A comparative study of methods for dynamic survival analysis by Wieske K. de Swart, Marco Loog, Jesse H. Krijthe, Jesse H. Krijthe

“…On the ADNI dataset the best performing method was Random Survival Forest with the last visit benchmark and super landmarking with an average tdAUC of 0.96 and brier score of 0.07. …”
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Evolution of Potential Distribution Areas and Cultivation Zones of <i>Morchella esculenta</i> (L.) Pers. Under Climate Warming: Application of Ensemble Models and Production Dynami... by Yi Huang, Guanghua Zhao, Jingtian Yang, Liyong Yang, Yang Yang, Wuzhi Jiaba, Zixi Shama, Jian Yang

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Ozone dry deposition through plant stomata: multi-model comparison with flux observations and the role of water stress as part of AQMEII4 Activity 2 by A. M. Khan, O. E. Clifton, O. E. Clifton, J. O. Bash, S. Bland, N. Booth, P. Cheung, L. Emberson, J. Flemming, E. Fredj, S. Galmarini, L. Ganzeveld, O. Gazetas, O. Gazetas, I. Goded, C. Hogrefe, C. D. Holmes, L. Horváth, V. Huijnen, Q. Li, P. A. Makar, I. Mammarella, G. Manca, J. W. Munger, J. W. Munger, J. L. Pérez-Camanyo, J. Pleim, L. Ran, R. San Jose, D. Schwede, S. J. Silva, R. Staebler, S. Sun, S. Sun, A. P. K. Tai, A. P. K. Tai, A. P. K. Tai, E. Tas, T. Vesala, T. Vesala, T. Weidinger, Z. Wu, Z. Wu, L. Zhang, P. C. Stoy

“…We find that modeled water stress effects are too strong in a temperate–boreal transition forest. Some single-point models overestimate summertime <span class="inline-formula">eg_s</span> in a seasonally water-limited Mediterranean shrubland. …”
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Transmission and control of Plasmodium knowlesi: a mathematical modelling study. by Natsuko Imai, Michael T White, Azra C Ghani, Chris J Drakeley

“…<h4>Methods</h4>A multi-host, multi-site transmission model was developed, taking into account the three areas (forest, farm, and village) where transmission is thought to occur. …”
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Uso da terra e perda de solo na Bacia Hidrográfica do Rio Colônia, Bahia Land use and soil loss in the Colônia River Watershed, Bahia by Vinícius de A. Silva, Mauricio S. Moreau, Ana M. S. dos S. Moreau, Neylor A. C. Rego

“…The SWAT software was used for obtaining a digital thematic map for every sub-basin of Colonia River Watershed, soil loss quantification in every sub-basin and in the forms of uses obtained by theoretical concept, simulating the inclusions of areas of permanent preservation (APP), as well as, forest in all surface of the sub-basins. …”
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Spatial-temporal distribution patterns change of grassland formation in Inner Mongolia since the 1980s by Anan Zhang, Jiakui Tang, Na Zhang, Xuefeng Xu, Wuhua Wang, Xiaofan Li, Maojin Li, Kaihui Li, Mengquan Wu, Shuohao Cai

“…Spatial distribution maps of grassland formations for four periods (1981–2020) were simulated and validated through field surveys and independent datasets. …”
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Attribution and scarcity analysis of blue and green water resources in a river basin under climate and environmental change by Yang Liu, Zhaoyang Zeng, Chengguang Lai, Sijing He, Jie Jiang, Zhaoli Wang

“…Among different LULC types, forest and cropland are significant drivers of both BW and GW changes. …”
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Determination of future gully erosion risk and its spatially quantitative interpretability of driving factors in regional scale using machine learning algorithms by Xin Liu, Dichen Wang, Mingming Guo, Xingyi Zhang, Zhuoxin Chen, Zhaokai Wan, Jielin Liu

“…The GERM was realized by four machine learning algorithms including Random Forest (RF), XGBoost, K-Nearest Neighbor (KNN), and Multi-layer perceptron of artificial neural networks (ANN-MLP). …”
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Spatial modeling of snow water equivalent in the high atlas mountains via a lumped process-based approach by Siham Acharki, Abdelghani Boudhar, Ayoub Bouihrouchane, Mostafa Bousbaa, Ismail Karaoui, Haytam Elyoussfi, Bouchra Bargam, El Mahdi El Khalki, Abdessamad Hadri, Abdelghani Chehbouni

“…The reanalysis data was downscaled and bias corrected using machine learning models (e.g. random forest). To validate results, we compared simulated snow cover area (fSCA) (transformed from SWE simulation) with fSCA issued from MODIS. …”
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Developing a multivariate drought index to assess drought characteristics based on the SWAT-Copula method in the Poyang Lake basin, China by Liping Guo, Fusheng Chen, Bin Wang, De Li Liu, Xiulong Chen, Hongsheng Huang, Hua Bai, Kaitao Liao, Zhiming Xia, Keyu Xiang, Linchao Li, Taihui Zheng, Qiang Yu

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Characterization of a thermostable uricase derived from Thermoactinospora rubra YIM 77501T and its heat-resistant mechanism by Xin Yan, Xin Yan, Xin Yan, Lan-Xin Tang, Lan-Xin Tang, Lan-Xin Tang, Maite Ortúzar, Maite Ortúzar, Li-Quan Yang, Li-Quan Yang, Li-Quan Yang, Peng Sang, Peng Sang, Na-Ying Jin, Na-Ying Jin, Lin-Hua Li, Lin-Hua Li, Zheng-Feng Yang, Zheng-Feng Yang, Zheng-Feng Yang, Yan-Wen Wang, Yan-Wen Wang, Wen-Jun Li, Wen-Jun Li, Wen-Jun Li, Wei Hu, Wei Hu, Yi-Rui Yin, Yi-Rui Yin, Yi-Rui Yin, Yi-Rui Yin

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A Novel Dataset for Experimentation With Intrusion Detection Systems in SCADA Networks Using IEC 60870-5-104 Standard by M. Agus Syamsul Arifin, Deris Stiawan, Bhakti Yudho Suprapto, Susanto, Tasmi Salim, Mohd Yazid Idris, Mohamed Shenify, Rahmat Budiarto

“…We then evaluated six Intrusion Detection System (IDS) models using different machine learning algorithms, i.e.: Artificial Neural Network, Categorical Na&#x00EF;ve Bayes, Decision Tree, K-Nearest Neighbors, Gradient Boosting, and Random Forest. The Decision Tree and Random Forest models achieved the highest accuracy of 93.66%. …”
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Pistachio Shell Powder as an Additive in Molded Pulp Products by Leila Fereidooni, Ana Rita C. Morais, Richard Venditti, Marcel Kropat, Jeff Shook, Mark B. Shiflett

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Three-stage hybrid modeling for real-time streamflow prediction in data-scarce regions by Awad M. Ali, Mohammed Abdallah, Babak Mohammadi, Hussam Eldin Elzain

“…The method was tested on four PERSIANN family precipitation products (2005–2019) using two conceptual hydrological models (CHM: HBV and GR6J) and three machine learning models (ML: Random Forest Regression, Boosted Regression Forest, and CatBoost Regression), with VMD applied to improve model inputs.New hydrological insights: Our results highlight that integrating VMD significantly enhances the reliability of hydrological simulations driven by satellite precipitation data, particularly during low-flow periods. …”
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Assessing CO2 Fluxes for European Peatlands in ORCHIDEE‐PEAT With Multiple Plant Functional Types by Liyang Liu, Chunjing Qiu, Yi Xi, Elodie Salmon, Aram Kalhori, Rebekka R. E. Artz, Christophe Guimbaud, Matthias Peichl, Joshua L. Ratcliffe, Koffi Dodji Noumonvi, Efrén López‐Blanco, Jiří Dušek, Tiina Markkanen, Torsten Sachs, Mika Aurela, Thu‐Hang Nguyen, Annalea Lohila, Ivan Mammarella, Philippe Ciais

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Modeling the Potential Habitat Gained by Planting Sagebrush in Burned Landscapes by Julie A. Heinrichs, Michael S. O’Donnell, Elizabeth K. Orning, David A. Pyke, Mark A. Ricca, Peter S. Coates, Cameron L. Aldridge

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Spatial Distribution Pattern of Aromia bungii Within China and Its Potential Distribution Under Climate Change and Human Activity by Liang Zhang, Ping Wang, Guanglin Xie, Wenkai Wang

“…ABSTRACT Aromia bungii is a pest that interferes with the health of forests and hinders the development of the fruit tree industry, and its spread is influenced by changes in abiotic factors and human activities. …”
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Climate Warming Increases the Voltinism of Pine Caterpillar (<i>Dendrolimus spectabilis</i> Butler): Model Predictions Across Elevations and Latitudes in Shandong Province, China by Yongbin Bao, Teri Gele, Xingpeng Liu, Zhijun Tong, Jiquan Zhang

“…The pine caterpillar (<i>Dendrolimus spectabilis</i> Bulter, Lepidoptera: Lasiocampidae) is a destructive insect threatening forest communities across Eurasia. The pest is polyvoltine, and under global warming, more favorable temperatures can lead to additional generations. …”
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Monitoring and Evaluation of Ecological Environment Changes in Dongzhuang Reservoir Basin in Shaanxi Province Based on Remote Sensing Ecological Index by Quan Wenting, Zhang Shuyu, Liu Yan, Wang Weidong

“…The CA-Markov model based on IDRISI software was used to simulate the ecological environment of the Dongzhuang reservoir basin in 2030. …”
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A novel approach of generating pseudo revisited soil sample data based on environmental similarity for space-time soil organic carbon modelling by Wenkai Cui, Lin Yang, Lei Zhang, Chenconghai Yang, Chenxu Zhu, Chenghu Zhou

“…Validation results showed the approach significantly improved predictive accuracy, with an RMSE of 5.28 t/ha (31.6 % lower than global parameter optimization and 10.7 % lower than spatiotemporal Random Forest) and an R2 improved from 0.319 (spatiotemporal Random Forest) to 0.456. …”
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