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In this study, the generalizability and distributivity of three different chaotic systems within an industrial robotics time series dataset are explained using an annotated artificial intelligence algorithm. A time series dataset derived from Industrial Robotics processes was created. The created data set was transformed into the Runge-Kutta system consisting of fourth-order differential equations for normalization. Among the processed data, data related to x-y-z position variables were passed through Lorenz, Chen and Rossler chaos systems and these data underwent chaotic transformation. The data of the x variable and angle variables among the x-y-z data that underwent chaotic transformation were put into the InterpretML model, which is an Annotated Artificial Intelligence model, and the effects of the angle variables on the x position variable were explained. As a result of the explanation, InterpretML Local analysis was determined with a sensitivity of 0.05 for the Rossler chaos system, 0.15 for Chen and 0.25 for Lorenz; Rossler explained the global analysis with 0.17 precision for the chaos system, Chen 0.255 and Lorenz 0.35. According to these sensitivity results, it has been observed that the Rossler chaos system provides more accurate results in InterpretML local and global analysis than other chaos systems and explains it more consistently. This study sheds important light on the literature on analyzing the distributive and generalization properties of chaos systems and understanding chaos systems.
In this study, the generalizability and distributivity of three different chaotic systems within an industrial robotics time series dataset are explained using an annotated artificial intelligence algorithm. A time series dataset derived from Industrial Robotics processes was created. The created data set was transformed into the Runge-Kutta system consisting of fourth-order differential equations for normalization. Among the processed data, data related to x-y-z position variables were passed through Lorenz, Chen and Rossler chaos systems and these data underwent chaotic transformation. The data of the x variable and angle variables among the x-y-z data that underwent chaotic transformation were put into the InterpretML model, which is an Annotated Artificial Intelligence model, and the effects of the angle variables on the x position variable were explained. As a result of the explanation, InterpretML Local analysis was determined with a sensitivity of 0.05 for the Rossler chaos system, 0.15 for Chen and 0.25 for Lorenz; Rossler explained the global analysis with 0.17 precision for the chaos system, Chen 0.255 and Lorenz 0.35. According to these sensitivity results, it has been observed that the Rossler chaos system provides more accurate results in InterpretML local and global analysis than other chaos systems and explains it more consistently. This study sheds important light on the literature on analyzing the distributive and generalization properties of chaos systems and understanding chaos systems.
COVID-19-associated intensive care unit (ICU) admissions were recognized as critical health issues that contributed to morbidity and mortality in SARS-CoV-2-infected patients. Severe symptoms in COVID-19 patients are often accompanied by cytokine release syndrome. Here, we analyzed publicly available data from the Yale IMPACT cohort to address immunological misfiring and sex differences in early COVID-19 patients. In 2020, SARS-CoV-2 was considered far more pathogenic and lethal than other circulating respiratory viruses, and the inclusion of SARS-CoV-2 negative patients in IMPACT cohorts confounds many findings. We ascertained the impact of several important biological variables such as days from symptom onset (DFSO); pre-existing risk factors, including obesity; and early COVID-19 treatments on significantly changed immunological measures in ICU-admitted COVID-19 patients that survived versus those that did not. Deceased patients had 19 unique measures that were not shared with ICU patients including increased granzyme-B-producing GzB+CD8+ T cells and interferon-γ. Male COVID-19 patients in ICU experienced many more changes in immunological and clinical measures than female ICU patients (25% vs. ~16%, respectively). A total of 13/124 measures including CCL5, CCL17, IL-18, IFNα2, Fractalkine, classical monocytes, T cells, and CD4Temra exhibited significant sex differences in female vs. male COVID-19 patients. A total of nine measures including IL-21, CCL5, and CD4Temra differed significantly between female and male healthy controls. Immunosuppressed patients experienced the most decreases in CD4Temra and CD8Tem cell numbers. None of the early COVID-19 treatments were effective in reducing levels of IL-6, a major component of the cytokine storm. Obesity (BMI >30) was the most impactful risk factor for COVID-19-related deaths and worst clinical outcomes. Our analysis highlights the contribution of biological sex, risk factors, and early treatments with respect to COVID-19-related ICU admission and progression to morbidity and mortality.
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