Satellite retrievals of total phosphorus in Taihu Lake using Sentinel-2 images and an optimized XGBoost model by Jiayu Cui, Shiqiang Wu, Jiangyu Dai, Wanyun Xue, Yue Zhang, Jiayi You, Xueyan Lv, Xuan Yang

“…This study provides a practical reference for constructing a satellite retrieval model to determine inactive water quality parameters, and also provides strong technical support for the construction of an air-space-ground integrated monitoring network, especially in the context of open sharing of satellite data, and offers a feasible path for realizing large-scale and low-cost water quality supervision.…”
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MaxEnt model reveals northward and elevational expansion of climate suitability for Camellia oleifera growth in China by Xiaojun Wang, Shumei Xiao, Guangxu Liu, Mingying Quan, Chunxia Zhang

“…Based on the bioclimatic scenario data, the MaxEnt model is used to simulate and predict the climate suitability of C. oleifera from 1980s to 2100, and analyzing the spatiotemporal characteristics and changes of climate suitability. (1) The AUC value of the model is 0.929, indicating very good evaluation results. …”
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Population Pharmacokinetic Modeling of Total and Unbound Pamiparib in Glioblastoma Patients: Insights into Drug Disposition and Dosing Optimization by Charuka Wickramasinghe, Seongho Kim, Yuanyuan Jiang, Xun Bao, Yang Yue, Jun Jiang, Amy Hong, Nader Sanai, Jing Li

“…Nonlinear mixed-effects modeling was performed using Monolix (2024R1) to simultaneously fit the total and unbound drug plasma concentration data. …”
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Hydrodynamic Processes of Incipient Meander Chute Cutoffs: Laboratory Experiments With Implications for Morphodynamics and Depth‐Averaged Modeling by Jason T.‐Y. Lin, Esteban Lacunza, Roberto Fernández, Marcelo H. García, Bruce Rhoads, Jim Best, Jessica Z. LeRoy, Gary Parker

“…This paper investigates three‐dimensional mean flow structure, turbulent flow structure, and bed shear stress distribution from high‐resolution flow velocity data in a fixed‐bed, sediment‐free physical model. …”
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Detection of Invasive Species (Siam Weed) Using Drone-Based Imaging and YOLO Deep Learning Model by Deepak Gautam, Zulfadli Mawardi, Louis Elliott, David Loewensteiner, Timothy Whiteside, Simon Brooks

“…First detected in Australia in northern Queensland in 1994 and later in the Northern Territory in 2019, there is an urgent need to determine the extent of its incursion across vast, rugged areas of both jurisdictions and a need for distribution mapping at a catchment scale. This study tests drone-based RGB imaging to train a deep learning model that contributes to the goal of surveying non-native vegetation at a catchment scale. …”
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Bayesian spatiotemporal modelling and mapping of malaria risk among children under five years of age in Ghana by Wisdom Kwami Takramah, Yaw Asare Afrane, Justice Moses K. Aheto

“…Bayesian Hierarchical spatiotemporal models were specified to estimate and map spatiotemporal variations in the relative risk of malaria. …”
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Generative priors-constraint accelerated iterative reconstruction for extremely sparse photoacoustic tomography boosted by mean-reverting diffusion model: Towards 8 projections by Teng Lian, Yichen Lv, Kangjun Guo, Zilong Li, Jiahong Li, Guijun Wang, Jiabin Lin, Yiyang Cao, Qiegen Liu, Xianlin Song

“…By modeling the degradation process from a high-quality image under full-view scanning (512 projections) to a sparse image with stable Gaussian noise (i.e., mean state), a mean-reverting diffusion model is trained to learn prior information of the data distribution. …”
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Analysis of travel-time to HIV treatment in sub-Saharan Africa reveals inequities in access to antiretrovirals by Justin T. Okano, Andrea Low, Luckson Dullie, Wongani Mzumara, Harriet Nuwagaba-Biribonwoha, Sally Blower

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Evaluating the Uncertainty and Predictive Performance of Probabilistic Models Devised for Grade Estimation in a Porphyry Copper Deposit by Raymond Leung, Alexander Lowe, Arman Melkumyan

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A spatial bearing fault classification method based on improved APSMOTE-WKMFA by CHEN Chao, YANG Chenhao, XU Haosen, WAN Ouying, HAN Liling

“…ObjectiveAiming at the problem of low accuracy of classification of time domain features of spatial bearings, time domain indicators and wavelet packet decomposition algorithms are combined to obtain the time-frequency distribution features of spatial bearings. …”
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Evaluation of a Hub-and-Spoke Model to Enhance Healthcare Professionals’ Practice of Antimicrobial Stewardship (AMS) Programmes in the Volta Region of Ghana by Mairead McErlean, Eneyi Kpokiri, Preet Panesar, Emily E. Cooper, Jonathan Jato, Emmanuel Orman, Hayford Odoi, Araba Hutton-Nyameaye, Samuel O. Somuah, Isaac Folitse, Thelma A. Aku, Inemesit O. Ben, Melissa Farragher, Leila Hail, Cornelius C. Dodoo, Yogini H. Jani

“…<b>Background</b>: Antimicrobial resistance (AMR) poses a critical global health challenge, particularly in resource-limited settings. A hub-and-spoke model, decentralising expertise and distributing resources to peripheral facilities, has been proposed as a strategy to enhance the antimicrobial stewardship (AMS) capacity in low- and middle-income countries. …”
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3D outcrop geologic modeling applied to test the role of brittle structures on permoporosity of poorly lithified reservoirs by Camila Faria de Albuquerque, Aline Theophilo Silva, Mathieu Moriss, Claudio Limeira Mello

“…Two models were built: one using traditional methods for industry where properties were uniformly distributed throughout the grid and a second model where sectors were delimited and populated with data from deformed rocks, highlighting the impact of tectonic structures (faults and deformation bands) on fluid flow, reducing the permeability by one order of magnitude, and increasing the flow time between wells by more than 10%. …”
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Predicting the suitable habitat of the invasive alien plant Lactuca serriola using Biomod2 model with ArcGIS by Dong-Mei Kou, Yan Sun, Li-Guo Long, Jia-Guo Wang, Jia-Wei Wu, Ting Long, Wei-Jie Li

“…Estimating the suitable habitat distribution of L. serriola and identifying the main environmental variables influencing its spread through modeling can provide essential baseline data for preventing its potential impacts. …”
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Supply–Demand Spatial Patterns of Cultural Services in Urban Green Spaces: A Case Study of Nanjing, China by Qinghai Zhang, Ruijie Jiang, Xin Jiang, Yongjun Li, Xin Cong, Xing Xiong

“…This study uses Nanjing, China, as a case study to develop an indicator framework for urban green space CES supply and demand, leveraging multi-source data. By employing spatial autocorrelation analysis (Bivariate Moran’s I) and a coupling coordination model, this research systematically assesses the spatial patterns of CESs in urban parks and green spaces. …”
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Dynamic Evolution, Spatial Differences, and Convergence of Land Green Use Efficiency in Resource-based Cities by BIAN Zhiqiang, ZHANG Qianhua

“…［Methods］ Based on data from 114 resource-based cities in China from 2006 to 2020, the spatial and temporal characteristics, distribution dynamics, spatial differences, and convergence features of land green use efficiency in resource-based cities were analyzed by using the super efficiency SBM model to measure land green use efficiency, in combination with Kernel density estimation, Dagum Gini coefficient, coefficient of variation, and fixed effects model. …”
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Restricted mean survival time approach versus time-varying coefficient Cox model for quantifying treatment effect when hazards are non-proportional by Tianyuan Gu, Zhaojin Chen, Yu Yang Soon, Joseph Wee, Bee-Choo Tai

“…An intensive simulation study was conducted to compare the performance of FPM to the Cox TVC model under PH and non-PH assumptions. The survival time t was assumed to follow the Piecewise Exponential distribution with various censoring patterns generated from the Uniform distribution. …”
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Spatial analysis of G.f.fuscipes abundance in Uganda using Poisson and Zero-Inflated Poisson regression models. by Albert Mugenyi, Dennis Muhanguzi, Guy Hendrickx, Gaëlle Nicolas, Charles Waiswa, Steve Torr, Susan Christina Welburn, Peter M Atkinson

“…<h4>Methodology</h4>Entomological data for the period 2008-2018 as used in the model were obtained from various sources and systematically assembled using a structured protocol. …”
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Slow True Polar Wander Around Varying Equatorial Axes Since 320 Ma by Bram Vaes, Douwe J. J. vanHinsbergen

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Spatial modelling of the shared impact of sexual health knowledge and modern contraceptive use among women with disabilities in Africa by Obasanjo Afolabi Bolarinwa, Clifford Odimegwu, Aliu Mohammed, Ezra Gayawan

“…The data were analysed using both spatial and Bayesian inference to account for the shared component model patterns between sexual health knowledge and modern contraceptive use among women with disabilities while accounting for factors unique to each outcome. …”
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Study on Short Term Temperature Forecast Model in Jiangxi Province based on LightGBM Machine Learning Algorithm by Kanghui SUN, An XIAO, Houjie XIA

“…In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.…”
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