Slope Stability from a Hydrological Perspective: Taking Typical Soil Slope as an Example by Yuelu Zhu, Yaoting Xiao

“…The results show that, from 1970 to 2010, the spatial-temporal distribution of soil moisture content in the Weihe River Basin showed an increasing trend. …”
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Convective-scale forecast errors over Taiwan: an ensemble analysis of error covariances and their influences by Kao-Shen Chung, Chih-Chien Tsai, Li-Hsin Chen, Shu-Chih Yang

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Evaluating the performances of SVR and XGBoost for short-range forecasting of heatwaves across different temperature zones of India by Srikanth Bhoopathi, Nitish Kumar, Somesh, Manali Pal

“…In a nutshell, the study attempts to highlight the capability of advanced ML techniques combined with spatial climate data to enhance the prediction of extreme heatwave events. …”
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Disaster-causing mechanism and prevention and control vision orientation of different types of coal seam floor water disasters in China by Yifan ZENG, Huicong ZHU, Qiang WU, Houzhu WANG, Xianjie FU, Tieji WANG, Xirui WANG, Jiulin FAN, Rongjie HU, Xiangjun CAI, Xuedong KAN, Shengbao GAO

“…On the basis of summarizing the current concept of coal seam floor water disaster detection, prediction and control technology, combined with the frontier development direction, it is pointed out and suggested that the three-dimensional dynamic reconstruction of the whole life cycle mining-induced mutation characteristics of the background geological gene of the coal seam floor, the upgrading of the concept of coal seam floor water inrush prediction and prediction suitable for the dynamic geological environment of space-time differentiation, the application of low disturbance and strong intervention with mining\water control and mutual grouting treatment technology, and the establishment of long-term monitoring, diagnosis and treatment platform for geological ecosystem after restoration should be carried out to build a large system of full-time and space-time prevention and control of coal seam floor water disaster, and keep up with the development of new formats in the whole coal industry under the background of new productivity.…”
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Digital twin and contact analysis of ultra-long distance coiled tubing operation structures [version 2; peer review: 1 approved, 2 approved with reservations] by Wenlan Wei, Jiarui Cheng, Hao Qu, Maliang Wang, Wenyuan Wang

“…Results The research results show that the digital twin coiled tubing and contact algorithm can effectively predict the contact state of coiled tubing operations; the verification model shows that the contact algorithm can accurately analyze the contact state of different well trajectories. …”
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Challenges of COVID-19 Case Forecasting in the US, 2020-2021. by Velma K Lopez, Estee Y Cramer, Robert Pagano, John M Drake, Eamon B O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter P Mueller, Jade Xiao, Johannes Bracher, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Sen Pei, Jeffrey Shaman, Teresa K Yamana, Samuel R Tarasewicz, Daniel J Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L Hill, Joshua Kaminsky, Elizabeth C Lee, Joseph C Lemaitre, Justin Lessler, Claire P Smith, Shaun Truelove, Matt Kinsey, Luke C Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V Prasad, Jo W Walker, Alexander E Webber, Rachel B Slayton, Matthew Biggerstaff, Nicholas G Reich, Michael A Johansson

“…We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. …”
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Plasmodium vivax malaria endemicity in Indonesia in 2010. by Iqbal R F Elyazar, Peter W Gething, Anand P Patil, Hanifah Rogayah, Elvieda Sariwati, Niken W Palupi, Siti N Tarmizi, Rita Kusriastuti, J Kevin Baird, Simon I Hay

“…Detailed understanding of the contemporary spatial distribution of this parasite is needed to combat it. …”
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SFMattingNet: A Trimap-Free Deep Image Matting Approach for Smoke and Fire Scenes by Shihui Ma, Zhaoyang Xu, Hongping Yan

“…However, due to the lack of smoke and fire image matting datasets for model training, existing image matting methods exhibit significant errors in predicting the alpha values of smoke and fire targets, leading to unrealistic composite images. …”
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Balancing accuracy versus precision: Enhancing the usability of sub-seasonal forecasts by Etienne Dunn-Sigouin, Erik W. Kolstad, C. Ole Wulff, Douglas J. Parker, Richard J. Keane

“…Forecasts are essential for climate adaptation and preparedness, such as in early warning systems and impact models. A key limitation to their practical use is often their coarse spatial grid spacing. …”
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Detecting Sensitive Spectral Bands and Vegetation Indices for Potato Yield Using Handheld Spectroradiometer Data by Diego Gomez, Pablo Salvador, Juan Fernando Rodrigo, Jorge Gil

“…Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. …”
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A hybrid deep learning framework for skin disease localization and classification using wearable sensors by Xiaoling Zhao, Huixin Zhang, Qian Zheng, Caihong Jing

“…This CNN-based multimodal fusion approach improves the model’s ability to capture spatial relationships and enhances classification performance. …”
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Evaluating the Quality of Light Emitted by Smartphone Displays by Nina Piechota, Krzysztof Skarżyński, Kamil Kubiak

“…It covered the color gamut, channel linearity response, refresh rate, flickering, spatial radiation distribution, luminance, uniformity, and static contrast. …”
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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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A Patch-Wise Mechanism for Enhancing Sparse Radar Echo Extrapolation in Precipitation Nowcasting by Yueting Wang, Hou Jiang, Tang Liu, Ling Yao, Chenghu Zhou

“…Spatial visualizations of radar echoes reveal PW's superior in predicting localized and intense precipitation. …”
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Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation by Wenzhong Li, Chengshuai Liu, Caihong Hu, Chaojie Niu, Runxi Li, Ming Li, Yingying Xu, Lu Tian

“…Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. …”
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Automated detection of sea cucumbers in turbid subtidal marine habitats: An explainable approach by Cheryl Chu, Yi-Fei Gu, Adrian Wong, Bayden D. Russell

“…Our study includes Eigen-CAM, a novel visualization tool that enhances model interpretability and transparency during model prediction. …”
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Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration by Jonathan E. Lee, Min Zhu, Ziqiao Xi, Kun Wang, Yanhua O. Yuan, Lu Lu

“…However, these simulations are often computationally expensive due to highly coupled physics and large spatial-temporal simulation domains. Surrogate modelling with data-driven machine learning has become a promising alternative to accelerate physics-based simulations. …”
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Trend analysis of the coupling effect between new urbanization and resources-environment in the Guangdong-Hong Kong-Macao Greater Bay area by Hongyi Dou, Guoqin Zhang

“…This study constructs an urbanization-resource-environment system dynamics (SD) model that highlights the new urbanization characteristics to simulate and predict the development trends from 1990 to 2035 at urban cluster and city scales. …”
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Enhancing seabed sediment classification with multibeam echo-sounding and self-training: a case study from the East Sea of South Korea by Changhoon Lee, Sujung Park, Daeung Yoon, Bo-Yeon Yi, Moonsoo Lim

“…To mitigate sample scarcity and class imbalance, a semi-supervised self-training loop iteratively added high-confidence pseudo-labels to the training set.ResultsField validation in the East Sea (Republic of Korea) showed that the Extreme Gradient Boosting model achieved the highest accuracy. Overall prediction accuracy increased from 60.81 % with the baseline workflow to 72.73 % after applying data interpolation, enhanced feature extraction, and self-training.DiscussionThe proposed combination of U-Net interpolation, multi-scale texture features, and semi-supervised learning significantly improves sediment classification where MBES data are incomplete and sediment samples are sparse. …”
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Mapping indicator species of segetal flora for result-based payments in arable land using UAV imagery and deep learning by Caterina Barrasso, Robert Krüger, Anette Eltner, Anna F. Cord

“…Additionally, we investigated the potential of spatial co-occurrence and canopy height heterogeneity to predict the presence of species difficult to detect by UAVs. …”
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