An artificial intelligence optimization of NOx conversion efficiency under dual catalytic mechanism reaction based on multi-objective gray wolf algorithm by Zhiqing Zhang, Zicheng He, Yuguo Wang, Feng Jiang, Weihuang Zhong, Bin Zhang, Yanshuai Ye, Zibin Yin, Dongli Tan

“…In this study, a fuzzy gray relational analysis coupled with random forest (RF) and back propagation artificial neural network (BP-ANN) model was developed. …”
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The genus Arthrinium (Ascomycota, Sordariomycetes, Apiosporaceae) from marine habitats from Korea, with eight new species by Sun Lul Kwon, Myung Soo Park, Seokyoon Jang, Young Min Lee, Young Mok Heo, Joo-Hyun Hong, Hanbyul Lee, Yeongseon Jang, Ji-Hyun Park, Changmu Kim, Gyu-Hyeok Kim, Young Woon Lim, Jae-Jin Kim

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Does environmental public policy act as a slowdown for urban expansion? A 2012-2023 analysis with Landsat images by Jorge Alberto Escandón-Calderón, Columba Jazmín López-Gutiérrez, Demian Vázquez-Muñoz, Marco Antonio Gálvez-Lomelín, Marcela Rosas-Chavoya

“…Landsat 7 and 8 images were used to perform a supervised classification with Random Forest algorithm with which the surface of different land use classes was estimated for three years 2012, 2018, and 2023. …”
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Ecosystem health assessment based on the V-O-R-S framework for the Upper Ganga Riverine Wetland in India by Alka Yadav, Mitthan Lal Kansal, Aparajita Singh

“…The findings reveal substantial land-use changes during this period, with a 245% increase in built-up and a 41% decline in forest cover. Consequently, the WEHI declined from 0.75 in 2000 to 0.58 in 2020, marking a 23% decrease over the period. …”
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The influences of social support expressed from doctors and disclosed from peers on patient decision-making: an analysis from the online health community by Beibei Yang, Wei Lu, Yan Xuan, Chongqi Hao, Xiaojun Huang

“…We select Haodf.com as our data source and we collected information for all new patients of 2,989 doctors. we use Jieba word segmentation tool and TF-IDF algorithm to segment word and find keywords. Then random forest algorithm is used to classify 750 training texts and 250 test texts. …”
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Internet of things-driven approach integrated with explainable machine learning models for ship fuel consumption prediction by Van Nhanh Nguyen, Nghia Chung, G.N. Balaji, Krzysztof Rudzki, Anh Tuan Hoang

“…Indeed, five different MLs were employed including linear regression, decision tree, random forest, XGBoost, and Gradient Boosting Regression. …”
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Grazing regime rather than grazing intensity affect the foraging behavior of cattle by You Wang, Rui Yu, Xin Li, Ronghao Chen, Jiahui Liu

“…Five machine learning models—XGBoost, Random Forest, Decision Tree, Extra Trees, and CatBoost—were employed to classify cattle's behavior and to assess the impact of grazing strategies on these behaviors. …”
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Contribution of Organic Carbon, Moisture Content, Microbial Biomass-Carbon, and Basal Soil Respiration Affecting Microbial Population in Chronosequence Manganese Mine Spoil by S. Dash and M. Kujur

“…Relative distribution and composition of the microbial population were estimated from six different chronosequence manganese mine spoil (MBO0, MBO2, MBO4, MBO6, MBO8, MBO10) and forest soil (FS). The variation was seen in moisture content (6.494±0.210-11.535±0.072)%, organic carbon (0.126±0.001- 3.469± 0.099)%, MB-C (5.519±1.371- 646.969± 11.428) μg.g-1 of soil. …”
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To Predict the Requirement of Pharmacotherapy by OGTT Glucose Levels in Women with GDM Classified by the IADPSG Criteria by Gülen Yerlikaya, Veronica Falcone, Tina Stopp, Martina Mittlböck, Andrea Tura, Peter Husslein, Wolfgang Eppel, Christian S. Göbl

“…Also, the combination of clinical risk factors (age, BMI, parity, and pharmacotherapy in previous gestation) showed an acceptable predictive accuracy (ROC-AUC: 72.1, 95% CI: 65.0–79.2), which was further improved when glycemic parameters were added (ROC-AUC: 77.5, 95% CI: 71.5–83.9). Random forest analysis revealed the highest variable importance for G0, G60, and age. …”
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Meteorological Analysis of Floods in Ghana by S. O. Ansah, M. A. Ahiataku, C. K. Yorke, F. Otu-Larbi, Bashiru Yahaya, P. N. L. Lamptey, M. Tanu

“…The first episodes of floods caused by heavy rainfall during the major rainy season in 2018 occurred in Accra (5.6°N and 0.17°W), a coastal town, and Kumasi (6.72°N and 1.6°W) in the forest region on the 18th and 28th of June, respectively. …”
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Soil Conservation Benefits of Ecological Programs Promote Sustainable Restoration by Renjie Zong, Nufang Fang, Yi Zeng, Xixi Lu, Zhen Wang, Wei Dai, Zhihua Shi

“…Notably, two critical programs that synergize forest conservation, cropland conversion, and human well‐being in China's less developed regions account for approximately 85% of the soil conservation benefits. …”
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Development, results and prospects of the spring durum wheat breeding in Russia (post-Soviet states) by P. N. Malchikov, M. G. Myasnikova

“…The article outlines a brief historical background on the introduction to cultivation, distribution and breeding of spring durum wheat in the steppe and forest-steppe regions of Eurasia (the countries of the former USSR: Russia, Ukraine, and Kazakhstan). …”
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Metabolites Profiling of Manilkara mabokeensis Aubrév Bark and Investigation of Biological Activities by Xavier Worowounga, Rami Rahmani, Armel-Frederic Namkona, Sylvie Cazaux, Jean-Laurent Syssa-Magalé, Hubert Matondo, Jalloul Bouajila

“…Manilkara mabokeensis Aubrév is a tree that belongs to the Sapotaceae family, native to the tropical forest in Latin America, Asia, Australia, and Africa. …”
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Peptide nucleic acid (PNA) clamps reduce amplification of host chloroplast and mitochondria rRNA gene sequences and increase detected diversity in 16S rRNA gene profiling analysis... by Usman Hussain, Jim Downie, Amy Ellison, Sandra Denman, James McDonald, Marine C. Cambon

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Pregnancy outcomes in patients with psoriasis, psoriatic arthritis, or axial spondyloarthritis receiving ixekizumab by A. Egeberg, L. Iversen, A. B. Kimball, S. Kelly, E. Grace, H. Patel, W. Xu, G. Gallo, E. Riedl, S. R. Feldman

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Improvement in the Accuracy of the Postclassification of Land Use and Land Cover Using Landsat 8 Data Based on the Majority of Segment-Based Filtering Approach by Fajar Yulianto, Gatot Nugroho, Galdita Aruba Chulafak, Suwarsono Suwarsono

“…Three digital classification approaches, namely, maximum likelihood (ML), random forest (RF), and the support vector machine (SVM), were applied to test the improvement in the accuracy of LULC postclassification using the MaSegFil approach, based on annual cloud-free Landsat 8 satellite imagery data from 2019. …”
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Sources of characters useful for breeding in hulless barley by N. V. Tetyannikov, N. A. Bome

“…Their genotypes were evaluated in the northern forest steppe environments of Tyumen Province (2015– 2017) according to the guidelines developed by the N.I. …”
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Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan by John Karongo, Joseph Ivivi Mwaniki, John Ndiritu, Victor Mokaya

“…We use five Machine Learning (ML) techniques, including Random Forest (RF), Decision Tree (DT), Extreme Gradient Boosting (XGboost), Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict 2021 end-of-season sorghum yield in conflict affected Upper Nile and Western Bahr El Gazal states. …”
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Investigation of groundwater quality indices and health risk assessment of water resources of Jiroft city, Iran, by machine learning algorithms by Sobhan Maleky, Maryam Faraji, Majid Hashemi, Akbar Esfandyari

“…The analysis of hydrochemical parameters, including arsenic (As), fluoride (F), nitrate (NO3), and nitrite (NO2), in 408 samples revealed that concentrations of F, NO3, and NO2 were below the WHO standard threshold, but levels of As exceeded the permissible value. The random forest model with the highest accuracy (R 2 = 0.986) was the best prediction model, while logistic regression (R 2 = 0.98), decision tree (R 2 = 0.979), K-nearest neighbor (R 2 = 0.968), artificial neural network (R 2 = 0.955), and support vector machine (R 2 = 0.928) predicted GWQI with lower accuracy. …”
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Improving the perceived contribution of sustainable tourism through the residents’ empowerment and support for tourism development: A different age group context by Lorena Bašan, Ivana Perušina, Marija Ham

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