Cold start is one of the most problematic combustion phases for diesel engines. During this phase, a large proportion of pollutants are produced within the cylinder due to misfiring and incomplete combustion due to the low engine temperatures. Furthermore, exhaust after-treatment devices work...

In this paper we present a strategy for speeding up the estimation of expected maximum flows through reliable networks. Our strategy tries to minimize the repetition of computational effort while evaluating network states sampled using the crude Monte Carlo method. Computational experiments with...

Successful and rapid startup of proton exchange membrane fuel cells (PEMFCs) at subfreezing temperatures (also called cold start) is of great importance for their commercialization in automotive and portable devices. In order to maintain good proton conductivity, the water content in the...

Cold start is one of the most problematic combustion phases for diesel engines. During this phase, a large proportion of pollutants are produced within the cylinder due to misfiring and incomplete combustion due to the low engine temperatures. Furthermore, exhaust after-treatment devices work...

Die Qualitätssicherung bei der Produktion von Solarzellen ist ein entscheidender Faktor, um langfristige Leistungsgarantien auf Solarpanels gewähren zu können. Die vorliegende Arbeit leistet hierzu einen Beitrag zur automatisierten Fehlererkennung auf Wafern, indem Elektrolumineszenz-Bilder...

Building on the extensive production of provenance data recently, this article explains how we can expand the purview of computational analysis in humanistic and social sciences by exploring how digital methods can be applied to provenances. Provenances document chains of events of ownership and...

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency....