Incubation temperature induced developmental plasticity of cold responsive physiological phenotypes in Japanese Quails

Embryonic development is a critical period for phenotype formation.Environmental variation during embryonic development can induce changes in postnatal phenotypes of animals.The thyroxine secretion and aerobic metabolic activity of small birds are important phenotypes closely related to their winter survival.In the context of climate change,it is necessary to determine whether temperature variation during incubation in birds leads to developmental plasticity of these cold responsive phenotypes.We incubated Japanese Quail(Coturnix japonica)eggs at 36.8℃,37.8℃,and 38.8℃,and raised the chicks to 35-day old at 22℃with same raising conditions,then all the quails were exposed to gradually temperature dropping environment(from 15℃to 0℃).After cold treatment,serum T3 level,resting metabolic rate,skeletal muscle and liver metabolomes of the birds were measured.The serum T3 levels were significantly lower in the 38.8℃group and significantly higher in the 36.8℃group compared to the 37.8℃group.The metabolic rate in the 38.8℃group was significantly lower compared to the 37.8℃group.Compared with the 37.8℃group,metabolites involved in the tricarboxylic acid cycle in the liver were significantly lower in the 38.8℃group,and metabolites related to lipid oxidation metabolism and fatty acid biosynthesis were significantly lower in the skeletal muscles in the 38.8℃group but significantly higher in the 36.8℃group.These results indicate that incubation temperature variation can lead to developmental plasticity in cold responsive physiological phenotypes.Higher incubation temperature may impair the capacity of birds coping with cold challenge.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

Target maneuver recognition is a prerequisite for air combat situation awareness,trajectory prediction,threat assessment and maneuver decision.To get rid of the dependence of the current target maneuver recognition method on empirical criteria and sample data,and automatically and adaptively complete the task of extracting the target maneuver pattern,in this paper,an air combat maneuver pattern extraction based on time series segmentation and clustering analysis is proposed by combining autoencoder,G-G clustering algorithm and the selective ensemble clustering analysis algorithm.Firstly,the autoencoder is used to extract key features of maneuvering trajectory to remove the impacts of redundant variables and reduce the data dimension;Then,taking the time information into account,the segmentation of Maneuver characteristic time series is realized with the improved FSTS-AEGG algorithm,and a large number of maneuver primitives are extracted;Finally,the maneuver primitives are grouped into some categories by using the selective ensemble multiple time series clustering algorithm,which can prove that each class represents a maneuver action.The maneuver pattern extraction method is applied to small scale air combat trajectory and can recognize and correctly partition at least 71.3%of maneuver actions,indicating that the method is effective and satisfies the requirements for engineering accuracy.In addition,this method can provide data support for various target maneuvering recognition methods proposed in the literature,greatly reduce the workload and improve the recognition accuracy.

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making.Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available.However,in real-world applications,manual sample labeling is often time-consuming and laborious.In addition,airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time.To solve these problems,in this paper,an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning,ensemble learning,semi-supervised learning,and Tri-training algorithm,abbreviated as Online Ensemble Semi-supervised Classification Framework(OESCF),is proposed.The framework is divided into four parts:basic classifier offline training stage,online recognition model initialization stage,target maneuver online recognition stage,and online model update stage.Firstly,based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement,basic classifiers are trained offline by making full use of labeled and unlabeled sample data.Secondly,the dynamic density clustering algorithm of the target maneuver is performed,statistical information of each cluster is calculated,and a set of micro-clusters is obtained to initialize the online recognition model.Thirdly,the ensemble K-Nearest Neighbor(KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances.Finally,to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat,the parameters of the model are updated online using error-driven representation learning,exponential decay function and basic classifier obtained in the offline training stage.The experimental results on several University of California Irvine(UCI)datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models,and the results show that the proposed model achieves higher classification accuracy.