Auditory risk recognition is socially transmitted across territory borders in wild birds by Jakub Szymkowiak

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Site Specific Stem Volume Models for Pinus patula and Pinus oocarpa by Herbert Malata, Elisha S. Ngulube, Edward Missanjo

“…Sustainable management of timber forests requires availability and adequacy of models for accurate estimation of tree volumes. …”
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Enhanced antioxidant performance of phlorotannin compounds from brown seaweed Sargassum tenerrimum via nanoliposomal encapsulation by Esmat Mohammadi, Bahareh Shabanpour, Parastoo Pourashouri, Vahide Payamnoor, Salim Sharifian

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A Postcolonial Insight into African Onomastics in Europhone Translation: A study of D. O. Fagunwa’s Selected Yoruba Narrative Names by Damola E. Adeyefa

“…Fagunwa’s Yoruba novels – Ògbójú Ọdẹ nínú Igbó Irúnmalẹ̀ (2005) and Ìrèké-Oníbùdó (2005) –and their French translations – Le preux chasseur dans la forêt infestée de démons (1989) and La fortune sourit aux audacieux(1989) – by Olaoye Abioye respectively; as well as Louis Camara’s, an Ivorian francophone, translation of Soyinka’s translation The Forest of a Thousand Daemons (1982); originally from Fagunwa’s Ogboju into French-- La Forêt aux Mille Demons (2010). …”
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Law diversities for climate change: legal pluralism and climate governance in Indonesia by Muhammad Insan Tarigan, Raisha Hafandi

“…Therefore, the formulation of Indonesia's NDC policy documents, especially those related to the forest and land use (FOLU) sector, is recommended to increase the inclusiveness of local communities, local governments, and non-governmental organizations. …”
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DRG2 as a Biomarker to Enhance the Predictive Efficacy of PD-L1 Immunohistochemistry Assays by Muralidharan Mani, Seong Hee Choi, Hyuk Nam Kwon, Jeong Woo Park

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Assessing Wildfire Risk in South Korea Under Climate Change Using the Maximum Entropy Model and Shared Socioeconomic Pathway Scenarios by Jukyeong Choi, Heemun Chae

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Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms by Dong Duan, Xiaojing Feng, Ruizhe Zhang, Xiaoyu Chen, Hongzhi Zhang

“…The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission.…”
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Spatial-Temporal Evolution Characteristics and Influencing Factors of Vegetation NDVI in Hebei Province by LIAN Xi, CHENG Yao, YUAN Jidong, YUAN Zeshen

“…In order to explore the vegetation growth status and influencing factors,Hebei Province is taken as the research area.Based on satellite remote sensing data and meteorological data,the Mann-Kendall trend test,Sen slope,and partial correlation analysis are used to explore the temporal and spatial distribution characteristics of vegetation in Hebei Province,and the influencing factors of vegetation NDVI change in Hebei Province are analyzed by combining the correlation among NDVI,climatic factors,and human activities.The results show that ① the NDVI value of vegetation in Hebei Province from 2001 to 2020 shows an overall growth trend,with an average annual growth rate of 2.84×10^-3.Specifically,it shows a rapid growth trend from 2001 to 2005 and a slow growth trend from 2005 to 2020, with fluctuations.②The NDVI value of vegetation in the study area increases gradually from south to north,and the vegetation improvement area (81.71%) is greater than that in the degraded area (13.79%),and the improvement area is mainly concentrated in Chengde and Zhangjiakou City.③Human activities (GDP) are one of the main influencing factors affecting the growth of NDVI,followed by climatic factors (precipitation and temperature).The change in GDP has a significant impact on vegetation growth,and the improvement of vegetation in Hebei Province is mainly related to forest land and grassland.Among the climatic factors,precipitation is the main influencing factor.…”
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FARMER-HERDERS CONFLICTS IN NIGERIA: ITS CHALLENGES AND INTERVENTIONS by M.O. Mazeli, Binta Side

“… Climate change has forced herders to move out of the desert to the forest, searching for a greener posture but not ethnic war. …”
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Generating the Flood Susceptibility Map for Istanbul with GIS-Based Machine Learning Algorithms by Zehra Koyuncu, Ömer Ekmekcioğlu

“…It is worth mentioning that this is the first time this approach has been used for flood hazard mapping studies in Turkey. Random forest (RF), stochastic gradient boosting (SGB), and XGBoost algorithms were used. …”
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A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets by Idongesit E. Eteng, Udeze L. Chinedu, Ayei E. Ibor

“…Thus, we propose an ensemble approach that stacks five classifiers - Support Vector Machine, Decision Trees, Random Forests, Gaussian Na¨?ve Bayes, and k-Nearest Neighbour, and uses the Logistic Regression meta-classifier to make predictions based on a stacking algorithm and novel pipeline. …”
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Application of machine learning in asphalt and concrete material testing: A comprehensive review by Khorshidi Meisam, Dave Eshan, Sias Jo

“…The review provides a comprehensive comparison of various ML models, including Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), Gradient Boosting (GB), and Gaussian Process Regression (GPR), assessing their strengths and limitations in predicting material performance. …”
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Factors associated with vaccine default in Southern Ghana based on data from the RTSS malaria vaccine trial in Cape Coast by Vincent Bio Bediako, Josephine Akua Ackah, Theophilus Junior Yankey, Joshua Okyere, Emmanuella Acheampong, Bernard Afriyie Owusu, Wonder Agbemavi, Adanna Uloaku Nwameme, Edward Mberu Kamau, Emmanuel Asampong

“…Classification models (Binary logistic regression and Random Forest) were performed to identify critical socio-demographic, health and RTS, S related predictors. …”
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Microstructural Features of Freezing and Thawing-Creep Damages for Concrete Mixed with Fly Ash by Bing Li, Lian-ying Zhang, Ming Li, Hai Pu, Chao Ma, Pei-tao Qiu

“…The microfracture morphology of the concrete was found to include five types of brittle fractures—river, step, cascade, hemispherical, and irregular patterns—and two types of ductile fractures—dimple and peak forest patterns. Two sets of experiments in which (1) the fly ash content (m=35%) was kept constant and the number of freeze-thaw cycles increased, and (2) the number of freeze-thaw cycles (n=120) was kept constant, and the fly ash content was increased were carried out. …”
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Development and Application of Urban Landslide Vulnerability Assessment Methodology Reflecting Social and Economic Variables by Yoonkyung Park, Ananta Man Singh Pradhan, Ungtae Kim, Yun-Tae Kim, Sangdan Kim

“…The proposed methodology was developed based on the landslide susceptibility maps that Korean Forest Service utilizes to identify landslide source areas. …”
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Avicennia alba, an Additional Potential Carbon Sequester in Mangrove Ecosystems by Nur Hasyimah Ramli, Nursyazni Abdul Rahim, Nur Azimah Osman, Norrizah Jaafar Sidik, Nabilah Mawi, Nor Bazilah Razali, Farah Ayuni Farinordin

“… Mangrove forests have exceptional carbon sequestration capacity for mitigating climate change impacts. …”
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Estimation of Landslides and Road Capacity after August 8, 2017, MS7.0 Jiuzhaigou Earthquake Using High-Resolution Remote Sensing Images by Xiao Fu, Qing Zhu, Chao Liu, Naiwen Li, Wenhua Zhuang, Zhengli Yang, Heng Lu, Min Tang

“…As the earthquake-stricken area is located in the mountainous region with forest and low residential density, the main damage is to vegetation and roads by earthquake-triggered landslides. …”
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Machine Learning and Multi-Omics Integration to Reveal Biomarkers and Microbial Community Assembly Differences in Abnormal Stacking Fermentation of Sauce-Flavor <i>Baijiu</i> by Shuai Li, Yueran Han, Ming Yan, Shuyi Qiu, Jun Lu

“…This study used three machine learning models (Logistic Regression, KNN, and Random Forest) combined with multi-omics (metagenomics and flavoromics) to develop a classification model for abnormal fermentation. …”
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Improving catalysts and operating conditions using machine learning in Fischer-Tropsch synthesis of jet fuels (C8-C16) by Parisa Shafiee, Bogdan Dorneanu, Harvey Arellano-Garcia

“…Moreover, various machine-learning models (Random Forest (RF), Gradient Boosted, CatBoost, and artificial neural networks (ANN)) were evaluated to predict CO conversion and C8-C16 selectivity. …”
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