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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Exploring the effect of multi-modal intervention against cognitive decline on atrophy and small vessel disease imaging markers in the AgeWell.de imaging study by Frauke Beyer, Lukas Kleine, Andrea Zülke, Melanie Luppa, Toralf Mildner, Jochen Gensichen, Thomas Frese, David Czock, Birgitt Wiese, Hans-Helmut König, Hanna Kaduszkiewicz, Wolfgang Hoffmann, Jochen René Thyrian, Arno Villringer, Steffi Riedel-Heller, A.Veronica Witte

“…Preliminary evidence suggested an association of the intervention, increased cerebral blood flow and systolic blood pressure reductions.Abbreviations: ECT, entorhinal cortex thickness; FW, free water fraction; WHO, world health organization; AD, Alzheimer’s disease; VCI, vascular cognitive impairment; FINGER, Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability; MTL, medial temporal lobe; MIND, Mediterranean-DASH Intervention for Neurodegenerative Delay diet; cSVD, cerebral small vessel disease; WMH, white matter hyperintensities of presumed vascular origin; PSMD, peak width of the mean diffusivity distribution; WW-FINGERS, world wide FINGER studies; CAIDE, Cardiovascular Risk Factors, Aging, and Incidence of Dementia; GPP, general practitioner praxis; MRI, magnetic resonance imaging; MST, Mnemonic Similarity Test; TE, echo time; TR, repetition time; FA, flip angle; FOV, field of view; GRAPPA, GeneRalized Autocalibrating Partial Parallel Acquisition; CMRR, Center for Magnetic Resonance Research; BOLD, blood oxygenation level dependent; pcASL: pseudo-continuous arterial spin labeling; EPI, echo-planar imaging; FLAIR, fluid attenuated inversion recovery; CBF, cerebral blood flow; QA, quality assessment; GM, gray matter; HCV, hippocampal volume; eICV, estimated intracranial volume; DWI, diffusion-weighted imaging; MD, mean diffusivity; FA, fractional anisotropy
TBSS: tract-based spatial statistics; CSF, cerebral spinal fluid; ISI, inter-stimulus interval; LDI, lure discrimination index; REC, recognition score; CG, control group; IG, intervention group; MoCA, Montreal Cognitive Assessment; CASMIN, Comparative Analysis of Social Mobility in Industrial Nations; BMI, body mass index; SBP/DBP, systolic/diastolic blood pressure; OSF, open science framework; LMM, linear mixed model; ANOVA, analysis of covariance.…”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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

Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Controls of Slab Subduction and Tearing on the Magmatism of Wrangell Volcanoes in South‐Central Alaska by Meng Liu, Haiying Gao

“…Our model reveals three key features, including (a) the presence of the subducting Yakutat slab with apparent velocity reductions near the trench and within its flat segment, (b) two slab segments beneath the Wrangell volcanic field, differing in steepness, depth, and seismic velocity, and aligning spatially with the northwestern and southeastern volcano clusters, and (c) the existence of slab windows between the Yakutat and Wrangell slabs and between the northwestern and southeastern portions of the Wrangell slab. …”
Get full text

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. …”
Get full text

LAGOS-US LANDSAT: Remotely sensed water quality estimates for U.S. lakes over 4 ha from 1984 to 2020 by Patrick J. Hanly, Katherine E. Webster, Patricia A. Soranno

“…Two random forest models were fit for each variable: Holdout-data (75/25 spatially representative train-test split) and Full-data (trained on all data). …”
Get full text

A Multi-Modal Attentive Framework That Can Interpret Text (MMAT) by Vijay Kumari, Sarthak Gupta, Yashvardhan Sharma, Lavika Goel

“…Questions such as &#x201C;What temperature is my oven set to?&#x201D; need the models to understand objects in the images visually and then spatially identify the text associated with them. …”
Get full text

Advances on the Links Between Turbulent and Submeso‐ to Mesoscales During EUREC4A by E. Gauvrit, M.‐N. Bouin, J.‐M. Delouis, F. Boulanger

Get full text

MPAR-RCNN: a multi-task network for multiple person detection with attribute recognition by S. Raghavendra, S. K. Abhilash, Venu Madhav Nookala, Jayashree Shetty, Praveen Gurunath Bharathi

“…This study introduces an innovative MTL framework designed to incorporate Multi-Person Attribute Recognition (MPAR) within a single-model architecture. Named MPAR-RCNN, this framework unifies object detection and attribute recognition tasks through a spatially aware, shared backbone, facilitating efficient and accurate multi-label prediction. …”
Get full text

Evaluation of potential productivity in coniferous forests by integrating field data and aerial laser scanning in Hidalgo, México by Rodrigo Ramos-Madrigal, Héctor M. de los Santos-Posadas, José René Valdez-Lazalde, Efraín Velasco-Bautista, Gregorio Ángeles-Pérez, Alma Delia Ortiz-Reyes

“…The Hossfeld IV anamorphic model adjusted as MEM and autocorrelation corrected model showed the best performance for predicting DH growth with R2adj of 0.87 and RMSE of 2.11 m. …”
Get full text

Unsupervised semantic label generation in agricultural fields by Gianmarco Roggiolani, Julius Rückin, Marija Popović, Jens Behley, Cyrill Stachniss, Cyrill Stachniss

“…Using our generated labels to train deep learning models boosts our prediction performance on previously unseen fields with respect to unseen crop species, growth stages, or different lighting conditions. …”
Get full text