Journal of University of Chinese Academy of Sciences

Numerical simulation of Marangoni flow on droplet impingement on a superheated pool

ZHAO Shuo, ZHANG Jie, NI Mingjiu

2024, 41 (3): 289-297. DOI: 10.7523/j.ucas.2023.063

We numerically investigate the dynamic behaviors of a droplet impacting onto the non-volatile superheated liquid pool, and focuse on the influence of the Marangoni effect resulted from the inhomogeneous temperature distribution at the surface of liquid pool. Particularly, the time evolution of the flow field and the temperature field inside the pool during and after the impacting are studied. We find that the pool surface, made of oil, is cooled quickly as the ethanol droplet approaches, and a large temperature gradient is produced along the radial direction. Correspondingly, the Marangoni force directs radially inwards to resist the radial outward vapor stress, and a “jet” like structure is formed at the position where the vapor layer becomes thinnest. As the time advances, the Marangoni force becomes dominant and the “jet” is pushed radially inwards until the rear of the droplet and form the downward plume flow. In addition, as the viscosity of the liquid pool increases, the flow induced by the vapor stress is suppressed which further reduce the heat exchange efficiency near the interface.

Structure-preserving numerical method for a class of stochastic Poisson systems

LIU Qianqian, WANG Lijin

2024, 41 (3): 298-305. DOI: 10.7523/j.ucas.2022.015

Abstract ( 350 ) PDF (0KB) ( 0 )

In this paper, we consider the structure-preserving numerical simulation of a class of stochastic Poisson systems, i.e. the stochastic Lotka-Volterra systems. We propose a stochastic Poisson integrator for the systems which can preserve the Poisson structure and the Casimir functions of the systems, and prove that the numerical integrator has root mean-square convergence order 1. Numerical experiments are performed to verify the theoretical results.

Turbulence properties in interstellar medium around Geminga TeV halo

CHENG Haolin, ZHU Hui, CHEN Tianlu, TIAN Wenwu, CUI Xiaohong, WU Dan, GAO Qi

2024, 41 (3): 306-311. DOI: 10.7523/j.ucas.2022.076

Abstract ( 241 ) PDF (0KB) ( 0 )

Space power spectrum analysis is a common method to study the turbulence property of interstellar medium. In this paper, we applied this method to analyze a neutral hydrogen cloud near Geminga with data from the GALFA (galactic Arecibo L-band feed array) HI survey. The spatial power spectrum of the cloud can be well described by a power-law with an index of -4.0±0.1 which is steeper than the turbulent power spectrum of local galactic interstellar medium (the spectral index is larger than -3.0). We considered several physical conditions that may lead to the steeper spectrum and found none of the thin HI slice along the line of sight direction, highly ordered magnetic field perpendicular to line of sight direction or energy loss during the energy cascade be responsible for the steeper spectrum. This indicates that the turbulence property around Geminga is very different to the local galactic interstellar medium which may account for the abnormally low diffusion coefficient in Gemninga TeV halo.

Comparisons among common methods of calculating stellar masses and star formation rates for normal galaxies

LI Cuihuan, LI Guodong, TSAI Chao-Wei, DANZENG Luobu

2024, 41 (3): 312-320. DOI: 10.7523/j.ucas.2022.052

Abstract ( 343 ) PDF (0KB) ( 0 )

Stellar mass and star formation rate are essential properties of galaxies. They are also an important basis for understanding the evolution of baryonic matter distribution on the cosmic time scale. Astronomers have developed many methods to estimate the stellar mass and star formation rate of a single galaxy using multi-waveband data. This paper evaluates the variations of the results from different stellar mass and star formation rate estimation methods using multi-wavelength sky survey data of a sample of normal galaxies from the Salon Digital Sky Survey. Our study shows that the results of various stellar mass estimation methods are not significantly different. However, the degree of deviation between the estimated star formation rates from different methods is significant and critical. Therefore, to avoid inaccurate interpretation caused by the discrepancy of different estimation methods, a similar observation data set and the same star formation rate estimation method should be used for each galaxy when comparing star formation rates between galaxies or analyzing star formation rates of a large galaxy sample.

Allometric equation and biomass estimation of Eucalyptus in Fujian

ZHENG Xiaoman, WENG Xian, OU Linglong, REN Yin

2024, 41 (3): 321-333. DOI: 10.7523/j.ucas.2022.074

Abstract ( 693 ) PDF (0KB) ( 0 )

The estimation of forest biomass at individual tree scale is the basis for the estimation of forest biomass at the regional scale. This paper aims at developing a reliable and effective allometric equation for Eucalyptus in Fujian in order to improve the estimation accuracy of Eucalyptus biomass in this area and to provide basic supporting data for the sustainable forestry development of Eucalyptus. This study takes Eucalyptus, a major fast-growing and productive tree species in Southern China, as the research object. Using 90 Eucalyptus woods harvested in the field, the partitioning of Eucalyptus biomass among organs are studied, the optimal allometric equations are constructed, and the Eucalyptus root/shoot ratios are calculated and applied to estimate Eucalyptus root biomass. Results show Eucalyptus has the following biomass allocation strategies: the biomass proportion of trunks increases with increasing stand age, while that of branches, leaves, and roots decreases. The most feasible and effective way to estimate Eucalyptus root biomass is to use root/shoot ratio data with stand age. In the construction of the allometric equation for Eucalyptus biomass, the multiplicative power equation is better than the linear equation, and the optimal independent variable varied by organ type. This paper provides data and theoretical support for the accurate estimation of Eucalyptus plantation biomass, and has implications for species growth patterns, survival strategies, and even forest ecological management.

Spatial and temporal distribution characteristics and influential factors of PM_2.5 pollution in Beijing-Tianjin-Hebei

SU Mengqian, SHI Yusheng

2024, 41 (3): 334-344. DOI: 10.7523/j.ucas.2023.025

Abstract ( 634 ) PDF (0KB) ( 0 )

The fine particulate matter PM_2.5 could be harmful to human health and the atmospheric environment. Beijing-Tianjin-Hebei is one of the most serious regions in China in terms of atmospheric PM_2.5 pollution. Based on PM_2.5 concentrations data, natural factors data, and human activity factors data, this study used kriging interpolation and statistical analysis to explore the spatial and temporal distribution characteristics of atmospheric PM_2.5 pollution in 13 cities of Beijing-Tianjin-Hebei in 2017 and then used correlation analysis models and factor analysis models to explore its influential factors. The results show that in Beijing-Tianjin-Hebei, 1) PM_2.5 concentrations are low in the north and high in the south. The gradient of annual average concentrations between the southern and northern cities can reach up to 64μg/m³. 2) PM_2.5 concentrations are high in winter and low in summer, high in the morning and evening, and low in the afternoon. PM_2.5 concentration in winter is 1.3-2.8 times higher than in summer, and the daily differences in PM_2.5 concentrations in all seasons are between 11-29μg/m³. 3) Atmospheric PM_2.5 pollution is closely related to natural factors. Terrain and topography affect the processes of PM_2.5 aggregation, transport, and dispersion. Wind speed, sunshine hours, and relative humidity are the dominant meteorological factors affecting atmospheric PM_2.5 pollution, and PM_2.5 concentrations have the strongest correlation with meteorological factors in winter. 4) Atmospheric PM_2.5 pollution is closely related to human activities, which can be summarized into social economy factor, industrial pollutant discharge factor, and urban construction factor. The results of this study will help fill the gaps in air pollution prevention and control in Beijing-Tianjin-Hebei.

A mixed geographically weighted regression model with varying-coefficient spatial lag

TANG Zhipeng, WU Ying, XIONG Shifeng, HUANG Huan

2024, 41 (3): 345-356. DOI: 10.7523/j.ucas.2022.081

Abstract ( 332 ) PDF (0KB) ( 0 )

Spatial correlation and spatial heterogeneity are the theoretical basis of spatial econometrics. In order to solve the local problem of spatial lag of dependent variables, this study extended the existing mixed geographically weighted regression model with constant-coefficient spatial lag, and proposed a mixed geographically weighted regression model with varying-coefficient spatial lag. The mixed geographically weighted regression model with varying-coefficient spatial lag combines spatial correlation with spatial heterogeneity, and covers most of the model forms of geographically weighted regression. Based on the parameterization reconstruction method and likelihood ratio test, the coefficient estimation method, significance test of this model and the discriminant test of varying-coefficient are given respectively. Both in Monte Carlo simulation and practical application, the results show that the mixed geographically weighted regression model with varying-coefficient spatial lag renders itself well for the fitting and forecasting effect on dependent variable. The mixed geographically weighted regression model with varying-coefficient spatial lag provides a support for setting up a suitable model form for quantitative research on spatial effects.

Clustering routing algorithm for WSN based on BBO optimized K-means

PENG Cheng, TAN Chong, LIU Hong, ZHENG Min

2024, 41 (3): 357-364. DOI: 10.7523/j.ucas.2022.065

Abstract ( 298 ) PDF (0KB) ( 0 )

Aimed at the problems of limited energy and short network lifetime in wireless sensor network,BBOK-GA based on biogeographic algorithm optimization K-means was proposed.In the clustering stage, biogeographic algorithm optimization K-means was firstly used to prevent K-means from falling into the local optimum. According to the energy factor and distance factor, a new fitness function was designed to select optimal cluster heads and complete the clustering. And genetic algorithm was used to search the optimal routing path towards base station for cluster heads. The simulation results indicate that BBOK-GA reduces the network energy consumption，increases the network throughput and extends the network life time compared to LEACH, LEACH-C, and K-GA.

Impact of atmospheric stability on vertical wind shear and wind veer in atmospheric boundary layer

LIANG Zhi, SHI Yu, ZHANG Zhe, HU Fei

2024, 41 (3): 365-374. DOI: 10.7523/j.ucas.2022.063

Abstract ( 346 ) PDF (0KB) ( 0 )

Vertical wind shear in the atmospheric boundary layer (ABL) is an important factor affecting safety in high-rise buildings, aviation and wind energy industry, therefore the detection and study of vertical wind shear is very important for application and research. In this paper, the accuracy of Lidar and meteorological mast (met mast) on the vertical wind shear was verified by field measurements, and the influence of atmospheric stability on vertical wind shear was studied. The results showed that: 1) the atmospheric stability had a significant effect on wind speed shear and wind direction veer, and the correlation coefficients of wind speed shear and wind direction veer with atmospheric stability are 0.48 and 0.54, respectively; 2) The wind speed shear increased with the potential temperature gradient, and remained 0.35-0.4 when the potential temperature gradient reaches 0.08 K·m^-1; 3) Under the neutral atmospheric condition, the wind direction of the upper and lower layers was more consistent, and the wind direction deflected counterclockwise with the increase of height under stable atmospheric condition, and deflected clockwise with the increase of height under the unstable atmospheric condition. The conclusion in this paper modeled the relationship between wind shear and atmospheric stability by the vertical observation of wind field, which is a good reference and valuable for the research and application related to the vertical structure of wind field.

Remote sensing extraction method of agricultural greenhouse based on an improved U-Net model

WANG Yinda, PENG Ling, CHEN Deyue, LI Weichao

2024, 41 (3): 375-386. DOI: 10.7523/j.ucas.2023.060

Abstract ( 454 ) PDF (0KB) ( 0 )

The agricultural greenhouse is a kind of agricultural facility, which is divided into transparent and non-transparent according to the surface transmittance. The large-scale statistics of agricultural greenhouses are of great significance to the survey of agricultural facilities, the formulation of agricultural policies, and the planning of county economic development. Aiming at the problem that manual statistics are time-consuming and laborious, this paper utilizes the convolutional neural network to extract agricultural greenhouses information from high-resolution remote sensing images. To solve the problems of insufficient semantic information extraction in remote sensing images and insufficient utilization of edge information of the U-Net model, this paper proposes the following improvements: 1) The semantic segmentation task is optimized, and ConvNeXt and attention mechanism is utilized to extract deep semantic information of agricultural greenhouses in remote sensing images. 2) The edge detection task is introduced, and the gated convolution layer and concate operation are used to fuse the semantic features of the encoder and the image gradient output by the decoder, and then the edge information is combined to optimize the segmentation results. After testing, the improved model can extract both transparent and non-transparent agricultural greenhouses information at the same time and the recognition effect is good, which is greatly improved compared with the traditional method.

SAR-communication integration technology under the framework of unmatched filtering

SUN Wen, SUN Jili, LU Hongliang

2024, 41 (3): 387-397. DOI: 10.7523/j.ucas.2022.085

Abstract ( 332 ) PDF (0KB) ( 0 )

Integrated waveform design is the key to the realization of radar communication integration technology. Orthogonal frequency division multiplexing (OFDM) signal is regarded as the most potentially integrated waveform signal. However, the OFDM integrated signal has the following problems in practical application: under the framework of matched filtering, the communication signals contained in the OFDM integrated signal lead to high sidelobes and pseudo peaks in the two-dimensional fuzzy function, which affects the performance of synthetic aperture radar (SAR) imaging. OFDM integrated signal has a high peak-to average power ratio (PAPR), which can not give full play to the linear amplification performance of the power amplifier, and then affect the detection range and communication performance. Aiming at the above problems, this paper applies the CP (cyclic prefix)-based unmatched filter imaging algorithm to integrated signal processing and proposes an integrated signal design method based on discrete fourier transform (DFT) precoding to eliminate the influence of communication signals on imaging performance. The PAPR of the signal is controlled within an acceptable range to achieve good compatibility between SAR imaging and communication performance. Finally, the effectiveness of the proposed method is proved by experiments.

Cooperative traffic signal control method for multi-intersection: an approach based on spatiotemporal dependence multi-agent reinforcement learning

WANG Zhaorui, YAN Yan, ZHANG Baoxian

2024, 41 (3): 398-410. DOI: 10.7523/j.ucas.2023.076

Abstract ( 336 ) PDF (0KB) ( 0 )

In the face of increasingly serious traffic congestion, intelligent traffic signal control has become an indispensable means to improve the performance of urban road network. In this paper, a spatiotemporal traffic light control (STLight) based on multi-agent reinforcement learning algorithm is proposed. Through the spatiotemporal dependent module (STDM) based on the attention mechanism, STLight can extract the initial traffic observation data as spatiotemporal features, so as to effectively capture the spatiotemporal dependence relationship between intersections. In addition, based on the extracted spatiotemporal characteristics, STLight further introduces global spatiotemporal information to each agent on the basis of the multi-agent reinforcement learning algorithm based on the centralized training decentralized execution framework, so as to further improve the cooperation ability among multi-agents. The experimental results show that STLight has significant advantages in improving the performance of urban road networks, and helps to alleviate the traffic congestion problem of current large-scale urban road networks.

SA-YOLO: self-adaptive loss object detection method under imbalance samples

SU Yapeng, CHEN Gaoshu, ZHAO Tong

2024, 41 (3): 411-426. DOI: 10.7523/j.ucas.2023.013

Abstract ( 543 ) PDF (0KB) ( 0 )

The phenomenon of sample imbalance refers to the excessive number of background easy samples in the dataset but too few foreground hard samples, which means the sample suffers from inter-class imbalance and hard-easy imbalance. Most of the existing object detection methods are two-stage detectors based on proposed regions or one-stage detectors based on regression. When applied to imbalanced samples, it is impossible to avoid the over-dependence of the prediction bounding box generated in training on a large number of negative samples, which leads to overfitting of the model and low detection accuracy, poor accuracy and generalization. In order to achieve efficient and accurate object detection under imbalanced samples, a new SA-YOLO self-adaptive loss object detection method is proposed in the paper. 1) To address the sample imbalance problem, we propose the SA-Focal Loss function, which adjusts the loss adaptively for different datasets and training stages to balance inter-class samples and hard-easy samples. 2) In this paper, we construct the CSPDarknet53-SP network architecture based on the multi-scale feature prediction mechanism, which enhances the extraction ability of global features of difficult small target samples and improves the detection accuracy of difficult samples. To verify the performance of the SA-YOLO method, extensive simulation experiments are conducted on the sample imbalance dataset and the COCO dataset respectively. The results show that compared with the optimal metrics of YOLO series method, SA-YOLO reaches 91.46% of mAP in the imbalance dataset, which improves 10.87%, and the enhancement of AP₅₀ for all kinds of objects is more than 2%, with excellent specialization; mAP₅₀ in the COCO dataset is upgraded by 1.58%, and all indexes are not lower than the optimal value, with good effectiveness.

Shuttling mechanism the bistable rotaxane based on the radical interaction by quantum chemical calculations

WANG Tao, LI Xiaoyi

2024, 41 (3): 427-431. DOI: 10.7523/j.ucas.2022.055

Abstract ( 258 ) PDF (0KB) ( 0 )

We designed a switchable bistable rotaxane consisted of the cyclobis (paraquat-p-phenylene) bisradical dicationic (CBPQT^2(·+)) ring and a main chain, concluding the recognition sites 4,4'-bipyridinium radical cationic (BIPY^·+) and 2,6-dioxynaphthalen(DOP). The density functional theory (DFT) was used to analyze the motion mechanism of ring along the main chain A. Quantum mechanics calculations were used to analyze the noncovalent interaction between the CBPQT^2(·+) ring and the two recognition sites. It proves that the generation and dissociation of the trisradical tricationic complex controlled by the redox reaction could drive the reciprocating motion of the CBPQT^2(·+) ring along the main chain between the two recognition sites.

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