Stacking data analysis method for Langmuir multi-probe payload by Jin Wang, Jin Wang, Duan Zhang, Qinghe Zhang, Qinghe Zhang, Xinyao Xie, Fangye Zou, Qingfu Du, Qingfu Du, V. Manu, Yanjv Sun

“…This study uses a stacking algorithm to process m-NLP data and incorporates the International Reference Ionosphere (IRI) model to correct the predicted electron density (Ne) values. …”
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Player Tracking Data and Psychophysiological Features Associated with Mental Fatigue in U15, U17, and U19 Male Football Players: A Machine Learning Approach by José E. Teixeira, Pedro Afonso, André Schneider, Luís Branquinho, Eduardo Maio, Ricardo Ferraz, Rafael Nascimento, Ryland Morgans, Tiago M. Barbosa, António M. Monteiro, Pedro Forte

“…The data preparation process involved managing absent values, applying normalization techniques, and selecting relevant features. …”
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Leveraging advanced deep learning and machine learning approaches for snow depth prediction using remote sensing and ground data by Haytam Elyoussfi, Abdelghani Boudhar, Salwa Belaqziz, Mostafa Bousbaa, Karima Nifa, Bouchra Bargam, Abdelghani Chehbouni

“…The dataset was processed and normalized for optimal performance, and hyperparameters were fine-tuned using a randomized search method. …”
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Hyperspectral estimation of chlorophyll content in grapevine based on feature selection and GA-BP by YaFeng Li, XinGang Xu, WenBiao Wu, Yaohui Zhu, LuTao Gao, XiangTai Jiang, Yang Meng, GuiJun Yang, HanYu Xue

“…It was shown that SNV-based processed hyperspectral data combined with GA-BP has great potential for efficient chlorophyll monitoring in grapevine. …”
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A systematic scoping review of mentor training in medical education between 2000 and 2024 by Jun Rey Leong, Adele Yi Dawn Lim, Nila Ravindran, Darius Wei Jun Wan, Varsha Rajalingam, Jun Kiat Lua, Hannah Yi Fang Kwok, Krish Sheri, Victoria Jia En Fam, Ranitha Govindasamy, Nur Amira Binte Abdul Hamid, Michael Dunn, Lalit Kumar Radha Krishna

“…It also highlights that more programs are employing longitudinal mentoring processes to guide the inculcation of desired mentoring characteristics amongst prospective mentors. …”
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Optimizing flood resilience in China’s mountainous areas: Design flood estimation using advanced machine learning techniques by Xuemei Wang, Ronghua Liu, Chaoxing Sun, Xiaoyan Zhai, Liuqian Ding, Xiao Liu, Xiaolei Zhang

“…Study region: China Study focus: We developed machine learning (ML) models for design flood estimation in mountainous catchments (≤ 500 km²) across China. This process considered different ML algorithms (random forest, extreme gradient boosting, and support vector regression), model scopes (nation and hydrological zones), and feature input sets (1–14 features) to optimize model development strategies. …”
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Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia by Hao Chen, Shurui Zhang, Hiromi Matsumoto, Nanami Tsuchiya, Chihiro Yamada, Shunsuke Okasaki, Atsushi Miyasaka, Kentaro Yumoto, Daiki Kanou, Fumihiro Kashizaki, Harumi Koizumi, Kenichi Takahashi, Masato Shimizu, Nobuyuki Horita, Takeshi Kaneko

“…The low-code approach enables medical professionals with limited technical expertise to effectively employ data science in their clinical decision-making process. This approach proved to be a valuable tool in the analysis of CAP patient data.…”
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Bridging the Gap: Limitations of Machine Learning in Real-World Prediction of Heavy Metal Accumulation in Rice in Hunan Province by Qing-Qian Peng, Xia Zhou, Hang Zhou, Ye Liao, Zi-Yu Han, Lu Hu, Peng Zeng, Jiao-Feng Gu, Rong Zhang

“…This study systematically evaluated the predictive performance of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Residual Neural Networks (ResNet), using a multi-source soil–rice dataset comprising 57,200 samples from Hunan Province. …”
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Characteristic, relationship and impact of thermokarst lakes and retrogressive thaw slumps over the Qinghai-Tibetan plateau by Wenwen Li, Denghua Yan, Yu Lou, Baisha Weng, Lin Zhu, Yuequn Lai, Yunzhe Wang

“…We further employed the eXtreme gradient boosting algorithm and ICESat-2 ATL08 laser altimetry data to quantify changes in water storage due to TLs. …”
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Functional traits driving invasion risk and potential distribution of alien plants in oasis agroecosystems by Shengtianzi Dong, Shengtianzi Dong, Tiantian Qin, Tiantian Qin, Zhifang Xue, Wenchao Guo, Hanyue Wang, Hanyue Wang, Hongbin Li, Hongbin Li

“…Alien invasive plants pose a significant threat to global agricultural production, with functional traits playing a critical role in their spread and establishment processes. However, relevant research is scarce in oasis agroecosystems, which are more sensitive to global change. …”
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Comparison of sample preparation methods for higher heating values in various sugarcane varieties using near-infrared spectroscopy by Kantisa Phoomwarin, Khwantri Saengprachatanarug, Jetsada Posom, Seree Wongpichet, Kittipong Laloon, Arthit Phuphaphud

“…Spectral data were pre-processed using seven techniques to minimize noise, and four variable selection algorithms–Variable Importance in Projection, Successive Projection Algorithm, Genetic Algorithm, and correlation-based selection via Partial Least Squares Regression–were employed to improve modelling accuracy.In parallel, four machine learning models–AdaBoost Regressor, Gradient Boosting, K-Nearest Neighbors, and Random Forest–were applied to the same dataset for Higher heating value prediction. …”
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Penguin guano is an important source of climate-relevant aerosol particles in Antarctica by Matthew Boyer, Lauriane Quéléver, Zoé Brasseur, Barry McManus, Scott Herndon, Mike Agnese, David Nelson, Joseph Roscioli, Frederik Weis, Sergej Sel, Giselle L. Marincovich, Francisco J. Quarin, Angela Buchholz, Carlton Xavier, Pablo J. Perchivale, Veli-Matti Kerminen, Markku Kulmala, Tuukka Petäjä, Xu-Cheng He, Svetlana Sofieva-Rios, Hilkka Timonen, Minna Aurela, Luis Barreira, Aki Virkkula, Eija Asmi, Doug Worsnop, Mikko Sipilä

“…Abstract Gaseous ammonia, while influential in atmospheric processes, is critically underrepresented in atmospheric measurements. …”
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Simulating High-redshift Galaxies: Enhancing UV Luminosity with Star Formation Efficiency and a Top-heavy IMF by Tae Bong Jeong, Myoungwon Jeon, Hyunmi Song, Volker Bromm

“…To achieve this, we conduct cosmological hydrodynamic zoom-in simulations, modifying baryonic subgrid physics, and post-process our simulation results to confirm the observability of our simulated galaxies. …”
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Enhancing Fe tolerance in Al–Cu alloys by controlling the microstructure and mechanical properties with Mn and Cr additions by Jihao Li, Zhilei Xiang, Zian Yang, Wenchao Sun, Yang Han, Jingcun Huang, Xinshuo Gu, Xiaozhao Ma, Ziyong Chen

“…The variety and morphology of Fe-rich phases significantly affect fracture behavior, and the cracks in dendrite-shaped phases form in multiple directions, boosting crack resistance during tensile deformation. …”
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Mushroom production on digestate: Mineral composition of cultivation compost, mushrooms, spent mushroom compost and spent casing by Agnieszka Jasinska, Ketil Stoknes, Przemyslaw Niedzielski, Anna Budka, Miroslaw Mleczek

“…Produced in the process of anaerobic digestion, the effluent called digestate is rich in nutrients and can be used as a growing media for mushrooms. …”
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From Continent to Ocean: Investigating the Multi-Element and Precious Metal Geochemistry of the Paraná-Etendeka Large Igneous Province Using Machine Learning Tools by J. J. Lindsay, H. S. R. Hughes, C. M. Yeomans, J. C. Ø. Andersen, I. McDonald

“…Large Igneous Provinces, and by extension the mantle plumes that generate them, are frequently associated with platinum-group element (PGE) ore deposits, yet the processes controlling the metal budget in plume-derived magmas remains debated. …”
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Construction of a disease risk prediction model for postherpetic pruritus by machine learning by Zheng Lin, Yuan Dou, Ru-yi Ju, Ping Lin, Yi Cao

“…Divide all the data into five pieces, and then use each piece as a verification set and the others as a training set for training and verification, this process is repeated 100 times. Five models, logistic regression, random forest (RF), k-nearest neighbor, gradient boosting decision tree and neural network, were built in the training set using machine learning methods, and the performance of these models was evaluated in the test set.ResultsSeven non-zero characteristic variables from the Lasso regression results were selected for inclusion in the model, including age, moderate pain, time to recovery from rash, diabetes, severe pain, rash on the head and face, and basophil ratio. …”
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Cyclic Peptide MV6, an Aminoglycoside Efficacy Enhancer Against <i>Acinetobacter baumannii</i> by Natalia Roson-Calero, Jimmy Lucas, María A. Gomis-Font, Roger de Pedro-Jové, Antonio Oliver, Clara Ballesté-Delpierre, Jordi Vila

“…MV6 showed a better boosting effect for aminoglycosides, especially netilmicin, exceeding that of PAβN. …”
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Identifying leptospirosis hotspots in Selangor: uncovering climatic connections using remote sensing and developing a predictive model by Muhammad Akram Ab Kadir, Rosliza Abdul Manaf, Siti Aisah Mokhtar, Luthffi Idzhar Ismail

“…Machine learning algorithms, including support vector machine (SVM), Random Forest (RF), and light gradient boosting machine (LGBM) were employed to develop predictive models for leptospirosis hotspot areas. …”
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Numerical simulation of Boger nanofluids with heat source, magnetic field, and Cattaneo–Christov heat flux model between two parallel permeable porous plates via finite difference... by Qadeer Raza, Xiaodong Wang

“…Nanofluids are used in applications like electronics cooling, automotive engine cooling, industrial processes, solar energy systems, and heat exchangers. …”
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