Sierra Espuña (Librillos, 2023) by Miguel Ángel González Espinosa

“…It presents a green mantle composed of a pine forest as a result of the repopulation undertaken by Ricardo Codorníu more than a century ago, with species such as Aleppo pine, maritime pine, black pine, laricio pine and other pine varieties. …”
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Automated Bitcoin Trading dApp Using Price Prediction from a Deep Learning Model by Zhi Zhan Lua, Chee Kiat Seow, Raymond Ching Bon Chan, Yiyu Cai, Qi Cao

“…By leveraging random forest (RF), long short-term memory (LSTM), and bi-directional LSTM (Bi-LSTM) models, the cryptocurrency trading system is equipped with strong predictive capacity and is able to optimize trading strategies for Bitcoin. …”
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Exploration of transfer learning techniques for the prediction of PM10 by Michael Poelzl, Roman Kern, Simonas Kecorius, Mario Lovrić

“…Common ML models such as Random Forests, Multilayer Perceptrons, Long-Short-Term Memory, and Convolutional Neural Networks are explored to predict particulate matter in both cities. …”
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Effect of Weathering on Cd Mobilization in Different Sedimentary Bedrock Soils by Yongqiang Ning, Minglong Yang, Chutong Liu, Jiazhong Huang, Tianyi Wang, Yong Pang, Quanheng Li

“…The results of major element oxides (K₂O, MgO, Na₂O, Fe₂O₃, and CaO) imply that Cd in soil primarily stems from the weathering of bedrocks. However, random forest analysis reveals that the soil formation processes of greywacke, mudstone, and marl lead to the loss of Cd in the soil, while those of shale and limestone result in the input of Cd into the soil. …”
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Evaluation of extracts from Phyllostachys makinoi for their antibacterial and accelerated wound healing potential by Te-Hua Liu, Ju-Yun Wu, Meng-Chun Cheng, Bang-Yuan Chen, Tsung-Yu Tsai

“…Abstract Phyllostachys makinoi, an endemic bamboo species in Taiwan, is underutilized, despite its rich forest resources. Known for its antioxidant, anti-inflammatory, and antibacterial properties, this study explores the antimicrobial, anti-inflammatory, and wound-healing activities of P. makinoi extracts. …”
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Did Turkey Experience Reductions in Air Pollution During The Covid-19 Lockdown and Partial Lockdown? by İsmet Yener, Kazım Onur Demirarslan

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Land Use Changes and Their Effects on the Value of Ecosystem Services in the Small Sanjiang Plain in China by Jing Chen, Bo-Ming Sun, Dan Chen, Xin Wu, Long-Zhu Guo, Gang Wang

“…We found that cropland sprawl was predominant and occurred in forest, wetland, and grassland areas in the small Sanjiang plain from 1980 to 2010. …”
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Waterbirds habitat mapping using unmanned aerial vehicle in Belawan Mangrove Ecosystems, North Sumatera, Indonesia by Thoha Achmad Siddik, Fauziyah Laely Nur, Jumilawaty Erni, Marwan Mochammad Akbar

“…The results of mapping using UAVs obtained a mangrove forest waterbird habitat covering an area of 1,01 hectares. …”
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Estimating soil profile salinity under vegetation cover based on UAV multi-source remote sensing by Zhenhai Luo, Meihua Deng, Min Tang, Rui Liu, Shaoyuan Feng, Chao Zhang, Zhen Zheng

“…For depths of 10 ~ 20 cm and 20 ~ 30 cm, the random forest (RF) models, incorporating spectral index and texture data, demonstrated superior accuracy with R2 values of 0.666 and 0.714. …”
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Modeling and Mapping of Aboveground Biomass and Carbon Stock Using Sentinel-2 Imagery in Chure Region, Nepal by Ananta Poudel, Him Lal Shrestha, Niraj Mahat, Garima Sharma, Sahara Aryal, Rupesh Kalakheti, Basanta Lamsal

“…The concerns about climate change in recent decades have heightened the need for effective methods for assessing and reporting forest biomass and Carbon Stocks (CS) at local, national, continental, and global scales. …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). …”
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Self-directed learning versus traditional didactic learning in undergraduate medical education: a systemic review and meta-analysis by Jasmine Aulakh, Hana Wahab, Christine Richards, Satesh Bidaisee, Prakash V. A. K Ramdass

“…Key words used were “self-directed learning” AND “undergraduate medical education.” Forest plots were generated with the Open Meta-analyst Software, comparing SDL and TDL. …”
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Optimum Conditions and LC-ESI-MS Analysis of Phenolic Rich Extract from Eucalyptus marginata L. under Maceration and Ultrasound-Assisted Extraction Methods Using Response Surface M... by Soumaya Hasni, Ghayth Rigane, Hanene Ghazghazi, Hajer Riguene, Amir Bouallegue, Olfa Khedher, Moufida A. Oueslati, Ridha Ben Salem

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Characterization and Machine Learning-Driven Property Prediction of a Novel Hybrid Hydrogel Bioink Considering Extrusion-Based 3D Bioprinting by Rokeya Sarah, Kory Schimmelpfennig, Riley Rohauer, Christopher L. Lewis, Shah M. Limon, Ahasan Habib

“…To address this gap, our research presents predictive models using machine learning (ML) algorithms, including polynomial fit (PF), decision tree (DT), and random forest (RF), to estimate bioink viscosity based on component weights and shear rate. …”
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Pawpaws prevent predictability: A locally dominant tree alters understory beta‐diversity and community assembly by Anna C. Wassel, Jonathan A. Myers

“…We tested these hypotheses in a large, temperate oak‐hickory forest plot containing a locally dominant tree species, pawpaw (Asimina triloba; Annonaceae), an understory tree species that occurs in dense, clonal patches in forests throughout the east‐central United States. …”
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Late Pennsylvanian vegetation dynamics of the Donets Basin, Ukraine by Nataliya Boyarina

“…The Late Pennsylvanian vegetation consisted of plant communities of wetland marattialean fern-dominated forests on coastal lowlands and wetland lycopsid-fern forests on deltaic plains in the Kasimovian as well as wetland marattialean fern-dominated forests with new dominants on coastal lowlands and wetland lycopsid-pteridosperm-calamitalean-fern forests with new dominants on deltaic plains in the early Gzhelian that were formed according to the evolutionary progressive model of phytocoenogenesis under conditions of an expansion of coastal lowlands and deltaic plains inthe long-term period of a relatively stable higher sea level with frequent sea level fluctuations during the late Kasimovian–early-mid-Gzhelian interglacial interval. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
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Effects of Land-Use Dynamics on Soil Organic Carbon and Total Nitrogen Stock, Western Ethiopia by Yitayh Leul, Mohammed Assen, Shimeles Damene, Asmamaw Legass

“…Soil organic carbon (SOC) and total nitrogen (TN) stock are key indicators of soil quality in tropical regions; however, their status is often degraded, especially due to massive deforestation in natural forest areas associated with extensive agricultural land use. …”
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Fine-Grained Building Classification in Rural Areas Based on GF-7 Data by Mingbo Liu, Ping Wang, Peng Han, Longfei Liu, Baotian Li

“…In the supervised classification stage, we compared different classification models, including Extreme Gradient Boosting (XGBoost) and Random Forest classifiers. The best-performing XGBoost model achieved an overall roof type classification accuracy of 88.89%. …”
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