Jilin Province of China, 1949–1979: History of Regional and Demographic Development by Svetlana B. Makeeva

“…Jilin’s regional and demographic development from 1949 to 1979 was characterized by increased birth and decreased mortality rates, rapid population growth and that of urban areas, accelerated urbanization, and migrations from other provinces to industrial, forest and rural territories of Jilin. …”
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Effect of climate on thermal response in cows of different racial groups in lower tropic by Raúl Andrés Molina-Benavides, Sandra Milena Perilla-Duque, Rómulo Campos-Gaona, Hugo Sánchez-Guerrero, Juan Camilo Rivera-Palacio, Luis Armando Muñoz-Borja, Daniel Ricardo Jiménez-Rodas

“…The information was analyzed using descriptive statistics, correlation matrices and Random Forest models, through the R software. Results. From the physiological data from automatic collection systems, the response variables that would allow the evaluation of thermoregulation processes were analyzed using big data. …”
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Assessing the Effects of Land Use Practices on Land Cover in Bubare Sub-County Rubanda District. by Kembabazi, Naome

“…This study examined the impact of land use practices on land cover in Bubare sub-county, Rubanda district, addressing three main objectives: to identify and characterize land use practices in Bubare, to assess how farming systems influence forest cover, and to evaluate the effectiveness of land use policies in managing local land resources. …”
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Molecular epidemiology of anaplasmosis in small ruminants along a human-livestock-wildlife interface in Uganda by Keneth Iceland, Kasozi, Simon Peter, Musinguzi

“…Small ruminants located at the forest edge (<0.3 km) showed higher A. ovis prevalence than those found inland with infections present in the midland regions associated with increased agricultural activity. …”
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Molecular epidemiology of anaplasmosis in small ruminants along a human-livestock-wildlife interface in Uganda by Keneth Iceland, Kasozi, Susan Christina, Welburn

“…Small ruminants located at the forest edge (<0.3 km) showed higher A. ovis prevalence than those found inland with infections present in the midland regions associated with increased agricultural activity. …”
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Using the alternative model of personality disorders for DSM-5 traits to identify personality types, and the relationship with disordered eating, depression, anxiety and stress by Tanya Gilmartin, Caroline Gurvich, Joanna F. Dipnall, Gemma Sharp

“…A systematic four-step process using hierarchical, k-means, and random forest cluster analyses were used to identify the best fitting cluster solution in the data. …”
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Sierra Espuña (Librillos, 2023) by Miguel Ángel González Espinosa

“…It presents a green mantle composed of a pine forest as a result of the repopulation undertaken by Ricardo Codorníu more than a century ago, with species such as Aleppo pine, maritime pine, black pine, laricio pine and other pine varieties. …”
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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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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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Efficient Feature Selection and Hyperparameter Tuning for Improved Speech Signal-Based Parkinson’s Disease Diagnosis via Machine Learning Techniques by Deepak Painuli, Suyash Bhardwaj, Utku Kose

“…This study investigates 12 machine learning models—logistic regression (LR), support vector machine (SVM, linear/RBF), K-nearest neighbor (KNN), Naïve bayes (NB), decision tree (DT), random forest (RF), extra trees (ET), gradient boosting (GbBoost), extreme gradient boosting (XgBoost), adaboost, and multi-layer perceptron (MLP)—to develop a robust ML model capable of reliably identifying PD cases. …”
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Divergence of alpine plant populations of three Gentianaceae species in the Qinling sky Island by Peng-Cheng Fu, Bing-Jie Mo, He-Xin Wan, Shu-Wen Yang, Rui Xing, Shan-Shan Sun

“…The redundancy and gradient forest analyses revealed that several temperature- and precipitation-related variables mainly contributed to shaping the genetic differentiation among the Qinling populations and others, indicating that the three species exhibited a similar pattern of adaptations to local environments. …”
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Sources and Radiative Impact of Carbonaceous Aerosols Using Four Years Ground-Based Measurements over the Central Himalayas by Priyanka Srivastava, Manish Naja, T. R. Seshadri

“…The role of crop residue burning in northern India and forest fires is shown to be dominant in spring while local heating-purpose emissions dominate in winter. …”
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The process and logical mechanism of agricultural production space contraction in mountainous areas based on actor-network theory：A case study of Lishi Village in Longde County, Ni... by CHEN Kunqiu, CHEN Yunya, LIANG Yajia, ZHENG Yuhan

“…[Results] The study found that: (1) The translation of actor networks at different stages, the entry and exit of heterogeneous actors within the networks, and the transformation of the actor networks goals comprehensively contributed to the contraction of agricultural production space through the combined effects of human and non-human actors. (2) During the transformation of the actor network in Lishi Village, the key actor changed from the local government to the young labor force, and the obligatory point of passage (OPP) changed from “returning farmland to forest and grassland” to “developing specialty farming to maximize economic income”. (3) The agricultural production space in Lishi Village has gone through two stages: explicit contraction under the ecological objective and implicit contraction under the economic objective. (4) The contraction of agricultural production space in mountainous areas follows the mechanisms of environmental logic, policy-driven logic, and multi-subject logic. …”
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Water Quality Variations in the Lower Yangtze River Based on GA-RF Model From GF-1, Landsat-8, and Sentinel-2 Images by Wentao Hu, Shuanggen Jin, Yuanyuan Zhang

“…In this article, GF-1, Landsat-8, and Sentinel-2 data are jointly used to develop a genetic algorithm-random forest (GA-RF) water quality inversion model weighted by the entropy method. …”
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Sierra Espuña (Librillos, 2023) by Miguel Ángel González Espinosa

“…It presents a green mantle composed of a pine forest as a result of the repopulation undertaken by Ricardo Codorníu more than a century ago, with species such as Aleppo pine, maritime pine, black pine, laricio pine and other pine varieties. …”
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Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning by Min Liang, Zhiwen Zhang, Langming Wu, Mafeng Chen, Shifan Tan, Jian Huang

“…Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA). …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), na&#x00EF;ve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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Predicting nighttime black ice using atmospheric data for efficient winter road maintenance patrols by Jinhwan Jang

“…In this context, the present study investigates machine learning techniques, including Random Forest, CatBoost, and Deep Neural Networks, for forecasting nighttime icing on rural highways in Korea. …”
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How does playfulness (re)frame the world? Evidence for selective cognitive and behavioral redirecting in times of adversity by Xiangyou Shen, Zoe Crawley

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Multiple PM Low-Cost Sensors, Multiple Seasons’ Data, and Multiple Calibration Models by S Srishti, Pratyush Agrawal, Padmavati Kulkarni, Hrishikesh Chandra Gautam, Meenakshi Kushwaha, V. Sreekanth

“…The ML models included (i) Decision Tree, (ii) Random Forest (RF), (iii) eXtreme Gradient Boosting, and (iv) Support Vector Regression (SVR). …”
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