Complexity
 Journal metrics
Acceptance rate39%
Submission to final decision69 days
Acceptance to publication51 days
CiteScore2.690
Impact Factor2.591
 Submit

Improve the Resilience of Multilayer Supply Chain Networks

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Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

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Chief Editor, Prof Sayama, is currently researching complex dynamical networks, human and social dynamics, artificial life, and interactive systems while working at Binghamton University, State University of New York.

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Global Attractivity for Lasota–Wazewska-Type System with Patch Structure and Multiple Time-Varying Delays

This paper aims to study the asymptotic behavior of Lasota–Wazewska-type system with patch structure and multiple time-varying delays. Based on the fluctuation lemma and some differential inequality techniques, we prove that the positive equilibrium is a global attractor of the addressed system with small time delay. Finally, we provide an example to illustrate the feasibility of the theoretical results.

Research Article

A Hybrid Grey Prediction Model for Small Oscillation Sequence Based on Information Decomposition

Grey prediction model has good performance in solving small data problem, and has been widely used in various research fields. However, when the data show oscillation characteristic, the effect of grey prediction model performs poor. To this end, a new method was proposed to solve the problem of modelling small data oscillation sequence with grey prediction model. Based on the idea of information decomposition, the new method employed grey prediction model to capture the trend characteristic of complex system, and ARMA model was applied to describe the random oscillation characteristic of the system. Crops disaster area in China was selected as a case study and the relevant historical eight-year data published by government department were substituted to the proposed model. The modelling results of the new model were compared with those of other traditional mainstream prediction models. The results showed that the new model had evidently superior performance. It indicated that the proposed model will contribute to solve small oscillation problems and have positive significance for improving the applicability of grey prediction model.

Research Article

Improvement of Heavy Load Robot Positioning Accuracy by Combining a Model-Based Identification for Geometric Parameters and an Optimized Neural Network for the Compensation of Nongeometric Errors

The positioning accuracy of a robot is of great significance in advanced robotic manufacturing systems. This paper proposes a novel calibration method for improving robot positioning accuracy. First of all, geometric parameters are identified on the basis of the product of exponentials (POE) formula. The errors of the reduction ratio and the coupling ratio are identified at the same time. Then, joint stiffness identification is carried out by adding a load to the end-effector. Finally, residual errors caused by nongeometric parameters are compensated by a multilayer perceptron neural network (MLPNN) based on beetle swarm optimization algorithm. The calibration is implemented on a SIASUN SR210D robot manipulator. Results show that the proposed method possesses better performance in terms of faster convergence and higher precision.

Research Article

Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement

This paper presents a method of depression recognition based on direct measurement of affective disorder. Firstly, visual emotional stimuli are used to obtain eye movement behavior signals and physiological signals directly related to mood. Then, in order to eliminate noise and redundant information and obtain better classification features, statistical methods (FDR corrected t-test) and principal component analysis (PCA) are used to select features of eye movement behavior and physiological signals. Finally, based on feature extraction, we use kernel extreme learning machine (KELM) to recognize depression based on PCA features. The results show that, on the one hand, the classification performance based on the fusion features of eye movement behavior and physiological signals is better than using a single behavior feature and a single physiological feature; on the other hand, compared with previous methods, the proposed method for depression recognition achieves better classification results. This study is of great value for the establishment of an automatic depression diagnosis system for clinical use.

Research Article

Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

Real estate market is a complex system. A rational real estate market is not only helpful to people's living standards but also beneficial to countries’ macroeconomic stability. Is Chinese real estate market rational? This paper attempts to study the efficiency of Chinese real estate market by using the complexity-entropy binary causal plane method. We firstly discuss the formation mechanism of real estate price, which provides a theoretical basis for testing the efficiency, and compute the real estate market efficiency of 70 main Chinese cities. The results show that neither the whole market nor the main cities have reached the weak efficiency, and the efficiency and complexity of each city are different, and the relationship between them is significantly negative. In addition, this paper also compares the efficiency and complexity of Chinese real estate market with American real estate market. Then, some suggestions for the healthy development of Chinese real estate market in the future are put forward.

Research Article

Impulsive Switching Epidemic Model with Benign Worm Defense and Quarantine Strategy

The issue on how to effectively control Internet malicious worms has been drawn significant attention owing to enormous threats to the Internet. Due to the rapid spreading of malicious worms, it is necessary to explore the integrated measures to automatically mitigate the propagation on the Internet. In this paper, a novel worm propagation model is established, which combines both impulsive quarantine and benign worm implementation. Then, sufficient conditions for the global stability of worm-free periodic solution and the permanence of the benign worm are obtained. Finally, the effects of quarantine strategy are assessed and some feasible strategies that can constrain the propagation of malicious worm are provided by numerical simulation.

Complexity
 Journal metrics
Acceptance rate39%
Submission to final decision69 days
Acceptance to publication51 days
CiteScore2.690
Impact Factor2.591
 Submit
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