Wetland degradation monitoring using multi-temporal remote sensing data and watershed land degradation index by I. Ridwan, S. Kadir, N. Nurlina

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Analysis of the CHS Gene Family Reveals Its Functional Responses to Hormones, Salinity, and Drought Stress in Moso Bamboo (<i>Phyllostachys edulis</i>) by Shiying Su, Xueyun Xuan, Jiaqi Tan, Zhen Yu, Yang Jiao, Zhijun Zhang, Muthusamy Ramakrishnan

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The Fabrication and Evaluation of a Potential Biomaterial Produced with Stem Cell Sheet Technology for Future Regenerative Medicine by Shukui Zhou, Ying Wang, Kaile Zhang, Nailong Cao, Ranxing Yang, Jianwen Huang, Weixin Zhao, Mahbubur Rahman, Hong Liao, Qiang Fu

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Construction land transition in Qinghai-Tibet Plateau and its eco-environmental effects during 2000-2020 by ZHANG Zining, LUO Junqiang, ZHANG Peilei, RONG Han, LIU Haiyang, AN Shuang, CHANG Genying

“…Early expansion mainly occupied unused land, whereas later expansion predominantly encroached on ecological land such as cropland, forest, and grassland. Regional differences in land-use transitions were evident: the Northern Tibet Plateau and Qaidam Basin showed higher proportions of transitions to water bodies, water conservancy land, and unused land, while the expansion mainly occupied grasslands and unused land; in other regions, construction land predominantly shifted between cropland and grassland. (3) The <i>IRSEI</i> values of construction land in the regions of Qinghai-Tibet Plateau were ranked as follows: Sichuan-Tibet alpine canyon region &gt; Qilian Mountains region &gt; Southern Tibet valley region &gt; Qinghai Plateau &gt; Qaidam Basin &gt; Northern Tibet Plateau. (4) Overall, construction land transition demonstrated negative ecological effects. …”
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Clinical evidence of acupuncture for amnestic mild cognitive impairment: A systematic review and meta-analysis of randomized controlled trials by Guangyao Lin, Stella Lim Jin Yie, Shanshan Guo, Xuanling Li, Lianwei Xu

“…The results of this meta-analysis were exhibited with forest plots. Sensitivity analyses were conducted to determine the robustness of the pooled results, and publication bias was estimated by Egger's and Begg's tests. …”
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The contribution of different polyphenol compositions from chokeberry produced in China to cellular antioxidant and antiproliferative activities by Ningxuan Gao, Xu Si, Wenzhong Han, Ersheng Gong, Chi Shu, Jinlong Tian, Yuehua Wang, Jiyue Zhang, Binxu Li, Bin Li

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Integrated analysis of bioinformatics, mendelian randomization, and experimental validation reveals novel diagnostic and therapeutic targets for osteoarthritis: progesterone as a p... by Ziyu Weng, Chenzhong Wang, Bo Liu, Yi Yang, Yueqi Zhang, Chi Zhang

“…Methods In this study, Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine learning techniques were employed to identify hub genes. …”
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Machine Learning Model Reveals Land Use and Climate’s Role in Caatinga Wildfires: Present and Future Scenarios by Rodrigo N. Vasconcelos, Mariana M. M. de Santana, Diego P. Costa, Soltan G. Duverger, Jefferson Ferreira-Ferreira, Mariana Oliveira, Leonardo da Silva Barbosa, Carlos Leandro Cordeiro, Washington J. S. Franca Rocha

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Photosynthetic characteristics of Paris polyphylla var. chinensis in response to different light intensities and soil water contents by Yaling Zhang, Yaling Zhang, Rong Xu, Rong Xu, Rong Xu, Zhaozhao Wang, Juan Zhang, Xinghao Tang, Yu Chen, Xiao-Li Yan, Lu-Ping Qu, Lu-Ping Qu

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Spatiotemporal analysis of oil palm land clearing by M.K. Rosyidy, E. Frimawaty

“…This study shows that the area of oil palm land from 2015 to 2019 has increased along with a decrease in land used, such as forests and others. The area of oil palm land 2014 was 2,071,345 hectares, while the area in 2019 was 2,110,545 hectares. …”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…The models were evaluated using popular evaluation metrics in IDS modeling, intra- and inter-model comparisons were performed between models and with state-of-the-art works. Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. …”
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Prevalence of Food Insecurity and Its Associated Factors among Adult People with Human Immunodeficiency Virus in Ethiopia: A Systematic Review and Meta-Analysis by Hagos Degefa Hidru, Haftay Gebremedhine, Alem Gebretsadik, Hirut Teame, Hadush Negash, Meresa Berwo Mengesha

“…Moreover, quality appraisal of the included studies, publication bias was checked using the funnel symmetry test, and heterogeneity was checked using forest plot and inverse variance square (I2). The searches were restricted to articles published in the English language only, and Medical Subject Headings (MeSH terms) was used to help expand the search in advanced PubMed search. …”
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The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population–Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis... by Musa Jaiteh, Edith Phalane, Yegnanew A Shiferaw, Refilwe Nancy Phaswana-Mafuya

“…Logistic regression, support vector machines, random forests, and decision trees will be used. A cross-validation technique will be used to divide the training sample into k-folds, including a validation set, and models will be trained on each fold. …”
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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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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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