Modeling actinic flux and photolysis frequencies in dense biomass burning plumes by J.-L. Tirpitz, S. F. Colosimo, N. Brockway, R. Spurr, M. Christi, S. Hall, K. Ullmann, J. Hair, T. Shingler, R. Weber, J. Dibb, R. Moore, E. Wiggins, V. Natraj, N. Theys, J. Stutz

“…Systematic biases between the model and observations are within 2 %, indicating that the uncertainties are most likely due to variability in the input data caused by the inhomogeneity of the plume as well as 3D RT effects not captured in the model. …”
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Modelling Arctic lower-tropospheric ozone: processes controlling seasonal variations by W. Gong, S. R. Beagley, K. Toyota, H. Skov, J. H. Christensen, A. Lupu, D. Pendlebury, J. Zhang, U. Im, Y. Kanaya, A. Saiz-Lopez, R. Sommariva, R. Sommariva, P. Effertz, P. Effertz, J. W. Halfacre, N. Jepsen, R. Kivi, T. K. Koenig, K. Müller, C. Nordstrøm, I. Petropavlovskikh, I. Petropavlovskikh, P. B. Shepson, W. R. Simpson, S. Solberg, R. M. Staebler, D. W. Tarasick, R. Van Malderen, M. Vestenius

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Studying the Uptake of Toxic Metals by Plants using New Mathematical Modeling by Hussein Hassan Kharnoob, Hasan Ahmed Hasan

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Examination of Strength Modeling Reliability of Physical Tests on Structural Concrete Columns by Sher Ali Mirza

“…The columns were subjected to short-term loads producing pure axial force, axial force combined with symmetrical single-curvature bending, or pure bending. Major variables included the concrete strength, the end eccentricity ratio, the slenderness ratio, the longitudinal reinforcing steel index for reinforced concrete or the structural steel index for composite columns, and the transverse reinforcement (tie/hoop) volumetric ratio. …”
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The Weight Minimization of a UAV Wing Component Through Structural Optimization by Andreas Psarros, Georgios Savaidis

“…This study focuses on the structural optimization of a composite wing element for an unmanned aerial vehicle. …”
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Primary prevention cardiovascular disease risk prediction model for contemporary Chinese (1°P-CARDIAC): Model derivation and validation using a hybrid statistical and machine-learn... by Yekai Zhou, Celia Jiaxi Lin, Qiuyan Yu, Joseph Edgar Blais, Eric Yuk Fai Wan, Emmanuel Wong, Kathryn Tan, David Chung-Wah Siu, Kai Hang Yiu, Esther Wai Yin Chan, Doris Yu, William Wong, Tak-Wah Lam, Ian Chi Kei Wong, Ruibang Luo, Celine S L Chui

“…A basic model was developed with the integration of statistically significant and important subset of risk variables by least absolute shrinkage and selection operator (LASSO) regression. …”
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The Truck Platooning Routing Optimization Model Based on Multicommodity Network Flow Theory by Zexi Zhang

“…The output of the routing optimization model could both reflect the composition of each truck platooning on each link and directly show the routings of each truck. …”
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Parametric LCA model for Ti6Al4V powder production by Christian Spreafico, Baris Ördek

“…This holistic, multi-variable optimization approach provides unprecedented, actionable insights by identifying optimal operational settings, not just sensitivities, for enhancing the sustainability of Ti6Al4V powder production, overcoming limitations of prior static or phase-specific parametric models.…”
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The Application of Vibroacoustic Mean and Peak-to-Peak Estimates to Assess the Rapidly Changing Thermodynamic Process of Converting Energy Obtained from Various Fuel Compositions U... by Marek Waligórski, Maciej Bajerlein, Wojciech Karpiuk, Rafał Smolec, Jakub Pełczyński

“…The influence of dimethyl ether on combustion efficiency was quantified using performance indicators, emission parameters, and vibration estimates (compared to diesel fuel). Mathematical models of combustion and its variability were created using the mean, peak-to-peak amplitude, root mean square error, and peak amplitudes of vibration accelerations, which were also represented using vibration graphics. …”
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Use of response surface methodology (RSM) for composite blends of low grade broken rice fractions and full-fat soybean flour by a twin-screw extrusion cooking process by Nahemiah Danbaba, Iro Nkama, Mamudu Halidu Badau

“…The p-value and lack-of-fit tests of the models could well explain the observed variability and therefore could be used to establish production setting for the twin-screw extruder. …”
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Modelling of biological age in stable and acute exacerbations of chronic obstructive pulmonary disease by Yujiao Wang, Ting Mu, Yufen Fu, Yuxin Wang, Guoping Li

“…The dataset was partitioned into training and validation sets at a 7:3 ratio, and LASSO regression was applied to refine the model's variable composition. To assess the ability of different variables to discriminate current disease status, we developed the initial model and three subsequent models, with the following variables added in the new model: Chronological age (CA), BA, and biological age acceleration. …”
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Estimation of the compressive strength of ultrahigh performance concrete using machine learning models by Rakesh Kumar, Divesh Ranjan Kumar, Warit Wipulanusat, Chanachai Thongchom, Pijush Samui, Baboo Rai

“…The models trained on the UHPC mixture dataset with 15 input variables included the group method of data handling, recurrent neural networks, long short-term memory, and bidirectional long short-term memory (Bi-LSTM). …”
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A spike is a spike: On the universality of spike features in four epilepsy models by Armen Sargsyan, Pablo M. Casillas‐Espinosa, Dmitri Melkonian, Terence J. O'Brien, Gilles vanLuijtelaar

“…The slow component showed a much larger variability across the rat models. Significance Despite differences in the morphology of the epileptiform activity in different models, the frequency composition of the spike component of single SWCs is identical and does not depend on the particular epilepsy model. …”
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Probabilistic Neural Networks (PNNs) for Modeling Aleatoric Uncertainty in Scientific Machine Learning by Farhad Pourkamali-Anaraki, Jamal F. Husseini, Scott E. Stapleton

“…This paper investigates the use of probabilistic neural networks (PNNs) to model aleatoric uncertainty, which refers to the inherent variability in the input-output relationships of a system, often characterized by unequal variance or heteroscedasticity. …”
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Recent advances in studying natural bioactive polysaccharides with in vitro gastrointestinal digestion models by Jing-Ya Zeng, Xu Yang, Jian-Hui Xiao, Yan Yang, Mi-shuai Zhang, Ru-Ming Liu

“…Given the complexity and individual variability of the human digestive system, in vitro gastrointestinal digestion models have become essential tools for assessing the digestive characteristics and bioavailability of polysaccharides. …”
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Mouse models for pseudoxanthoma elasticum: genetic and dietary modulation of the ectopic mineralization phenotypes. by Qiaoli Li, Haitao Guo, David W Chou, Annerose Berndt, John P Sundberg, Jouni Uitto

“…These mice provide novel model systems to study the pathomechanisms and the reasons for strain background on phenotypic variability of PXE.…”
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Aeromagnetic anomalies and structural model of the Chicxulub multiring impact crater, Yucatan, Mexico by Mario Rebolledo-Vieyra, Jaime Urrutia-Fucugauchi, Héctor López-Loera

“…A structural model of the Chicxulub crater is derived from aeromagnetic anomaly modeling, borehole information and magnetic mineral data. …”
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COBRAme: A computational framework for genome-scale models of metabolism and gene expression. by Colton J Lloyd, Ali Ebrahim, Laurence Yang, Zachary A King, Edward Catoiu, Edward J O'Brien, Joanne K Liu, Bernhard O Palsson

“…Genome-scale models of metabolism and macromolecular expression (ME-models) explicitly compute the optimal proteome composition of a growing cell. …”
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Selective Spectrum Antibiotic Modulation of the Gut Microbiome in Obesity and Diabetes Rodent Models. by Deepak K Rajpal, Jean-Louis Klein, David Mayhew, Joyce Boucheron, Aaron T Spivak, Vinod Kumar, Karen Ingraham, Mark Paulik, Lihong Chen, Stephanie Van Horn, Elizabeth Thomas, Ganesh Sathe, George P Livi, David J Holmes, James R Brown

“…We show that specific changes in microbial community composition are associated with obesity and glycemic control phenotypes. …”
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Multi-scale machine learning model predicts muscle and functional disease progression by Silvia S. Blemker, Lara Riem, Olivia DuCharme, Megan Pinette, Kathryn Eve Costanzo, Emma Weatherley, Jeff Statland, Stephen J. Tapscott, Leo H. Wang, Dennis W. W. Shaw, Xing Song, Doris Leung, Seth D. Friedman

“…A three-stage random forest model was developed to predict annualized changes in muscle composition and a functional outcome (timed up-and-go (TUG)). …”
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