three categories of reasons for testing, including symptoms, disease-unrelated reasons, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same vaccine effectiveness parameter, and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. The performance of our proposed method is demonstrated through simulation studies

Linking the SO2 emission of cement plants to the sulfur characteristics of their limestones: A study of 80 NSP cement lines in China

In a properly operated new suspension preheater (NSP) cement line, the SO2 emission is mainly originated from sulfides in the raw meal, and limestone, occupying about 85% wt. of the raw meal, is the dominant sulfur source. However, the sulfur characteristics of limestones and then their influences on the SO2 emission have not been clarified yet. In the present study, 80 NSP cement lines with SO2 emission > 200 mg/Nm3 were recorded, the sulfur content and species as well as pyrite morphology of limestones were analyzed and then correlated to their resulting SO2 emission. The results show that the SO2 emission of stack gas increases linearly with the SO3 content of limestone used, and sulfates lead to a 50% reduction in SO2 emission relative to sulfides. Compared with average SO2 emission, euhedral pyrite leads to a slightly higher SO2 emission, whereas metasomatic pyrite results in a lower SO2 emission, which can be attributed to the effects of accompanying elements (Ti, F, K, and Al etc.) on the desulfurization reaction and clinkerization in the whole NSP cement line. The relationships proposed can be used to predict the SO2 emission based on the sulfur characteristics of limestone and to rationally utilize high-sulfur limestone in cement industry

Bond-Slip Behavior of Basalt Fiber Reinforced Polymer Bar in Concrete Subjected to Simulated Marine Environment: Effects of BFRP Bar Size, Corrosion Age, and Concrete Strength

Basalt Fiber Reinforced Polymer (BFRP) bars have bright potential application in concrete structures subjected to marine environment due to their superior corrosion resistance. Available literatures mainly focused on the mechanical properties of BFRP concrete structures, while the bond-slip behavior of BFRP bars, which is a key factor influencing the safety and service life of ocean concrete structures, has not been clarified yet. In this paper, effects of BFRP bars size, corrosion age, and concrete strength on the bond-slip behavior of BFRP bars in concrete cured in artificial seawater were investigated, and then an improved Bertero, Popov, and Eligehausen (BPE) model was employed to describe the bond-slip behavior of BFRP bars in concrete. The results indicated that the maximum bond stress and corresponding slip decreased gradually with the increase of corrosion age and size of BFRP bars, and ultimate slip also decreased sharply. The ascending segment of bond-slip curve tends to be more rigid and the descending segment tends to be softer after corrosion. A horizontal end in bond-slip curve indicates that the friction between BFRP bars and concrete decreased sharply