Risk of Rare Disasters, Euler Equation Errors and the Performance of the C-CAPM

This paper shows that the consumption-based asset pricing model (C-CAPM) with low-probability disaster risk rationalizes large pricing errors, i.e., Euler equation errors. This result is remarkable, since Lettau and Ludvigson (2009) show that leading asset pricing models cannot explain sizeable pricing errors in the C-CAPM. We also show (analytically and in a Monte Carlo study) that implausible estimates of risk aversion and time preference are not puzzling in this framework and emerge as a result of rational pricing errors. While this bias essentially removes the pricing error in the traditional endowment economy, a production economy with stochastically changing investment opportunities generates large and persistent empirical pricing errors

Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles

Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting

An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified

Molecular genetics of congenital atrial septal defects

Congenital heart defects (CHD) are the most common developmental errors in humans, affecting 8 out of 1,000 newborns. Clinical diagnosis and treatment of CHD has dramatically improved in the last decades. Hence, the majority of CHD patients are now reaching reproductive age. While the risk of familial recurrence has been evaluated in various population studies, little is known about the genetic pathogenesis of CHD. In recent years significant progress has been made in uncovering genetic processes during cardiac development. Data from human genetic studies in CHD patients indicate that the genetic aetiology was presumably underestimated in the past. Inherited mutations in genes encoding cardiac transcription factors and sarcomeric proteins were found as an underlying cause for familial recurrence of non-syndromic CHD in humans, in particular cardiac septal defects. Notably, the cardiac phenotypes most frequently seen in mutation carriers are ostium secundum atrial septal defects (ASDII). This review outlines experimental approaches employed for the detection of CHD-related genes in humans and summarizes recent findings in molecular genetics of congenital cardiac septal defects with an emphasis on ASDII

Evaluation of T1 relaxation time in prostate cancer and benign prostate tissue using a Modified Look-Locker inversion recovery sequence

Purpose of this study was to evaluate the diagnostic performance of T1 relaxation time (T1) for differentiating prostate cancer (PCa) from benign tissue as well as high- from low-grade PCa. Twenty-three patients with suspicion for PCa were included in this prospective study. 3 T MRI including a Modified Look-Locker inversion recovery sequence was acquired. Subsequent targeted and systematic prostate biopsy served as a reference standard. T1 and apparent diffusion coefficient (ADC) value in PCa and reference regions without malignancy as well as high- and low-grade PCa were compared using the Mann-Whitney U test. The performance of T1, ADC value, and a combination of both to differentiate PCa and reference regions was assessed by receiver operating characteristic (ROC) analysis. T1 and ADC value were lower in PCa compared to reference regions in the peripheral and transition zone (p < 0.001). ROC analysis revealed high AUCs for T1 (0.92; 95%-CI, 0.87-0.98) and ADC value (0.97; 95%-CI, 0.94 to 1.0) when differentiating PCa and reference regions. A combination of T1 and ADC value yielded an even higher AUC. The difference was statistically significant comparing it to the AUC for ADC value alone (p = 0.02). No significant differences were found between high- and low-grade PCa for T1 (p = 0.31) and ADC value (p = 0.8). T1 relaxation time differs significantly between PCa and benign prostate tissue with lower T1 in PCa. It could represent an imaging biomarker for PCa

Clinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms

Multidrug-resistant pathogens represent one of the biggest global healthcare challenges. Molecular diagnostics can guide effective antibiotics therapy but relies on validated, predictive biomarkers. Here we present a novel, universally applicable workflow for rapid identification of antimicrobial resistance (AMR) biomarkers from clinical Escherichia coli isolates and quantitatively evaluate the potential to recover causal biomarkers for observed resistance phenotypes. For this, a metagenomic plasmid library from 1,110 clinical E. coli isolates was created and used for high-throughput screening to identify biomarker candidates against Tobramycin (TOB), Ciprofloxacin (CIP), and Trimethoprim Sulfamethoxazole (TMP-SMX). Identified candidates were further validated in vitro and also evaluated in silico for their diagnostic performance based on matched genotype phenotype data. AMR biomarkers recovered by the metagenomics screening approach mechanistically explained 77% of observed resistance phenotypes for Tobramycin, 76% for Trimethoprim-Sulfamethoxazole, and 20% Ciprofloxacin. Sensitivity for Ciprofloxacin resistance detection could be improved to 97% by complementing results with AMR biomarkers that are undiscoverable due to intrinsic limitations of the workflow. Additionally, when combined in a multiplex diagnostic in silico panel, the identified AMR biomarkers reached promising positive and negative predictive values of up to 97 and 99%, respectively. Finally, we demonstrate that the developed workflow can be used to identify potential novel resistance mechanisms

Optimal design and operation of maritime energy systems based on renewable methanol and closed carbon cycles

The phasing out of fossil fuels in the shipping sector is of key importance for reducing greenhouse gas emissions. Synthetic fuels based on renewable energy are a promising option for a sustainable maritime sector, with renewable methanol being one of the most widely considered energy carriers. However, the availability of renewable methanol is still limited and the costs associated with it are significantly higher than for conventional fuels, also because fuel synthesis must rely on carbon dioxide as a resource. Through the use of onboard carbon capture, the release of carbon dioxide during combustion can be avoided, and this closed cycle reduces the need for carbon sources. This paper investigates such a scenario by analyzing overall ship energy systems that use internal combustion engines with connected pre-combustion and post-combustion carbon capture technologies. The effect of these technologies on the techno-economic performance of a fully renewable energy system is investigated by setting up a mixed-integer optimization framework for the optimal design and operation of ship propulsion systems. The propulsion demand for the chosen case study consists of a typical operational profile of a ferry operating in the Baltic Sea. Comparison of the capture cases to a system solely based on renewable methanol reveals significant cost advantages of the closed carbon cycle systems. The baseline scenario has nearly 20% lower annual costs, with total capture rates of 90% in the post-combustion case and around 40% in the pre-combustion case. An extensive sensitivity analysis shows that these cost advantages are robust against various technological and economic boundary conditions. In the pre-combustion case, process heat demand reduction in combination with increased engine heat supply might enable higher capture rates beyond 90%. The results indicate that combining renewable fuels with onboard carbon capture creates opportunities for cost-effective, sustainable shipping