Towards climate-resilient conservation: Integrating genetics and environmental factors in determining adaptive units of a xeric shrub by Yong-Zhi Yang, Pei-Wei Sun, Chong-Yi Ke, Min-Xin Luo, Jui-Tse Chang, Chien-Ti Chao, Run-Hong Gao, Pei-Chun Liao

“…We analyzed RAD-seq data from 217 samples across 19 populations, integrating ecological niche modeling (ENM) and GradientForest (GF) to pinpoint adaptive genetic variations. …”
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Prediction of Multidimensional Poverty Status With Machine Learning Classification at Household Level: Empirical Evidence From Tanzania by Ngong'Ho Bujiku Sende, Snehanshu Saha, Leon Ruganzu, Saibal Kar

“…A variety of supervised machine-learning algorithms such as RBF Kernel in SVM, Linear Kernel in SVM, Polynomial Kernel in SVM, Random Forest, Logistic regression classifier, Decision tree, Gradient Boosting, K-Nearest Neighbours Classifier, Naïve Bayes Classifier, Artificial Neuron Network and Ensemble Learning Model were implemented to predict multidimensional poverty status for each dataset. …”
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Next-gen agriculture: integrating AI and XAI for precision crop yield predictions by R. N. V. Jagan Mohan, Pravallika Sree Rayanoothala, R. Praneetha Sree

“…Advanced regression models, including Decision Tree Regressor, Random Forest Regressor, and LightGBM Regressor, achieved exceptional predictive performance, with R² scores reaching 0.92, mean squared errors as low as 0.02, and mean absolute errors of 0.015. …”
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Structure and Composition of Mangrove Vegetation on Kelasa Island: Dominance of Rhizophora apiculata and Its Implications for Coastal Ecosystem Sustainability by Akhrianti Irma, Oka Arizona Mohammad, Harapan Putera Batubara Geothani

“…The mangrove community is largely dominated by R. apiculata, indicating a trend towards monospecific dominance with robust regeneration. The forest spans approximately 2.57 ha on the island’s eastern coastline, characterized by sandy coral fronts and muddy-rocky substrates. …”
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Compound events in Germany in 2018: drivers and case studies by E. Xoplaki, E. Xoplaki, F. Ellsäßer, F. Ellsäßer, J. Grieger, K. M. Nissen, J. G. Pinto, M. Augenstein, T.-C. Chen, T.-C. Chen, H. Feldmann, P. Friederichs, D. Gliksman, D. Gliksman, L. Goulier, K. Haustein, K. Haustein, J. Heinke, L. Jach, F. Knutzen, S. Kollet, J. Luterbacher, J. Luterbacher, N. Luther, S. Mohr, S. Mohr, C. Mudersbach, C. Müller, E. Rousi, F. Simon, L. Suarez-Gutierrez, L. Suarez-Gutierrez, L. Suarez-Gutierrez, S. Szemkus, S. M. Vallejo-Bernal, S. M. Vallejo-Bernal, S. M. Vallejo-Bernal, O. Vlachopoulos, F. Wolf

“…We also examine the interannual influence of droughts on surface water and the impact of water scarcity and heatwaves on agriculture and forests. We assess projected changes in compound events at different current and future global surface temperature levels, demonstrating the need for improved quantification of future extreme events to support adaptation planning. …”
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Joint Analysis of Morphological Parameters and In Silico Haemodynamics of the Left Atrial Appendage for Thrombogenic Risk Assessment by Maria Isabel Pons, Jordi Mill, Alvaro Fernandez-Quilez, Andy L. Olivares, Etelvino Silva, Tom de Potter, Oscar Camara

“…Statistical analysis, regression models, and random forests were used to analyse the differences between morphological and haemodynamics parameters, extracted from computational simulations built on 3D rotational angiography images, between patients with and without transient ischemic attack (TIA) or cerebrovascular accident (CVA). …”
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Comparative Study of Bulgarian Linden Honey (<i>Tilia</i> sp.) by Anastasiya Yankova-Nikolova, Desislava Vlahova-Vangelova, Desislav Balev, Nikolay Kolev, Stefan Dragoev, Biljana Lowndes-Nikolova

“…An overall assessment ranks linden honey from the Northern Central region, the richest in linden forests, as the highest quality among the six studied regions. …”
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The Wildcat That Lives in Me: A Review on Free-Roaming Cats (<i>Felis catus</i>) in Brazil, Focusing on Research Priorities, Management, and Their Impacts on Cat Welfare by Luana S. Gonçalves, Daiana de Souza Machado, Maria Eduarda Caçador, Giovanne Ambrosio Ferreira, Christopher R. Dickman, Maria Camila Ceballos, Fabio Prezoto, Aline Cristina Sant’Anna

“…More studies were conducted in Brazilian mainland areas (<i>n</i> = 23)—notably in Atlantic Forest—than on islands (<i>n</i> = 11). The review highlights potential impacts of cats on wildlife. …”
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The use of nano-structured cellulose to improve plywood: A review by L.C. Lirya Silva, F.O. Lima, S.N. Monteiro, A.R.G. Azevedo, A.L. Christoforo, B.S. Ferreira, D. Goveia, C.I. de Campos

“…The great availability of forest resources in Brazil enables the constant growth of industrial sectors that employ wood as a raw material. …”
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Feature selection based on Mahalanobis distance for early Parkinson disease classification by Mustafa Noaman Kadhim, Dhiah Al-Shammary, Ahmed M. Mahdi, Ayman Ibaida

“…Similarly, on the ''Parkinson Dataset with Replicated Acoustic Features'', the feature set was reduced from 45 to 18 features, achieving accuracy improvements ranging from 1.38 % to 13.88 %, with the Random Forest (RF) classifier achieving the best accuracy of 95.83 %. …”
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Harnessing synergy of machine learning and nature-inspired optimization for enhanced compressive strength prediction in concrete by Abba Bashir, Esar Ahmad, Shashivendra Dulawat, Sani I. Abba

“…This study assesses nine machine learning models, integrating conventional AI algorithms, such as artificial neural network (ANN), support vector regression (SVR), and random forest (RF) with nature-inspired optimization techniques including chicken swarm optimization (CSO), moth flame optimization algorithm (MFO), and whale optimization algorithm (WOA). …”
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The Impact of Selected Regimens of Chronic HIV‐Antiretroviral Treatment on Glycemic Control Markers and Correlates: A Systematic Review and Meta‐Analysis Protocol by Mlindeli Gamede, Mbulelo Aubrey Sosibo, Nontobeko Gumede, Mluleki Luvuno

“…Additionally, heterogeneity tests will be conducted using both Χ2 and I2 tests, meta‐analysis will be conducted using the Review Manager version 5.4 software (RevMan), and data will be presented in forest plots. Grading of Recommendations Assessment, Development, and Evaluation approach (GRADE) will be used to assess the strength of evidence in eligible reports. …”
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Assessment of environmental degradation and conservation in the Maracanã River Basin, eastern amazon by Lucas Lima Raiol, Yuri Antonio da Silva Rocha, Aline Maria Meiguins de Lima, Andrés Velastegui-Montoya

“…Coastal basins stand out for their continent-estuary interface and connection as corridors of mangrove forests. The Maracanã River Basin (MRB) represents this environment, holding various ecosystem services for the component municipalities, protected areas with highly sensitive environments and water demand, and potential for multiple water uses. …”
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Relationship between landscape and river ecosystem services by M. Dede, S. Sunardi, K.C. Lam, S. Withaningsih

“…As representations of the natural landscape, forests and grasslands have a positive and significant contribution to river ecosystem services. …”
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Species-Level Saltmarsh Vegetation Fractional Cover Estimation Based on Time Series Sentinel-2 Imagery with the Assistance of Sample Expansion by Jinghan Sha, Zhaojun Zhuo, Qingqing Zhou, Yinghai Ke, Mengyao Zhang, Jinyuan Li, Yukui Min

“…We chose the Yellow River Delta as the study area and utilized the time series Sentinel-2 imagery and random forest regression model for species-level FVC estimation with the assistance of FVC-WGAN-generated samples. …”
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Age group classification based on optical measurement of brain pulsation using machine learning by Martti Ilvesmäki, Hany Ferdinando, Kai Noponen, Tapio Seppänen, Vesa Korhonen, Vesa Kiviniemi, Teemu Myllylä

“…ML experiments utilized support vector machines and random forest learners, along with maximum relevance minimum redundancy and principal component analysis for feature selection. …”
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A Landscape-Clustering Zoning Strategy to Map Multi-Crops in Fragmented Cropland Regions Using Sentinel-2 and Sentinel-1 Imagery with Feature Selection by Guanru Fang, Chen Wang, Taifeng Dong, Ziming Wang, Cheng Cai, Jiaqi Chen, Mengyu Liu, Huanxue Zhang

“…These schemes are then optimized for each CHZ using a random forest classifier. The results demonstrate that the landscape-clustering zoning strategy achieves an overall accuracy of 93.52% and a kappa coefficient of 92.67%, outperforming the no-zoning method by 2.9% and 3.82%, respectively. …”
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XAI-Enhanced Machine Learning for Obesity Risk Classification: A Stacking Approach With LIME Explanations by Mohammad Azad, Md Faraz Kabir Khan, Sameh Abd El-Ghany

“…Our proposed model employs an ensemble approach, specifically a stacking algorithm, where the base estimators include the Light Gradient Boosting Machine (LGBM) classifier, the Logistic Regression (LR) classifier, and the Random Forest (RF) Classifier, and the Stochastic Gradient Descent (SGD) classifier is selected as the final estimator. …”
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Drivers of soil biophysical processes along an elevational gradient at Pico de Orizaba volcano, Mexico by Guillermo Angeles, Marycruz Álvarez-Jiménez, Fabien Anthelme, Isabelle Barois, José Luis Chaparro Carrillo, Nereyda Cruz-Maldonado, Thibaud Decaens, Martín de los Santos-Bailón, Stéphane Fourtier, Emmanuel García-Gutiérrez, David García-Segura, Rodrigo Gómez-Aguilar, Antonio Gómez-Anaya, Eduardo Hernández, Daniel Hernández-Cáceres, Leonor Jiménez, Beatriz E. Marín-Castro, Luis Merino-Martín, Marco A. Morales-Martínez, Edwin U. Monfil-León, René A. Palestina, Carlos Pimentel-Reyes, Ricardo Quiroz Reyes, Frédérique Reverchon, Hervé Rey, Catherine Roumet, Christina D. Siebe-Grabach, Katrin Sieron, Víctor Vázques Reyes, Monique Weemstra, Alexia Stokes

“…Data are provided for 6 elevations (3400–4600 m) ranging from pine forest to alpine prairie. We focused on soil biophysical properties beneath several keystone or community-structuring plant species with different growth forms: (1) tree (Pinus hartwegii Lindl.); shrub (Oxylobus arbutifolius (Kunth) A. …”
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Artificial intelligence applied to bed regulation in Rio Grande do Norte: Data analysis and application of machine learning on the "RegulaRN Leitos Gerais" platform. by Tiago de Oliveira Barreto, Fernando Lucas de Oliveira Farias, Nicolas Vinícius Rodrigues Veras, Pablo Holanda Cardoso, Gleyson José Pinheiro Caldeira Silva, Chander de Oliveira Pinheiro, Maria Valéria Bezerra Medina, Felipe Ricardo Dos Santos Fernandes, Ingridy Marina Pierre Barbalho, Lyane Ramalho Cortez, João Paulo Queiroz Dos Santos, Antonio Higor Freire de Morais, Gustavo Fontoura de Souza, Guilherme Medeiros Machado, Márcia Jacyntha Nunes Rodrigues Lucena, Ricardo Alexsandro de Medeiros Valentim

“…The results showed better accuracy (87.77%) and recall (87.77%) for the XGBoost model, and higher precision (87.85%) and F1-Score (87.56%) for the Random Forest and Gradient Boosting models, respectively. …”
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