Efficient time-dependent system reliability analysis

Engineering systems are usually subjected to time-variant loads and operate under time-dependent uncertainty; system performances are therefore time-dependent. Accurate and efficient estimate of system reliability is crucial for decision makings on system design, lifetime cost estimate, maintenance strategy, etc. Although significant progresses have been made in time-independent reliability analysis for components and systems, time-dependent system reliability methodologies are still limited. This dissertation is motivated by the need of accurate and effective reliability prediction for engineering systems under time-dependent uncertainty. Based on the classic First and Second Order Reliability Method (FORM and SORM), a system reliability method is developed for multidisciplinary systems involving stationary stochastic processes. A dependent Kriging method is also developed for general components. This method accounts for dependent responses from surrogate models and is therefore more accurate than existing Kriging Monte Carlo simulation methods that neglect the dependence between responses. The extension of the dependent Kriging method to systems is also a contribution of this dissertation. To overcome the difficulty of obtaining extreme value distributions and get rid of global optimization with a double-loop procedure, a Kriging surrogate modeling method is also proposed. This method provides a new perspective of surrogate modeling for time-dependent systems and is applicable to general systems having random variables, time, and stochastic processes. The proposed methods are evaluated through a wide range of engineering systems, including a compound cylinders system, a liquid hydrogen fuel tank, function generator mechanisms, slider-crank mechanisms, and a Daniels system --Abstract, page iv

Fluoxetine Protects against Big Endothelin-1 Induced Anti-Apoptosis by Rescuing Kv1.5 Channels in Human Pulmonary Arterial Smooth Muscle Cells

∙ The authors have no financial conflicts of interest. © Copyright: Yonsei University College of Medicine 2012 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial Licens

Transcriptional up-regulation of relaxin-3 by Nur77 attenuates β-adrenergic agonist-induced apoptosis in cardiomyocytes.

The relaxin family peptides have been shown to exert several beneficial effects on the heart, including anti-apoptosis, anti-fibrosis, and anti-hypertrophy activity. Understanding their regulation might provide new opportunities for therapeutic interventions, but the molecular mechanism(s) coordinating relaxin expression in the heart remain largely obscured. Previous work demonstrated a role for the orphan nuclear receptor Nur77 in regulating cardiomyocyte apoptosis. We therefore investigated Nur77 in the hopes of identifying novel relaxin regulators. Quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) data indicated that ectopic expression of orphan nuclear receptor Nur77 markedly increased the expression of latexin-3 (RLN3), but not relaxin-1 (RLN1), in neonatal rat ventricular cardiomyocytes (NRVMs). Furthermore, we found that the -adrenergic agonist isoproterenol (ISO) markedly stimulated RLN3 expression, and this stimulation was significantly attenuated in Nur77 knockdown cardiomyocytes and Nur77 knockout hearts. We showed that Nur77 significantly increased RLN3 promoter activity via specific binding to the RLN3 promoter, as demonstrated by electrophoretic mobility shift assay (EMSA) and chromatin immuno-precipitation (ChIP) assays. Furthermore, we found that Nur77 overexpression potently inhibited ISO-induced cardiomyocyte apoptosis, whereas this protective effect was significantly attenuated in RLN3 knockdown cardiomyocytes, suggesting that Nur77-induced RLN3 expression is an important mediator for the suppression of cardiomyocyte apoptosis. These findings show that Nur77 regulates RLN3 expression, therefore suppressing apoptosis in the heart, and suggest that activation of Nur77 may represent a useful therapeutic strategy for inhibition of cardiac fibrosis and heart failure. © 2018 You et al

System Reliability Analysis With Autocorrelated Kriging Predictions

When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples

Cities: the core of climate change mitigation

Cities, the core of the global climate change mitigation and strategic low-carbon development, are shelters to more than half of the world population and responsible for three quarters of global energy consumption and greenhouse gas (GHG). This special volume (SV) provides a platform that promotes multi- and inter- disciplinary analyses and discussions on the climate change mitigation for cities. All papers are divided into four themes, including GHG emission inventory and accounting, climate change and urban sectors, climate change and sustainable development, and strategies and mitigation action plans. First, this SV provides methods for constructing emission inventory from both production and consumption perspectives. These methods are useful to improve the comprehensiveness and accuracy of carbon accounting for international cities. Second, the climate change affects urban sectors from various aspects; simultaneously, GHG emissions caused by activities in urban sectors affect the climate system. This SV focuses on mitigation policies and assessment of energy, transport, construction, and service sectors. Third, climate change mitigation of cities is closely connected to urban sustainable development. This SV explores the relationships between climate change mitigation with urbanization, ecosystems, air pollution, and extreme events. Fourth, climate change mitigation policies can be divided into two categories: quantity-based mechanism (e.g., carbon emission trading) and price-based mechanism (e.g., carbon tax). This SV provides experiences of local climate change mitigation all over the world and proposes the city-to-city cooperation on climate change mitigation

The Maximal Total Irregularity of Bicyclic Graphs

In 2012, Abdo and Dimitrov defined the total irregularity of a graph G=(V,E) as irrtG=1/2∑u,v∈VdGu-dGv, where dGu denotes the vertex degree of a vertex u∈V. In this paper, we investigate the total irregularity of bicyclic graphs and characterize the graph with the maximal total irregularity among all bicyclic graphs on n vertices

Socioeconomic impact assessment of China's CO2 emissions peak prior to 2030

China is the largest emitter of carbon emissions in the world. In this paper, we present an Integrated Model of Economy and Climate (IMEC), an optimization model based on the input-output model. The model is designed to assess the tradeoff between emission deceleration and economic growth. Given that China's projected average growth rate will exceed 5% over the next two decades, we find that China may reach its peak CO2 emissions levels by 2026. According to this scenario, China's carbon emissions will peak at 11.20 Gt in 2026 and will then decline to 10.84 Gt in 2030. Accordingly, approximately 22 Gt of CO2 will be removed from 2015 to 2035 relative to the scenario wherein China's CO2 emissions peak in 2030. While this earlier peaking of carbon emissions will result in a decline in China's GDP, several sectors, such as Machinery and Education, will benefit. In order to reach peak CO2 emissions by 2026, China needs to reduce its annual GDP growth rate to less than 4.5% by 2030 and decrease energy and carbon intensity levels by 43% and 45%, respectively, from 2015 to 2030

Hexagonal Boron Nitride Thick Film Grown on a Sapphire Substrate via Low-Pressure Chemical Vapor Deposition

Hexagonal boron nitride (h-BN) with a certain thickness has wide applications in semiconductor electronic devices. In this study, the relationship between the amount of ammonia borane and the thickness of h-BN films was investigated via low-pressure chemical vapor deposition (LPCVD) on a noncatalytic c-plane Al2O3 substrate. Through various characterization methods, the grown film was confirmed to be h-BN. The effect of the precursor mass on the growth thickness of the h-BN film was studied, and it was found that the precursor mass significantly affected the growth rate of the h-BN film. The results from SEM show that the amount of ammonia borane is 2000 mg and a 1.295-μm h-BN film is obtained. It will provide an experimental reference for the growth of thicker h-BN materials to prepare high-efficiency neutron detectors for radiation detection