Explore the vast range of titles available.

In this paper, a cross-resolution acceleration method for topology optimization is proposed based on deep learning aiming at achieving precise and high-efficiency geometrically non-linear structure design. We develop a cross-resolution Pix2pix neural network (CR-Pix2pix NN) to build the high-dimensional mapping between the low-resolution intermediate configuration (IC) and the corresponding high-resolution optimized configuration. Architecture of CR-Pix2pix NN is composed of a cross-resolution generator and Markovian discriminator. The cross-resolution geometrically non-linear dataset utilized to train the deep learning model is created by solving stress constrained topology optimization model established by independent continuous mapping (ICM) method. The pre-trained CR-Pix2pix NN is capable of accurately predicting the high-resolution optimized configuration by inputting the low-resolution IC with the only one iteration step. Furthermore, the hyperparameters of the developed model are discussed to ensure the performance in accuracy and computational efficiency. The proposed method can be generalized to other precise topology optimization scenarios and engineering design.

Long-term mining activities have introduced heavy metals (HMs) into the soil, ultimately threatening environmental sustainability. Precisely forecasting the spatial patterns of HMs and performing risk evaluations in mining regions are essential for efficient pollution control. In this study, 213 topsoil samples were collected from the Fengfeng Mining Area, which has a 150-year mining history. To determine the spatial distribution of soil HM speciation, correlation analysis was conducted by integrating landform types, and visualization was carried out through Kriging interpolation. Results indicate that the mean levels of Cd, Cu, Pb, and Zn exceed their respective background values by 6.48, 1.61, 4.79, and 4.35 times. The bioavailability sequence is Cd > Pb > Zn > Cu, with elevated levels of bioavailable Cd and Pb observed in the western hilly region. Based on the secondary phase to primary phase ratio (RSP) and the risk assessment code (RAC), Pb and Cd were identified as posing high ecological risks, whereas Cu and Zn do not cause severe contamination. This study provides a scientific foundation for industrial transformation and sustainable development in resource-exhausted cities.

A challenge in designing a bistable structure is the need to have low energy input for state change while maximizing the load-carrying capability. Here, we present an optimization framework for a bistable structure such that the maximum load-bearing ability can be achieved based on the investigation on mechanical performance and surrogate models. Firstly, an analytical expression of radius for the second state of the bistable structure is derived and verified by numerical simulation using a two-point loading method. Then, the transforming process of a bistable structure is analyzed by the force-displacement curve, and the transformed load is identified as an indicator measuring the load-bearing capacity. Secondly, the influence of changing parameters, including length, ply angle, thickness, and radius of the bistable shell structure on the transformed load is carried out systematically to choose optimal design variables. Thirdly, the optimal model is established, targeting the transformed load with the constraint of coupling stress in the second stable state. Model selection is conducted to determine the surrogate model that maps design variables into objective and constraint functions. And then, the improved genetic algorithm is developed to solve the optimal model, and optimal results are analyzed and discussed. Ultimately, we achieve an optimal bistable structure with the maximum load-bearing capacity while satisfying constraint, which is validated by numerical simulation. These computational and optimal strategies can provide design ideas for new structural optimization design.

Soils are heterogeneous and microbial spatial distribution can clearly indicate the spatial characteristics of the soil carbon and nitrogen cycle. However, it is not clear how long-term fertilization affects the spatial distribution of microbial biomass in fluvo-aquic soil. We collected fluvo-aquic soil samples (topsoil 0-7.5 cm and sub-topsoil 7.5-20 cm) using a spatially-explicit design within three 40.5 m2 plots in each of four fertilization treatments. Fertilization treatments were: cropping without fertilizer inputs (CK); chemical nitrogen, phosphorus, and potassium fertilizer (NPK); chemical fertilizer with straw return (NPKS); and chemical fertilizer with animal manure (NPKM). Variables included soil microbial biomass carbon (MBC) and nitrogen (MBN), and MBC/MBN. For both soil layers, we hypothesized that: microbial biomass was lowest in CK but with the largest spatial heterogeneity; and microbial biomass was highest in NPKM and NPKS but with the lowest spatial heterogeneity. Results showed that: (1) Fertilization significantly increased MBC and MBN more in topsoil than sub-topsoil but had no MBC/MBN changes. (2) The coefficient of variation (CV) and Cochran's C showed that variation was largest in CK in topsoil and NPK in sub-topsoil and that variation of topsoil was generally lower than in sub-topsoil. The sample size of the three variables was largest in CK in topsoil but had little variation among the other treatments. (3) The trend-surface model showed that within-plot heterogeneity varied substantially with fertilization (NPKM = NPK > NPKS > CK), but Moran's I and the interpolation map showed that spatial variability with fertilization followed the order NPK > NPKS > CK = NPKM at a fine scale in topsoil. In sub-topsoil, the trend-surface model showed that within-plot heterogeneity followed the order NPKM = CK > NPK > NPKS and that the fine-scale pattern was NPKM>NPK = NPKS>CK. MBC had the highest spatial heterogeneity among the three variables in both soil layers. Our results indicate that the application of organic fertilizer (straw or manure) reduced the variation of MBC and MBN but increased the spatial variability of MBC and MBN. The spatial variation of the three variables was MBC > MBN > MBC/MBN regardless of whether variation was considered at the plot-scale or the fine-scale in both layers.

Adenosine receptors are P1 class of purinergic receptors that belong to G protein‐coupled receptors. There are 4 subtypes of adenosine receptors, namely A1, A2A, A2B, and A3. A2AR has a high affinity for the ligand adenosine. Under pathological conditions or external stimuli, ATP is sequentially hydrolyzed to adenosine by CD39 and CD73. The combination of adenosine and A2AR can increase the concentration of cAMP and activate a series of downstream signaling pathways, and further playing the role of immunosuppression and promotion of tumor invasion. A2AR is expressed to some extent on various immune cells, where it is abnormally expressed on immune cells in cancers and autoimmune diseases. A2AR expression also correlates with disease progression. Inhibitors and agonists of A2AR may be potential new strategies for treatment of cancers and autoimmune diseases. We herein briefly reviewed the expression and distribution of A2AR, adenosine/A2AR signaling pathway, expression, and potential as a therapeutic target. A2AR is expressed to some extent on various immune cells, where it is abnormally expressed on immune cells in cancers and autoimmune diseases. This review summarizes the expression and distribution of A2AR, adenosine/A2AR signaling pathway, expression, and potential as a therapeutic target.

Continuous fiber-reinforced polymer (CFRP) has been paid more attention to due to its advantages of high strength, high stiffness, and lightweight. However, it is challenging for topology optimization to achieve CRRP structure design. A topology optimization (TO) model with minimizing structural compliance under volume fraction constraint is established based on the independent continuous mapping (ICM) method. The continuous fiber angle optimization (CFAO) method solves the fiber orientation. Meanwhile, the local optimal solution problem caused by the CFAO method is greatly avoided by the direction of principal stress. The effectiveness of the method is verified by classic numerical examples. This paper has a significant reference for the TO design of CFRP structure.

Topology optimization is a common approach for material distribution in continuous structure due to its rigorous mathematical theory. However, with the increase of material types in design domain, the computational efficiency of traditional topology optimization for multiple materials problem is greatly decreased. In this paper, a novel deep learning-based topology optimization method is proposed to achieve multi-material structural design for improving computational efficiency. A large number of multi-material topological configurations are simulated by solid isotropic material with penalization (SIMP), to construct multi-material topology optimization dataset. Subsequently, ResUNet involved generative adversarial network (ResUNet-GAN) is developed for high-dimensional mapping from design parameters to the corresponding multi-material topological configuration. Finally, the ResUNet-GAN, trained by the multi-material dataset, is utilized to design multi-material topological configuration. Numerical simulations verify that the well-trained ResUNet-GAN is successfully applied to three types of cases: the cantilever beam with double materials, the cantilever beam with triple materials, and the half-MBB with triple materials. The deep learning-based topology optimization approach is superior to the conventional methods in terms of higher computational efficiency, performing the potential of such a data-driven method to accelerate the calculation of structural optimization design.

The buckling behaviours of C-tape-springs (CTS)plays a crucial role in the stability and safety of space deployable structures. First, this paper presents an axial compression analysis of 65Mn steel CTS through numerical simulations using ABAQUS, combining linear eigenvalue buckling and nonlinear analysis. Subsequently, the study examines the relationship between buckling critical loads and geometric parameters under different edge conditions, considering both scenarios with and without initial defects. The results indicate that the buckling critical load is positively correlated with CTS thickness and central angle. Under fixed edge conditions, critical buckling loads monotonically decrease with increasing length defect factors. For simply supported edge, critical loads exhibit an initial increase followed by reduction with rising length defect factor, while steady-state loads positively dependent on higher defect factors.

The traditional topology optimization method of continuum structure generally uses quadrilateral elements as the basic mesh. This approach often leads to jagged boundary issues, which are traditionally addressed through post-processing, potentially altering the mechanical properties of the optimized structure. A topology optimization method of Movable Morphable Smooth Boundary (MMSB) is proposed based on the idea of mesh adaptation to solve the problem of jagged boundaries and the influence of post-processing. Based on the ICM method, the rational fraction function is introduced as the filtering function, and a topology optimization model with the minimum weight as the objective and the displacement as the constraint is established. A triangular mesh is utilized as the base mesh in this method. The mesh is re-divided in the optimization process based on the contour line, and a smooth boundary parallel to the contour line is obtained. Numerical examples demonstrate that the MMSB method effectively resolves the jagged boundary issues, leading to enhanced structural performance.

Based upon the conservation of average energy a theorem about the periodic solutions of strongly nonlinear nonautomous systems with multi-degree-of freedom is proved. This theorem can not only decide the existence and stability of the periodic solutions, but at the same time can also give their first-order and second-order approximate expressions.