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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Multiscale computational framework linking alloy composition to microstructure evolution via machine learning and nanoscale analysis by Jaemin Wang, Hyeonseok Kwon, Sang-Ho Oh, Jae Heung Lee, Dae Won Yun, Hyungsoo Lee, Seong-Moon Seo, Young-Soo Yoo, Hi Won Jeong, Hyoung Seop Kim, Byeong-Joo Lee

“…Machine learning models trained on 750,000 CALPHAD-derived datapoints enabled rapid screening of two billion compositions based on thermodynamic criteria. …”
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Research on predicting the thermocompression deformation behavior of Mg–Li matrix composite using machine learning and traditional techniques by Dandan Li, Xiaoyu Hou, Yangfan Liu, Linhao Gu, Jinhui Wang, Jiaxuan Ma, Xiaoqiang Li, Zhi Jia, Qichi Le, Dexue Liu, Xincheng Yin

“…In this study, the Al3La/LAZ532 composite based on in-situ self-reaction technology was successfully prepared by adding La2O3 particles. …”
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The urinary microbiome distinguishes symptomatic urinary tract infection from asymptomatic older adult patients presenting to the emergency department by Evan S. Bradley, Celina Stansky, Abigail L. Zeamer, Ziyuan Huang, Lindsey Cincotta, Abigail Lopes, Linda Potter, Theresa Fontes, Doyle V. Ward, Vanni Bucci, Beth A. McCormick, John P. Haran

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