Identification of flood atmospheric patterns and simulation of rainfall to runoff conversion in Shiraz Watershed by Hamid Jorkesh, Mohammad Javad Amiri, Ahmad Nohegar

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Dynamic identification method for flow patterns of liquid-solid two-phase flow in horizontal pipelines by ZHUJUNLIANG Junliang, ZHAOZHIFENG, WANGSHUGANG, JIANG Shuang

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Identification of low-risk cases of invasive endocervical adenocarcinoma with Silva pattern-based classification: a systematic review and meta-analysis by Nikita Sinha, Olivia D. Lara, Kimberly Dessources, Emily P. Jones, Leslie H Clark, Russell Broaddus, Benjamin B. Albright

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Entropy-Assisted Quality Pattern Identification in Finance by Rishabh Gupta, Shivam Gupta, Jaskirat Singh, Sabre Kais

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Spherical Polar Pattern Matching for Star Identification by Jingneng Fu, Ling Lin, Qiang Li

“…To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. …”
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Identification of the bHLH Transcription Factor Family in Orah Mandarin and the Response of <i>CrbHLH46</i> to Low-Temperature Stress by Chaoying Chen, Xulin Li, Ke Wen, Tuo Yin, Ping Tian, Ke Zhao, Li Zhang, Xianyan Zhou, Xiaozhen Liu, Hanyao Zhang

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Haemodynamic Patterns of Severe Aortic Stenosis by A. E. Komlev, M. A. Saidova, T. E. Imaev, V. N. Shitov, R. S. Akchurin

“…The authors suggest the pilot haemodynamic classification of AS which includes 6 types (0-5) based on different combination of the following variables: left ventricle ejection fraction, stroke volume, mean aortic systolic pressure gradient. Severe AS with low transaortic pressure gradient in patients with depressed systolic function of the left ventricle (so called «low flow-low» gradient phenomenon) is referred to as the most frequent, classical haemodynamic pattern of low-gradient AS. …”
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Genome-wide identification, expression analysis of the R2R3-MYB gene family and their potential roles under cold stress in Prunus sibirica by Xin Zhao, Shipeng Wang, Hongrui Zhang, Shengjun Dong, Jianhua Chen, Yongqiang Sun, Yueyuan Zhang, Quangang Liu

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Identification and expression pattern analysis of soybean TrxG gene family by JIANG Ling, YANG Xiaofeng, PENG Ming, GU Xiaoyan , ZHOU Hong*

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Mid-Infrared Spectroscopy for Coffee Variety Identification: Comparison of Pattern Recognition Methods by Chu Zhang, Chang Wang, Fei Liu, Yong He

“…These methods were classified as highly effective methods (soft independent modelling of class analogy, support vector machine, back propagation neural network, radial basis function neural network, extreme learning machine, and relevance vector machine), methods of medium effectiveness (partial least squares-discrimination analysis, K nearest neighbors, and random forest), and methods of low effectiveness (Naive Bayes classifier) according to the classification accuracy for coffee variety identification.…”
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Amino Acid Patterns that Determine Antihyperuricemic Activity of Peptides: Identification and Predictive Analysis by Anastasia V. Smirnova, Sergey L. Tikhonov

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Identification of population multimorbidity patterns in 3.9 million patients from Bogota in 2018 by Carolina Saavedra-Moreno, Rafael Hurtado, Nubia Velasco, Andrea Ramírez

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Utilising Smart-Meter Harmonic Data for Low-Voltage Network Topology Identification by Ali Othman, Neville R. Watson, Andrew Lapthorn, Radnya Mukhedkar

“…The proposed approach leverages the unique properties of harmonic distortion to improve the accuracy of topology identification. This paper first analyses the influential factors affecting topology identification, establishing that harmonic distortion propagation patterns offer superior discrimination compared to RMS voltage. …”
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Identification and classification method of landslide pattern in the soil water index-based early warning system by Yulong Zhu, Bonan Wang, Yafen Zhang, Zhiguo Sun

“…As rainfall intensity rises, the slope failure pattern gradually changes from Pattern I (Sliding) during long-term low-intensity (LL) type rainfall, to Pattern II (Buckling), to Pattern III (Toppling), and finally to Pattern IV (Crumbling) during short-term high-intensity (SH) type rainfall. …”
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Spatial Identification and Distribution Pattern of the Complexity of Rural Poverty in China Using Multisource Spatial Data by Zhenyu Qi, Jinghu Pan, Yaya Feng

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Weak periodic signal identification in low amplitude seismic waves based on chaotic oscillator by Nazmi Yılmaz, Mahmut Akıllı

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Machine learning based identification of anoikis related gene classification patterns and immunoinfiltration characteristics in diabetic nephropathy by Jing Zhang, Lulu Cheng, Shan Jiang, Duosheng Zhu

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Intermittent hypoxemia during hemodialysis: AI-based identification of arterial oxygen saturation saw-tooth pattern by Hanjie Zhang, Andrea Nandorine Ban, Peter Kotanko

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Identification of plum PsNAC gene family and its expression patterns in development of fruit hollowness and browning by DENG Honghong, PENG Chao, LIANG Xi, ZHANG Ziyang, LIU Junwei, LI Binqi, WEI Mingkang, WANG Xueying, LI Liumin, CHEN Faxing

“…The most significant core elements included phytohormone-responsive elements, MYB binding sites, low temperature responsiveness, drought-inducible elements, and light-responsive elements. …”
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Study of Low-Level Wind Shear and Its Evolution Based on LIDAR and Aircraft Reports Identification by Jie DING, Lei ZHANG, Zeyong HU, Jiemin WANG, Kaijun ZHANG, Jiening LIANG, Yuan WANG, Zhida ZHANG, Lili XU, Jin WANG, Tao CAI

“…Low-level wind shear significantly impacts aviation safety and operational efficiency.Zhongchuan Airport， located in a mountainous inland region， experiences the influence of complex terrain and weather systems.Wind shear is frequently observed during the summer season.This study analyzes the spatial and temporal characteristics of low-level wind shear events at Zhongchuan Airport using aircraft reports spanning from 2009 to 2018.During the observation period from May 2016 to November 2017， a total of 18 low-level wind shear events were confirmed through aircraft verification.Two methods， namely the fixed and adaptive window methods， were compared to identify wind shear events using LIDAR data from the Windcube 400s-at instrument.The study also explored the continuous evolution and spatial characteristics of the three-dimensional wind shear structure.The results indicate that the frequency of low-level wind shear events has increased at a faster rate compared to the flight volume at Zhongchuan Airport over the past ten years.The peak months for low-level wind shear events at Zhongchuan Airport are April-July and August-October， influenced by local weather patterns.The peak months for low-level wind shear events at Zhongchuan Airport are April-July and August-October， influenced by local weather patterns.Wind shear induced by convective weather occurs in the weak or non-echo region surrounding the convective cloud， resulting from the convergence of updrafts and downdrafts outside the cloud， or the formation of a gust front following the downdraft's contact with the ground.The wind shear factor is correlated with the intensity of radar echoes.Compared to the fixed window method， the adaptive window method had a larger recognition range due to the different data sets included in the recognition window.Therefore， the adaptive window method was found to be more suitable for studying the three-dimensional evolution of wind shear structures.Therefore， the adaptive window method is better suited for studying the evolution of the three-dimensional wind shear structure.The wind shear in the vicinity of Zhongchuan Airport is characterized by a low spatial distribution， small horizontal scale， and short duration， primarily concentrated in the small-scale and γ mesoscale.In other words， the wind shear events occurred at relatively low altitudes， with horizontal scales mostly ranging between 1000~1500 m and 2000~2600 m， lasting less than 20 minutes.Furthermore， 40.5% of the wind shear events were attributed to the movement of the wind， primarily influenced by the background wind.The findings of this study contribute to a better understanding of the characteristics of wind shear， offering valuable insights for the identification， mechanisms， forecasting， and early warning of low-level wind shear events at Zhongchuan Airport.…”
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