Human Health Outcomes Linked to Ecosystem Changes of Urban Streams: An OneAquaHealth Protocol by Marilena ANASTASAKI, Catherine CHRONAKI, Anne MOEN, Eleni KOKINOU, Stratos KOKOLAKIS, Matenia KARAGIANNIDOU

“…The research will be conducted for a certain period so that data can be collected at various spatial and temporal scales to detect patterns over time and provide reliable predictions using machine learning models. …”
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Medical mesocosms and cohort differences in victim decisions in spousal violence in Sub-Saharan Africa by Patricia Elung'ata

“…Spatial interpolation addressed geographical clustering. …”
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Bioinformatics analysis for immune hub genes in BLIS subtype of triple-negative breast cancer by Hend Adel, Manal Abdel Wahed, Heba M. Afify

“…Conclusions Our findings provide a foundation for developing immune-targeted therapeutic approaches for BLIS-TNBC patients and provision the integration of machine learning models to predict treatment responses and optimize patient-specific strategies, potentially transforming the clinical management of this challenging breast cancer subtype.…”
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Evaluating the Relationship Between Vegetation Status and Soil Moisture in Semi-Arid Woodlands, Central Australia, Using Daily Thermal, Vegetation Index, and Reflectance Data by Mauro Holzman, Ankur Srivastava, Raúl Rivas, Alfredo Huete

“…Understanding the temporal changes in rainfall-driven vegetation in the thermal/optical spectra associated with increases in SM can allow us to predict the spatial impact of wet pulses on vegetation dynamics in extensive drylands.…”
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Plasmodium falciparum malaria endemicity in Indonesia in 2010. by Iqbal R F Elyazar, Peter W Gething, Anand P Patil, Hanifah Rogayah, Rita Kusriastuti, Desak M Wismarini, Siti N Tarmizi, J Kevin Baird, Simon I Hay

“…<h4>Background</h4>Malaria control programs require a detailed understanding of the contemporary spatial distribution of infection risk to efficiently allocate resources. …”
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Building consistency in explanations: Harmonizing CNN attributions for satellite-based land cover classification by Timo T. Stomberg, Lennart A. Reißner, Martin G. Schultz, Ribana Roscher

“…Furthermore, the unique characteristics of remote sensing imagery pose additional challenges for attribution interpretation: it primarily comprises continuous “stuff” classes rather than objects, exhibits fine-grained spatial variability, contains mixed pixels, is often multispectral, and exhibits spatially heterogeneity. …”
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Historical habitat mapping from black-and-white aerial photography: A proof of concept for post World War II Switzerland by Nica Huber, Matthias Bürgi, Christian Ginzler, Birgit Eben, Andri Baltensweiler, Bronwyn Price

“…Information regarding the spatial arrangement and extent of past habitats is important for understanding present biodiversity, restoration potential, and fighting extinction-debt effects. …”
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Rainfall forecasting in arid regions in response to climate change using ARIMA and remote sensing by Hany F. Abd-Elhamid, Amr M. El-Dakak, Martina Zeleňáková, Saleh O. K, Mohamed Mahdy, Samah H. Abd El Ghany

“…Climate changes in increasing at alarming rates which caused outward changes in the hydrological cycle parameters such as temperature and precipitation. The spatial and temporal distribution of rainfall has changed all over the world. …”
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Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations by Meilin He, Tao Chen, Yuanjin Pan, Lv Zhou, Yifei Lv, Lewen Zhao

“…Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. …”
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Coseismic crustal seismic velocity changes associated with the 2024 MW 7.5 Noto earthquake, Japan by Nicolas Paris, Yuji Itoh, Florent Brenguier, Qing-Yu Wang, Yixiao Sheng, Tomomi Okada, Naoki Uchida, Quentin Higueret, Ryota Takagi, Shin’ichi Sakai, Satoshi Hirahara, Shuutoku Kimura

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Estimation of Soil Salinity by Combining Spectral and Texture Information from UAV Multispectral Images in the Tarim River Basin, China by Jiaxiang Zhai, Nan Wang, Bifeng Hu, Jianwen Han, Chunhui Feng, Jie Peng, Defang Luo, Zhou Shi

“…Spatial distribution maps of soil salinity were then generated for each model. …”
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AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images by Binyao Wang, Ya’nan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang

“…To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. In the feature extraction stage, the model incorporates an Adaptive Mix Attention (AMA) Block, balancing robust feature extraction with low computational overhead through spatial mixing and Efficient Multi-Scale Attention (EMA). …”
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LoadSeer: Exploiting Tensor Graph Convolutional Network for Power Load Forecasting With Spatio-Temporal Characteristics by Jiahao Zhang, Bin Yu, Hanbin Lai, Lin Liu, Jinghui Zhou, Fengliang Lou, Yili Ni, Yan Peng, Ziheng Yu

“…Existing spatio-temporal prediction methods can only handle one factor in each dimension of time and space. …”
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Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Two-decade spatiotemporal variations in ground-level ozone over Ontario, Canada by Zhou Zang, Jane Liu, Erjia Ge, Yi Zhang

“…Understanding long-term trends and spatially explicit details of O3 is important for supporting air quality management in Ontario.MethodWe construct a high-resolution (daily, 10 km) dataset of maximum daily 8-hour average O3 (MDA8 O3) over Ontario from 2004 to 2023, through a two-step machine learning model. …”
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Leveraging ResNet50 With Swin Attention for Accurate Detection of OCT Biomarkers Using Fundus Images by S. Tamilselvi, M. Suchetha, Rajiv Raman

“…Therefore, regular screening of diabetes patients for the early detection of retinal abnormalities is essential to prevent the development and progression of DR and DME. The proposed model predicts DME-associated biomarkers, typically identified in Optical Coherence Tomography (OCT), using 2D fundus images. …”
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A novel coal-rock recognition method in coal mining face based on fusing laser point cloud and images by Yang Liu, Lei Si, Zhongbin Wang, Miao Chen, Xin Li, Dong Wei, Jinheng Gu

“…Subsequently, the 2D target candidate box is calculated through the improved Mask R-CNN, and the frustum point cloud in the 2D target candidate box is extracted to reduce the calculation scale and spatial search range. Then, the self-attention PointNet is constructed to segment the fused point cloud within the frustum range, and the bounding box regression network is used to predict the bounding box parameters. …”
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Remote sensing reveals the role of forage quality and quantity for summer habitat use in red deer by Thomas Rempfler, Christian Rossi, Jan Schweizer, Wibke Peters, Claudio Signer, Flurin Filli, Hannes Jenny, Klaus Hackländer, Sven Buchmann, Pia Anderwald

“…Results The combination of vegetation indices and optical traits greatly improved predictive power in both the biomass (R2 = 0.60, Root mean square error (RMSE) = 88.55 g/m2) and relative nitrogen models (R2 = 0.34, RMSE = 0.28%). …”
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Evaluation of 3D‐Var and 4D‐Var data assimilation on simulation of heavy rainfall events over the Indian region by Shivaji S. Patel, Ashish Routray, Vivek Singh, R. Bhatla, Rohan Kumar, Elena Surovyatkina

“…Similarly, it is found that satellite wind assimilation improved the statistical skill scores in predicting heavy precipitation in all three cases. Overall, the study suggested that the performance of the NCUM‐R model integrated with the 4D‐Var technique improved the model's forecast skill in the simulation of HREs.…”
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Evaluation of ground based spectral imaging for real time maize biomass monitoring by Andrea Szabó, Andrea Szabó, Nxumalo Gift Siphiwe, Nxumalo Gift Siphiwe, Erika Buday-Bódi, Erika Buday-Bódi, Blessing Ademola, János Tamás, János Tamás, Attila Nagy, Attila Nagy

“…Conversely, the chlorophyll prediction model showed improved accuracy, achieving an R² of 0.64, an MBE of 0.04 µg/g, and an NRMSE of 15.92%. …”
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