Negative Poisson's ratio (NPR) metamaterials exhibit outstanding advantages in load-bearing, energy absorption, and buffering, with broad prospects for applications aerospace, shipbuilding, related fields. However, the highly nonlinear performance-parameter relationship of NPR makes optimisation design targeting special mechanical properties challenging time-consuming. This paper proposes a multi-fidelity constrained sequential method based on lower-confidence-bound criterion (MF-CLCB)...

Engineering design optimization with expensive simulations is usually a computationally prohibitive process. As one of the most famous efficient global approaches, lower confidence bounding (LCB) approach has been widely applied to relieve this computational burden. However, LCB can be used only for problems single-fidelity level. In paper, variable-fidelity (VF-LCB) developed extend engineering multifidelity levels. First, VF-LCB function analytically derived adaptively select LF or HF...

Purpose Engineering design optimization involving computational simulations is usually a time-consuming, even computationally prohibitive process. To relieve the burden, adaptive metamodel-based (AMBDO) approaches have been widely used. This paper aims to develop an AMBDO approach, lower confidence bounding approach based on coefficient of variation (CV-LCB) balance exploration and exploitation objectively for obtaining global optimum under limited budget. Design/methodology/approach In...

Abstract In this paper, a variable-fidelity constrained lower confidence bound (VF-CLCB) criterion is presented for computationally expensive optimization problems (COPs) with two levels of fidelity. VF-CLCB, the hierarchical Kriging model adopted to objective and inequality constraints. Two infill sampling functions are developed based on constraints, respectively, an adaptive selection strategy set select elite sample points. Moreover, VF-CLCB criterion, parallel method noted as PVF-CLCB...

Graph partitioning is crucial in distributed graph-parallel computing systems, and it challenging for graph to optimize the communication cost load balancing together. Existing state-of-the-art works, such as Powerlyra TopoX, by randomly distributing edges of high-degree vertices, which inevitably brings a high that unbounded. This paper proposes partition model can minimize while maximizing balancing. More specifically, we combinatorial design problem. Our proposed provide high-quality...

Designated verifier signature scheme (DVS) is an impor- tant cryptographic primitive which can provide authentica- tion of a message without non-repudiation property tra- ditional signature. The security most existing schemes only proven in the random orale model. However, se- curity oracle model doesn't imply real world. In this paper, we proposed efficient ora- cle and show that tightly re- lated to Strong Diffie-Hellman assumption. prominent advantage pairing operation not involved...

To improve the performance of Quantum-inspired Evolutionary algorithm based on P Systems (QEPS), this paper presents an improved QEPS with a Dynamic Membrane Structure (QEPS-DMS) to solve knapsack problems. QEPS-DMS combines quantum-inspired evolutionary algorithms (QIEAs) system dynamic membrane structure. In QEPS-DMS, QIEA is considered as subalgorithm put inside each elementary one-level structure, which dynamically adjusted in process evolution by applying criterion for measuring...

Given a data matrix D, submatrix S of D is an order-preserving (OPSM) if there permutation the columns S, under which entry values each row in are strictly increasing. OPSM mining widely used real-life applications such as identifying coexpressed genes, and finding customers with similar preference. However, noise ubiquitous real matrices due to variable experimental conditions measurement errors, makes conventional algorithms inapplicable. No previous work has ever combated uncertain value...

Given a data matrix \( D \) , submatrix S of is an order-preserving (OPSM) if there permutation the columns under which entry values each row in are strictly increasing. OPSM mining widely used real-life applications such as identifying coexpressed genes and finding customers with similar preference. However, noise ubiquitous real matrices due to variable experimental conditions measurement errors, makes conventional algorithms inapplicable. No previous work on has ever considered uncertain...

In this paper, The necessary condition of isomorphism for self-complementary graph was gived by discusse the number subgraph with order 4 and 5. At final article feasibility sufficient judgements extend conditions to become self-complement discussed.

The outsourcing industrial Cluster is an open network embedding in the global value chain. Although China's clusters have achieved rapid development recent years, they to enforce their international competiveness compete with leading clusters. From perspective, paper studies type and necessity upgrade of clusters, proposes upgrading strategy Finally, analyzes makes use experiences Bangalore cluster.

SimRank is an important metric to measure the topological similarity between two nodes in a graph. In particular, single-source and top-k has numerous applications recommendation systems, network analysis, web mining, etc. Mathematically, given vertex, computation of single-machine mainly lies matrix-matrix operations. However, it almost impossible directly compute on large graphs. Thus, existing works yield main operations: series random walks, sparse matrix dense vector multiplication This...

Generating function is an effective method to solve combinatorial counting problem, but it most likely be neglected in mathematics teaching. In this paper, we provides a demonstration for teaching improvement by using the generating solving combination number and sum of preceding terms among sequence numbers.

Precipitation nowcasting, which predicts rainfall intensity in the near future, has been studied by meteorologists for decades. Currently, computer vision techniques, especially optical flow based methods, are widely adopted observatories since they deliver reasonable performance without need of model training. However, their is highly sensitive to parameters require a lot empirical knowledge optimize. With recent success deep learning (DL), machine researchers have started explore use...