A review of the design methods of complex topology structures for 3D printing

As a matter of fact,most natural structures are complex topology structures with intricate holes or irregular surface morphology.These structures can be used as lightweight infill,porous scaffold,energy absorber or micro-reactor.With the rapid advancement of 3D printing,the complex topology structures can now be efficiently and accurately fabricated by stacking layered materials.The novel manufacturing technology and application background put forward new demands and challenges to the current design methodologies of complex topology structures.In this paper,a brief review on the development of recent complex topology structure design methods was provided;meanwhile,the limitations of existing methods and future work are also discussed in the end.

Case-Based Reasoning Topological Complexity Calculation of Design for Components

Directly calculating the topolo gi cal and geometric complexity from the STEP (standard for the exchange of product model data, ISO 10303) file is a huge task. So, a case-based reasoning approac h is presented, which is based on the similarity between the new component and t he old one, to calculate the topological and geometric complexity of new compone nts. In order to index, retrieve in historical component database, a new way of component representation is brought forth. And then an algorithm is given to ext ract topological graph from its STEP files. A mathematical model, which describe s how to compare the similarity, is discussed. Finally, an example is given to s how the result.

A Spatio-Temporal Heterogeneity Data Accuracy Detection Method Fused by GCN and TCN

Spatio-temporal heterogeneous data is the database for decisionmaking in many fields,and checking its accuracy can provide data support for making decisions.Due to the randomness,complexity,global and local correlation of spatiotemporal heterogeneous data in the temporal and spatial dimensions,traditional detection methods can not guarantee both detection speed and accuracy.Therefore,this article proposes a method for detecting the accuracy of spatiotemporal heterogeneous data by fusing graph convolution and temporal convolution networks.Firstly,the geographic weighting function is introduced and improved to quantify the degree of association between nodes and calculate the weighted adjacency value to simplify the complex topology.Secondly,design spatiotemporal convolutional units based on graph convolutional neural networks and temporal convolutional networks to improve detection speed and accuracy.Finally,the proposed method is compared with three methods,ARIMA,T-GCN,and STGCN,in real scenarios to verify its effectiveness in terms of detection speed,detection accuracy and stability.The experimental results show that the RMSE,MAE,and MAPE of this method are the smallest in the cases of simple connectivity and complex connectivity degree,which are 13.82/12.08,2.77/2.41,and 16.70/14.73,respectively.Also,it detects the shortest time of 672.31/887.36,respectively.In addition,the evaluation results are the same under different time periods of processing and complex topology environment,which indicates that the detection accuracy of this method is the highest and has good research value and application prospects.

Effects of Complex System Topology on the Bak-Sneppen Evolution Model

In this paper, we investigate by numerical simulations the Bak-Sneppenmodel (BSM) for biological evolution on scale-free networks (SFNs) with various degree exponents γ. We find that the punctuated equilibrium is rather robust with respect to the network topology. Furthermore, we analyze the evolution of the criticalaverage fitness h fi ∗ and the exponent τ of h fi 0 avalanche as a function of α (i.e., the degree exponent γ). Our observations indicate the dependence of evolutionary dynamicsof BSM on the complex biosystem topology.

Software System Evolution Analysis Method Based on Algebraic Topology

The analysis of software system evolution is highly significant in software research as the evolution runs throughout the lifecycle of a software system. Considering a software system as an algebraic engineering system, we propose a software system evolution analysis method based on algebraic topology. First, from a complex network perspective, we abstract a software system into the software structural topology diagram. Then, based on the algebraic topology principle, we abstract each node in the software structural topology diagram into an algebraic component represented by a 6-tuple. We propose three kinds of operation relationships between two algebraic components, so that the software system can be abstracted into an algebraic expression of components. In addition, we propose three forms of software system evolution, which help to analyze the structure and evolution of system software and facilitate its maintenance and reconfiguration.

[Co(NH_3)_6]_2[Cd_8(C_2O_4)_(11)(H_2O)_4]·8H_2O: A 5-connected sqp topological metal-organic framework co-templated by Co(NH_3)_6^(3+) cation and (H_2O)_4 cluster

In our efforts to construct new metal-organic frameworks （MOFs） by template-directing method, a new cadmium oxalate, [Co（NH3）612[Cds（C204）ll（H20）4].8H20 （denoted HNU-1 ）, has been synthesized under hydrothermal condition in the presence of C0（NH3）6C13, The crystal structure of HNU-1 was determined by single-crystal X-ray diffraction （monoclinic, C2/c）, a = 11.126（2）A, b = 17.361 （4）,A, c = 16.119（3）A, fi = 102.40（3）, V = 3040.8（10） A and Z = 8. The open framework of HNU-1 contains 12-ring channels and exhibits a 5-connected sqp topological network with dinuclear Cd（ll） clusters acting as nodes. The Co（NH3）63＋ cations and unusual hydrogen-bonded （H20）4 clusters are found in the 12-ring channels with an alternative arrangement. It is believed that the （H20）4 clusters play a co-templating role in the crystallization of HNU-1.

Combining Topological Properties and Strong Ties for Link Prediction

Link prediction is an important task that estimates the probability of there being a link between two disconnected nodes. The similarity-based algorithm is a very popular method that employs the node similarities to find links. Most of these types of algorithms focus only on the contribution of common neighborhoods between two nodes. In sociological theory relationships within three degrees are the strong ties that can trigger social behaviors.Thus, strong ties can provide more connection opportunities for unconnected nodes in the networks. As critical topological properties in networks, nodes degrees and node clustering coefficients are well-suited for describing the tightness of connections between nodes. In this paper, we characterize node similarity by utilizing the strong ties of the ego network（i.e., paths within three degrees） and its close connections（node degrees and node clustering coefficients）. We propose a link prediction algorithm that combines topological properties with strong ties, which we called the TPSR algorithm. This algorithm includes TPSR2, TPSR3, and the TPSR4 indices. We evaluate the performance of the proposed algorithm using the metrics of precision and the Area Under the Curve（AUC）. Our experimental results show the TPSR algorithm to perform remarkably better than others.