Study of the dynamics of herbs productivity based on long-term monitoring data by D. A. Ivanov, O. V. Karaseva, M. V. Rublyuk

“…It has been determined that different groups of observation years differ in productivity and in the nature of its spatio-temporal variability, as well as in the factors that determine them and in the conditions that affect these factors. This makes, when predicting the yield of grasses of different ages, to create mathematical models of its dependence on landscape conditions for different time clusters.…”
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Machine learning-guided integration of fixed and mobile sensors for high resolution urban PM2.5 mapping by Tianshuai Li, Xin Huang, Qingzhu Zhang, Xinfeng Wang, Xianfeng Wang, Anbao Zhu, Zhaolin Wei, Xinyan Wang, Haolin Wang, Jiaqi Chen, Min Li, Qiao Wang, Wenxing Wang

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Structural Connectivity Shaped by Cryospheric Degradation Modulates Sediment Transport Dynamics in High Mountain Asia by Jinlong Li, Genxu Wang, Jiapei Ma, Peng Huang, Chunlin Song, Shouqin Sun, Linmao Guo, Kai Li, Haoyu Wang, Dongfeng Li, Admin Husic

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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

“…Live fuel moisture content (LFMC) significantly influences fire activity and behavior over different spatial and temporal scales. The ability to estimate LFMC is important to improve our capability to predict when and where large wildfires may occur. …”
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Comprehensive geospatial analysis of urban expansion dynamic in Lahore, Pakistan (1998–2023) by Sona Karim, Yaning Chen, Patient Mindje Kayumba, Ishfaq Ahmad, Hassan Iqbal

“…To contribute, we used high-resolution Landsat imagery to analyze the spatial diverging patterns of urban extent from 1998 to 2023 in Lahore. …”
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Yield gap in cowpea plants as function of water déficits during reproductive stage by Paulo J. O. P. Souza, Vivian D. da S. Farias, João V. N. Pinto, Hildo G. G. C. Nunes, Everaldo B. de Souza, Clyde W. Fraisse

“…The total deficiencies in the reproductive phase were spatialized considering the 30 locations in order to assess the temporal and spatial seasonality of water availability and the sowing period in the study region. …”
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FlexiNet: An Adaptive Feature Synthesis Network for Real-Time Ego Vehicle Speed Estimation by Abdalrahaman Ibrahim, Kyandoghere Kyamakya, Wolfgang Pointner

“…On the nuImages dataset, our model achieves an RMSE of 1.1358 m/s and an MAE of 0.9599 m/s, while on the KITTI dataset it records an RMSE of 1.9542 m/s and an MAE of 1.0610 m/s—reductions in error of up to 27.6% and 75.5% compared to baseline methods. …”
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Regulation of MAP2, GFAP, and calcium in the CA3 Region Following Kainic Acid Exposure to  organotypic hippocampal slice culture [version 3; peer review: 2 approved] by Aulanni'am Aulanni'am, Kusworini Handono, Ettie Rukmigarsari, Hidayat Sujuti, Machlusil Husna

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Metabolomics and network analysis reveal the mechanism of Salvia miltiorrhiza bunge extract in ameliorating cognitive dysfunction in sleep-deprived rats by Meiya Zhang, Xirui Huang, Mengxiang Dai, Shunbo Zhang, Chao Yin, Qiuyun You

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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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Impact of Subjective and Objective Green Space Characteristics on Mental Health Benefits: An Explainable Machine Learning Approach by Ke LI, Yipei MAO, Yongjun LI

“…Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. …”
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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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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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HMGB1 Inhibition Alleviates Chronic Nonbacterial Prostatitis by Suppressing M1 Polarization of Macrophages by Zhou J, Ding L, Chen J, Chen C, Jiang P, Mei Z, Jiang Q, Hua X

“…Co-immunofluorescence was used to analyze the functional phenotype of macrophages and spatial localization of HMGB1 in prostate of EAP mice. …”
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Getting NBA Shots in Context: Analysing Basketball Shots with Graph Embeddings by Schmid Marc, Schöpf Moritz, Kolbinger Otto

“…We introduce a graph neural network to process a graph based on player and ball tracking data to compute expected shot quality. We evaluate this model against other models focusing on calibration. …”
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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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Defending Data Poisoning Attack Through Watermarked Friendly Noise Luminosity Activated Dense Layered UNet for Classification of Lung Disease by M. Shyamala Devi, S. Priya

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cogsworth: A Gala of COSMIC Proportions Combining Binary Stellar Evolution and Galactic Dynamics by Tom Wagg, Katelyn Breivik, Mathieu Renzo, Adrian M. Price-Whelan

“…We provide a detailed explanation of the functionality of cogsworth and demonstrate its capabilities through a series of use cases: (1) we predict the spatial distribution of compact objects and runaways in both dwarf and Milky Way–like galaxies; (2) using a star cluster from a hydrodynamical simulation, we show how supernovae can change the orbits of stars in several ways; and (3) we predict the separation of disrupted binary stellar companions on the sky and create a synthetic Gaia color–magnitude diagram. …”
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WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

“…The most important predictor<span id="page2508"/> variables included near-surface soil temperatures (top 40 cm), vegetation spectral reflectance, and soil moisture. Our results, modeled from 138 site years across 26 sites, had relatively strong predictive skill, with a mean <span class="inline-formula"><i>R</i>²</span> of 0.51 and 0.70 and a mean absolute error (MAE) of 30 and 27 nmol m<span class="inline-formula">⁻²</span> s<span class="inline-formula">⁻¹</span> for daily and monthly fluxes, respectively. …”
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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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