Jing Wang

Shanghai Jiao Tong University

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Hairun Xie

Shanghai Micro Satellite Engineering Center

Miao Zhang

Commercial Aircraft Corporation of China (China)

Zheng Guo

Second Affiliated Hospital of Nanjing Medical University

Chen Yao

Putian University

Jiang Feng

Northeast Normal University

Jinfeng Zou

Princess Margaret Cancer Centre

Jing Zhu

Biogen (United States)

Yao Zheng

Zhejiang University

Ke Wang

Tianjin University

Jifa Zhang

Dalian University of Technology

Cooperative Institutions

Massachusetts Institute of Technology

Chinese Academy of Sciences

University of Electronic Science and Technology of China

Shanghai Jiao Tong University

Nanjing University of Aeronautics and Astronautics

Ocean University of China

Zhejiang University

Austrian Academy of Sciences

Huazhong University of Science and Technology

Institute of High Energy Physics

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Airport noise disturbs foraging behavior of Japanese pipistrelle bats

Ecology and Evolution (2022)

Weiwei Wang Huimin Gao Chengrong Li Yingchun Deng Daying Zhou

Abstract The expansion of anthropogenic noise poses an emerging threat to the survival and reproductive success of various organisms. Previous investigations have focused on the detrimental effects of anthropogenic noise on the foraging behavior in some terrestrial and aquatic animals. Nevertheless, the role of airport noise in impairing foraging activities of most wild animals has been neglected. Here, we aimed to assess whether foraging behavior in free‐living Japanese pipistrelle bats ( Pipistrellus abramus ) can be disturbed by airport noise. We used audio recording to monitor foraging activities of bats at 11 sites around the runway of a municipal airport. We quantified noise level and spectra, aircraft activity, habitat type, nightly temperature, wind speed, and moon phase for each site. The analysis revealed that noise level and aircraft activity were significant negative predictors for the number of bat passes and feeding buzzes around the runway, even after controlling for the effects of other environmental factors. There was no marked spectral overlap between bat echolocation pulses and airport noise in the presence and absence of low‐flying aircraft. The spectro‐temporal parameters of echolocation vocalizations emitted by bats were dependent on noise level, aircraft activity, and habitat type. These results provide correlative evidence that airport noise can reduce foraging activities of wild pipistrelle bats. Our findings add to the current knowledge of adverse impacts of airport noise on foraging bats in artificial ecosystems and provide a basis for further research on the mechanisms behind noise pollution near airports.

Human echolocation

10.1002/ece3.8976

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Prediction of transonic flow over supercritical airfoils using geometric-encoding and deep-learning strategies

Physics of Fluids (2023)

Zhiwen Deng Jing Wang Hongsheng Liu Hairun Xie BoKai Li

The Reynolds-averaged Navier-Stokes equation for compressible flow over supercritical airfoils under various flow conditions must be rapidly and accurately solved to shorten design cycles for such airfoils. Although deep-learning methods can effectively predict flow fields, the accuracy of these predictions near sensitive regions and their generalizability to large-scale datasets in engineering applications must be enhanced. In this study, a modified vision transformer-based encoder-decoder network is designed for the prediction of transonic flow over supercritical airfoils. In addition, four methods are designed to encode the geometric input with various information points and the performances of these methods are compared. The statistical results show that these methods generate accurate predictions over the complete flow field, with a mean absolute error on the order of 1e-4. To increase accuracy near the shock area, multilevel wavelet transformation and gradient distribution losses are introduced into the loss function. This results in the maximum error that is typically observed near the shock area decreasing by 50%. Furthermore, the models are pretrained through transfer learning on large-scale datasets and finetuned on small datasets to improve their generalizability in engineering applications. The results generated by various pretrained models demonstrate that transfer learning yields a comparable accuracy from a reduced training time.

Compressible flow

10.1063/5.0155383

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Citations (16)

Study on the Influence of Crown Arc Structure on Tire Grounding Performance

IJEIR (2021)

Shicheng Lu Aiqiang Li Meng Hui Jing Wang Lanian Tong

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Kármán Vortex Street in an Immiscible Two-component Bose-Einstein Condensate

Research Square (Research Square) (2021)

Xiaolin Li Xueping Ren Jing Wang Xiao-Bei Fan Yushan Zhou

Abstract In this paper, we investigate a two-component Gross-Pitaevskii system with a moving obstacle potential, which describes the vortex shedding for an immiscible binary Bose-Einstein condensate composed of different hyperfine states of 87 Rb atoms. Numerical simulations indicate that, the Kármán vortex street is discovered in one component, while the Kármán-like vortex street named "ghost vortex street" is formed in the other. The other typical patterns of vortex shedding, such as vortex dipoles, V-shaped vortex pairs and corresponding "ghost vortex shedding", can also be found in the wake of obstacle. The instantaneous and time-averaged drag force acting on obstacle potential are calculated, which show the contribution to drag force from vortex shedding. The parameter region for various vortex patterns, critical velocity for vortex emission, and superfluid Reynolds number are presented and discussed. Finally, we provide an experimental protocol for the above realization and observation.

Vortex shedding

Starting vortex

10.21203/rs.3.rs-209903/v1

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Product Design Sketch Decision Model Based on Improved Capsule Network

Computer-Aided Design and Applications (2024)

Zhou Qingyan Zhang Jincheng Wei Tangwei Li Hao Jing Wang

Sketch

10.14733/cadaps.2025.369-383

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The peroxisome proliferators-ativated receptor gamma (PPARG) gene polymorphisms and associations with body measurements of cattle

AFRICAN JOURNAL OF BIOTECHNOLOGY (2011)

Liushuai Hua Jing Wang Fuying Chen Hu Shenrong Hong Chen

Peroxisome proliferators-activated receptor gamma (PPARG) is an important regulator in the regulation of adipose differentiation and development. The mutations of the PPARG in human had been shown to be associated with type II diabetes, fat distribution and body weight. The functional importance of the PPARG makes it a good candidate to search molecular markers in marker assistant selection in cattle breeding. All the mRNA region of the PPARG gene within 760 individuals of four Chinese cattle breeds was scanned and four single nucleotide polymorphisms (SNPs) (-110G>C, -27C>T, +20A>G, +1344G>T) of the PPARG were detected in three Chinese indigenous cattle breeds (Qinchuan, Nangyang and Jiaxian cattle), rather than Chinese Holstein cattle. The mutations -110G>C, -27C>T and +20A>G located in the Exon1 of the PPARG and were under linkage disequilibrium. The individuals with these three mutations had smaller body measurements. This information could help animal scientists to develop genetic markers or biomarkers to assist with beef production. Key words: Peroxisome proliferators-activated receptor gamma (PPARG) gene, polymorphisms, cattle, association analysis.

Linkage Disequilibrium

10.5897/ajb10.731

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Citations (6)

Reproducible Cancer Biomarker Discovery in SELDI-TOF MS Using Different Pre-Processing Algorithms

PLoS ONE (2011)

Jinfeng Zou Guini Hong Xinwu Guo Lin Zhang Chen Yao

There has been much interest in differentiating diseased and normal samples using biomarkers derived from mass spectrometry (MS) studies. However, biomarker identification for specific diseases has been hindered by irreproducibility. Specifically, a peak profile extracted from a dataset for biomarker identification depends on a data pre-processing algorithm. Until now, no widely accepted agreement has been reached.In this paper, we investigated the consistency of biomarker identification using differentially expressed (DE) peaks from peak profiles produced by three widely used average spectrum-dependent pre-processing algorithms based on SELDI-TOF MS data for prostate and breast cancers. Our results revealed two important factors that affect the consistency of DE peak identification using different algorithms. One factor is that some DE peaks selected from one peak profile were not detected as peaks in other profiles, and the second factor is that the statistical power of identifying DE peaks in large peak profiles with many peaks may be low due to the large scale of the tests and small number of samples. Furthermore, we demonstrated that the DE peak detection power in large profiles could be improved by the stratified false discovery rate (FDR) control approach and that the reproducibility of DE peak detection could thereby be increased.Comparing and evaluating pre-processing algorithms in terms of reproducibility can elucidate the relationship among different algorithms and also help in selecting a pre-processing algorithm. The DE peaks selected from small peak profiles with few peaks for a dataset tend to be reproducibly detected in large peak profiles, which suggests that a suitable pre-processing algorithm should be able to produce peaks sufficient for identifying useful and reproducible biomarkers.

Biomarker Discovery

False Discovery Rate

Identification

10.1371/journal.pone.0026294

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A product design strategy based on combination of design of experiment and inversion of partial least square model

Journal of the Chemical Industry and Engineering Society of China (2010)

Jing Wang

Frequent product changeovers in the modern market require an efficient operating condition setup method for producing new products in the process industry.A novel product design strategy was developed based on the combination of design of experiment(DOE) and inversion of partial least square(PLS) model.The response surface methodology(RSM),a widely-used optimization method in DOE,was improved to vary the searching step-size with the accumulation of newly available experimental data.Then,the combination method of RSM and the inversion of PLS model was proposed to ensure both extrapolation and interpolation performance of the operating condition setup model.The proposed product design strategy could specify the desirable operating condition quickly and accurately with fewer new experiments in the design process.The strategy was illustrated by a numerical example and a real chemical process.Simulation results could verify its feasibility and effectiveness.

Design of experiments

Interpolation

Process design

Inverse transform sampling

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Flow field prediction of supercritical airfoils via variational autoencoder based deep learning framework

Physics of Fluids (2021)

Jing Wang Cheng He Runze Li Haixin Chen Chen Zhai

Effective access to obtain the complex flow fields around an airfoil is crucial in improving the quality of supercritical wings. In this study, a systematic method based on generative deep learning is developed to extract features for depicting the flow fields and predict the steady flow fields around supercritical airfoils. To begin with, a variational autoencoder (VAE) network is designed to extract representative features of the flow fields. Specifically, the principal component analysis technique is adopted to realize feature reduction, aiming to obtain the optimal dimension of features in VAE. Afterward, the extracted features are incorporated into the dataset, followed by the mapping from the airfoil shapes to features via a multilayer perception (MLP) model. Eventually, a composite network is adopted to connect the MLP and the decoder of VAE for predicting the flow fields given the airfoil. The proposed VAE network achieves compression of high-dimensional flow field data into ten representative features. The statistical results indicate the accurate and generalized performance of the proposed method in reconstructing and predicting flow fields around a supercritical airfoil. Especially, our method obtains accurate prediction results over the shock area, indicating its superiority in conducting turbulent flow under high Reynolds number.

Autoencoder

10.1063/5.0053979

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Citations (76)

Multiple Attribute Decision-making Analysis of Super Range Gun Interior Ballistic Design

Fire Control and Command Control (2005)

Jing Wang Yuan Ya-xiong

Based on the optimization theory,tow interior ballistic multiple attributes decision-making models are established for full charge and lower charge respectively.The application result in this paper shows that the model and related optimal method is benefit to the design of interior ballistic scheme and TOPSIS method is effective for multiple attributed decision-making.

TOPSIS

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