Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its ...
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Quality of life (QOL) is how the individual perceives himself as having an impact on his physical-emotional integrity, functional capacity, and self-esteem. Quality of life assessment tools can be generic or specific....
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Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its ...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Myocarditis, characterized by inflammation of the heart muscle, has seen a notable 62.2% increase in incidence over the past three decades, leading to 324,490 deaths in 2019. Despite advancements in understanding its etiology, several critical questions remain unresolved, highlighting the need for improved patient treatment strategies. computational methods play a crucial role in unraveling the complex interactions between pathogens and the immune system. This study explores the use of Physics-Informed Neural Networks (PINNs) to accelerate a newly developed model for myocardial edema formation in acute infectious myocarditis. The model describes the concentrations of pathogens and leukocytes using a nonlinear system of ordinary differential equations (ODEs). Our findings demonstrate a mean computational speedup of 5.93, while maintaining a Root Mean Squared Error of 0.0015. These results indicate that PINNs can accurately replicate the ODE system’s solutions, offering substantial computational efficiency for this application.
Complex non-linear systems biology models comprise relevant knowledge on processes of pharmacological interest. They are, however, too complex to be used in inferential settings, for example, to allow for the estimati...
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The goal of this study is to present three different cases of using artificial intelligence to address challenges in the industry, aiming to foster the 4th industrial revolution. Fully convolutional networks (FCN), mu...
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The goal of this study is to present three different cases of using artificial intelligence to address challenges in the industry, aiming to foster the 4th industrial revolution. Fully convolutional networks (FCN), multilayer perceptron (MLP) and convolutional neural networks (CNN) were used, respectively, for estimating initial velocity models for oil and gas exploration, predicting the concentration of
Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support...
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This study examines a scenario combining a prolonged solar minimum, like the historical Maunder Minimum, with the increased CO2 emissions characteristic of the industrial era. Two scenarios were developed in NCAR/CESM...
This study examines a scenario combining a prolonged solar minimum, like the historical Maunder Minimum, with the increased CO2 emissions characteristic of the industrial era. Two scenarios were developed in NCAR/CESM 2.0 implemented at LAMMOC/UFF to create simulations from 1850 to 2000, it contrasts differing radiative forcings from 1950 onward—one reflecting actual observed changes, including rising CO2 levels, and the other simulating a decrease in solar output like that during the Maunder Minimum but with continued CO2 growth. The results were validated against ERA5 data and 20th-century reanalysis. By calculating meridional averages at 30-degree latitude intervals, distinct regional impacts of the Maunder Minimum were identified. Notably the simulated Maunder Minimum reduced global warming by and even mitigated 70 % in HS in the last decade of the 20th century. However, this attenuation was lower in the HN, especially in the 30–60N region where no attenuation was observed.
Complex non-linear systems biology models comprise relevant knowledge on processes of pharmacological interest. They are, however, too complex to be used in inferential settings, for example, to allow for the estimati...
Complex non-linear systems biology models comprise relevant knowledge on processes of pharmacological interest. They are, however, too complex to be used in inferential settings, for example, to allow for the estimation of patient-specific parameters for individual dose optimisation. Thus, there is a need for simple models with interpretable components to infer the drug effect in a clinical setting. In particular, it is essential to accurately quantify and simulate the interindividual variability in the drug response in order to account for covariates like body weight, age and genetic disposition. To this end, non-linear model order reduction and simplification methods can be used if they maintain model interpretability during reduction and consider an entire population rather than just a single reference individual. We present a sample-based approach for robust model order reduction and propose two improvements for efficiency. In particular, we introduce a new sampling method to generate the virtual population based on transformed latin hypercube sampling. Thereby, the sample is stratified in the relevant parameter-space directions, which are identified using empirical observability Gramians. We illustrate our approach in application to a blood coagulation pathway model, where we reduce the complexity from a 62-dimensional highly non-linear to a six-dimensional and a nine-dimensional system of ordinary differential equations for two scenarios, respectively.
This work presents a resource allocation algorithm that considers the characteristics of regular applications to choose the subset of processors, accelerators, and networks that minimize their parallel execution time ...
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Model-informed precision dosing (MIPD) using therapeutic drug/biomarker monitoring offers the opportunity to significantly improve the efficacy and safety of drug therapies. Current strategies comprise model-informed ...
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