A novel ecological risk assessment method of potentially toxic elements based on soil nematode communities by Xiujuan Yang, Li Cao, Bijun Cheng, Huirong Duan, Zixuan Fu, Xiaofang Xu, Qianying Xiang, Shuhan Wang, Xiaoqing Yan, Zhihong Zhang, Hongmei Zhang

“…Applying general community indices and NBIs, Ridge and RF models can effectively predict the ecological risks of PTEs.…”
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Future projections of Siberian wildfire and aerosol emissions by R. K. Nurrohman, R. K. Nurrohman, T. Kato, H. Ninomiya, L. Végh, N. Delbart, T. Miyauchi, H. Sato, T. Shiraishi, R. Hirata

“…We integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their impacts. …”
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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

“…The water inrush mode of floor karst collapse column induced by dynamic/static load disturbance of overburden roof is proposed, and its mechanical starting conditions and disaster-causing mechanism are clarified. The generalized model of macro-micro geomechanical structure of coal seam floor fault is established. …”
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Can vegetation breakpoints in Eastern Mongolia rangeland be detected using Sentinel-1 coherence time series data? by Shuxin Ji, Ganzorig Gonchigsumlaa, Sugar Damdindorj, Tserendavaa Tseren, Densmaa Sharavjamts, Amartuvshin Otgondemberel, Enkh-Amgalan Gurjav, Munguntsetseg Puntsagsuren, Batnaran Tsabatshir, Tumendemberel Gungaa, Narantsetseg Batbold, Lukas Drees, Bayarchimeg Ganbayar, Dulamragchaa Orosoo, Bayartsetseg Lkhamsuren, Badamtsetseg Ganbat, Myagmarsuren Damdinsuren, Gantogoo Gombosuren, Batnyambuu Dashpurev, Thanh Noi Phan, Nandintsetseg Dejid, Thomas Müller, Lukas Lehnert

“…The breakpoints in October and November can be explained by increasing grazing pressure as the herders moved to the winter camps while those occurring in spring are associated with enhanced vegetation growth after herders left for summer camps. (3) From a spatial perspective, the random forest model predicts summer and winter pastures with homogeneous patterns. …”
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Feasibility of estimating the percentage of desert pavement using Tasseled Cap Transformation indices extracted from Landsat 8 images by farzaneh Fotouhi Firoozabad, Atefeh jebali

“…The obtained model can predict approximately 61% of surface pavement changes in the study area. …”
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Neural Network Constraints on the Cosmic-Ray Ionization Rate and Other Physical Conditions in NGC 253 with ALCHEMI Measurements of HCN and HNC by Erica Behrens, Jeffrey G. Mangum, Serena Viti, Jonathan Holdship, Ko-Yun Huang, Mathilde Bouvier, Joshua Butterworth, Cosima Eibensteiner, Nanase Harada, Sergio Martín, Kazushi Sakamoto, Sebastien Muller, Kunihiko Tanaka, Laura Colzi, Christian Henkel, David S. Meier, Víctor M. Rivilla, Paul P. van der Werf, ALMA Comprehensive High-resolution Extragalactic Molecular Inventory (ALCHEMI) collaboration

“…We combine the neural network with radiative transfer modeling to generate modeled integrated intensities, which we compare to measurements of HCN and HNC from the ALMA Large Program ALCHEMI. …”
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Relationship between Vegetation and Soil Moisture Anomalies Based on Remote Sensing Data: A Semiarid Rangeland Case by Juan José Martín-Sotoca, Ernesto Sanz, Antonio Saa-Requejo, Rubén Moratiel, Andrés F. Almeida-Ñauñay, Ana M. Tarquis

“…We found other periods of relevant increment in the predictability, such as March and April for Los Vélez, and from July to September for Bajo Aragón.…”
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Triple oxygen isotope composition of CO₂ in the upper troposphere and stratosphere by G. A. Adnew, G. A. Adnew, G. Koren, N. Mehendale, N. Mehendale, S. Gromov, M. Krol, M. Krol, T. Röckmann

“…Moreover, we found no significant spatial or hemispheric differences in <span class="inline-formula">Δ^′17</span>O(CO<span class="inline-formula">₂</span>) for the upper-tropospheric samples collected during the CARIBIC programme. …”
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Multi-source Data-driven Analysis of Deformation and Influencing Factors for Expansive Soil Canal Slopes by ZHANG Yuhan, HU Jiang, LI Xing

“…Furthermore, a self-explaining neural network (SENN) model incorporating an attention mechanism is developed to predict canal slope deformation. …”
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Visual Automatic Localization Method Based on Multi-level Video Transformer by Qiping ZOU, Botao LI, Saian CHEN, Xi GUO, Taohong ZHANG

“…This innovative model is developed to identify the clearest frame within a video sequence, a pivotal step for enhancing automated machining precision. …”
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Plasma-wall interaction impact of the ITER re-baseline by R.A. Pitts, A. Loarte, T. Wauters, M. Dubrov, Y. Gribov, F. Köchl, A. Pshenov, Y. Zhang, J. Artola, X. Bonnin, L. Chen, M. Lehnen, K. Schmid, R. Ding, H. Frerichs, R. Futtersack, X. Gong, G. Hagelaar, E. Hodille, J. Hobirk, S. Krat, D. Matveev, K. Paschalidis, J. Qian, S. Ratynskaia, T. Rizzi, V. Rozhansky, P. Tamain, P. Tolias, L. Zhang, W. Zhang

“…Conservative assessments of the W wall source, coupled with integrated modelling of W pedestal and core transport, demonstrate that the elimination of Be presents only a low risk to the achievement of the principal ITER Q = 10 DT burning plasma target. …”
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Mapping global distributions, environmental controls, and uncertainties of apparent topsoil and subsoil organic carbon turnover times by L. Zhang, L. Zhang, L. Yang, T. W. Crowther, C. M. Zohner, S. Doetterl, G. B. M. Heuvelink, G. B. M. Heuvelink, A. M. J.-C. Wadoux, A.-X. Zhu, Y. Pu, F. Shen, H. Ma, Y. Zou, C. Zhou, C. Zhou

“…We further reveal that the current Earth system models may underestimate <span class="inline-formula"><i>τ</i></span> by comparing model-derived maps with our observation-derived <span class="inline-formula"><i>τ</i></span> maps. …”
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Probabilistic Forecasting of Ground Magnetic Perturbation Spikes at Mid‐Latitude Stations by Michael Coughlan, Amy Keesee, Victor Pinto, Raman Mukundan, José Paulo Marchezi, Jeremiah Johnson, Hyunju Connor, Don Hampton

“…The models were also compared to a persistence model to ensure that the model using both datasets did not over‐rely on dB/dt values in making its predictions. …”
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Geospatial Data and Deep Learning Expose ESG Risks to Critical Raw Materials Supply: The Case of Lithium by Christopher J. M. Lawley, Marcus Haynes, Bijal Chudasama, Kathryn Goodenough, Toni Eerola, Artem Golev, Steven E. Zhang, Junhyeok Park, Eleonore Lèbre

“…The high AUC of the deep learning model demonstrates that public geospatial data can accurately predict natural resources conflicts, but we show that machine learning results are biased by proxies for population density and likely underestimate the potential for conflict in remote areas. …”
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Tree Species Classification at the Pixel Level Using Deep Learning and Multispectral Time Series in an Imbalanced Context by Florian Mouret, David Morin, Milena Planells, Cécile Vincent-Barbaroux

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Snow monitoring at strategic locations improves water supply forecasting more than basin-wide mapping by Mark S. Raleigh, Eric E. Small, Edward H. Bair, Cameron Wobus, Karl Rittger

“…Here we show that adding strategic measurements at snow hotspots – localized areas with untapped information for predicting streamflow – consistently outperforms spatially complete surveys that provide basin-average snowpack, both in basins with and without existing stations. …”
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Recurrent neural networks for anomaly detection in magnet power supplies of particle accelerators by Ihar Lobach, Michael Borland

“…We demonstrate that the RNN outperforms a reasonably complex physics-based model at predicting the PS temperatures and at anomaly detection. …”
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Thermal Avalanches Drive Logarithmic Creep in Disordered Media by Daniel J. Korchinski, Dor Shohat, Yoav Lahini, Matthieu Wyart

“…We show that these predictions hold both in numerical models of amorphous solids, as well as in experiments with thin crumpled sheets. …”
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Brief communication: Monitoring snow depth using small, cheap, and easy-to-deploy snow–ground interface temperature sensors by C. L. Bachand, C. L. Bachand, C. Wang, B. Dafflon, L. N. Thomas, L. N. Thomas, I. Shirley, S. Maebius, S. Maebius, C. M. Iversen, K. E. Bennett

“…We trained a random forest machine learning model to predict snow depth from variability in snow–ground interface temperature. …”
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Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors by Manuel Ballester, Jaromir Kaspar, Francesc Massanés, Srutarshi Banerjee, Alexander Hans Vija, Aggelos K. Katsaggelos

“…This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. …”
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