Climate-responsive vector control strategies for Aedes albopictus by Jesús Bellver-Arnau, Laura Blanco-Sierra, Santi Escartin, Simone Mariani, Frederic Bartumeus

“…Methods In this study, we employed a metapopulation model to simulate Ae. albopictus population dynamics and dispersal, optimizing the timing and spatial allocation of larvicidal treatments. …”
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Remote sensing perspective in exploring the spatiotemporal variation characteristics and post-disaster recovery of ecological environment quality, a case study of the 2010 Ms7.1 Yu... by Yaohui Liu, Yu Lin, Wenyi Liu, Jie Zhou, Jin Wang

“…Then, we analyzed the spatiotemporal characteristics of Yushu’s EEQ from 2001 to 2020 and explored spatial autocorrelation relationships. Last, the driving mechanism of EEQ recovery in Yushu was analyzed using the GeoDetector model. …”
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Spatiotemporal analysis of Anopheles gambiae larval sites and malaria transmission in Djilakh, Central Senegal by Assane Ndiaye, Camille Morlighem, Aminata Niang Diène, Moussa Kane, Abdoulaye Diallo, Fassiatou Tairou, Mohamed Abderemane Nourdine, Pape Cheikh Sarr, Lassana Konaté, Ousmane Faye, Oumar Gaye, El Hadji Amadou Niang, Catherine Linard, Ousmane Sy

“…Abstract Background The progress made against malaria has resulted in a nationwide reduction of the disease burden in Senegal. The observed overall low transmission levels are, however, marked by an important spatial heterogeneity with hotspots subsisting in several parts of the country. …”
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Forest cover restoration analysis using remote sensing and machine learning in central Malawi by Jabulani Nyengere, Precious Masuku, Sylvester Chikabvumbwa, Weston Mwase, Msaiwale Kathewera, Allena Laura Njala, Wilson Tchongwe, Isaac Tchuwa, Tiwonge I Mzumara, Chikondi Chisenga, Wilfred Kadewa, Emmanuel Chinkaka, Harineck Tholo

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Relative Humidity and Air Temperature Characteristics and Their Drivers in Africa Tropics by Isaac Kwesi Nooni, Faustin Katchele Ogou, Abdoul Aziz Saidou Chaibou, Samuel Koranteng Fianko, Thomas Atta-Darkwa, Nana Agyemang Prempeh

“…Semi-arid regions in the Sahelian–Guinean belt recorded moderate TEMP (25–30 °C) and RH (30–60%), while the Guinean coastal belt and Congo Basin experienced cooler, more humid conditions (TEMP < 20 °C, RH (60–90%). (2) Trend analysis using Mann–Kendal and Sen slope estimator analysis revealed spatial heterogeneity, with increasing TEMP and deceasing RH trends varying by region and season. (3) The warming rate was higher in arid and semi-arid areas, with seasonal rates exceeding annual averages (0.18 °C decade⁻¹). …”
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Impact of Extreme Climate Indices on Vegetation Dynamics in the Qinghai–Tibet Plateau: A Comprehensive Analysis Utilizing Long-Term Dataset by Hanchen Duan, Beiying Huang, Shulin Liu, Jianjun Guo, Jinlong Zhang

“…Both correlation analysis and random forest methodologies underscore the impact of extreme climate indices on vegetation variations, with the random forest model exhibiting superior capability in capturing nonlinear relationships. …”
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Tensor network analysis of the maple-leaf antiferromagnet spangolite by Philipp Schmoll, Harald O. Jeschke, Yasir Iqbal

“…Experimental measurements revealed a non-magnetic ground state at T ~ 8 K with magnetic properties dominated by dimerization. We propose a spatially anisotropic Heisenberg model for the Cu2+ spin-1/2 degrees of freedom on this geometrically frustrated and effectively two-dimensional maple-leaf lattice, featuring five symmetry inequivalent couplings with ferromagnetic bonds on hexagons and antiferromagnetic triangular bonds. …”
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Copper Stress Levels Classification in Oilseed Rape Using Deep Residual Networks and Hyperspectral False-Color Images by Yifei Peng, Jun Sun, Zhentao Cai, Lei Shi, Xiaohong Wu, Chunxia Dai, Yubin Xie

“…Copper (Cu) stress in crops not only leads to significant reductions in both yield and quality but also poses potential health risks to humans. …”
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Investigating Source Mechanisms for Nonlinear Displacement of GNSS Using Environmental Loads by Jian Wang, Wenlan Fan, Weiping Jiang, Zhao Li, Tianjun Liu, Qusen Chen

“…After removing the displacements caused by environmental loads, the root mean square (RMS) values of the coordinate time series decreased by 85.5% in the E direction, 77.4% in the N direction, and 89.8% in the U direction, with average reductions of 6.2%, 4.4%, and 16.7%, respectively. Lastly, it also comprehensively assesses the consistency between environmental loads and coordinate time series from the perspectives of the optimal noise model, velocity and uncertainty, and amplitude and phase. …”
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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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Combining a Standardized Growth Class Assessment, UAV Sensor Data, GIS Processing, and Machine Learning Classification to Derive a Correlation with the Vigour and Canopy Volume of... by Ronald P. Dillner, Maria A. Wimmer, Matthias Porten, Thomas Udelhoven, Rebecca Retzlaff

“…The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). …”
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Storm water runoff studies in built-up watershed areas using curve number and remote sensing techniques by Arati Reddy Nilap, H. N. Rajakumara, Ali Aldrees, Hasan Sh. Majdi, Wahaj Ahmad Khan

“…The model satisfactorily predicted runoff from the simulated data analysis with evaluation criteria NSE = 0.9945, MAE = 5.4121, r = 0.9975, R2 = 0.9949, RMSE = 6.8117 and PBias = 1.1436. …”
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Spatiotemporal dynamics of future hydrology in the Pearl River Basin: Controls of climate change and land surface by Ying Zhang, Jianping Gan

“…Driven by an optimized subset of General Circulation Models through a comprehensive framework of multi-level calibrations, we employed a well-validated Soil and Water Assessment Tool to predict streamflow and investigate the underlying dynamics in the basin. …”
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Likelihood testing of seismicity‐based rate forecasts of induced earthquakes in Oklahoma and Kansas by M. P. Moschetti, S. M. Hoover, C. S. Mueller

“…Abstract Likelihood testing of induced earthquakes in Oklahoma and Kansas has identified the parameters that optimize the forecasting ability of smoothed seismicity models and quantified the recent temporal stability of the spatial seismicity patterns. …”
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AI-based defect detection and self-healing in metal additive manufacturing by Jan Akmal, Kevin Minet-Lallemand, Jukka Kuva, Tatu Syvänen, Pilvi Ylander, Tuomas Puttonen, Roy Björkstrand, Jouni Partanen, Olli Nyrhilä, Mika Salmi

“…A convolutional neural network was trained (n = 211) and was verified with XCT. The model demonstrated prediction accuracy of 94% for the six classes of unfamiliar defective regions. …”
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Forecasting the Influence of Climate Change on Agroecosystem Services: Potential Impacts on Honey Yields in a Small-Island Developing State by Diana L. Delgado, María Eglée Pérez, Alberto Galindo-Cardona, Tugrul Giray, Carla Restrepo

“…The four models varied in terms of their predictions but showed that both honey yields and areas suitable for honey production will decrease under scenarios of climate change. …”
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Determining carbon storage of a complex peat stratigraphy using non– and minimal-invasive geophysical prospection techniques (Verlorener Bach and Loosbach valleys, southern Germany... by Anne Köhler, Ulrike Werban, Marco Pohle, Johannes Rabiger-Völlmer, Birgit Schneider, Anneli Wanger-O’Neill, Stefanie Berg, Peter Dietrich, Christoph Zielhofer

“…However, potential reductions in groundwater levels could lead to peat decomposition and carbon release.This study demonstrates the effectiveness of integrating EMI, DP-EC and ERT data for stratigraphic analysis, providing a comprehensive understanding of spatial sediment stratigraphies and carbon storage in the study area. …”
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Accurate and Efficient Fluid Flow Regime Classification Using Localized Texture Descriptors and Machine Learning by Manimaran Renganathan, Palani Thanaraj Krishnan, C. Christopher Columbus, T. Sunil Kumar

“…This paper presents an image-based framework for classifying fluid flow regimes into low and high-speed states by utilizing spatially localized texture features combined with machine learning techniques. …”
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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. …”
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Cross-Species Multi-Omics Analysis Reveals Myeloid-Driven Endothelial Oxidative Stress in Ischemic Stroke by Ziqi Cheng, Hua Zhu, Shi Feng, Yonggang Zhang, Xiaoxing Xiong

“…Methods: In this study, we integrated bulk RNA-sequencing (RNA-seq), single-cell RNA-seq (scRNA-seq), spatial transcriptomics, and flow cytometry data from human and mouse models of ischemic stroke. …”
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