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  1. 141
  2. 142

    Multidisciplinary Design Optimization of the NASA Metallic and Composite Common Research Model Wingbox: Addressing Static Strength, Stiffness, Aeroelastic, and Manufacturing Constr... by Odeh Dababneh, Timoleon Kipouros, James F. Whidborne

    Published 2025-05-01
    “…This study explores the multidisciplinary design optimization (MDO) of the NASA Common Research Model (CRM) wingbox, utilizing both metallic and composite materials while addressing various constraints, including static strength, stiffness, aeroelasticity, and manufacturing considerations. …”
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  3. 143

    A Generalized Benders Decomposition for Mixed-Integer Nonlinear Programming: Theory and Applications by Fadiah Hasna Nadiatul Haq, Diah Chaerani, Anita Triska

    Published 2024-11-01
    “…The GBD method is an extension of the Benders Decomposition (BD) method, effectively handles the characteristics of the MINLP  model, where the model has nonlinear properties and involves two types of variables, namely continuous variables and integer variables. …”
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  4. 144

    THE DEGENERATE STATE RECOGNITION METHOD OF ROLLING BEARING BASED ON LCD AND GMM-VPMCD HYBRID MODEL by LIU Jibiao, CHENG Junsheng, LIU Yanfei

    Published 2016-01-01
    “…Since variable predictive model based class discriminate( VPMCD) is a pattern recognition method in which the relationship between the feature values is adopted,it can be applied to the he degenerate state recognition of roller bearing. …”
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  5. 145

    Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

    Published 2025-12-01
    “…Biological analysis formed seven distinct mechanisms, each associated with specific biological functions and climate-driven effects on physiological and productive traits. 1) Blood traits were related to all milk components; 2) Lipid and energy metabolism, as well as kidney function, are related to the regulation of body temperature and milk composition; 3) Immunity and thyroid hormones are related to radiant thermal load; and 4) Homeostasis is the organic balance maintained between thermoregulatory, hormonal, hematological, productive, and biochemical functions, which are influenced by environmental variables. …”
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  8. 148

    Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

    Published 2024-04-01
    “…Correlation of friction coefficients and wear rates of copper/aluminum-graphite (Cu/Al-graphite) self-lubricating composites with their inherent material properties (composition, lubricant content, particle size, processing process, and interfacial bonding strength) and the variables related to the testing method (normal load, sliding speed, and sliding distance) were analyzed using traditional approaches, followed by modeling and prediction of tribological properties through five different ML algorithms, namely support vector machine (SVM), K-Nearest neighbor (KNN), random forest (RF), eXtreme gradient boosting (XGBoost), and least-squares boosting (LSBoost), based on the tribology experimental data. …”
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  9. 149
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    Robust Design Optimization of Viscoelastic Damped Composite Structures Integrating Model Order Reduction and Generalized Stochastic Collocation by Tianyu Wang, Chao Xu, Teng Li

    Published 2024-12-01
    “…This study presents a novel approach that integrates model order reduction (MOR) and generalized stochastic collocation (gSC) to enhance robust design optimization (RDO) of viscoelastic damped composite structures under material and geometric uncertainties. …”
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  12. 152

    Defining critical quality attributes and composition parameters for burn wound dressings: Antibiotic-anesthetic films as a model by María Florencia Sanchez, Laura Carolina Luciani-Giacobbe, Fiamma Barbieri, María Eugenia Olivera

    Published 2024-11-01
    “…This underscores the imperative need to establish precise critical quality attributes, a task undertaken within this study using an antibiotic-anesthetic film as a model. The aim was to establish the influence of critical composition and process parameters and optimize the formula.First, the quality target product profile was defined, and critical quality attributes were identified. …”
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  13. 153

    Antiviral Activity of Double-Stranded Ribonucleic Acid and Interferon Alpha Composition in the Model of Experimental Influenza Infection of Mice by S. G. Gamaley, М. O. Skarnovich, E. V. Makarevich, О. Yu. Mazurkov, L. N. Shishkina, О. S. Ivanova, G. M. Levagina, Е. D. Danilenko

    Published 2023-12-01
    “…The aim of this work was to study antiviral activity of intranasal forms of the pharmaceutical compositions containing yeast dsRNA and recombinant human interferon-alpha-2b in a model of lethal influenza infection in mice. …”
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  14. 154

    Impact of chemical composition on metabolizable energy and its prediction models in brewer's spent grains for broilers at different ages by Mingqiang Song, Kai Tian, Cong Ren, Youyou Liu, Xiaomeng Ye, Yuming Wang, Jingjing Xie, Feng Zhao

    Published 2025-08-01
    “…This study aimed to investigate the relationship between chemical composition and metabolizable energy (ME) in brewer’s spent grains (BSG), and to develop ME prediction models for fast-growing white feathered broilers at different ages. …”
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  15. 155

    A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence by Hatef Dastour, Quazi K. Hassan

    Published 2024-11-01
    “…Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. …”
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    Predicting 3-year all-cause mortality in rectal cancer patients based on body composition and machine learning by Xiangyong Li, Zeyang Zhou, Xiaoyang Zhang, Xinmeng Cheng, Chungen Xing, Yong Wu

    Published 2025-03-01
    “…SHAP values revealed that subcutaneous adipose tissue index (SAI), visceral adipose tissue index (VAI), skeletal muscle density (SMD), visceral-to-subcutaneous adipose tissue ratio (VSR), and subcutaneous adipose tissue density (SAD) were the five most important variables influencing all-cause mortality post-LaTME.ConclusionBy integrating body composition, multiple ML predictive models were developed and validated for predicting all-cause mortality after rectal cancer surgery, with the XGBoost model exhibiting the best performance.…”
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    Evolution of CFD numerical methods and physical models towards a full discrete approach by Caltagirone, Jean-Paul

    Published 2022-10-01
    “…The physical models and numerical methodologies of Computational Fluid Dynamics (CFD) are historically linked to the concept of continuous medium and to analysis where continuity, derivation and integration are defined as limits at a point. …”
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  20. 160

    A predictive model for body water and fluid balance using 3D smartphone anthropometry by Austin J. Graybeal, Abby T. Compton, Sydney H. Swafford, Caleb F. Brandner, Molly F. Johnson, Maria G. Kaylor, Hunter Haynes, Jon Stavres

    Published 2025-06-01
    “…Fluid overload and imbalance were determined using ECF/TBW and ECF/ICF, respectively, and subsequently predicted from the retained variables using receiver operating characteristic curve analyses and logistic regression.ResultsEstimates from each of the newly-developed prediction models were not significantly different from the estimates produced using BIS (all p ≥ 0.70) and revealed acceptable agreement (TBW: R2 = 0.91, RMSE = 3.24 L; ECF: R2 = 0.94, RMSE = 1.10 L; ICF: R2 = 0.87, RMSE = 2.29 L) when evaluated in the testing sample (n = 66), although proportional bias was observed (p < 0.001). …”
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