Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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Adaptive EMG Pattern Classification via Probabilistic Knowledge Transfer With Scale Mixture-Based Bayesian Sequential Learning by Seitaro Yoneda, Akira Furui

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Machine Learning-Driven Parametric Analysis of Eco-Friendly Ultrasonic Welding for AL6061-CU Alloy Joints by A. Karan, S. Arungalai Vendan, M. R. Nagaraj, M. Chaturvedi, S. Sivadharmaraj

“…In addition, scanning electron microscopy (SEM) pictures are examined to gain insights into the surface shape and assess the degree of weld production and performance. …”
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Comprehensive analysis of drilling responses in additively manufactured PLA using a regression—based statistical learning approach by Vishwadarshan, Gauthami Shetty, Raviraj Shetty, Supriya J P, Balaji V, Adithya Hegde

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Mixed Layer Depth Estimation From Multisource Remote Sensing Data Using Clustering-Machine Learning Method by Zengxin Guan, Kaijun Ren, Senliang Bao, Hengqian Yan, Huizan Wang, Yanlai Zhao, Jianbin Liu

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Biomechanical optimization and reinforcement learning provide insight into transition from ankle to hip strategy in human postural control by Seongwoong Hong, Sukyung Park

“…Abstract Human postural control strategies, categorized as ankle or hip strategies, adapt to varying perturbation magnitudes and support surface sizes. While numerous studies have characterized these strategies, few have explored the underlying mechanisms driving the transition from ankle to hip strategy. …”
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Quantifying the Geomorphological Susceptibility of the Piping Erosion in Loess Using LiDAR-Derived DEM and Machine Learning Methods by Sisi Li, Sheng Hu, Lin Wang, Fanyu Zhang, Ninglian Wang, Songbai Wu, Xingang Wang, Zongda Jiang

“…We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. …”
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Predicting Trunk Muscle Activity in Chronic Low Back Pain: Development of a Supervised Machine Learning Model by Sara Salamat, Vahideh Montazeri, Saeed Talebian

“… Introduction: Recently, machine learning adoption has significantly increased across various applications, including the prediction of diseases based on a person’s clinical profile. …”
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Machine learning models for predicting interaction affinity energy between human serum proteins and hemodialysis membrane materials by Simin Nazari, Amira Abdelrasoul

“…Various membrane materials with distinct characteristics, chemistry, and orientation are considered in conjunction with different proteins. …”
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Regional stream temperature modeling in pristine Atlantic salmon rivers: A hybrid deterministic–Machine Learning approach by Ilias Hani, André St-Hilaire, Taha B.M.J. Ouarda

“…Key explanatory variables include low cloud coverage, high wind speed quantiles, upstream land cover areal coverage, distance to the coast, watershed orientation, and topographical features describing surface curvature and elevation. The machine learning-based regionalization approach provides a robust approach for deriving water temperature model parameters from watershed attributes, provided flow measurements are available. …”
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Predicting mucosal healing in Crohn’s disease: development of a deep-learning model based on intestinal ultrasound images by Li Ma, Yuepeng Chen, Xiangling Fu, Jing Qin, Yanwen Luo, Yuanjing Gao, Wenbo Li, Mengsu Xiao, Zheng Cao, Jialin Shi, Qingli Zhu, Chenyi Guo, Ji Wu

“…Heat maps showing the deep-learning decision-making process revealed that information from the bowel wall, serous surface, and surrounding mesentery was mainly considered by the model. …”
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Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning by Zhaoyang Pan, Zhanhua Lu, Liting Zhang, Wei Liu, Xiaofei Wang, Shiguang Wang, Hao Chen, Haoxiang Wu, Weicheng Xu, Youqiang Fu, Xiuying He

“…Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. …”
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Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China by Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, Qimeng Liu, Yu Liu

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Automated Detection of Micro-Scale Porosity Defects in Reflective Metal Parts via Deep Learning and Polarization Imaging by Haozhe Li, Xing Peng, Bo Wang, Feng Shi, Yu Xia, Shucheng Li, Chong Shan, Shiqing Li

“…Aiming at the key technology of defect detection in precision additive manufacturing of highly reflective metal materials, this study proposes an enhanced SCK-YOLOV5 framework, which combines polarization imaging and deep learning methods to significantly improve the intelligent identification ability of small metal micro and nano defects. …”
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A Study on the Spatial Perception and Inclusive Characteristics of Outdoor Activity Spaces in Residential Areas for Diverse Populations from the Perspective of All-Age Friendly Des... by Biao Yin, Lijun Wang, Yuan Xu, Kiang Chye Heng

“…Guided by the principles of all-age friendly and inclusive design, this study innovatively integrates eye-tracking and multi-modal physiological monitoring technologies to collect both subjective and objective perception data of different age groups regarding outdoor activity spaces in residential areas through human factor experiments and empirical interviews. Machine learning methods are utilized to analyze the data, uncovering the differentiated response mechanisms among diverse groups and clarifying the inclusive characteristics of these spaces. …”
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Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning by Masashige Shiga, Masao Sorai, Tetsuya Morishita, Masaatsu Aichi, Naoki Nishiyama, Takashi Fujii

“…To enhance the interpretability of the machine learning approach, we proposed a patch-based analysis to identify local characteristic textural patterns that contribute significantly to the classification of individual rock samples. …”
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NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

“…What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of <span class="inline-formula">NO₂</span> profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. …”
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Machine learning in assessing the association between the size and structure of the ascending aortic wall in patients with aortic dilatation of varying severity by V. E. Uspenskiy, V. L. Saprankov, V. I. Mazin, D. G. Zavarzina, A. B. Malashicheva, O. B. Irtyuga, O. M. Moiseeva, M. L. Gordeev

“…To assess the association between pathological ascending aortic (AA) wall changes and its planimetric characteristics in non-syndromic non-familial (sporadic) aneurysm and dilation of the AA.Material and methods. …”
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Toward automated plantar pressure analysis: machine learning-based segmentation and key point detection across multicenter data by Carlo Dindorf, Jonas Dully, Steven Simon, Dennis Perchthaler, Stephan Becker, Hannah Ehmann, Christian Diers, Christoph Garth, Michael Fröhlich

“…The datasets were further standardized and augmented. The plantar surface was segmented into four regions (hallux, metatarsal area 1, metatarsal areas 2–5, and the heel) using a U-Net model, and deep learning regression models predicted the key points, such as interdigital space coordinates and the center of metatarsal area 1. …”
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The emergence of Brucella canis as a public health threat in Europe: what we know and what we need to learn by Vitomir Djokic, Luca Freddi, Fabrizio de Massis, Elina Lahti, Marielle H. van den Esker, Adrian Whatmore, Anna Haughey, Ana Cristina Ferreira, Giuliano Garofolo, Falk Melzer, Flavio Sacchini, Ad Koets, Stephen Wyllie, Alain Fontbonne, Guillaume Girault, Acacia Ferreira Vicente, John McGiven, Claire Ponsart

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