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    Multivariate Data Analysis to Assess Process Evolution and Systematic Root Causes Investigation in Tablet Manufacturing at an Industrial Scale—A Case Study Focused on Improving Tab... by Rita Mathe, Tibor Casian, Ioan Tomuta

    Published 2025-02-01
    “…The purpose of this work was to identify the root causes for the low and variable hardness of core tablets prepared using high-shear wet granulation through batch statistical modeling and to verify the short- and long-term effectiveness of the improvement actions. …”
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    Machine learning prediction of metabolic dysfunction-associated fatty liver disease risk in American adults using body composition: explainable analysis based on SHapley Additive e... by Yan Hong, Xinrong Chen, Ling Wang, Fan Zhang, ZiYing Zeng, Weining Xie

    Published 2025-06-01
    “…This study aimed to develop and validate machine learning (ML) models for predicting MAFLD using detailed body composition metrics and to explore the relative contributions of adipose tissue features through explainable ML techniques.MethodsData from the 2017–2018 National Health and Nutrition Examination Survey (NHANES) were used to construct predictive models based on anthropometric, demographic, lifestyle, and clinical variables. …”
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    A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

    Published 2025-05-01
    “…Additionally, we use meteorological data from a set of sensors installed at a meteorological station located in Belém to train multivariate statistical and machine learning (ML) models to predict precipitation. Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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    Preliminary analysis of patient assessment within a study on the impact of hydrokinetotherapy on body composition and metabolic disease risk in an adult population segment by Claudiu Cobuz, Sînziana Călina Silișteanu, Maricela Cobuz, Andrei-Ioan Costea, Elena Vizitiu Lakhdari

    Published 2025-12-01
    “…Similarly, the BMI data for men aged 40-60 indicated notable variability in body composition, consistent with the findings for men aged 61-80. …”
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    Fluid-Structure Interaction of Vibrating Composite Piezoelectric Plates Using Exponential Shear Deformation Theory by Korosh Khorshidi, Mahdi Karimi

    Published 2020-04-01
    “…After deriving the governing equations applying Hamilton’s principle, the natural frequencies of the fluid-structure system with simply supported boundary conditions are computed using the Galerkin method. The model is compared to the available results in the literature, and consequently the effects of different variables such as depth of fluid, the width of fluid, plate thickness, and aspect ratio on natural frequencies and mode shapes are displayed.…”
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    Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition by Zhaoxiong Han

    Published 2025-01-01
    “…The dataset comprises categorical information on Cation and Anion, along with numeric variables T(K) and xIL (mol%), serving as inputs for the models, while Viscosity (Pa.s) represents the output variable. …”
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    Analytical investigation on resolution calculation method for nonlinear temperature load of steel-concrete composite girders by Chun-Ming Zhang, Wei-Hong Wu, Wei Xian, Wei Xian

    Published 2025-05-01
    “…The analysis encompasses temperature-induced self-restraint stresses, secondary stresses, and axial deformation in variable-section continuous composite girders. Key findings reveal that code-specified thermal stresses exhibit opposing polarity characteristics at specific locations compared to other models. …”
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    Learning compositional sequences with multiple time scales through a hierarchical network of spiking neurons. by Amadeus Maes, Mauricio Barahona, Claudia Clopath

    Published 2021-03-01
    “…The hierarchical model redistributes the variability: it achieves high motif fidelity at the cost of higher variability in the between-motif timings.…”
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  18. 318

    Experimental Study on Fatigue Performance of Composite Box Girder With Steel Bottom Plate and Corrugated Steel Webs by Shijin Wang, Genhui Wang, Jiang Fan, Zichen Zhang

    Published 2024-01-01
    “…In order to study the fatigue performance and fatigue failure mode of the composite box girder with steel bottom plate and corrugated webs (SBCSWs), the model beam was made and the constant and variable amplitude fatigue experiment were performed. …”
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    Low-noise optimization design for underwater series-connected multi-sphere composite shell structure by Hefan LI, Guanjun ZHANG, Yuzhao KE

    Published 2025-06-01
    “…The radial basis function (RBF) neural network was used to establish a multidimensional mapping model between the design variables and the optimization objective. …”
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    Dynamic changes in AI-based analysis of endometrial cellular composition: Analysis of PCOS and RIF endometrium by Seungbaek Lee, Riikka K. Arffman, Elina K. Komsi, Outi Lindgren, Janette Kemppainen, Keiu Kask, Merli Saare, Andres Salumets, Terhi T. Piltonen

    Published 2024-12-01
    “…In conclusion, the AI model can potentially improve endometrial histology assessment by accelerating the analysis of the cellular composition of the tissue and by ensuring maximal objectivity for research and clinical purposes.…”
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