Distribution and Main Influencing Factors of Net Ecosystem Carbon Exchange in Typical Vegetation Ecosystems of Southern China by Yike Wang, Xia Liu, Weijia Lan, Shuxian Yin, Liya Fan, Boru Mai, Xuejiao Deng

“…This study investigated the NEE characteristics of typical evergreen coniferous forest ecosystems (ECFEs), tree-and-crop mixed ecosystems (TCMEs), and coastal crop ecosystems (CCEs) in southern China. …”
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Biotechnological Approach for Development and Characterization of Protein Feed for <i>Melipona quadrifasciata</i> by Patrícia Miranda-Pinto, Jullio Kennedy Castro Soares, Irys Hany Lima Gonzalez, Yuri Ribeiro Diogo, Lívia Soman de Medeiros, Luciana Chagas Caperuto, Patrícia Locosque Ramos, Tiago Maurício Francoy, Michelle Manfrini Morais

“…To address this, we developed a fermented protein feed using microorganisms from pollen of <i>Melipona quadrifasciata,</i> a species commonly found in the Brazilian Atlantic Forest. The fermented feed consisted of a protein bran mixture, sugar syrup, and an inoculant derived from species’ fermented pollen. …”
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Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model by Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi

“…We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. …”
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Multiple Dimensions Define Thresholds for Population Resilience of the Eastern Oyster, Crassostrea virginica by Megan K. La Peyre, Hongqing Wang, Shaye E. Sable, Wei Wu, Bin Li, Devin Comba, Carlos Perez, Melanie Bates, Lauren M. Swam

“…Two statistical approaches were applied, with each model highlighting a different operational definition of a threshold: random forest models identified a threshold as an abrupt change in the oyster abundance‐ salinity relationship, while Bayesian models identified an increased probability of oyster abundance dropping below a critical threshold, defined here as less than 50% of the 5‐year mean. …”
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Exploring the influence of age on the causes of death in advanced nasopharyngeal carcinoma patients undergoing chemoradiotherapy using machine learning methods by Mengni Zhang, Shipeng Zhang, Xudong Ao, Lisha Liu, Shunlin Peng

“…However, cumulative incidences of secondary malignant neoplasms were comparable between the two groups (P = 0.100). The random forest (RF) model demonstrated the highest concordance index of 0.701 among all models. …”
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Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors by Md Abdullah Al Mehedi, Shah Saki, Krutikkumar Patel, Chaopeng Shen, Sagy Cohen, Virginia Smith, Adnan Rajib, Emmanouil Anagnostou, Tadd Bindas, Kathryn Lawson

“…These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. …”
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Towards climate-resilient conservation: Integrating genetics and environmental factors in determining adaptive units of a xeric shrub by Yong-Zhi Yang, Pei-Wei Sun, Chong-Yi Ke, Min-Xin Luo, Jui-Tse Chang, Chien-Ti Chao, Run-Hong Gao, Pei-Chun Liao

“…We analyzed RAD-seq data from 217 samples across 19 populations, integrating ecological niche modeling (ENM) and GradientForest (GF) to pinpoint adaptive genetic variations. …”
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Glaucoma detection and staging from visual field images using machine learning techniques. by Nahida Akter, Jack Gordon, Sherry Li, Mikki Poon, Stuart Perry, John Fletcher, Thomas Chan, Andrew White, Maitreyee Roy

“…Among the ML models, the random forest (RF) classifier performed best with an F1 score of 96%.…”
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New Insights on Quality, Safety, Nutritional, and Nutraceutical Properties of Honeydew Honeys from Italy by Andrea Mara, Federica Mainente, Vasiliki Soursou, Yolanda Picó, Iratxe Perales, Asma Ghorab, Gavino Sanna, Isabel Borrás-Linares, Gianni Zoccatelli, Marco Ciulu

“…The honeydew elements, conductivity, color, antioxidant properties, total polyphenol content, hydroxymethylfurfural, major and trace elements, toxic and rare earth elements, and pesticide residues were measured in 59 samples of honeydew honey from forest, eucalyptus, fir, oak, and citrus sources. …”
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Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery by Edward N. Yap, Jie Huang, Joshua Chiu, Robert W. Chang, Bradley Cohn, Judith C. F. Hwang, Mary Reed

“…Patients’ EHR-based clinical history, vital signs, labs, and demographics were included in logistic regression, LASSO, decision tree, Random Forest, and XGBoost models. Area under the receiver operating characteristics curve (AUCROC) was used to compare model performance. …”
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Climate variability, population growth, and globalization impacting food security in Pakistan by Sohail Abbas, Ahtisham Haider, Shazia Kousar, Heli Lu, Siqi Lu, Fang Liu, Huan Li, Changhong Miao, Wanfu Feng, Muhammad Irfan Ahamad, Muhammad Sajid Mehmood, Rana Muhammad Zulqarnain

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Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer by Yuan Zeng, Yuhao Chen, Dandan Zhu, Jun Xu, Xiangting Zhang, Huiya Ying, Xian Song, Ruoru Zhou, Yixiao Wang, Fujun Yu

“…Four machine learning algorithms, known as random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM) and naive Bayes (NB), were used to develop models for predicting the risk of lower extremity DVT occurrence in GC patients. …”
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Multimedia: multimodal mediation analysis of microbiome data by Hanying Jiang, Xinran Miao, Margaret W. Thairu, Mara Beebe, Dan W. Grupe, Richard J. Davidson, Jo Handelsman, Kris Sankaran

“…The software includes modules for regularized linear, compositional, random forest, hierarchical, and hurdle modeling, making it well-suited to microbiome data. …”
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Petrophysical Regression regarding Porosity, Permeability, and Water Saturation Driven by Logging-Based Ensemble and Transfer Learnings: A Case Study of Sandy-Mud Reservoirs by Shenghan Zhang, Yufeng Gu, Yinshan Gao, Xinxing Wang, Daoyong Zhang, Liming Zhou

“…Additionally, to highlight the validating effect, three sophisticated predictors, including k-nearest neighbors (KNN), support vector regression (SVR), and random forest (RF), are introduced as competitors to implement a contrast. …”
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Naming the untouchable – environmental sequences and niche partitioning as taxonomical evidence in fungi by Faheema Kalsoom Khan, Kerri Kluting, Jeanette Tångrot, Hector Urbina, Tea Ammunet, Shadi Eshghi Sahraei, Martin Rydén, Martin Ryberg, Anna Rosling

“…Based on environmental amplicon sequencing from a well-studied Swedish pine forest podzol soil, we generate 68 distinct species hypotheses of Archaeorhizomycetes, of which two correspond to the only described species in the class. …”
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Combined Liver Stiffness and Α-fetoprotein Further beyond the Sustained Virologic Response Visit as Predictors of Long-Term Liver-Related Events in Patients with Chronic Hepatitis... by Sheng-Hung Chen, Hsueh-Chou Lai, Wen-Pang Su, Jung-Ta Kao, Po-Heng Chuang, Wei-Fan Hsu, Hung-Wei Wang, Tsung-Lin Hsieh, Hung-Yao Chen, Cheng-Yuan Peng

“…Cox regression and random forest models identified the key factors, including longitudinal LS and noninvasive test results, that could predict LREs, including hepatocellular carcinoma, during prespecified follow-ups from 2010 to 2021. …”
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The Wildcat That Lives in Me: A Review on Free-Roaming Cats (<i>Felis catus</i>) in Brazil, Focusing on Research Priorities, Management, and Their Impacts on Cat Welfare by Luana S. Gonçalves, Daiana de Souza Machado, Maria Eduarda Caçador, Giovanne Ambrosio Ferreira, Christopher R. Dickman, Maria Camila Ceballos, Fabio Prezoto, Aline Cristina Sant’Anna

“…More studies were conducted in Brazilian mainland areas (<i>n</i> = 23)—notably in Atlantic Forest—than on islands (<i>n</i> = 11). The review highlights potential impacts of cats on wildlife. …”
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Integrated Machine Learning Approaches for Landslide Susceptibility Mapping Along the Pakistan–China Karakoram Highway by Mohib Ullah, Haijun Qiu, Wenchao Huangfu, Dongdong Yang, Yingdong Wei, Bingzhe Tang

“…To address this, this study assessed the performance of six machine learning models, including Convolutional Neural Networks (CNNs), Random Forest (RF), Categorical Boosting (CatBoost), their CNN-based hybrid models (CNN+RF and CNN+CatBoost), and a Stacking Ensemble (SE) combining CNN, RF, and CatBoost in mapping landslide susceptibility along the Karakoram Highway in northern Pakistan. …”
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Application of deep learning and feature selection technique on external root resorption identification on CBCT images by Nor Hidayah Reduwan, Azwatee Abdul Aziz, Roziana Mohd Razi, Erma Rahayu Mohd Faizal Abdullah, Seyed Matin Mazloom Nezhad, Meghna Gohain, Norliza Ibrahim

“…The performance of four DLMs including Random Forest (RF) + Visual Geometry Group 16 (VGG), RF + EfficienNetB4 (EFNET), Support Vector Machine (SVM) + VGG, and SVM + EFNET) and four hybrid models (DLM + FST: (i) FS + RF + VGG, (ii) FS + RF + EFNET, (iii) FS + SVM + VGG and (iv) FS + SVM + EFNET) was compared. …”
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Comparing the effectiveness, safety and tolerability of interventions for depressive symptoms in people with multiple sclerosis: a systematic review and network meta-analysis proto... by Amalia Karahalios, Allan G Kermode, Yvonne C Learmonth, Julia Lyons, Stephanie Campese, Alexandra Metse, Claudia H Marck

“…We plan to provide summary measures including forest plots, a geometry of the network, surface under the cumulative ranking curve, and a league table, and perform subgroup analyses. …”
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