Recognition of Maize Tassels Based on Improved YOLOv8 and Unmanned Aerial Vehicles RGB Images by Jiahao Wei, Ruirui Wang, Shi Wei, Xiaoyan Wang, Shicheng Xu

“…The tasseling stage of maize, as a critical period of maize cultivation, is essential for predicting maize yield and understanding the normal condition of maize growth. …”
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Arsenic health risk in shallow groundwater of the alluvial plains in the lower Yellow River, China: driving mechanisms of climate change and human activities by Wengeng Cao, Yu Fu, Yu Ren, Xiangzhi Li, Yanyan Wang, Le Song

“…In this study, we developed a robust machine learning model framework to predict the spatial variation of arsenic levels in shallow groundwater within the alluvial plains of the lower Yellow River. …”
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Pattern transition recognition based on transfer learning for exoskeleton across different terrains by Yifan Gao, Jianbin Zheng, Yang Gao, Ziyao Chen, Jing Tang, Liping Huang

“…In the study, a novel transfer learning method based on temporal convolutional network spatial attention (TCN-SA) is applied for pattern transition recognition under triple physical loads on different terrains. …”
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GCN-Former: A Method for Action Recognition Using Graph Convolutional Networks and Transformer by Xueshen Cui, Jikai Zhang, Yihao He, Zhixing Wang, Wentao Zhao

“…The model integrates the Transformer architecture with traditional GCNs, leveraging the Transformer’s powerful capability for handling long-sequence data and the effective capture of spatial dependencies by GCNs. …”
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Investigating Mortality Uncertainty Using the Block Bootstrap by Xiaoming Liu, W. John Braun

“…This paper proposes a block bootstrap method for measuring mortality risk under the Lee-Carter model framework. In order to take account of all sources of risk (the process risk, the parameter risk, and the model risk) properly, a block bootstrap is needed to cope with the spatial dependence found in the residuals. …”
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Global Feature Focusing and Information Enhancement Network for Occluded Pedestrian Detection by ZHENG Kaikui, JI Kangyou, LI Jun, LI Qiming

“…CBAM adjusts the importance of each channel and spatial location in the feature maps through operations like global average pooling, maxpooling, and small fully connected neural networks in both channel and spatial attention dimensions. …”
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High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data by Y. Wang, H. Wang, B. Zhang, P. Liu, X. Wang, S. Si, L. Xue, Q. Zhang, Q. Wang

“…Based on the established emission model, we predicted that the benefits of vehicle electrification in reducing vehicle emissions could reach 40 %–80 %. …”
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Investigating socioeconomic deprivation and antibiotic prescribing among older medicare patients using an instrumental variable approach by Mayar Al Mohajer, David Slusky, David Nix, Catia Nicodemo

“…The IV analysis then examined the relationship between predicted SDI and antibiotic days supplied (ln). Linear regression models estimated associations between SDI and its components, and antibiotic days supplied, adjusting for prescriber, beneficiary, and geographic factors. …”
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Using New York City’s Geographic Data in an Innovative Application of Generative Adversarial Networks (GANs) to Produce Cooling Comparisons of Urban Design by Yuanyuan Li, Lina Zhao, Hao Zheng, Xiaozhou Yang

“…This model can rapidly generate visual LST predictions under various configuration scenarios. …”
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Freeze–Thaw-Induced Degradation Mechanisms and Slope Stability of Filled Fractured Rock Masses in Cold Region Open-Pit Mines by Jun Hou, Penghai Zhang, Ning Gao, Wanni Yan, Qinglei Yu

“…Based on regression fitting using 0–25 FT cycles, regression model predictions indicate that when the number of <i>FT</i> cycles exceeds 42, the slope safety factor drops below 1.0, entering a critical instability state. …”
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Optimization Design and Test Analysis of Rice Electric Binder Knotter Based on ADAMS by Difa Bao, Jufei Wang, Zhi Liang, Chongcheng Chen, Wuxiong Weng, Shuhe Zheng, Jinbo Ren

“…Based on the ADAMS software, a simulation model of the knotter operation was constructed. Using the Box–Behnken design (BBD) method and response surface analysis of variance, a regression prediction model for knotter operation evaluation indicators was established, and the multi-objective optimization of the knotter’s operation quality was performed. …”
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Vegetation greening does not significantly enhance ecosystem resilience in the Northern Hemisphere by Jingjing Zhang, Xingming Hao, Yongchang Liu, Xuewei Li, Qixiang Liang, Fan Sun, Mengtao Ci, Yupeng Li

“…Greening is asynchronous with ecosystem resilience in the context of vegetation restoration, thus highlighting the uncertainty in predicting the future sustainability of ecosystems. …”
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The Samples and Binary Fractions of Red Supergiants in M31 and M33 by the HST Observations by Min Dai, Shu Wang, Biwei Jiang, Ying Li

“…These results are in good agreement with predictions from the Binary Population and Spectral Synthesis binary evolution model.…”
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Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning by Jonne Pohjankukka, Timo A. Räsänen, Timo P. Pitkänen, Arttu Kivimäki, Ville Mäkinen, Tapio Väänänen, Jouni Lerssi, Aura Salmivaara, Maarit Middleton

“…Additionally, we provided an approach for evaluating spatial prediction uncertainty based on the models’ internal prediction agreement. …”
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Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor by Sinesipho Ngamile, Sinesipho Ngamile, Mahlatse Kganyago, Sabelo Madonsela, Sabelo Madonsela, Vuyelwa Mvandaba

“…IntroductionWater quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS.MethodsHigh-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions.ResultsThe results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. …”
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Machine learning for the rElapse risk eValuation in acute biliary pancreatitis: The deep learning MINERVA study protocol by Mauro Podda, Adolfo Pisanu, Gianluca Pellino, Adriano De Simone, Lucio Selvaggi, Valentina Murzi, Eleonora Locci, Matteo Rottoli, Giacomo Calini, Stefano Cardelli, Fausto Catena, Carlo Vallicelli, Raffaele Bova, Gabriele Vigutto, Fabrizio D’Acapito, Giorgio Ercolani, Leonardo Solaini, Alan Biloslavo, Paola Germani, Camilla Colutta, Savino Occhionorelli, Domenico Lacavalla, Maria Grazia Sibilla, Stefano Olmi, Matteo Uccelli, Alberto Oldani, Alessio Giordano, Tommaso Guagni, Davina Perini, Francesco Pata, Bruno Nardo, Daniele Paglione, Giusi Franco, Matteo Donadon, Marcello Di Martino, Dario Bruzzese, Daniela Pacella

“…The model includes the following steps: the spatial transformation of variables using kernel Principal Component Analysis (kPCA), the creation of 2D images from transformed data, the application of convolutional filters, max-pooling, flattening, and final risk prediction via a fully connected layer. …”
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A novel on-line dual sensing system for soil property measurement and mapping by Rukayat Afolake Oladipupo, Muhammad Abdul Munnaf, Parsat Sanganta, Ajit Borundia, Abdul Mounem Mouazen

“…Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. …”
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ThermalGS: Dynamic 3D Thermal Reconstruction with Gaussian Splatting by Yuxiang Liu, Xi Chen, Shen Yan, Zeyu Cui, Huaxin Xiao, Yu Liu, Maojun Zhang

“…Thermal infrared (TIR) images capture temperature in a non-invasive manner, making them valuable for generating 3D models that reflect the spatial distribution of thermal properties within a scene. …”
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A Near-Real-Time Operational Live Fuel Moisture Content (LFMC) Product to Support Decision-Making at the National Level by Akli Benali, Giuseppe Baldassarre, Carlos Loureiro, Florian Briquemont, Paulo M. Fernandes, Carlos Rossa, Rui Figueira

“…The model predicted well the temporal LFMC variability across most of the sampling sites. …”
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Deep Learning Approach for Estimating Workability of Self-Compacting Concrete from Mixing Image Sequences by Zhongcong Ding, Xuehui An

“…We propose a deep learning approach to better utilize the spatial and temporal information obtained from image sequences of the self-compacting concrete- (SCC-) mixing process to recover SCC characteristics in terms of the predicted slump flow value (SF) and V-funnel flow time (VF). …”
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