Study on the Rheological Properties and Constitutive Model of Shenzhen Mucky Soft Soil

In order to obtain the basic parameters of numerical analysis about the time-space effect of the deformation occurring in Shenzhen deep soft-soil foundation pit, a series of triaxial consolidated-undrained shear rheology tests on the peripheral mucky soft soil of a deep foundation pit support were performed under different confining pressures. The relations between the axial strain of the soil and time, as well as between the pore-water pressure of the soil and time, were achieved, meanwhile on the basis of analyzing the rheological properties of the soil, the relevant rheological models were built. Analysis results were proved that the rheology of Shenzhen mucky soft soil was generally viscous, elastic, and plastic, and had a low yield stress between 90 and 150 kPa. The increase in pore-water pressure made the rheological time effect of the mucky soft soil more remarkable. Thus, the drainage performance in practical engineering should be improved to its maximum possibility extent to decrease the soft-soil rheological deformation. Lastly, a six-component extended Burgers model was employed to fit the test results and the parameters of the model were determined. Findings showed that the extended Burgers model could satisfactorily simulate the various rheological stages of the mucky soft soil. The constitutive model and the determination of its parameters can be served as a foundation for the time-space effect analysis on the deformation of deep soft-soil foundation pits

Effects of dietary chromium on growth, amino acid content and proteomic changes in Sea Cucumber Apostichopus japonicus

781-786To assess the effect of dietary chromium, the growth rate, amino acid content and proteomic changes in the sea cucumber Apostichopus japonicus were compared, when fed with the diets amended with chromium-treated (58.27 mg Cr/kg dry weight) Macrocystis pyrifera and a Cr-free control diet (5.83 mg Cr/kg dry weight). After 10 days, the dietary chromium exposure decreased its growth rate and the amino acid content also changed. The proteomic changes were analyzed in A. japonicus after it was fed for 10 days with Cr-added and Cr-free control diet. The total of 1587 proteins were identified, of which 28 proteins were identified as differentially regulated proteins in sea cucumber to Cr stress. Among them, 10 proteins were identified. In summary, this work reported toxic effects in sea cucumber A. japonicus after dietary exposure to Cr

2,2′,4,4′,6,6′-Hexamethyl-N-(3-phthalimidoprop­yl)-N,N′-(propane-1,3-di­yl)dibenzene­sulfonamide

In the title compound, C32H38N3O6S2, an inter­mediate in the synthesis of polyamine drugs, the dihedral angle between the phenyl rings of the two 2,4,6-trimethyl­benzene­sulfonyl groups is 27.1 (3)°. In the crystal structure, mol­ecules are linked by inter­molecular N—H⋯O hydrogen bonds, thereby forming an infinite one-dimensional chain propagating along [010]

N,N′-Bis(2-cyano­ethyl)-4,4′-dimethyl-N,N′-(butane-1,4-di­yl)dibenzene­sulfonamide

The complete mol­ecule of the title compound, C24H30N4O4S2, is generated by a crystallographic inversion centre. In the crystal, weak C—H⋯O inter­actions link the mol­ecules, forming infinite sheets

2-(3-Bromo­prop­yl)isoindoline-1,3-dione

In the title compound, C11H10BrNO2, the dihedral angle between the five- and six-membered rings of the phthalamide system is 1.00 (16)°. There are no significant inter­molecular inter­ations except for van der Waals contacts

Multi-Behavior Hypergraph-Enhanced Transformer for Sequential Recommendation

Learning dynamic user preference has become an increasingly important component for many online platforms (e.g., video-sharing sites, e-commerce systems) to make sequential recommendations. Previous works have made many efforts to model item-item transitions over user interaction sequences, based on various architectures, e.g., recurrent neural networks and self-attention mechanism. Recently emerged graph neural networks also serve as useful backbone models to capture item dependencies in sequential recommendation scenarios. Despite their effectiveness, existing methods have far focused on item sequence representation with singular type of interactions, and thus are limited to capture dynamic heterogeneous relational structures between users and items (e.g., page view, add-to-favorite, purchase). To tackle this challenge, we design a Multi-Behavior Hypergraph-enhanced Transformer framework (MBHT) to capture both short-term and long-term cross-type behavior dependencies. Specifically, a multi-scale Transformer is equipped with low-rank self-attention to jointly encode behavior-aware sequential patterns from fine-grained and coarse-grained levels. Additionally, we incorporate the global multi-behavior dependency into the hypergraph neural architecture to capture the hierarchical long-range item correlations in a customized manner. Experimental results demonstrate the superiority of our MBHT over various state-of-the-art recommendation solutions across different settings. Further ablation studies validate the effectiveness of our model design and benefits of the new MBHT framework. Our implementation code is released at: https://github.com/yuh-yang/MBHT-KDD22.Comment: Published as a KDD'22 full pape

Propulsion Performance Analysis of Wave-powered Boats

With the development of oceanographic research and marine environment protection, mobile marine platforms are applied for ocean observation for a long journey. Wave-powered boats are capable of applying wave motion to propel itself and make a long-duration survey. This paper presents the dynamics of the wave-powered boat under the excitation of the heave motion and pitch motion. Taking the wave-powered boat with double fins as an example, the heave and pitch motions of the boat are obtained by ANSYS-AQWA firstly. Then the relationship between propulsion performance and three factors, including  wave height, wave period, and restoring stiffness of torsion spring, was analyzed through multibody dynamics software ADAMS. With the increase of sea state from level 1 to level 4 the average propulsion speed increased from 0.4m/s to 1.4m/s. Under the same wave height and period, with the increase of restoring stiffness of torsion spring from 0.0125N·m/deg to 0.3N·m /deg, the propulsion speed of the wave-powered boat increases first and then decreases, and there exists an optimum stiffness. Through the calculation it is found that when the restoring stiffness of torsional spring is increased from 0.025N·m /deg to 0.2N·m /deg with the sea state level 1 to 4, the wave powered boat has better propulsion performance

CMFDFormer: Transformer-based Copy-Move Forgery Detection with Continual Learning

Copy-move forgery detection aims at detecting duplicated regions in a suspected forged image, and deep learning based copy-move forgery detection methods are in the ascendant. These deep learning based methods heavily rely on synthetic training data, and the performance will degrade when facing new tasks. In this paper, we propose a Transformer-style copy-move forgery detection network named as CMFDFormer, and provide a novel PCSD (Pooled Cube and Strip Distillation) continual learning framework to help CMFDFormer handle new tasks. CMFDFormer consists of a MiT (Mix Transformer) backbone network and a PHD (Pluggable Hybrid Decoder) mask prediction network. The MiT backbone network is a Transformer-style network which is adopted on the basis of comprehensive analyses with CNN-style and MLP-style backbones. The PHD network is constructed based on self-correlation computation, hierarchical feature integration, a multi-scale cycle fully-connected block and a mask reconstruction block. The PHD network is applicable to feature extractors of different styles for hierarchical multi-scale information extraction, achieving comparable performance. Last but not least, we propose a PCSD continual learning framework to improve the forgery detectability and avoid catastrophic forgetting when handling new tasks. Our continual learning framework restricts intermediate features from the PHD network, and takes advantage of both cube pooling and strip pooling. Extensive experiments on publicly available datasets demonstrate the good performance of CMFDFormer and the effectiveness of the PCSD continual learning framework.Comment: 12pages,7 figure

Multiscale isogeometric optimization for cellular structure with multiple prototypes

This paper presents a multiscale concurrent isogeometric design method for the cellular structure composed of the multiple prototype microstructures. Based on the unified-skeleton modeling scheme, the graded microstructures obtained from a same prototype have similar topology and good connectivity between each other. For the different prototypes, the configurations are rearranged and added the thin-wall material in common interfaces. Thus, the geometric features as well as the structural skeletons can have a smooth transition. Through combining the numerical homogenization method with the interpolation technique, the effective properties of graded microstructures can be estimated efficiently. For the multiscale scheme, the allocation of microstructures is optimized by a material distribution-based method while the configurations of prototypes are designed by the isogeometric parameterized level set method. The proposed method inherits the high accuracy and high efficiency of isogeometric analysis and the smooth boundaries and distinct interfaces of level set method. The cellular structure can be freely customized with favorable connectivity