Search results for: Li Minghui

Authors: Li Minghui, Min Shaorong, Zhang Jun

Abstract:

This paper aims for studying the operational efficiency of surface warship’s motorized evasion to acoustic homing torpedo. It orderly developed trajectory model, self-guide detection model, vessel evasion model, as well as anti-torpedo error model in three-dimensional space to make up for the deficiency of precious researches analyzing two-dimensionally confrontational models. Then, making use of the Monte Carlo method, it carried out the simulation for the confrontation process of evasion in the environment of MATLAB. At last, it quantitatively analyzed the main factors which determine vessel’s survival probability. The results show that evasion relative bearing and speed will affect vessel’s survival probability significantly. Thus, choosing appropriate evasion relative bearing and speed according to alarming range and alarming relative bearing for torpedo, improving alarming range and positioning accuracy and reducing the response time against torpedo will improve the vessel’s survival probability significantly.

Keywords: acoustic homing torpedo, vessel evasion, monte carlo method, torpedo defense, vessel's survival probability

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Authors: Zhibin Lei, Minghui Yang

Abstract:

Ningwu-Jingle basin is a structural syncline which has undergone a complex tectono-thermal history since Cretaceous. It stretches along the strike of the northern Lvliang Mountains which are the most important mountains in the middle and west of North China Craton. The Mesozoic units make up of the core of Ningwu-Jingle Basin, with pre-Mesozoic units making up of its flanks. The available low-temperature thermochronology implies that Ningwu-Jingle Basin has experienced two stages of uplifting: 94±7Ma to 111±8Ma (Albian to Cenomanian) and 62±4 to 75±5Ma (Danian to Maastrichtian). In order to constrain its tectono-thermal history in the Cenozoic, both apatite (U-Th-Sm)/He and fission track dating analysis are applied on 3 Middle Jurassic and 3 Upper Triassic sandstone samples. The central fission track ages range from 74.4±8.8Ma to 66.0±8.0Ma (Campanian to Maastrichtian) which matches well with previous data. The central He ages range from 20.1±1.2Ma to 49.1±3.0Ma (Ypresian to Burdigalian). Inverse thermal modeling is established based on both apatite fission track data and (U-Th-Sm)/He data. The thermal history obtained reveals that all 6 sandstone samples cross the high-temperature limit of fission track partial annealing zone by the uppermost Cretaceous and that of He partial retention zone by the uppermost Eocene to the early Oligocene. The result indicates that the middle and west of North China Craton is not stable in the Cenozoic.

Keywords: apatite fission track thermochronology, apatite (u–th)/he thermochronology, Ningwu-Jingle basin, North China craton, tectono-thermal history

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Authors: Minghui Zhang, Sirine Fkaier, Sabri Fernana, Svetlana Kiseleva, Denis Kiselev

Abstract:

The real-time identification of bacteria and fungi is difficult because they emit much weaker signals than pollen. In 2020, Plair developed Rapid-E+, which extends abilities of Rapid-E to detect smaller bioaerosols such as bacteria and fungal spores with diameters down to 0.3 µm, while keeping the similar or even better capability for measurements of large bioaerosols like pollen. Rapid-E+ enables simultaneous measurements of (1) time-resolved, polarization and angle dependent Mie scattering patterns, (2) fluorescence spectra resolved in 16 channels, and (3) fluorescence lifetime of individual particles. Moreover, (4) it provides 2D Mie scattering images which give the full information on particle morphology. The parameters of every single bioaerosol aspired into the instrument are subsequently analysed by machine learning. Firstly, pure species of microbes, e.g., Bacillus subtilis (a species of bacteria), and Penicillium chrysogenum (a species of fungal spores), were aerosolized in a bioaerosol chamber for Rapid-E+ training. Afterwards, we tested microbes under different concentrations. We used several steps of data analysis to classify and identify microbes. All single particles were analysed by the parameters of light scattering and fluorescence in the following steps. (1) They were treated with a smart filter block to get rid of non-microbes. (2) By classification algorithm, we verified the filtered particles were microbes based on the calibration data. (3) The probability threshold (defined by the user) step provides the probability of being microbes ranging from 0 to 100%. We demonstrate how Rapid-E+ identified simultaneously microbes based on the results of Bacillus subtilis (bacteria) and Penicillium chrysogenum (fungal spores). By using machine learning, Rapid-E+ achieved identification precision of 99% against the background. The further classification suggests the precision of 87% and 89% for Bacillus subtilis and Penicillium chrysogenum, respectively. The developed algorithm was subsequently used to evaluate the performance of microbe classification and quantification in real-time. The bacteria and fungi were aerosolized again in the chamber with different concentrations. Rapid-E+ can classify different types of microbes and then quantify them in real-time. Rapid-E+ enables classifying different types of microbes and quantifying them in real-time. Rapid-E+ can identify pollen down to species with similar or even better performance than the previous version (Rapid-E). Therefore, Rapid-E+ is an all-in-one instrument which classifies and quantifies not only pollen, but also bacteria and fungi. Based on the machine learning platform, the user can further develop proprietary algorithms for specific microbes (e.g., virus aerosols) and other aerosols (e.g., combustion-related particles that contain polycyclic aromatic hydrocarbons).

Keywords: bioaerosols, laser-induced fluorescence, Mie-scattering, microorganisms

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