Development of Quantitative Structure–Anti-Inflammatory Relationships of Alkaloids by Cristian Rojas, Doménica Muñoz, Ivanna Cordero, Belén Tenesaca, Davide Ballabio

“…The performance of the models was quantified by means of the non-error rate (<i>NER</i>) statistical parameter.…”
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AI-driven diagnosis and health management of autonomous electric vehicle powertrains: An empirical data-driven approach by Hicham El hadraoui, Adila El maghraoui, Oussama Laayati, Erroumayssae Sabani, Mourad Zegrari, Ahmed Chebak

“…A suite of supervised ML classifiers, decision trees, gradient-boosted trees, random forests, and artificial neural networks are evaluated using experimental vibration data collected from bench-mounted IMs operating under three distinct conditions: healthy, bearing fault, and static eccentricity. …”
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Classification of grassland community types and palatable pastures in semi-arid savannah grasslands of Kenya using multispectral Sentinel-2 imagery by James M. Muthoka, Pedram Rowhani, Edward E. Salakpi, Heiko Balzter, Heiko Balzter, Alexander S. Antonarakis

“…Sentinel-2 imagery was processed using MESMA to classify the fractional cover of four key grass species (Cynodon, Setaria, Themeda, and Kunthii) along with non-grass land cover types (bare ground, forests, shrubs, and water). An iterative endmember selection method optimized the classification, achieving a root mean square error (RMSE) of 23.5% and a 6% improvement in the overall accuracy compared to the unoptimized models. …”
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A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan, Zhixin Qin

“…Furthermore, we propose an anomaly detection framework based on STL decomposition and dual lonely forests. This framework improves sensitivity to sudden feature changes and detection robustness through a weighted fusion strategy of global trends and residual anomalies. …”
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Identification of Patients With Congestive Heart Failure From the Electronic Health Records of Two Hospitals: Retrospective Study by Daniel Sumsion, Elijah Davis, Marta Fernandes, Ruoqi Wei, Rebecca Milde, Jet Malou Veltink, Wan-Yee Kong, Yiwen Xiong, Samvrit Rao, Tara Westover, Lydia Petersen, Niels Turley, Arjun Singh, Stephanie Buss, Shibani Mukerji, Sahar Zafar, Sudeshna Das, Valdery Moura Junior, Manohar Ghanta, Aditya Gupta, Jennifer Kim, Katie Stone, Emmanuel Mignot, Dennis Hwang, Lynn Marie Trotti, Gari D Clifford, Umakanth Katwa, Robert Thomas, M Brandon Westover, Haoqi Sun

“…We trained and compared the performance of logistic regression, random forests, and RoBERTa models. We measured model performance using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). …”
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Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe by C. Poppe Terán, C. Poppe Terán, C. Poppe Terán, B. S. Naz, B. S. Naz, H. Vereecken, H. Vereecken, R. Baatz, R. A. Fisher, H.-J. Hendricks Franssen, H.-J. Hendricks Franssen

“…GPP was underestimated by CLM5<span class="inline-formula">_PFT</span>, especially in deciduous forests (bias of <span class="inline-formula">−43.76</span> %). …”
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Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…Several ML models, including Random Forests (RF), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBoost), Artificial Neural Networks (ANN), Decision Trees (DT), and Linear Regression (LR), were trained and evaluated. …”
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First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors by Celeste McFarlane, Paulson Kasereka Isevulambire, Raymond Sinsi Lumbuenamo, Arnold Murphy Elouma Ndinga, Ranil Dhammapala, Xiaomeng Jin, V. Faye McNeill, Carl Malings, R. Subramanian, Daniel M. Westervelt

“…The raw PurpleAir data from September 2019 through February 2020 strongly correlated with the BAM-1020 measurements (R2 = 0.88) but also exhibited a mean absolute error (MAE) of approximately 14 µg m−3. Employing two calibration models, namely, multiple linear regression and random forests, decreased the MAE to 3.4 µg m−3 and increased R2 to 0.96. …”
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Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture by Mo Zhang, Yong Ge, Jianghao Wang

“…The results showed that parameter calibration significantly enhanced sub-surface soil moisture simulation, reducing root mean square error (RMSE) by 61.2 % to 69.8 %, from 0.09 to 0.03. …”
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Can machine-learning algorithms improve upon classical palaeoenvironmental reconstruction models? by P. Sun, P. B. Holden, H. J. B. Birks, H. J. B. Birks

“…The first layer applies three different ensemble machine-learning models (random forests, extra random trees, and LightGBM), trained on the modern taxon assemblage and associated environmental data to make reconstructions based on the three different models, while the second layer uses multiple linear regression to integrate these three reconstructions into a consensus reconstruction. …”
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Status of the Sapo National Park elephant population and implications for conservation of elephants in Liberia by Yaw Boafo, Massalatchi Sani

“… Dung counts are used to estimate abundance and distribution of elephants in tropical forests and give precise population estimates (Barnes, 2002). …”
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Aided Greenway Design Approach Based on Internet Big Data and AIGC Fine-Tuning Model by Yifan WU, Lu MENG, Liang LI

“…In addition, the use of various fine-tuning models can realize the tasks of generating error control and drawing style migration.ConclusionThe approach proposed in the research has some limitations. …”
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Inverse modelling of New Zealand's carbon dioxide balance estimates a larger than expected carbon sink by B. Bukosa, S. Mikaloff-Fletcher, G. Brailsford, D. Smale, E. D. Keller, E. D. Keller, W. T. Baisden, M. U. F. Kirschbaum, D. L. Giltrap, L. Liáng, S. Moore, R. Moss, S. Nichol, J. Turnbull, A. Geddes, D. Kennett, D. Hidy, Z. Barcza, L. A. Schipper, A. M. Wall, S.-I. Nakaoka, H. Mukai, A. Brandon

“…The largest differences are in New Zealand's South Island, in regions dominated by mature indigenous forests, generally considered to be near equilibrium, and certain grazed pasture regions. …”
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