Leaf Water Storage Capacity Among Eight US Hardwood Tree Species: Differences in Seasonality and Methodology by Natasha Scavotto, Courtney M. Siegert, Heather D. Alexander, J. Morgan Varner

“…The changing forest structure, notably the decline of oak’s (<i>Quercus</i>) dominance and encroachment of non-oak species in much of the upland hardwood forests of the eastern United States, challenges our understanding of how species-level traits scale up to control the forest hydrologic budget. …”
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Exploring Partnerships between Local Communities and Timber Companies: An Experiment Using the Role-Playing Games Approach by Herry Purnomo, Philippe Guizol, Guillermo A. Mendoza

“…The paper examines a forest plantation company in South Sumatra, Indonesia, which has cooperated with local communities since 2000. …”
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Evaluation of the Impacts of Land Use on Water Quality: A Case Study in The Chaohu Lake Basin by Juan Huang, Jinyan Zhan, Haiming Yan, Feng Wu, Xiangzheng Deng

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Annual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023 by Siya Li, Quansheng Ge, Fubao Sun, Qiulei Ji, Wenbin Liu, Ronggao Liu, Duanyang Xu, Zexing Tao

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A novel spectral transformation technique based on special functions for improved chest X-ray image classification. by Abeer Aljohani

“…All our simulation is performed in computation softwares, Matlab and Python. …”
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Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation by Rong Mei, ChengJiang Yu

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The annual dynamic dataset of high-resolution crop water use in China from 1991 to 2019 by Minglei Wang, Wenjiao Shi

“…Firstly, we estimated the yearly crop blue and green water use at the site scale by incorporating more localized input parameters using a dynamic water balance model. Then, the random forest model was combined with site-scale simulation results to generate spatial predictions of blue and green water for each crop from 1991 to 2019. …”
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Time-series urban green space mapping and analysis through automatic sample generation and seasonal consistency modification on Sentinel-2 data: A case study of Shanghai, China by Zhuoqun Chai, Keyao Wen, Hao Fu, Mengxi Liu, Qian Shi

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Machine learning analysis of molecular dynamics properties influencing drug solubility by Zeinab Sodaei, Saeid Ekrami, Seyed Majid Hashemianzadeh

“…Molecular dynamics (MD) simulation is a powerful computational tool for modeling various physicochemical properties, particularly solubility. …”
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Spatial and Temporal Characteristics of Land Use Changes in the Yellow River Basin from 1990 to 2021 and Future Predictions by Yali Cheng, Yangbo Chen

“…Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. …”
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Systemic model of segregation and transformation of urban solid waste by Alex Rengifo Rojas, Nelino Florida Rofner, Luis Morales y Chocano, José Raúl Rodríguez Lichtenheldt, Julio Pedro Atoche Cornejo

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Investigating the Sensitivity of Modelled Ozone Levels in the Mediterranean to Dry Deposition Parameters by André Barreirinha, Sabine Banzhaf, Markus Thürkow, Carla Gama, Michael Russo, Enrico Dammers, Martijn Schaap, Alexandra Monteiro

“…Adjustments were made to the vegetation type dependent Jarvis functions for temperature and vapour pressure deficit, as well as to the maximum stomatal conductance across four land use types: arable land, crops, deciduous broadleaf forest, and coniferous evergreen forest. The model’s baseline run showed a widespread overestimation of ozone. …”
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A Unified Surrogate Framework for Data-Driven Reliability Analysis of Mechanical Systems from Low to Multi-DOF by Lun Shao, Alexandre Saidi, Abdel-Malek Zine, Mohamed Ichchou

“…The methodology integrates four main components: (i) probabilistic uncertainty modeling for mass, damping, and stiffness, (ii) Latin Hypercube Sampling (LHS) to efficiently explore parameter variations, (iii) Monte Carlo simulation (MCS) for estimating failure probabilities under stochastic excitations, and (iv) machine learning models, including Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Neural Networks (NNs), to predict structural responses and failure probabilities. …”
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Development of Procymidone and Difenoconazole Resistance in <i>Alternaria alternata</i>, the Causal Agent of Kiwifruit Brown Spot Disease by Yahui Liu, Manfei Bao, Yanxin Wang, Chuanqing Zhang

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Preliminary Study to Determine the Key Limiting Indicator of Cropland Soil Quality on the Tibetan Plateau by Dianqing Gong, Zhaofeng Wang, Yili Zhang, Bo Wei, Binghua Zhang, Xiaoyang Hu, Min Deng, Changjun Gu

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The impact of sound land use management to reduce runoff by Z.A. Buisan, A.E. Milano, P.D. Suson, D.S. Mostrales, C.S. Taclendo, J.G. Blasco

“…This can, however, result to increase in close canopy forest (112.3%), grassland (125.7%), and open forest (4.3%). …”
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Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas by ZHAO Zejin, SUN Wei, ZHOU Bin, ZHANG Xuan, WANG Gaoxu, WU Wei, LI Wenjie, YAO Ye

“…For hydrological forecasting in karst areas,existing research mainly uses hydrological models based on physical mechanisms,while rare research focuses on machine learning models.To explore the applicability of machine learning models in karst areas, this paper utilizes the LSTM model and random forest model to simulate the daily runoff and field floods at Tangdian hydrological station,using the Shadian River basin in Yunnan Province as the study area.The modified Xin'anjiang model for karst areas is taken as a reference.The results show that both the machine learning model and the modified Xin'anjiang model have achieved good results in simulating the daily runoff process, with the LSTM model showing better simulation results.In the simulation of floods,the modified Xin'anjiang model achieves Class A forecast accuracy.The machine learning models have better forecast results for the 6-hour forecasting period than the modified Xin'anjiang model,while the forecast results for the 24-hour forecasting period do not meet the accuracy requirements of the forecast operation.The study provides a reference for hydrological forecasting in karst areas by studying the characteristics and forecasting accuracy of two machine learning models and a hydrologic model.…”
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Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision by Janni Yuval, Paul A. O'Gorman, Chris N. Hill

“…The NN parameterization leads to stable simulations that replicate the climate of the high‐resolution simulation with similar accuracy to a successful random‐forest parameterization while needing far less memory. …”
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Enhanced Satellite Monitoring of Dryland Vegetation Water Potential Through Multi‐Source Sensor Fusion by J. Du, J. S. Kimball, J. S. Guo, S. A. Kannenberg, W. K. Smith, A. Feldman, A. Endsley

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Natural Model based Design in Context: an Effective Method for Environmental Problems by Eric D. Kameni, Theo P. van der Weide, Wouter T. de Groot

“…Analyzing complex problem domains is not easy. Simulation tools support decision makers to find the best policies. …”
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