Fire in the Wildland-Urban Interface: Preparing a Firewise Plant List for WUI Residents by Anna L. Behm, Alan J. Long, Martha C. Monroe, Cotton K. Randall, Wayne C. Zipperer, Leslie Annie Hermansen-Baez

“…Extension agents, nursery personnel, landscape architects, or urban foresters may use this publication for preparing local firewise plant lists in the wildland-urban interface (WUI). …”
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Temperature and Humidity Prediction Based on Machine Learning by Xiong Yanqi

“…Support Vector Machine(SVM), Neural Network(NN), and Random Forest(RF). to analyze their accuracy in predicting temperature and humidity. …”
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Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques by Mahmut Bağcı, Pınar Kaya Soylu

“…Accuracy scores show that random forest, logistic regression, and support vector classifiers perform competitively for the ORF range classification in both short- and long-term applications. …”
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Tropical Hardwood Hammocks in Florida by Annisa Karim, Martin B. Main

“…Today, the term hammock is used in Florida to describe forest habitats that are typically higher in elevation than surrounding areas and that are characterized by hardwood forests of broad-leaved evergreens. …”
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Tropical Hardwood Hammocks in Florida by Annisa Karim, Martin B. Main

“…Today, the term hammock is used in Florida to describe forest habitats that are typically higher in elevation than surrounding areas and that are characterized by hardwood forests of broad-leaved evergreens. …”
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Point Process applied to fire outbreaks in São Paulo by Vinicius Crispim Tavares Monteiro, Manoel Alves de Oliveira, Ricardo Alves de Olinda, Lucas Cardoso Pereira

“…Predictive models based on Poisson processes were fitted, highlighting the most vulnerable areas, particularly in the Atlantic Forest and Cerrado biomes. …”
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Candidate Species for Florida Aquaculture: Gulf Killifish, Fundulus grandis by Shane W. Ramee, Joshua T. Patterson, Cortney L. Ohs, Matthew DiMaggio

“…DiMaggio, and published by the Program in Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, January 2016. …”
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The place of natural resources in the model of transition to a green economy in Azerbaijan by Humbatova Sevil

“…A key component of the green economy index is the “natural resources group” sub-index, which includes 10 sub-indicators: volumes of fresh water, water consumption, oil and gas production, land fund structure, fishing, and forest areas. The author also highlights methodological issues that arise in comparative studies, including the dependence of some indicators' absolute values on the size of the country and geographical location. …”
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Depictions Of Environmental Destruction By British Colonizers In Ole Kulets “The Hunter” and “Vanishing Herds” by Johnson, Ocan, Francis Akena, Adyanga

“…This article examines the ecological legacy of 70 years of British colonial rule in Kenya and to identify some of the ecological challenges faced by the post-colonial Kenyan state, which are reflected in the rapid depletion of the country's forest cover, the alarming loss of the country's wildlife heritage, accentuated by the threat of poaching and human-wildlife conflict. …”
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Environmental, socioeconomic, and sociocultural drivers of monkeypox transmission in the Democratic Republic of the Congo: a One Health perspective by Guangyu Lu, Zeyin Chong, Enyu Xu, Ce Na, Kaixuan Liu, Liying Chai, Pengpeng Xia, Kai Yang, Guoqiang Zhu, Jinkou Zhao, Olaf Müller

“…The GM (1, n) model, based on the proportion of primary forest, index of economic well-being, and mean annual precipitation, predicted the epidemic trend (revealed relative error: 2.69). …”
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Improving machine learning predictions to estimate fishing effort using vessel's tracking data by J. Samarão, A. Moreno, M.B. Gaspar, M.M. Rufino

“…We found that a simple moving average applied to speed, employed as a pre-processing technique using ten neighbouring points, showed up to 3 % improvement in results. Random Forest and XGBoost gave the best performances among the models compared (18 % change), using the variables Latitude, Longitude, Speed, Time, and Month (accuracies near 99 %)(61 % change). …”
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Population Genomics Reveals Elevated Inbreeding and Accumulation of Deleterious Mutations in White Raccoon Dogs by Yinping Tian, Yu Lin, Yue Ma, Jiayi Li, Sunil Kumar Sahu, Jiale Fan, Chen Lin, Zhiang Li, Minhui Shi, Fengping He, Lianduo Bai, Yuan Fu, Zhangwen Deng, Huabing Guo, Haimeng Li, Qiye Li, Yanchun Xu, Tianming Lan, Zhijun Hou, Yanling Xia, Shuhui Yang

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Effective leaf rust resistance genes of wheat in Novosibirsk Province in connection with the variability of the <i>Puccinia triticina</i> population by L. P. Sochalova, N. I. Boyko, A. A. Poteshkina, V. V. Piskarev

“…In 2015–2019, affliction of Thatcher lines (Tc) and cultivars under the disease pressure was assessed, and P. triticina structure in the Ob forest-steppe of Novosibirsk Province was monitored. …”
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Easy Data Augmentation untuk Data yang Imbalance pada Konsultasi Kesehatan Daring by Anisa Nur Azizah, Misbachul Falach Asy'ari, Ifnu Wisma Dwi Prastya, Diana Purwitasari

“…Kemudian, percobaan menyelidiki proses augmentasi kami menggunakan pengklasifikasi Random Forest, Naïve Bayes, dan metode berbasis peningkatan seperti XGBoost dan ADABoost, yang menghasilkan peningkatan akurasi rata-rata sebesar 0,63. …”
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Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach by Hongyang Wang, Junpeng Ding, Shuochen Wang, Long Li, Jinqiu Song, Dongsheng Bai

“…Feature engineering was initially performed, followed by a comparative analysis of five machine learning algorithms (Logistic Regression, SVM, Random Forest, XGBoost, and LightGBM) and the deep learning TabNet model. …”
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Impacts of Recent Climate Trends and Human Activity on the Land Cover Change of the Abbay River Basin in Ethiopia by Asaminew Abiyu Cherinet, Denghua Yan, Hao Wang, Xinshan Song, Tianlin Qin, Mulualem T. Kassa, Abel Girma, Batsuren Dorjsuren, Mohammed Gedefaw, Hejia Wang, Otgonbayar Yadamjav

“…In the thirteen years’ period, forest land and water resource decreased by 3429.62 km2 and 81.45 km2, respectively. …”
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Ten Machine Learning Models for Predicting Preoperative and Postoperative Coagulopathy in Patients With Trauma: Multicenter Cohort Study by Xiaojuan Xiong, Hong Fu, Bo Xu, Wang Wei, Mi Zhou, Peng Hu, Yunqin Ren, Qingxiang Mao

“…Among the ML models, the random forest model was the most effective predictor. …”
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End-to-End Stroke Imaging Analysis Using Effective Connectivity and Interpretable Artificial Intelligence by Wojciech Ciezobka, Joan Falco-Roget, Cemal Koba, Alessandro Crimi

“…The graph convolutional architecture is also compared to legacy methods such as random forest and support vector machine providing a complete benchmark. …”
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SOIL TILLAGE VARIANTS AND SULPHUR FERTILIZATION AFFECT NODULATION, N2 FIXATION AND YIELD OF BAMBARA GROUNDNUT (Vigna subterreana) IN SOUTHEAST NIGERIA by M.A.N. Anikwe, Nnamnani D.E, Ikengannyia E.E, J.C. Eze, Obidike-Ugwu, E. O

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Influence of environmental variables and anthropogenic activities on soda-saline lakes chemistry in northern Tanzania: A remote sensing and GIS approach by Azaria Stephano Lameck, Brian Rotich, Abdalrahman Ahmed, Harison K. Kipkulei, Peto Akos, Emil Boros

“…Land use/land cover changes results indicated dynamics in the various classes with an overall decrease in areas under water bodies (−39.80 %), forests (−22.57 %) and bareland (−36.18) while agricultural land (111.01 %) built-up areas (434.72 %), shrubs and grasses (72.77 %) increased in area coverage over the 23 years study period (2000–2023). …”
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