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The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive (e.g. in the case of depot operations) or highly inefficient (e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for low-speed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.
Within the framework of the project a genderand diversity-oriented teaching evaluation and modern, media-supported blended learning approaches were used in order to achieve the intended goals. First research results of the literature and status quo analysis were already implemented and tested in newly designed teaching approaches, for example in a multidisciplinary introductory lecture of civil engineering at RWTH Aachen University.
The main objective of the BATIMASS project was to address how the energy balance in relatively lightweight steel buildings can be improved by building in ‘active thermal mass’ (ATM) into the building fabric. This was achieved through concept design, dynamic thermal modelling and testing of a number of potentially viable systems and concepts. A significant programme of thermal simulation modelling was undertaken utilising the thermally equivalent slab (TES) concept to model the passive thermal capacity effect of profiled, composite metal floor decks. It is apparent from the modelling results that thermal mass is a highly complex phenomenon which is highly dependent upon building type, occupancy patterns, climate and many other aspects of the building design and servicing strategy. The ATM systems developed, both conceptually and for prototype testing, focussed on water-cooled composite slabs, the Cofradal floor system and the phase change material (PCM) Energain. In addition to laboratory testing of prototypes, whole building monitoring was undertaken at the Kubik building in Spain and the RWTH test building in Germany. Advanced thermal modelling was also undertaken to estimate the likely benefits of the ATM concept designs developed and for comparison with the test results. In addition to thermal testing, structural tests were conducted on composite floor specimens incorporating embedded water pipes. This Final Report presents the results of the activities carried out under this RFCS contract RFSR CT 2012 00033. The work carried out is reported in six major sections corresponding to the technical Work Packages of the project. Only summaries of the work carried out are provided in this report; all work undertaken is fully reported in the formal project deliverables.
Given the strong increase in regulatory requirements for business processes the management of business process compliance becomes a more and more regarded field in IS research. Several methods have been developed to support compliance checking of conceptual models. However, their focus on distinct modeling languages and mostly linear (i.e., predecessor-successor related) compliance rules may hinder widespread adoption and application in practice. Furthermore, hardly any of them has been evaluated in a real-world setting. We address this issue by applying a generic pattern matching approach for conceptual models to business process compliance checking in the financial sector. It consists of a model query language, a search algorithm and a corresponding modelling tool prototype. It is (1) applicable for all graph-based conceptual modeling languages and (2) for different kinds of compliance rules. Furthermore, based on an applicability check, we (3) evaluate the approach in a financial industry project setting against its relevance for decision support of audit and compliance management tasks.
We analyze the trading behavior of individual investors in option-like securities, namely bankissued warrants, and thus expand the growing literature of investors behavior to a new kind of securities. A unique data set from a large German discount broker gives us the opportunity to analyze the trading behavior of 1,454 investors, making 89,958 transactions in 6,724 warrants on 397 underlyings. In different logit regression, we make use of the facts that investors can speculate on rising and falling prices of the underlying with call and put warrants and that we also have information about the stock portfolios of the investors. We report several facts about the trading behavior of individual investors in warrants that are consistent with the literature on the behavior of individual investors in the stock market. The warrant investors buy calls and sell puts if the price of the underlying has decreased over the past trading days and they sell calls and buy puts if the price of the underlying has increased. That means, the investors follow negative feedback trading strategies in all four trading categories observed. In addition, we find strong evidence for the disposition effect for call as well as put warrants, which is reversed in December. The trading behavior is also influenced if the underlying reaches some exceptionally prices, e.g. highs, lows or the strike price. We show that hedging, as one natural candidate to buy puts, does not play an important role in the market for bank-issued warrants.
C-terminal truncation of a metagenome-derived detergent protease for effective expression in E. coli
(2010)
Recently, a new alkaline protease named HP70 showing highest homology to extracellular serine proteases of Stenotrophomonas maltophilia and Xanthomonas campestris was found in the course of a metagenome screening for detergent proteases (Niehaus et al., submitted for publication). Attempts to efficiently express the enzyme in common expression hosts had failed. This study reports on the realization of overexpression in Escherichia coli after structural modification of HP70. Modelling of HP70 resulted in a two-domain structure, comprising the catalytic domain and a C-terminal domain which includes about 100 amino acids. On the basis of the modelled structure the enzyme was truncated by deletion of most of the C-terminal domain yielding HP70-C477.
This structural modification allowed effective expression of active enzyme using E. coli BL21-Gold as the host. Specific activity of HP70-C477 determined with suc-l-Ala-l-Ala-l-Pro-l-Phe-p-nitroanilide as the substrate was 30 ± 5 U/mg compared to 8 ± 1 U/mg of the native enzyme. HP70-C477 was most active at 40 °C and pH 7–11; these conditions are prerequisite for a potential application as detergent enzyme. Determination of kinetic parameters at 40 °C and pH = 9.5 resulted in KM = 0.23 ± 0.01 mM and kcat = 167.5 ± 3.6 s⁻¹. MS-analysis of peptide fragments obtained from incubation of HP70 and HP70-C477 with insulin B indicated that the C-terminal domain influences the cleavage preferences of the enzyme. Washing experiments confirmed the high potential of HP70-C477 as detergent protease.
In this paper we present CAESAR, an intelligent domestic service robot. In domestic settings for service robots complex tasks have to be accomplished. Those tasks benefit from deliberation, from robust action execution and from flexible methods for human–robot interaction that account for qualitative notions used in natural language as well as human fallibility. Our robot CAESAR deploys AI techniques on several levels of its system architecture. On the low-level side, system modules for localization or navigation make, for instance, use of path-planning methods, heuristic search, and Bayesian filters. For face recognition and human–machine interaction, random trees and well-known methods from natural language processing are deployed. For deliberation, we use the robot programming and plan language READYLOG, which was developed for the high-level control of agents and robots; it allows combining programming the behaviour using planning to find a course of action. READYLOG is a variant of the robot programming language Golog. We extended READYLOG to be able to cope with qualitative notions of space frequently used by humans, such as “near” and “far”. This facilitates human–robot interaction by bridging the gap between human natural language and the numerical values needed by the robot. Further, we use READYLOG to increase the flexible interpretation of human commands with decision-theoretic planning. We give an overview of the different methods deployed in CAESAR and show the applicability of a system equipped with these AI techniques in domestic service robotics
Numerical models have become an essential part of snow avalanche engineering. Recent
advances in understanding the rheology of flowing snow and the mechanics of entrainment and
deposition have made numerical models more reliable. Coupled with field observations and historical
records, they are especially helpful in understanding avalanche flow in complex terrain. However, the
application of numerical models poses several new challenges to avalanche engineers. A detailed
understanding of the avalanche phenomena is required to specify initial conditions (release zone
dimensions and snowcover entrainment rates) as well as the friction parameters, which are no longer
based on empirical back-calculations, rather terrain roughness, vegetation and snow properties. In this
paper we discuss these problems by presenting the computer model RAMMS, which was specially
designed by the SLF as a practical tool for avalanche engineers. RAMMS solves the depth-averaged
equations governing avalanche flow with first and second-order numerical solution schemes. A
tremendous effort has been invested in the implementation of advanced input and output features.
Simulation results are therefore clearly and easily visualized to simplify their interpretation. More
importantly, RAMMS has been applied to a series of well-documented avalanches to gauge model
performance. In this paper we present the governing differential equations, highlight some of the input
and output features of RAMMS and then discuss the simulation of the Gatschiefer avalanche that
occurred in April 2008, near Klosters/Monbiel, Switzerland.