Conservation in action: Cost-effective UAVs and real-time detection of the globally threatened swamp deer (Rucervus duvaucelii) by Ravindra Nath Tripathi, Karan Agarwal, Vikas Tripathi, Ruchi Badola, Syed Ainul Hussain

“…This study exemplifies how combining UAVs with deep learning can facilitate species monitoring and population count estimation and be adopted by forest managers to support conservation decisions.…”
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Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction by Waqar A. Sulaiman, Charithea Stylianides, Andria Nikolaou, Andria Nikolaou, Zinonas Antoniou, Ioannis Constantinou, Lakis Palazis, Anna Vavlitou, Theodoros Kyprianou, Theodoros Kyprianou, Efthyvoulos Kyriacou, Antonis Kakas, Antonis Kakas, Marios S. Pattichis, Andreas S. Panayides, Constantinos S. Pattichis, Constantinos S. Pattichis

“…Using machine learning models, including Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP), we identified GB as the best performing model outperforming the other models with an AUC of 0.730, accuracy of 69.93%, sensitivity of 88.20%, and specificity of 40.95% on the original dataset. …”
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Long-acting family planning uptake and associated factors among women in the reproductive age group in East Africa: multilevel analysis by Ermias Bekele Enyew, Abiyu Abadi Tareke, Habtamu Setegn Ngusie, Mulugeta Desalegn Kasaye, Shimels Derso Kebede, Mahider Shimelis Feyisa

“…The pooled prevalence of long-acting contraceptive uptake with a 95% confidence interval (CI) was reported and presented in a forest plot for East African countries using STATA version 14.1. …”
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Explainable machine learning model for predicting decline in platelet count after interventional closure in children with patent ductus arteriosus by Song-Yue Zhang, Yi-Dong Zhang, Hao Li, Qiao-Yu Wang, Qiao-Fang Ye, Xun-Min Wang, Tian-He Xia, Yue-E He, Xing Rong, Ting-Ting Wu, Rong-Zhou Wu

“…The extra tree algorithm was used for feature selection and four ML algorithms [random forest (RF), adaptive boosting, extreme gradient boosting, and logistic regression] were established. …”
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Capital formation and production of carbon emissions in low-carbon development by E.S. Siregar, S.U. Sentosa, A. Satrianto

“…Low carbon development is a new platform to maintain economic growth through reducing carbon emissions and reducing the use of natural resources, because it was predicted that reducing emissions will increase economic growth while preventing forest loss, improving air quality and living standards, and reducing mortality rates.METHODS: Utilizing a quantitative method, this study integrates a novel viewpoint by combining low-carbon development with related emission factors. …”
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Functional role of termites as soil engineers: Their influence on soil organic carbon and nitrogen in pine and mahogany stands Alas Bromo by Febriani Sri Rezeki, Ariyanto Dwi Priyo, Cahyono Ongko, Dewi Widyatmani Sih, Sumani Sumani, Komariah Komariah, Tarmadi Didi, Setiawan Khoirul Himmi, Wikantyoso Bramantyo, Zaki Muhamad Khoiru

“…Alas Bromo University Forest has various stands that can influence the presence of soil macrofauna, one of which is termites. …”
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Wear Behavior Analysis and Gated Recurrent Unit Neural Network Prediction of Coefficient of Friction in Al10Cu-B₄C Composites by Mihail Kolev, Ludmil Drenchev, Veselin Petkov, Rositza Dimitrova, Krasimir Kolev, Boris Yanachkov

“…Additionally, feature importance analysis using Random Forest models identified reinforcement-related features as the dominant predictors for both COF and mass wear. …”
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Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy by Jing Xue, Menghan Wu, Jing Zhang, Jiayang Yang, Guannan Lv, Baojun Qu, Yanping Zhang, Xia Yan, Xia Yan, Jianbo Song, Jianbo Song

“…Logistic regression (LR), Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO) methods were utilized to select optimal imaging features, leading to a combined prediction model developed using a random forest (RF) algorithm. Model performance was assessed using the area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA), with Shapley Additive exPlanations (SHAP) values for interpretation.ResultsThe samples were split into training (70%) and validation (30%) sets. …”
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Adaptability of spring oat yield in the environments of the Near-Irtysh area in Omsk Province by P. N. Nikolaev, N. I. Aniskov, O. A. Yusova, I. V. Safonova

“…The experimental part of the work was carried out in 2011–2017 on the experimental fields of Omsk Agrarian Scientific Center located in the southern forest-steppe area. Agricultural practice used in the experiments was conventional for West Siberia. …”
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Establishment and Validation of a Machine‐Learning Prediction Nomogram Based on Lymphocyte Subtyping for Intra‐Abdominal Candidiasis in Septic Patients by Jiahui Zhang, Wei Cheng, Dongkai Li, Guoyu Zhao, Xianli Lei, Na Cui

“…A machine‐learning random forest model was used to select important variables, and multivariate logistic regression was used to analyze the factors influencing IAC. …”
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Identification and validation of respiratory subphenotypes in patients with COVID-19 acute respiratory distress syndrome undergoing prone position by Mônica R. da Cruz, Pedro Azambuja, Kátia S. C. Torres, Fernanda Lima-Setta, André M. Japiassú, Denise M. Medeiros

“…A K-means clustering implementation designed for joint trajectory analysis was utilized for the unsupervised classification of the development cohort. A random forest model was trained on the labeled development cohort and used to validate the subphenotypes in the validation cohort. …”
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Genome-wide identification of short-chain dehydrogenases/reductases genes and functional characterization of ApSDR53C2 in melanin biosynthesis in Arthrinium phaeospermum by Jiao Liao, Yisi Wang, Han Liu, Sijia Liu, Peng Yan, Hang Chen, Shujiang Li, Shujiang Li

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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…Machine learning classifiers, including support vector machine, random forest, adaptive boosting, and k-nearest neighbors, were trained using weak supervision and minority oversampling to handle sparse and imbalanced pain labels. …”
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Engineering of SauriCas9 with enhanced specificity by Xiaoqi Zhang, Chen Tao, Miaomiao Li, Sufang Zhang, Puping Liang, Yan Huang, Huihui Liu, Yongming Wang

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Characteristics of Land Use Change and Evaluation of Ecological Sensitivity in Chongqing by XIE Xianjian, GOU Qiantao, WU Han

“…There had been a significant reduction in both cropland and grassland, while the areas of forest land and urban-rural construction land had increased markedly from 2000 to 2020. (2) Over the course of 20 years, the average value of the comprehensive ecological sensitivity index rose from 1.037 to 1.045, indicating an overall improvement in the ecological environment of the study area. …”
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A Simple Machine Learning-Based Quantitative Structure–Activity Relationship Model for Predicting pIC₅₀ Inhibition Values of FLT3 Tyrosine Kinase by Jackson J. Alcázar, Ignacio Sánchez, Cristian Merino, Bruno Monasterio, Gaspar Sajuria, Diego Miranda, Felipe Díaz, Paola R. Campodónico

“…<b>Methods:</b> Using a dataset which was 14 times larger than those employed in prior studies (1350 compounds with 1269 molecular descriptors), we trained a random forest regressor, chosen due to its superior predictive performance and resistance to overfitting. …”
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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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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and multilayer perceptron (MLP) were applied across four datasets, with the input parameters including body weight of pigs (BW), inside temperature (IT), inside relative humidity (IRH), and inside CO2 concentration (ICO2) of pig barns. …”
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Impacts of Land Use on Soil Erosion: RUSLE Analysis in a Sub-Basin of the Peruvian Amazon (2016–2022) by Moises Ascencio-Sanchez, Cesar Padilla-Castro, Christian Riveros-Lizana, Rosa María Hermoza-Espezúa, Dayan Atalluz-Ganoza, Richard Solórzano-Acosta

“…The deforestation rate was 17.99 km² year⁻¹ and by 2022 the forested area reached 237.65 km². In conclusion, the transition from forest to farmland was related to the most critical erosion values. …”
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Human–nature connectedness and sustainability across lifetimes: A comparative cross‐sectional study in France and Colombia by Gladys Barragan‐Jason, Maxime Cauchoix, Paula A. Diaz‐Valencia, Arielle Syssau‐Vaccarella, Solène Hemet, Camilo Cardozo, Suzanne M. Skevington, Philipp Heeb, Camille Parmesan

“…We also investigated the links between human–nature connectedness, pro‐environmental behaviours, well‐being and two indicators of opportunity to experience nature (i.e. degree of urbanisation and forest cover around the participants' municipality of residence). …”
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