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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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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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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Èdè Àyàn: The Language of Àyàn in Yorùbá Art and Ritual of Egúngún by Oláwọlé Fámúlẹ̀

“…As among other Yorùbá deities (òrìsạ̀) that live in the spiritual realm in certain but uncommon natural environments (forests, trees, rivers, streams, and mountains, among others), Òrìsà Àyàn is thought to reside in wood (Vil ̣ - lepastour 2015, 3). …”
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Atmospheric Black Carbon Evaluation in Two Sites of San Luis Potosí City During the Years 2018–2020 by Valter Barrera, Cristian Guerrero, Guadalupe Galindo, Dara Salcedo, Andrés Ruiz, Carlos Contreras

“…One of the main findings was the dominance of annual mean concentrations of BC originating from fossil fuels (BCff) on the north site in the city was 0.97 and on the south site (BCff) was 0.91 due to some forest fires during the monitoring period. This study presented information from two zones of a growing city in Mexico to generate new air pollutant indicators to have a better understanding of pollutant interactions in the city, to decrease the emission precursor sources, and reduce the health risks in the population.…”
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Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis by Jiayu Cao, Yuhui Yang, Xi Liu, Yang Huang, Qianqian Xie, Aliaksei Kadushkin, Mikhail Nedelko, Di Wu, Noel J. Aquilina, Xuehua Li, Xiaoming Cai, Ruibin Li

“…The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. …”
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Microplastic contamination in different tissues of commercial fish in estuary area by N.D. Takarina, O.M. Chuan, A. Adiwibowo, F.N.A. Jeffery, N.Z.A.B.N.M. Zamri, M.A. Adidharma

“…Four fish sampling sites were identified according to the predominant land use, with settlements in the upper reaches, ponds in the central area, and mangrove forests in the lower reaches. Fish samples were taken the gastrointestinal tract, gills and muscle to calculated the microplastic content and identify its shape and size. …”
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Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index by Leyi Zhang, Xia Li, Xiuhua Liu, Zhiyang Lian, Guozhuang Zhang, Zuyu Liu, Shuangxian An, Yuexiao Ren, Yile Li, Shangdong Liu

“…The land-use variations in forests, shrubs, grasslands, and croplands driven by ecological restoration and agricultural policies exerted a positive impact on RSEI. …”
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Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes by Kian Draper, Ben Li, Muzammil Syed, Farah Shaikh, Abdelrahman Zamzam, Batool Jamal Abuhalimeh, Kharram Rasheed, Houssam K. Younes, Rawand Abdin, Mohammad Qadura

“…In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. …”
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Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations by Hengyi Ji, Yidan Xu, Ganghui Teng

“…We systematically compared the performances of the following seven ML models in predicting egg production rate and egg weight: random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), least squares support vector machine (LSSVM), k-nearest neighbors (kNN), XGBoost, and LightGBM. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…The results of the machine learning analysis indicated that the model built using the random forest algorithm performed the best, with an area under the curve of 0.879. …”
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AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism by Lucindah N. Fry-Nartey, Cyril Akafia, Ursula S. Nkonu, Spencer B. Baiden, Ignatus Nunana Dorvi, Kwasi Agyenkwa-Mawuli, Odame Agyapong, Claude Fiifi Hayford, Michael D. Wilson, Whelton A. Miller, Samuel K. Kwofie

“…Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. …”
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High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. …”
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Influence of land cover change on atmospheric organic gases, aerosols, and radiative effects by R. Vella, R. Vella, M. Forrest, A. Pozzer, A. Pozzer, A. P. Tsimpidi, T. Hickler, T. Hickler, J. Lelieveld, J. Lelieveld, H. Tost

“…Human activities have extensively altered natural vegetation cover, primarily by converting forests into agricultural land. In this work, a global atmospheric chemistry–climate model, coupled with a dynamic global vegetation model, was employed to study the impacts of perturbing the biosphere through human-induced land use change, thereby exploring changes in BVOC emissions and the atmospheric aerosol burden. …”
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A novel multi-model estimation of phosphorus in coal and its ash using FTIR spectroscopy by Arya Vinod, Anup Krishna Prasad, Sameeksha Mishra, Bitan Purkait, Shailayee Mukherjee, Anubhav Shukla, Nirasindhu Desinayak, Bhabesh Chandra Sarkar, Atul Kumar Varma

“…In this article, we explore the potential of FTIR spectroscopy combined with machine learning models (piecewise linear regression—PLR, partial least square regression—PLSR, random forest—RF, and support vector regression—SVR) for quantifying the phosphorus content in coal and coal ash. …”
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Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer by Tiankai Xu, Chu Chu, Shuyu Xue, Tongchao Jiang, Ying Wang, Wen Xia, Huanxin Lin

“…Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined immune infiltration and tumor mutation burden of the genes within our model and scrutinized divergences in the immune microenvironment between high- and low-risk groups. …”
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An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) by Ran Ni, Yongjie Huang, Lei Wang, Hongjie Chen, Guorui Zhang, Yali Yu, Yinglan Kuang, Yuyan Tang, Xing Lu, Hong Liu

“…Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). …”
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Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). …”
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