Contact Analysis of Separation Between Concrete Slab and Cushion Layer in Tianshengqiao Concrete-Faced Rockfill Dam

Tianshengqiao concrete-faced rockfill dam (CFRD), with a maximum height of 178m, is the highest dam of the same kind in China and the second highest in the world. During the construction of the dam, some problems special for high CFRDs occurred, such as deficient of cushion layer and separation of concrete slab from cushion layer. In this paper, a finite element analysis is made to understand the deformation of the cushion layer and the separation between the slab and the cushion. Direct constraints method and Coulomb friction law are used to simulate the contact behavior between the deformable concrete slab and the cushion layer. The methods are shown to be effective through a comparison of the numerical results with in-situ measurements. The mechanism of occurrence of the separation between the slab and the cushion is discussed. Valuable suggestions are made for further design and construction of high concrete-faced rockfill dams

Geometric Multi-Model Fitting by Deep Reinforcement Learning

This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e.g., laser scanned point clouds). We formulate multi-model fitting problem as a sequential decision making process. We then use a deep reinforcement learning algorithm to learn the optimal decisions towards the best fitting result. In this paper, we have compared our method against the state-of-the-art on simulated data. The results demonstrated that our approach significantly reduced the number of fitting iterations

Cooperative Hardware-Prompt Learning for Snapshot Compressive Imaging

Snapshot compressive imaging emerges as a promising technology for acquiring real-world hyperspectral signals. It uses an optical encoder and compressively produces the 2D measurement, followed by which the 3D hyperspectral data can be retrieved via training a deep reconstruction network. Existing reconstruction models are trained with a single hardware instance, whose performance is vulnerable to hardware perturbation or replacement, demonstrating an overfitting issue to the physical configuration. This defect limits the deployment of pre-trained models since they would suffer from large performance degradation when are assembled to unseen hardware. To better facilitate the reconstruction model with new hardware, previous efforts resort to centralized training by collecting multi-hardware and data, which is impractical when dealing with proprietary assets among institutions. In light of this, federated learning (FL) has become a feasible solution to enable cross-hardware cooperation without breaking privacy. However, the naive FedAvg is subject to client drift upon data heterogeneity owning to the hardware inconsistency. In this work, we tackle this challenge by marrying prompt tuning with FL to snapshot compressive imaging for the first time and propose an federated hardware-prompt learning (FedHP) method. Rather than mitigating the client drift by rectifying the gradients, which only takes effect on the learning manifold but fails to touch the heterogeneity rooted in the input data space, the proposed FedHP globally learns a hardware-conditioned prompter to align the data distribution, which serves as an indicator of the data inconsistency stemming from different pre-defined coded apertures. Extensive experiments demonstrate that the proposed method well coordinates the pre-trained model to indeterminate hardware configurations.Comment: 11 figures, 4 table

A novel explicit design method for complex thin-walled structures based on embedded solid moving morphable components

In this article, a novel explicit approach for designing complex thin-walled structures based on the Moving Morphable Component (MMC) method is proposed, which provides a unified framework to systematically address various design issues, including topology optimization, reinforced-rib layout optimization, and sandwich structure design problems. The complexity of thin-walled structures mainly comes from flexible geometries and the variation of thickness. On the one hand, the geometric complexity of thin-walled structures leads to the difficulty in automatically describing material distribution (e.g., reinforced ribs). On the other hand, thin-walled structures with different thicknesses require various hypotheses (e.g., Kirchhoff-Love shell theory and Reissner-Mindlin shell theory) to ensure the precision of structural responses. Whereas for cases that do not fit the shell hypothesis, the precision loss of response solutions is nonnegligible in the optimization process since the accumulation of errors will cause entirely different designs. Hence, the current article proposes a novel embedded solid component to tackle these challenges. The geometric constraints that make the components fit to the curved thin-walled structure are whereby satisfied. Compared with traditional strategies, the proposed method is free from the limit of shell assumptions of structural analysis and can achieve optimized designs with clear load transmission paths at the cost of few design variables and degrees of freedom for finite element analysis (FEA). Finally, we apply the proposed method to several representative examples to demonstrate its effectiveness, efficiency, versatility, and potential to handle complex industrial structures

Evaluating and Enhancing Iron Removal via Filterable Iron Precipitates Formation during Coal-Waste Bioleaching

Iron removal via jarosite precipitate formation is a commonly used technique in various hydrometallurgical processes. Excess iron removal often becomes essential to an overall metal recovery circuit. This is particularly important to processes involving iron-bearing minerals. A technique, which involved the use of pyrite to generate acid for leaching, for iron removal is critical to enabling the process. Iron removal using CaO or similar reagents is expensive and often results in lost product. In the present study, various compounds that facilitate jarosite formation, namely Na2SO4, NH4OH, KCl, and KOH, were utilized and their effect in precipitation was observed. Visual Minteq assisted simulations were run in order to evaluate favorable conditions for iron removal. Morphology and elemental composition of precipitates were analyzed using scanning electron microscopy equipped with energy-dispersive X-ray spectroscopy, and the phase purity was identified using X-ray diffraction analysis

Knockdown of Brm and Baf170, Components of Chromatin Remodeling Complex, Facilitates Reprogramming of Somatic Cells

© Copyright 2015, Mary Ann Liebert, Inc. 2015. The SWI/SNF (SWItch/Sucrose NonFermentable or BAF, Brg/Brahma-associated factors) complexes are epigenetic modifiers of chromatin structure and undergo progressive changes in subunit composition during cellular differentiation. For example, in embryonic stem cells, esBAF contains Brg1 and Baf155, while their homologs, Brm and Baf170, are present in BAF of somatic cells. In this study, we sought to determine whether Brm and Baf170 play any roles in induced pluripotent stem cell (iPSC) reprogramming by using shRNA-mediated knockdown studies in the mouse model. We found that knocking down Brm during early, mid, and late stages (days 3, 6, and 9 after initial iPSC induction) and knocking down Baf170 during late-stage (day 9) reprogramming improve the numbers of iPSC colonies formed. We further showed that inhibition of these somatic BAF components also promotes complete reprogramming of partially reprogrammed somatic cells (pre-iPSCs). Finally, we found that the expression of Brm and Baf170 during reprogramming was regulated by Jak/Stat3 activity. Taken together, these data suggest that inhibiting somatic BAF improves complete reprogramming by facilitating the activation of the pluripotency circuitry