‘Tobolyak’: an oat cultivar for universal use by M. N. Fomina, Yu. S. Ivanova, O. A. Pay, N. A. Bragin

“…The average grain yield for the years of study (2014–2019) under the environmental conditions of the northern forest-steppe (Tyumen Province) was 5.88 t/ha (+0.41 t/ha to the reference). …”
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Factors associated with contracting border malaria: A systematic and meta-analysis. by Tichaona Fambirai, Moses Chimbari, Tafadzwa Mhindu

“…Pooled odds ratios, inverse variance statistic (I2), Luis Furuya-Kanamori (LFK) index, and forest plot were computed. Findings from this study suggest night outdoor activities (POR 2.87 95% CI, 1.17 7,01), engaging in forestry activities (POR 2.76 95% CI, 2.08 3.67), working in mines (POR 197 95% CI, 175 22171), access to poor housing structure (POR 3.42 95% CI, 2.14 5.46), and cross-border movement (POR 50.86 95% CI, 12.88 200.85) none use of insecticide-treated nets (POR 5.09 95% CI, 2.44 10.63) were all significantly associated with contracting malaria within border regions. …”
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A New Approach to Determine the Total Airborne N Input into the Soil/Plant System Using 15N Isotope Dilution (ITNI): Results for Agricultural Areas in Central Germany by Rolf W.B. Russow, Frank Bahme, Heinz-Ulrich Neue

“…The atmospheric deposition of nitrogen (N) in the environment is of great concern due to its impact on natural ecosystems including affecting vegetation, reducing biodiversity, increasing tree growth in forests, and the eutrophication of aquatic systems. …”
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Spatial and Temporal Relationships Between Roe and Red Deer in an Alpine Area by Valerio Donini, Luca Pedrotti, Francesco Ferretti, Elisa Iacona, Lucrezia Lorenzetti, Francesca Cozzi, Luca Corlatti

“…Spatial analysis suggested a higher probability of roe deer presence in forested habitats, at lower elevations, and in areas with gentler slopes. …”
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Prion forensics: a multidisciplinary approach to investigate CWD at an illegal deer carcass disposal site by Marc D. Schwabenlander, Jason C. Bartz, Michelle Carstensen, Alberto Fameli, Linda Glaser, Roxanne J. Larsen, Manci Li, Rachel L. Shoemaker, Gage Rowden, Suzanne Stone, W. David Walter, Tiffany M. Wolf, Peter A. Larsen

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Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score by Mike Nsubuga, Timothy Mwanje Kintu, Helen Please, Kelsey Stewart, Sergio M. Navarro

“…Methods Data from 4,109 trauma patients at Soroti Regional Referral Hospital, a rural hospital in Uganda, were used to train and evaluate four ML models: Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). …”
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On-Farm Experimentation with Improved Maize Seed and Soil Amendments in Southern Ghana: Productivity Effects in Small Holder Farms by E. Marfo-Ahenkora, K. J. Taah, E. Owusu Danquah, E. Asare-Bediako

“…To contribute to addressing these challenges in maize production, two on-farm experiments were conducted each in the semi deciduous forest and coastal savannah agroecological zones (AEZs) of Ghana during the major and minor cropping seasons of 2017. …”
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Clasificación de uso y cobertura del suelo a través de algoritmos de aprendizaje automático: revisión bibliográfica by René Tobar-Díaz, Yan Gao, Jean François Mas, Víctor Hugo Cambrón-Sandoval

“…Para dicha revisión se utilizaron únicamente artículos científicos publicados entre el año 2000 al 2020 y que consideraran alguno de los siguientes algoritmos para la clasificación de UCS: k vecinos más cercanos (K-nearest neighbor-KNN), bosque aleatorio (random forest-RF), máquina de soporte de vectores (support vector machine-SVM), redes neuronales artificiales (artificial neural network-ANN) y árboles de decisión (decision trees-DT). …”
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A dataset from the Cryptogamia-Lichenes section of the Herbarium Universitatis Taurinensis (TO) by Rosanna Piervittori, Deborah Isocrono, Enrica Matteucci, Mariagrazia Morando, Luca Dessì, Laura Guglielmone, Heimo Rainer, Stefano Martellos, Andrea Moro, Pier Luigi Nimis, Matteo Conti, Sergio Enrico Favero-Longo

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UAV-Multispectral Based Maize Lodging Stress Assessment with Machine and Deep Learning Methods by Minghu Zhao, Dashuai Wang, Qing Yan, Zhuolin Li, Xiaoguang Liu

“…The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. …”
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Impact of tourist and recreational activities on the indicators of soil-ecological monitoring of the adjacent territory of Lake Teletskoe (Altai Mountains) by Olga A. Elchininova

“…Because of the tourist activities in the coastal zone of the mountain-forest belt of Lake Teletskoe, a developed path network transforming its natural ecosystems appeared. …”
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The Variations of Satellite-Based Ecosystem Water Use and Carbon Use Efficiency and Their Linkages with Climate and Human Drivers in the Songnen Plain, China by Bo Li, Fang Huang, Shuai Chang, Ning Sun

“…Deciduous broad-leaved forests had the highest average WUE but the lowest CUE. …”
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Dissolved Gas Analysis for Fault Prediction in Power Transformers Using Machine Learning Techniques by Sahar R. Al-Sakini, Ghassan A. Bilal, Ahmed T. Sadiq, Wisam Abed Kattea Al-Maliki

“…The MLMs used for transformer fault diagnosis were random forest (RF), backpropagation neural network (BPNN), K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), and Naive Bayes (NB). …”
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Risk Factors for Contralateral Occult Papillary Thyroid Carcinoma in Patients with Clinical Unilateral Papillary Thyroid Carcinoma: A Case-Control Study by Liu Yihao, Li Shuo, Xi Pu, Wang Zipeng, Sun Hanlin, Chang Qungang, Wang Yongfei, Yin Detao

“…Univariate and multivariate logistic regression analyses were conducted to assess the association between COPTC and clinical-pathological characteristics, as well as the relation between the diameter of the occult lesions and predictors. The forest plot was plotted to visualize the prediction factors from the output of the multivariate regression analysis. …”
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« Une femme en Côte d’Ivoire, une femme au Burkina Faso » by François Ruf

“…Côte d’Ivoire, with its diversity of forest and savannah regions, and its neighbour to the north, Burkina Faso, are historically linked to the creation and growth of the Ivorian village plantation economy based on the ‘coffee-cocoa’ pairing. …”
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A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway by Xian Liu, Jian Lu, Zeyang Cheng, Xiaochi Ma

“…In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. …”
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Sensitivity assessment and simulation of ecosystem services in response to land use change in arid regions: Empirical evidence from Xinjiang, China by Xiaoyun Li, Chunsheng Wu

“…The expense was the losses of forest land and grassland, with reductions of 5.84% and 4.15%, respectively. …”
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Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study by Kanika Godani, Vishma Prabhu, Priyanka Gandhi, Ayushi Choudhary, Shubham Darade, Rupal Kathare, Prathiba Hande, Ramesh Venkatesh

“…Six supervised ML models—ANCOVA, Random Forest (RF) regression, K-Nearest Neighbor, Support Vector Machine, Extreme Gradient Boosting, and Lasso regression—were trained using an 80:20 training-to-testing split. …”
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Ensemble machine learning-based extrapolation of Penman-Monteith-Leuning evapotranspiration data by Vahid Nourani, Ramin Ahmadi, Yongqiang Zhang, Dominika Dąbrowska

“…This study applies several machine learning (ML) models—including a backpropagation neural network (BPNN), an adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and long short-term memory (LSTM)—to simulate PML-V2 ET in the Ahar Chay basin, Northwestern Iran. The Seto mixed forest site in Japan, characterized by a contrasting ecosystem, served as a cross-validation site to further validate the methodology. …”
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HMOX1 as a potential drug target for upper and lower airway diseases: insights from multi-omics analysis by Enhao Wang, Shazhou Li, Yang Li, Tao Zhou

“…Candidate genes were further screened using Gene Set Enrichment Analysis (GSEA) and Random Forest (RF) algorithms. Causal inference between candidate genes and upper and lower airway diseases (CRSwNP, allergic rhinitis (AR), and asthma (AS)) was conducted using bidirectional two-sample Mendelian randomization (TwoSampleMR) analysis. …”
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