scholarly journals Symbolic Input-Output Conformance Checking for Model-Based Mutation Testing

2016 ◽  
Vol 320 ◽  
pp. 3-19 ◽  
Author(s):  
Bernhard K. Aichernig ◽  
Martin Tappler
Keyword(s):  
Mutation Testing ◽  
Input Output ◽  
Conformance Checking ◽  
Model Based

scholarly journals Model-based mutation testing via symbolic refinement checking

2015 ◽  
Vol 97 ◽  
pp. 383-404 ◽  
Author(s):  
Bernhard K. Aichernig ◽  
Elisabeth Jöbstl ◽  
Stefan Tiran
Keyword(s):  
Mutation Testing ◽  
Model Based ◽  
Refinement Checking

scholarly journals Carbon emissions and driving forces of China’s power sector: Input-output model based on the disaggregated power sector

2020 ◽  
Vol 268 ◽  
pp. 121925 ◽  
Author(s):  
Fang Luo ◽  
Yi Guo ◽  
Mingtao Yao ◽  
Wenqiu Cai ◽  
Meng Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Driving Forces ◽  
Input Output ◽  
Power Sector ◽  
Model Based

scholarly journals Model-based mutation testing—Approach and case studies

2016 ◽  
Vol 120 ◽  
pp. 25-48 ◽  
Author(s):  
Fevzi Belli ◽  
Christof J. Budnik ◽  
Axel Hollmann ◽  
Tugkan Tuglular ◽  
W. Eric Wong
Keyword(s):  
Case Studies ◽  
Mutation Testing ◽  
Model Based ◽  
Testing Approach

Specification-Driven Conformance Checking for Virtual/Silicon Devices using Mutation Testing

2020 ◽  
pp. 1-1
Author(s):  
Haifeng Gu ◽  
Jianning Zhang ◽  
Mingsong Chen ◽  
Tongquan Wei ◽  
Li Lei ◽  
...  
Keyword(s):  
Mutation Testing ◽  
Conformance Checking ◽  
Silicon Devices

scholarly journals Quantification of dynamic cerebral autoregulation and CO2 dynamic vasomotor reactivity impairment in essential hypertension

2020 ◽  
Vol 128 (2) ◽  
pp. 397-409
Author(s):  
Vasilis Z. Marmarelis ◽  
Dae C. Shin ◽  
Mareike Oesterreich ◽  
Martin Mueller
Keyword(s):  
Essential Hypertension ◽  
Cerebral Autoregulation ◽  
Input Output ◽  
Physiological Mechanisms ◽  
Control Subjects ◽  
Vasomotor Reactivity ◽  
Model Based ◽  
Arterial Blood ◽  
Transfer Function Analysis ◽  
Dynamic Cerebral Autoregulation

The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO2 tension are also taken into account, because of the significant effects of CO2 dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different ( P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms. NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.

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