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Target maneuver trajectory prediction is an important prerequisite for air combat situation awareness and threat assessment. Aiming at the problem of low prediction accuracy in traditional trajectory prediction methods, combined with the chaotic characteristics of the target maneuver trajectory time series, a target maneuver trajectory prediction model based on chaotic theory and improved genetic algorithm-Volterra neural network (IGA-VNN) model is proposed, mathematically deducing and analyzing the consistency between Volterra functional model and back propagation (BP) neural network in structure. Firstly, the C-C method is used to reconstruct the phase space of the target trajectory time series, and the maximum Lyapunov exponent of the time series of the target maneuver trajectory is calculated. It is proved that the time series of the target maneuver trajectory has chaotic characteristics, so the chaotic method can be used to predict the target trajectory time series. Then, the practicable Volterra functional model and BP neural network are combined together, learning the advantages of both and overcoming the difficulty in obtaining the high-order kernel function of the Volterra functional model. At the same time, an adaptive crossover mutation operator and a combination mutation operator based on the difference degree of gene segments are proposed to improve the traditional genetic algorithm; the improved genetic algorithm is used to optimize BP neural network, and the optimal initial weights and thresholds are obtained. Finally, the IGA-VNN model of chaotic time series is applied to the prediction of target maneuver trajectory time series, and the experimental results show that its estimated performance is obviously superior to other prediction algorithms.

Gomphocerus sibiricus L., the dominant insect species in the alpine and subalpine grassland, overwinters with diapause at egg stage. In this study, cold tolerance and related cryoprotectants of G. sibiricus eggs were investigated. In particular, the supercooling point (SCP), water content, carbohydrates (trehalose, glucose, fructose, glycogen), polyols (glycerol, inositol, sorbitol), fat, and amino acids contents were evaluated at different developmental stages of G. sibiricus eggs collected under natural conditions. The SCPs of eggs were very low (-32.83 to -22.61°C) at mid-diapause. Water content gradually increased during development. The fructose, glycerol, and sorbitol contents were significantly higher in diapausing eggs than in early embryogenesis stage and post-diapause development stage. Glycogen content was high throughout the whole developmental period. The trehalose, glucose, and inositol contents were low during diapause compared to that in early embryogenesis stage and post-diapause development stage. There were no significant differences in the fat content of eggs among all development stages. The total amino acid contents in eggs in the early embryogenesis and at the start of diapause were higher than that in post-diapause eggs. The contents of Glu, Asp, Leu, Pro and Arg during diapause were significantly higher than those during post-diapause development. Results indicate that G. sibiricus eggs have a high supercooling capacity. Successful overwintering can be attributed to the accumulation of glycerol, fructose, sorbitol, and amino acids (Glu, Asp, Leu, Pro and Arg). These findings provide insight into the mechanisms underlying the adaptation of G. sibiricus to cold conditions.

The multi-objective weapon-target assignment problem, which aims to generate reasonable assignment to meet the objectives, is a typical optimization problem with complex constraints. In order to get close to the actual air combat, the game process between both sides at war is introduced to construct a three objective mathematical model, which includes the damage of the enemy, the cost of missiles, and the damage value of fighting capacity. Considering the NP-complete nature of multi-objective weapon-target assignment problem, an improved intelligent algorithm (named as D-NSGA-III-A) on the basis of non-dominated sorting genetic algorithm III (NSGA-III) is proposed. In this improved algorithm, first, the non-dominated sorting based on dominance degree matrix is proposed to reduce the unnecessary or repetitive comparisons in ranking schemes, so as to further decrease the time consumption. Second, diversity and convergence are taken into account resorting to the niching information and the dominance ratio when selecting individuals. Third, the adaptive operator selection mechanism, which selects the operators adaptively according to the information of generations from a pool where single point crossover and all bits crossover operators are included, is employed to seek a balance between intensification and diversification within the decision space and to improve the quality of Pareto solutions. From the experiments, the combination of above technologies obtains better Pareto solutions and time performance for solving the static multi-objective target assignment (SMWTA) problem than NSGA-III, MP-ACO, NSGA-II, MOPSO, MOEA/D, and DMOEA-εC.

A new approach to solving weapon-target assignment (WTA) problem is proposed in this paper. Firstly, relative superiority that lays the foundation for assignment is calculated based on the combat power energy of the fighters. Based on the relative superiority, WTA problem is formulated. Afterwards, a hybrid algorithm consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) is introduced and furthermore applied to solve the assignment formulation. Finally, the proposed approach is validated by eight representative benchmark functions and two concrete cooperative air combat examples. The results show that the approach proposed in this paper achieves good performances in solving WTA problem in cooperative air combat.

In the clutter environment, radar/infrared sensors are used to track the background of maneuvering targets. Aiming at the shortcomings of traditional probabilistic data association theory in solving multiple target echoes and numbers in measurement, it is presented by combining interactive multiple model (IMM) and multiple detection probabilistic data association filter (MDPDAF) in a multi-sensor (radar and infrared) scenario known as IMM/MS-MDPDAF algorithm. The IMM algorithm has the ability to adapt to the target high maneuver and clutter environment. The MS-MDPDAF algorithm can detect multiple effective target echoes, considering various uncertainties in the clutter environment. According to the multi-detection mode of radar sensor, the target is effectively measured and the state vector is updated. Then the data fusion and probability data association theory are used to calculate the corresponding probability and state prediction, estimation and update under the Bayesian framework. The simulation results show that the IMM/MS-MDPDAF algorithm can improve the effectiveness and tracking accuracy of target, and has better tracking performance than IMM/MSPDAF.

The outcome of surgery alone for advanced gastric cancer is unsatisfactory. Local recurrence rate and distant metastasis rate are still high in postoperative patients. In recent years, people have paid more attention to neo- adjuvant chemotherapy, because it can reduce tumor loading, degrade the staging, increase the surgery excision rate and improve the long-term survival. Preoperative regional arterial infusion chemotherapy can directly act on the tumor endothelial cells with higher local drug concentration, but with fewer systemic side effects. So it may offer an effective treatment for advanced gastric cancer. The indications, complications, therapeutic evaluation, timing of surgery, chemotherapy regimens and pathomorphological changes and so on of the preoperative regional arterial infusion chemotherapy are summarized in this review.