Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries by Ignacio R. Bartol, Martin S. Graffigna Palomba, Mauricio E. Tano, Shaheen A. Dewji

“…The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. …”
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The Experience of Being Born: A Natural Context for Learning to Suckle by Jeffrey R. Alberts, April E. Ronca

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Fatigue Detection of Air Traffic Controllers Through Their Eye Movements by Yi Hu, Haoran Shen, Hui Pan, Wenbin Wei

“…This study involved human-in-the-loop simulations to collect eye movement and fatigue data from air traffic controllers and students. …”
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Modelling potential impacts of climate change on the oak spatial distribution (Case study: Ilam and Lorestan provinces) by Mohammad Safaei, Hani Rezayan, Parviz Zeaiean Firouzabadi, Ali asghar Torahi

“…Examining the effects of climate change on the oak spatial distribution, as the main species of Zagros forests and its ecological and economic values is of significant importance. …”
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Application of the Finite Difference Parabolic Equation Model in Forestry Remote Telemetry by Qi Guo, Yubin Lan, Xiaoling Deng, Yinliang Diao

“…The Debye–Cole dual dispersion model is used to simulate the effective permittivity of vegetation. …”
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Improving the Routing Security in Wireless Sensor Networks using Neutrosophic Set and Machine Learning Models by Hanadi Ahmad Simmak, Ahmed A El-Douh, Tareef S Alkellezli, Rabih Sbera, Darin shafek, Ahmed Abdelhafeez

“…Our approach makes use of real-time feature analysis of simulated WSN routing data. We create a strong foundation for categorization. …”
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Monte Carlo Guidance for Better Imaging of Boreal Lakes in the Wavelength Region of 400–800 nm by Vinh Nguyen Du Le

“…The bottoms of boreal forest lakes are mainly composed of woody debris, and thus spectral imaging revealing contrast of woody debris can be used to estimate lake depth. …”
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Impact of Sea Surface Temperature Variability at Different Ocean Basins on Dust Activities in the Gobi Desert and North China by Guanyu Liu, Jing Li, Zhongjing Jiang, Xichen Li

“…Two random forest regression models were established to link the meteorological variables to boreal Spring Dust Activity Frequency (SDAF). …”
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Digitally twin driven ship cooling pump fault monitoring system and application case by Shaojuan Su, Zhe Miao, Yong Zhao, Nanzhe Song

“…Using the random forest algorithm for data training and testing, the results showed that the root mean square error for the training set was 0.0037873, and for the test set, it was 0.008929, indicating high accuracy in predicting the status of cooling pumps. …”
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Modeling ecosystem services in Armenia using InVEST: a scenario-based approach with NextGISWeb integration for public awareness and engagement by E. Kazakov, E. Bukvareva

“…This integration of advanced modeling techniques with accessible web-based dissemination aims to influence strategic policy-making in forest management, water resource allocation, and urban planning. …”
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Seasonal Peak Snow Predictability Derived From Early‐Season Snow in North America by Jessica D. Lundquist, Rhae Sung Kim, Michael Durand, Laura R. Prugh

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Fingerprint of anthropogenic climate change detected in long-term western North American fire weather trends by Laura E. Queen, Sam Dean, Dáithí Stone, Piyush Jain, James Renwick, Nathanael Melia, Yukiko Imada

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Prediction and optimization of struvite recovery from wastewater by machine learning by TONG Ying, JIANG Shaojian, KANG Bingyan, LENG Lijian*, LI Hailong

“…The recovery of nitrogen and phosphorus from simulated wastewater in the form of struvite was investigated through a Machine Learning (ML)-based approach. …”
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Effect of Imputation Methods in the Classifier Performance by Pinar Cihan, Oya Kalıpsız, Erhan Gökçe

“…Theaim of this study is to examine mean, median, nearest neighbors, mice andmissForest methods to impute the simulated missing data which is the randomlyremoved with varying frequencies (5 to 25% by 5%) from original veterinarydataset. …”
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Interpretable multimodal machine learning analysis of X-ray absorption near-edge spectra and pair distribution functions by Tanaporn Na Narong, Zoe N. Zachko, Steven B. Torrisi, Simon J. L. Billinge

“…Abstract We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. …”
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Impact of changes in land use in the flow of the Pará River Basin, MG by Evandro L. Rodrigues, Marcos A. T. Elmiro, Francisco de A. Braga, Claudia M. Jacobi, Rafael D. Rossi

“…An increase of about 10% in the basin flow was estimated, as a function of changes in land use, when simulating the removal of the original 'Cerrado' vegetation and of the seasonal semideciduous forest for pasture implementation in 38% of the basin.…”
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Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques by K. Varalakshmi, J. Kumar

“…This paper introduces an ensemble-based framework combining Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM) to improve fault prediction and maintenance efficiency. …”
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Analysis of Hydrological Response to Climate and Land Use Land Cover Changes in Jewha Watershed Using SWAT Model, Ethiopia by Dereje Gebrie Habte, Satishkumar Belliethathan, Tenalem Ayenew

“…The LULC classes, including vegetation cover (Forest and shrubland), experienced a shift from 35.94% in 1984 to 27.32% in 2015. …”
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Évaluer la perception de notifications d’alerte scénarisées dans différents contextes en France : enjeux et perspectives by Johnny Douvinet, Camille Cavalière, Esteban Bopp, Karine Weiss, Karine Emsellen, Béatrice Gisclard, Gilles Martin, Mathieu Coulon

“…Natural events that have recently occurred in France (forest fires, flash floods) remind how necessary it is to be able to alert the population in appropriate time and with explicit messages, specifying the nature of the danger in progress and what should be done about it. …”
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Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease by Colin Birkenbihl, Madison Cuppels, Rory T. Boyle, Hannah M. Klinger, Oliver Langford, Gillian T. Coughlan, Michael J. Properzi, Jasmeer Chhatwal, Julie C. Price, Aaron P. Schultz, Dorene M. Rentz, Rebecca E. Amariglio, Keith A. Johnson, Rebecca F. Gottesman, Shubhabrata Mukherjee, Paul Maruff, Yen Ying Lim, Colin L. Masters, Alexa Beiser, Susan M. Resnick, Timothy M. Hughes, Samantha Burnham, Ilke Tunali, Susan Landau, Ann D. Cohen, Sterling C. Johnson, Tobey J. Betthauser, Sudha Seshadri, Samuel N. Lockhart, Sid E. O’Bryant, Prashanthi Vemuri, Reisa A. Sperling, Timothy J. Hohman, Michael C. Donohue, Rachel F. Buckley

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