Military heterogeneous networks often suffer from node or edge failures due to attacks, which lead to whole network topology segmentation or even paralysis. Therefore, structural invulnerability is the major technical bottleneck that impedes the combat capability of military heterogeneous system. Nodes are mainly considered in current methods, with little attention paid to edges. As such, current methods cannot accurately evaluate the structural invulnerability of military heterogeneous networks. This paper presents a new method of evaluating the survivability of military heterogeneous networks, based on network structure entropy. A model of survivability is proposed based on the network irreversibility which considers not only the nodes but also the edges. The node criticality is calculated by the level flow betweenness. Edge criticality is put forward based on the edge correlation factor and information transmission efficiency. Simulation results show that this invulnerability measure has the characteristics of high sensitivity and high precision, which will provide a theoretical basis for designing and optimizing the structure of the military heterogeneous network.

Current command and control (C2) network models typically do not account for differences in attributes of nodes that belong to the same class; in addition, there is no provision in these models for adequate description of operational relationships that exist between multiple combat entities with different attributes in real-world scenarios. To address these issues, we propose a method to model multilevel command and control supernetworks (MC2S) based on attribute synergy prioritization and hypergraph theory. First, the characteristics of C2 supernetworks are analyzed to abstract their nodes and hyperedges, based on which, we propose the MC2S model, which consists of three layers and five networks. Second, the Term Frequency–Inverse Document Frequency (TF-IDF) and spatial distance algorithms are used to construct an attribute synergy prioritization-based strategy for the generation of intra-layer MC2S hyperedges, while local-world (LW) selection is used to create an LW model-based method for the generation of inter-layer MC2S hyperedges. Through simulation experiments, we show that our model exhibits (approximately) scale-free properties as well as small-world properties, excellent cooperative engagement capabilities (CEC), and has a high level of network invulnerability. These findings will serve as a useful reference for the description of complex nodal (edge) relationships in C2 networks, network performance analyses, and deployment of complex combat mission networks.

An innovative multi-attribute decision making (MADM) approach is put forward in this paper, which is demonstrated to be more precise than other methods currently available, especially, when the evaluations given by the decision maker(s) involve the complexity of time dependent and interdependent data. As for dynamic MADM interval-valued intuitionistic fuzzy environments with the time and attribute weights completely unknown, the new approach achieves the precision improvement by overcoming the drawbacks, the traditional geometric average operator existing: these fail to consider the relationships of the integrated data as well as the influences of attribute weights and time preference factor. The new method is systematically defined in a seven step approach; steps within the approach consist of the definition of multiperiod power-weighted geometric average operators and relative optimization models to compute attribute weights and time weights. A case analysis on the option of a superior company about venture capital funding is provided to demonstrate the method proposed; validation studies present a quantitative cross-comparison study and a sensitivity analysis of the new approach.

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

In the current command and control (C2) supernetwork model it is difficult to quantify and describe the complex relationships in C2 networks accurately. This paper presents a new modeling method for C2 supernetworks based on hypergraph theory. Firstly, a C2 supernetwork model with three layers and five networks is built. Secondly, the generation strategies for five types of hyperedges are given. Finally, we verify the feasibility and effectiveness of both the model and the method using a simulation of an air defense C2 network. The simulation results confirm that the model can reflect the complex relationships and characteristics present in a C2 network.

Diabetes mellitus is a disease that has reached epidemic proportions globally in recent years. Consequently, the prevention and treatment of diabetes have become key social challenges. Most of the research on diabetes risk factors has focused on correlation analysis with little investigation into the causality of these risk factors. However, understanding the causality is also essential to preventing the disease. In this study, a causal discovery method for diabetes risk factors was developed based on an improved functional causal likelihood (IFCL) model. Firstly, the issue of excessive redundant and false edges in functional causal likelihood structures was resolved through the construction of an IFCL model using an adjustment threshold value. On this basis, an IFCL-based causal discovery algorithm was designed, and a simulation experiment was performed with the developed algorithm. The experimental results revealed that the causal structure generated using a dataset with a sample size of 2000 provided more information than that produced using a dataset with a sample size of 768. In addition, the causal structures obtained with the developed algorithm had fewer redundant and false edges. The following six causal relationships were identified: insulinplasma glucose concentration, plasma glucose concentrationbody mass index (BMI), triceps skin fold thicknessBMI and age, diastolic blood pressureBMI, and number of times pregnantage. Furthermore, the reasonableness of these causal relationships was investigated. The algorithm developed in this study enables the discovery of causal relationships among various diabetes risk factors and can serve as a reference for future causality studies on diabetes risk factors.