Rapid identification of high-temperature Daqu Baijiu with the same aroma type by UV-VIS sensor of HBT combined with Zn2+ by Yanmei Zhu, Yuanyuan Su, Yipeng Cang, Hengye Chen, Wanjun Long, Wei Lan, Xue Jiang, Haiyan Fu

“…The specific mechanism of light signal change was mainly based on the competitive coordination effect of pyrazines and other nitrogen-containing compounds in high-temperature Daqu Baijiu and small molecular probe HBT on Zn2+ and the excited state intramolecular proton transfer (ESIPT) mechanism of HBT itself. Results: The random forest results showed that the prediction set classification accuracy was improved from 62.37% to 100%. …”
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Clinical validation and optimization of machine learning models for early prediction of sepsis by Xi Liu, Meiyi Li, Xu Liu, Yuting Luo, Dong Yang, Hui Ouyang, Jiaoling He, Jinyu Xia, Fei Xiao, Fei Xiao, Fei Xiao

“…We trained models in predicting sepsis by machine learning (ML) methods, including logistic regression, decision tree, random forest (RF), multi-layer perceptron, and light gradient boosting. …”
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Effects of Long-Term Nitrogen Fertilization on Nitrous Oxide Emission and Yield in Acidic Tea (<i>Camellia sinensis</i> L.) Plantation Soils by Fuying Jiang, Yunni Chang, Jiabao Han, Xiangde Yang, Zhidan Wu

“…N₂O flux was positively correlated with N rates, water-filled pore space (WFPS), soil temperature (T_soil), and inorganic N (NH₄⁺-N and NO₃⁻-N), while showing a negative correlation with soil pH. Random forest (RF) modeling identified WFPS, N rates, and Tsoil as the most important variables influencing N₂O flux. …”
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Ultrasound-based radiomics and clinical factors-based nomogram for early intracranial hypertension detection in patients with decompressive craniotomy by Zunfeng Fu, Lin Peng, Laicai Guo, Chao Qin, Yanhong Yu, Jiajun Zhang, Yan Liu

“…Radiomics features were extracted from ONS images, and feature selection methods were applied to construct predictive models using logistic regression (LR), support vector machine (SVM), random forest (RF), and K-Nearest Neighbors (KNN). Clinical-ultrasound variables were incorporated into the model through univariate and multivariate logistic regression. …”
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A study on the classification of coastal wetland vegetation based on the Suaeda salsa index and its phenological characteristics by Weicheng Huang, Xianyun Fei, Weiwei Yang, Zhen Wang, Yajun Gao, Hong Yan

“…The four phenological metrics were combined with spectral and textural features to classify the vegetation using the random forest (RF) algorithm. In order to demonstrate the efficacy of the constructed vegetation indices, this study employed both the NDVI and the existing Suaeda salsa Vegetation Index (SSVI) to calculate the phenological metrics for classification purposes. …”
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Development and validation of a prognostic prediction model for elderly gastric cancer patients based on oxidative stress biochemical markers by Xing-Qi Zhang, Ze-Ning Huang, Ju Wu, Chang-Yue Zheng, Xiao-Dong Liu, Ying-Qi Huang, Qi-Yue Chen, Ping Li, Jian-Wei Xie, Chao-Hui Zheng, Jian-Xian Lin, Yan-Bing Zhou, Chang-Ming Huang

“…Predictive models for five-year overall survival (OS) were constructed using random forest (RF), decision tree (DT), and support vector machine (SVM) methods, and validated using area under the curve (AUC) and calibration plots. …”
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Contrasting spatial variations between above and below-ground net primary productivity in global grasslands by Ying Hu, Yue Yang, Yu Wei, Xiaozhen Li, Yue Jiao, Jiapei Liao, Ruiyu Fu, Lichong Dai, Zhongmin Hu

“…Among the nine machine learning methods compared, the random forest model provided the highest accuracy for productivity estimation (R2 = 0.63 ∼ 0.89). …”
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Establishment of a Breeding Approach Combined with Gamma Ray Irradiation and Tissue Regeneration for Highbush Blueberry by Xuan Yu, Haidi Yuan, Yihong Jin, Chuizheng Xia, Jiani Zhu, Jiali Che, Jiao Yang, Xiaofei Wang, Bingsong Zheng, Shufang Yang, Cristian Silvestri, Fuqiang Cui, Jianfang Zuo

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Double degree programs and education for sustainable development implementation: Experience of Ukrainian and UK universities cooperation in wartime by Iryna Skliar, Mykola Kostel’, Olena Melnyk, Kseniia Bliumska-Danko

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Unravelling spatiotemporal propagation processes among meteorological, soil, and evaporative flash droughts from a three-dimensional perspective by Chen Hu, Dunxian She, Gangsheng Wang, Liping Zhang, Zhaoxia Jing, Zhihong Song, Jun Xia

“…Additionally, we utilized the random forest model to identify critical factors influencing flash drought propagation. …”
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Fine-scale satellite-based monitoring of temperature and vegetation cover in microclimates, distribution ranges, and landscape connectivity for Neurergus kaiseri (Kaiser’s mountain... by Peyman Karami, Sajad Tavakoli, Mina Esmaeili

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Evaluating the Performance of Artificial Intelligence-Based Large Language Models in Orthodontics—A Systematic Review and Meta-Analysis by Farraj Albalawi, Sanjeev B. Khanagar, Kiran Iyer, Nora Alhazmi, Afnan Alayyash, Anwar S. Alhazmi, Mohammed Awawdeh, Oinam Gokulchandra Singh

“…The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42–0.44]. …”
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Artificial-Intelligence-Based Investigation on Land Use and Land Cover (LULC) Changes in Response to Population Growth in South Punjab, Pakistan by Tanweer Abbas, Muhammad Shoaib, Raffaele Albano, Muhammad Azhar Inam Baig, Irfan Ali, Hafiz Umar Farid, Muhammad Usman Ali

“…Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). …”
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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…The ensemble learning algorithms used include AdaBoost (AD), Gradient Boosting Classifier (GBM), Random Forest (RF) and Extra Trees (ET). We used an accuracy metric to measure the performance of the algorithms. …”
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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

“…The least absolute shrinkage and selection operator (LASSO) regression and random forest (RF) algorithm were applied to identify key mortality-related dietary factors, which were subsequently incorporated into risk prediction nomogram models. …”
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Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively.Purpose: The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV.Patients and Methods: Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system.Results: The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. …”
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Pembingkaian Perjuangan Tanah Orang Mentawai Dalam Konflik Kehutanan by Nurhayati Nurhayati, Afrizal Afrizal, Jendrius Jendrius

“…The geographical location and growth patterns have never been touched by human intervention, making the Mentawai forest have a unique composition. Implementation of economic growth in land control through market mechanisms and state intervention. …”
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A new signature associated with anoikis predicts the outcome and immune infiltration in nasopharyngeal carcinoma by Yonglin Luo, Wenyang Wei, Yaxuan Huang, Jun Li, Weiling Qin, Quanxiang Hao, Jiemei Ye, Zhe Zhang, Yushan Liang, Xue Xiao, Yonglin Cai

“…A risk score based on t 3-ARG feature was developed to stratify NPC patients into two distinct risk groups using the optimal model, Random Survival Forest. NPC patients with high-risk scores experienced notably shorter progression-free survival in comparison to those with low-risk scores. …”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. …”
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Recommendations for developing, documenting, and distributing data products derived from NEON data by Jeff W. Atkins, Kelly S. Aho, Xuan Chen, Andrew J. Elmore, Rich Fiorella, Wenqi Luo, Danica Lombardozzi, Claire Lunch, Leah Manak, Luis X. dePablo, Allison N. Myers‐Pigg, Sydne Record, Tong Qiu, Samuel Reed, Benjamin Ruddell, Brandon Strange, Christa L. Torrens, Kelsey Yule, Andrew D. Richardson

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