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In the grinding process, acceleration signals in both the time and frequency domains are valuable for monitoring and controlling vibration patterns, as factors such as rotational speed and the grinding head design significantly influence machining quality, efficiency, and finishing performance. This study analyzes the acceleration signals by dividing them into three distinct stages, pairing this analysis with microscopic morphology to investigate the grinding behavior of carbon fiber-reinforced polymer (CFRP). The findings reveal that high-frequency and low-amplitude vibrations enhance polishing efficiency and quality, whereas low-frequency and high amplitudes adversely affect grinding quality. Acceleration vibrations are more stable during the intermediate grinding stage compared to the initial and final stages, which helps reduce surface roughness, regardless of the rotational speed or grinding head mesh size. In addition, a coarse mesh (#40) results in an uneven surface due to a large amount of removed material, whereas a fine one (#120) results in lower material removal but continuous vertical vibrations due to the impact with the grinding surface, also resulting in poor surface quality. Thus, controlling the tool's size and rotational speed is essential in reducing the amplitude of the vibration, allowing for maximizing the grinded CFRP surface quality. [ABSTRACT FROM AUTHOR]

BackgroundCardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. ObjectiveThe aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. MethodsRetrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). ResultsThe cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. ConclusionsOur case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.