Analysis of the forming characteristics for Cu/Al bimetal tubes produced by the spinning process

Tube spinning technology represents a process with high forming precision and good flexibility and is increasingly being used in the manufacture of bimetal composite tubular structures. In the present study, a forming analysis of clad tube and base tube in spinning process was conducted through numerical simulations and experiments. There was an equivalent stress transition on the interface since the stress transmission was retarded from clad tube to base tube. The yield strength became a main consideration during a design bimetal composite tube. Meanwhile, the strain distributions in axial direction, tangential direction, and radial direction were also investigated to determine the deformation characteristics of each component. As the press amount increased, the strain of clad tube changed more than base tube. As the feed rate increased, the strain decreased in axial direction and tangential direction but almost unchanged in radial direction. Simultaneously, a method for controlling the wall thickness of the clad tube and the base tube is proposed. These results to guide the design of bimetal tube composite spinning process have the certain meanings

Emergence of Bending Power Law in Higher-Order Networks

In the past two decades, a series of important results have been established in the empirical and theoretical modeling of complex networks, although considered are mainly pairwise networks. However, with the development of science and technology, an increasing number of higher-order networks with many-body interactions have gradually moved to the center stage of research when real-life systems are investigated. In the paper, the concept of higher-order degree is introduced to higher-order networks, and a bending power law (BPL) model with continuous-time growth is proposed. The evolution mechanism and topological properties of the general higher-order network are studied. The batch effect of low dimensional simplex is considered. The model is analyzed by using the mean-field method and Poisson process theory. The stationary average higher-order degree distribution of simplices is expressed analytically. The obtained analytical results agree well with those observed through simulations. In particular, this paper shows that the higher-order degree distribution of simplices in the network processes a property of bending power law, and the scale-free property of the higher-order degree is controlled by the higher-order edge, the simplex dimension and the feature parameter of the model. The BPL model of higher-order networks not only generalizes the NGF model, but also the famous scale-free model of complex networks to higher-order networks

Current Status, Problems and Suggestions of Import and Export of Goods Trade between China and Chile

As one of the largest trading countries in the world, China continues to expand its trade relations with various countries, which has promoted the development of China-Chile trade in goods. Bilateral goods trade has consistently exhibited robust growth. This paper aims to delve into the current status of goods trade between China and Chile, analyze the existing challenges, and propose feasible recommendations. Through comprehensive data analysis, this research unveils the key features and challenges of their trade relationship, providing substantial support for enhancing cooperation.To begin with, this paper provides a detailed overview of the scale of China-Chile trade, emphasizing the mutually beneficial nature of their relationship. Simultaneously, it highlights the primary categories of Chinese exports to the Chilean market and Chile’s key import demands from China. Furthermore, an in-depth analysis of the major issues confronting the current merchandise trade between the two nations is conducted, encompassing concerns such as trade imbalances, product structures, and investment levels. Finally, recommendations are put forth to address these challenges, primarily focusing on enhancing policy coordination to promote bilateral investments, improving product structures to augment the value of traded goods, and optimizing investment structures to deepen bilateral cooperation

Competitive and Weighted Evolving Simplicial Complexes

A simplex-based network is referred to as a higher-order network, in which describe that the interactions can include more than two nodes. This paper first proposes a competitive evolving model of higher-order networks. We notice the batch effect of low-dim simplices during the growth of such a network. We obtain an analytical expression for the distribution of higher-order degrees by employing the theory of Poisson processes and the mean field method and use computers to simulate higher-order networks of competitions. The established results indicate that the scale-free behavior for the (d-1)-dim simplex with respect to the d-order degree is controlled by the competitiveness factor. As the competitiveness increases, the d-order degree of the (d-1)-dim simplex is bent under the logarithmic coordinates. Second, by considering the weight changes of the neighboring simplices, as triggered by the selected simplex, a new weighted evolving model in higher-order networks is proposed. The results of the competitive evolving model of higher-order networks are used to analyze the weighted evolving model so that obtained are the analytical expressions of the higher-order degree distribution and higher-order strength density function of weighted higher-order networks. The outcomes of the simulation experiments are consistent with the theoretical analysis. Therefore, the weighted network belongs to the collection of competition networks

Prototype as Query for Few Shot Semantic Segmentation

Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referring to only a few annotated examples named support images. One of the characteristics of FSS is spatial inconsistency between query and support targets, e.g., texture or appearance. This greatly challenges the generalization ability of methods for FSS, which requires to effectively exploit the dependency of the query image and the support examples. Most existing methods abstracted support features into prototype vectors and implemented the interaction with query features using cosine similarity or feature concatenation. However, this simple interaction may not capture spatial details in query features. To alleviate this limitation, a few methods utilized all pixel-wise support information via computing the pixel-wise correlations between paired query and support features implemented with the attention mechanism of Transformer. These approaches suffer from heavy computation on the dot-product attention between all pixels of support and query features. In this paper, we propose a simple yet effective framework built upon Transformer termed as ProtoFormer to fully capture spatial details in query features. It views the abstracted prototype of the target class in support features as Query and the query features as Key and Value embeddings, which are input to the Transformer decoder. In this way, the spatial details can be better captured and the semantic features of target class in the query image can be focused. The output of the Transformer-based module can be viewed as semantic-aware dynamic kernels to filter out the segmentation mask from the enriched query features. Extensive experiments on PASCAL-$5^{i}$ and COCO-$20^{i}$ show that our ProtoFormer significantly advances the state-of-the-art methods.Comment: under revie

A Region-Shrinking-Based Acceleration for Classification-Based Derivative-Free Optimization

Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible. In this paper, we study the framework of classification-based derivative-free optimization algorithms. By introducing a concept called hypothesis-target shattering rate, we revisit the computational complexity upper bound of this type of algorithms. Inspired by the revisited upper bound, we propose an algorithm named "RACE-CARS", which adds a random region-shrinking step compared with "SRACOS" (Hu et al., 2017).. We further establish a theorem showing the acceleration of region-shrinking. Experiments on the synthetic functions as well as black-box tuning for language-model-as-a-service demonstrate empirically the efficiency of "RACE-CARS". An ablation experiment on the introduced hyperparameters is also conducted, revealing the mechanism of "RACE-CARS" and putting forward an empirical hyperparameter-tuning guidance

AlGaInP light-emitting diodes with SACNTs as current-spreading layer

Transparent conductive current-spreading layer is important for quantum efficiency and thermal performance of light-emitting diodes (LEDs). The increasing demand for tin-doped indium oxide (ITO) caused the price to greatly increase. Super-aligned carbon nanotubes (SACNTs) and Au-coated SACNTs as current-spreading layer were applied on AlGaInP LEDs. The LEDs with Au-coated SACNTs showed good current spreading effect. The voltage bias at 20 mA dropped about 0.15 V, and the optical power increased about 10% compared with the LEDs without SACNTs