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ObjectiveThis study aimed to understand the epidemic characteristics of Diarrheagenic Escherichia coli （DEC） in domestic meat products.MethodsThe molecular characteristics of 86 strains of DEC were analyzed by whole genome sequencing technology， and the dominant pathological types， STs， and serotypes were identified. The phylogenetic relationship between DEC strains derived from meat products in China was determined through whole genome single nucleotide polymorphism.ResultsThe results showed that the pathological types of DEC in meat products were mainly EAEC. Eighty-six DEC strains were divided into eight virulence genotypes， including 48 ST types （including six new ST types）， and 55 O：H serotypes. ST11 and CC10 were the predominant ST and clonal complex， respectively. O157， O15， and O6 were the predominant serogroups. DEC strains exhibited high genetic heterogeneity， the same pathological type had low homology， and they were in different evolutionary branches.ConclusionThere is no correspondence between DEC pathological types and ST types and serotypes. The pathological types cannot be directly judged by monitoring ST types and serotypes， but the most numerous ST types and serotypes reveal the main hazard characteristics and prevention of DEC. The determination method of DEC can be supplemented and optimized according to the results of whole genome sequencing， which can provide data support for the traceability and epidemiological investigation of DEC in the future.

Aiming at the difficulty of analyzing the meshing contact characteristics of gear transmission system under the big data formed by multiple working conditions and uncertain parameters, a meshing contact characteristics analysis method of gear transmission system based on data mining technology is proposed. Based on the principle of multi-dimensional Gaussian distribution and the finite element model of gear transmission system, the meshing contact characteristic data set of the system is constructed. The correlation between system parameters and meshing contact characteristics is analyzed using the maximum information coefficient, which provides a candidate feature subset for the prediction model. Then, the prediction model of meshing contact characteristics of the system is established using support vector machine and random forest algorithm, which realizes the efficient prediction of meshing contact characteristics of the system. The results show that the prediction error of the prediction model based on support vector machine is the smallest, and the average absolute percentage error is 3.87%, which is far less than the theoretical calculation error. Under the optimal feature subset, the prediction error index of contact characteristics of the prediction model based on support vector machine decreases significantly, and its average absolute percentage error reaches 3.03%, which is 21.71% lower than the prediction error of contact characteristics before optimization. These have verified the accuracy and effectiveness of the proposed method.

Aimed at the problems of unmanned marine exploration vessels, such as the short voyage time and the limited sensing ability caused by sensor failure under complex marine environments, a long-range unmanned ocean-air stereo exploration vessel driven by wind and solar energy is developed. An elevating ducted wind turbine is designed for high efficiency and low starting wind speed, and a deployable solar photovoltaic power generation system is developed. Therefore, wind power and solar energy can be utilized to realize a hybrid system, which overcomes the instability of single energy power supply, and effectively ensures the endurance of unmanned exploration vessel. Then, a ship-borne tethered ummanned aerial vehicle (UAV) system is developed with an autonomous takeoff and landing control section. Finally, the information fusion technology of ship borne and airborne sensors is adopted to greatly improve the perception ability of the unmanned ship to the surrounding environment and the function of three-dimension detection of sea and air. The unmanned surface vessel (USV) proposed in this paper is permitted to perform the assigned task with different types of loading equipment according to the scenarios.

To realize the practical scale application of the spar-type floating offshore wind turbine (FOWT) in the medium depth sea areas, a novel 6 MW spar-type floating offshore wind turbine is analyzed by model test and numerical simulation under extreme conditions. The response of main freedom degrees, the mooring tense and the stress at the danger point are explored by a 1∶65.3 scale model at the State Key Laboratory of Ocean Engineering in Shanghai JiaoTong University. Coupled motion response of the spar-type floating wind turbine is calculated by using numerical simulation software in time domain. The results of the numerical simulation and model test are compared and analyzed in time and frequency domain. The maximum deviation between numerical simulation and model test is less than 12%, which shows that the numerical simulation results are in good agreement with the model test results. The dynamic response energy of the FOWT is mainly concentrated at low frequency and wave frequency. Moreover, the whole FOWT system has an excellent survivability under extreme conditions. Finally, the ultimate load of the wind turbine is predicted, which provides the necessary theoretical basis and calculation parameters for the structural strength calculation.

In order to determine single-observer passive coherent locations using illuminators of opportunity, we propose a jointing angle and Time Difference Of Arrival (TDOA) Weighted Least Squares (WLS) location method. First, we linearize the DOA and TDOA measurement equations. We establish the localization problem as the WLS optimization model by considering the errors in the location equations. Then, we iteratively solve the WLS optimization. Finally, we conduct a performance analysis of the proposed method. Simulation results show that, unlike the TDOA-only method, which needs at least three illuminators to locate a target, the jointing DOA and TDOA method requires only one illuminator. It also has a higher localization accuracy than the TDOA-only method when using the same number of illuminators. The proposed method yields a lower mean square error than the least squares algorithm, which makes it possible to approach the Cramér-Rao lower bound at a relatively high TDOA noise level. Moreover, on the basis of the geometric dilution of precision, we conclude that the positions of the target and illuminators are also important factors affecting the localization accuracy.