Machine Learning Approach for Predicting Hypertension Based on Body Composition in South Korean Adults by Jeong-Woo Seo, Sanghun Lee, Mi Hong Yim

“…In the hypertension prediction model, the most important variables for men were age, skeletal muscle mass (SMM), and body fat mass (BFM), in that order. …”
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Methodology for Developing a Composite Social Development Index (CSDI): Essential Decisions and Results by Mehdi Mobaraki, Zohre Shahbazi, Najme Mostafavi

“…However, measuring complex social issues, such as social development with their various dimensions and dispersed information as a single variable poses significant challenges. As a result, composite indicators are typically employed. …”
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Mechanical Performance of Group Stud Connectors in Steel–Concrete Composite Beams with Straddle Monorail by Lei-Ting Jiao, Zhen-Hao Wu, Yong-Fei Zhao, Ji-Zhi Zhao, Shu-Ke Wang

“…This study investigates the behavior of group stud connectors by conducting push-out tests on four specimens, comprising three full-scale models and one 1:3 scaled model. Variables such as the number of connectors, arrangement, and specimen size were explored. …”
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Random shear resistance of a headed-stud connector in composite steel-concrete beam by Tomasz Domański, Mariusz Maślak

“…On its basis, the degree of simplification of such evaluation is estimated, as well as its consequences, resulting from the use of a conventional standard model in this respect.…”
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Deciphering and predicting algal bloom variability using size-fractionated organic matter and machine learning in a complex watershed by Yun Kyung Lee, Haeseong Oh, Bo-Mi Lee, Jin Hur

“…Understanding the drivers of algal bloom variability is critical for managing eutrophication in freshwater reservoirs, yet most predictive models underrepresent the role of dissolved organic matter (DOM) composition. …”
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Linear Vibrations Analysis of a Composite Sandwich Conical Shell Manufactured by Additive Technologies by Kostiantyn V. Avramov, Borys V. Uspenskyi, Borys H. Liubarskyi, Oleksii A. Smetskykh

Machine-Learning-Driven Analysis of Wear Loss and Frictional Behavior in Magnesium Hybrid Composites by Barun Haldar, Hillol Joardar, Arpan Kumar Mondal, Nashmi H. Alrasheedi, Rashid Khan, Murugesan P. Papathi

“…The performance evaluation showed that ML models effectively predicted friction behavior and wear behavior of magnesium-based hybrid composites using tribological test data. …”
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Unlocking chickpea flour potential: AI-powered prediction for quality assessment and compositional characterisation by Ali Zia, Muhammad Husnain, Sally Buck, Jonathan Richetti, Elizabeth Hulm, Jean-Philippe Ral, Vivien Rolland, Xavier Sirault

“…However, the inherent variability in the composition of chickpea flour, influenced by genetic diversity, environmental conditions, and processing techniques, poses significant challenges to standardisation and quality control. …”
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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

“…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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SUMMARIA — A XAI Support Methodology by Generating Composite Linguistic Summaries of Qualitative Data by Carlos Rafael Rodríguez-Rodríguez, Yeleny Zulueta-Veliz, Dainys Gainza-Reyes

“…Abstract The main stream of Linguistic Data Summarization involves modeling numerical attributes using linguistic variables, which makes it difficult addressing real-world problems with qualitative or mixed data. …”
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Landscape Composition and Forest Structure Shape Phyllostomid Bat Assemblages in the Atlantic Forest Remnants by Ricardo Bovendorp, Eduardo Mariano-Neto, Albérico Queiroz, Deborah Faria

“…We applied structural equation modeling to test the direct and indirect effects of landscape and local variables. …”
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Body Composition Profiles and Associated Factors in Adolescents UndergoingLong-term Regular Exercise by WANG Yutong, GUO Xiaoyuan, DU Hanze, PAN Hui, WANG Wei, ZHANG Mei, BAN Bo, LI Ping, ZHANG Xinran, ZHANG Qiuping, SUN Hongshuang, LI Rong, CHEN Shi

“…Linear regression models examined associations between training type (direct-contact vs. non-contact sports) and follow-up body fat percentage, BMI, and waist circumference as dependent variables, adjusting for covariates.ResultsThe study included 110 adolescents (39 female, 71 male) with median age 13.21 years (IQR: 12.46-14.33). …”
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Spectral optical properties of soot: laboratory investigation of propane flame particles and their link to composition by J. Heuser, C. Di Biagio, J. Yon, M. Cazaunau, A. Bergé, A. Bergé, E. Pangui, M. Zanatta, M. Zanatta, L. Renzi, A. Marinoni, S. Inomata, C. Yu, V. Bernardoni, S. Chevaillier, D. Ferry, P. Laj, P. Laj, M. Maillé, D. Massabò, D. Massabò, F. Mazzei, F. Mazzei, G. Noyalet, H. Tanimoto, B. Temime-Roussel, R. Vecchi, V. Vernocchi, P. Formenti, B. Picquet-Varrault, J.-F. Doussin

“…The established relationship can provide a useful parameterisation for models to estimate the absorption from combustion aerosols and their BC and BrC contributions.…”
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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

“…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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Inverse Association of Longitudinal Variations in Fat Tissue Radiodensity and Area by Giulia Besutti, Marta Ottone, Efrem Bonelli, Simone Canovi, Roberto Farì, Francesco Farioli, Annarita Pecchi, Guido Ligabue, Massimo Pellegrini, Pierpaolo Pattacini, Paolo Giorgi Rossi

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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

“…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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Nutritional quality of Piptocoma discolor and Cratylia argentea as a non-timber forest products for animal feed in the Caquetá province by Faver Álvarez Carrillo, Fernando Casanoves, Yolanda Cuellar Medina, Jhoyner Felipe Ortiz Meneses, Victor Julio Balanta Martinez, Gustavo Adolfo Celis Parra

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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

“…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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Fish fauna and its environmental relationship in an endorheic basin of Zacatecas, Mexico by Fernando Solís-Carlos, Gorgonio Ruiz-Campos, María de Lourdes Lozano-Vilano, Juana Claudia Leyva-Aguilera

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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

“…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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