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Block ramps are ecologically oriented drop structures with adequate energy dissipation and partially moderate flow velocities. A special case is given with crossbar block ramps, where the upstream and downstream level difference is reduced by a series of basins. To prevent the total structure from failing, the stability of single boulders within the crossbars and the bed material in between must be guaranteed. The present paper addresses the stability of bed material and scour development for various flow regimes. Any bed material erosion may affect the stability of the crossbar boulders, which in turn can result in major damages of the ramp. Therefore new design approaches are developed to choose an appropriate bed material size and to avoid failures of crossbar block ramp structures.
In this work, a spore-based biosensor is evaluated to monitor the microbicidal efficacy of sterilization processes applying gaseous hydrogen peroxide (H2O2). The sensor is based on interdigitated electrode structures (IDEs) that have been fabricated by means of thin-film technologies. Impedimetric measurements are applied to study the effect of sterilization process on spores of Bacillus atrophaeus. This resilient microorganism is commonly used in industry to proof the sterilization efficiency. The sensor measurements are accompanied by conventional microbiological challenge tests, as well as morphological characterizations with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The sensor measurements are correlated with the microbiological test routines. In both methods, namely the sensor-based and microbiological one, a tailing effect has been observed. The results are evaluated and discussed in a three-dimensional calibration plot demonstrating the sensor's suitability to enable a rapid process decision in terms of a successfully performed sterilization.
The sterilization of packages in aseptic food processes is highly significant to maintain a consumer-safe product with extended shelf-life. Today, the sterilization of food packages is predominantly accomplished by gaseous hydrogen peroxide (H2O2) in combination with heat. In order to monitor this sterilization process, calorimetric gas sensors as differential set-up of two platinum temperature sensors representing a catalytically active (additionally deposition of MnO2) and a passive segment have been recently developed. The temperature rise of the exothermic decomposition serves as an indicator of the present H2O2 concentration. In the present work, a theoretical approach considering the sensor’s thermochemistry and physical transport phenomena was formulated to evaluate the temperature rise based on the energy content of gaseous H2O2. In a further part of this work, three polymers have been analyzed with respect to their application as passivation materials. The examined polymers are photoresist SU-8, perfluoroalkoxy (PFA) and fluorinated ethylene propylene (FEP). Thermal analyses by means of differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) have been conducted to determine the operation limits of the polymers. The overall chemical resistance and stability of the polymers against the harsh environmental conditions during the sterilization process have been examined by attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR).
In this work, the catalyst manganese(IV) oxide (MnO2), of calorimetric gas sensors (to monitor the sterilization agent vaporized hydrogen peroxide) has been investigated in more detail. Chemical analyses by means of X-ray-induced photoelectron spectroscopy have been performed to unravel the surface chemistry prior and after exposure to hydrogen peroxide vapor at elevated temperature, as applied in the sterilization processes of beverage cartons. The surface characterization reveals a change in oxidation states of the metal oxide catalyst after exposure to hydrogen peroxide. Additionally, a cleaning effect of the catalyst, which itself is attached to the sensor surface by means of a polymer interlayer, could be observed.
In this work, a sensor to evaluate sterilization processes with hydrogen peroxide vapor has been characterized. Experimental, analytical and numerical methods were applied to evaluate and study the sensor behavior. The sensor set-up is based on planar interdigitated electrodes. The interdigitated electrode structure consists of 614 electrode fingers spanning over a total sensing area of 20 mm2. Sensor measurements were conducted with and without microbiological spores as well as after an industrial sterilization protocol. The measurements were verified using an analytical expression based on a first-order elliptical integral. A model based on the finite element method with periodic boundary conditions in two dimensions was developed and utilized to validate the experimental findings.
Sterilisation processes are compulsory in medicine, pharmacy, and food industries to prevent infections of consumers and microbiological contaminations of products. Monitoring the sterilisation by conventional microbiological methods is time- and lab-consuming. To overcome this problem, in this work a novel biosensor has been proposed. The sensor enables a fast method to evaluate sterilisation processes. By means of thin-film technology the sensor's transducer structures in form of IDEs (interdigitated electrodes) have been fabricated on a silicon substrate. Physical characterisation of the developed sensor was done by AFM, SEM, and profilometry. Impedance analyses were conducted for the electrical characterisation. As microbiological layer spores of B. atrophaeus have been immobilised on the sensing structure; spores of this type are a well-known sterilisation test organism. Impedance measurements at a fixed frequency over time were performed to monitor the immobilisation process. A sterilisation process according to aseptic filling machines was applied to demonstrate the sensor functionality. After both, immobilisation and sterilisation, a change in impedance could successfully be detected.
Development of a subject-oriented reference process model for the telecommunications industry
(2016)
Generally the usage of reference models can be structured top-down or bottom-up. The practical need of agile change and flexible organizational implementation requires a consistent mapping to an operational level. In this context, well-established reference process models are typically structured top-down. The subject-oriented Business Process Management (sBPM) offers a modeling concept that is structured bottom-up and concentrates on the process actors on an
operational level. This paper applies sBPM to the enhanced Telecom Operations Map (eTOM), a well-accepted reference process model in the telecommunications industry. The resulting design artifact is a concrete example for a combination of a bottom-up and top-down developed reference model. The results are evaluated and confirmed in practical context through the involvement of the industry body TMForum.
The impact of surgical staplers on tissues has been studied mostly in an empirical manner. In this paper, finite element method was used to clarify the mechanics of tissue stapling and associated phenomena. Various stapling modalities and several designs of circular staplers were investigated to evaluate the impact of the device on tissues and mechanical performance of the end-to-end colorectal anastomosis. Numerical simulations demonstrated that a single row of staples is not adequate to resist leakage due to non-linear buckling and opening of the tissue layers between two adjacent staples. Compared to the single staple row configuration, significant increase in stress experienced by the tissue at the inner staple rows was observed in two and three rows designs. On the other hand, adding second and/or third staple row had no effect on strain in the tissue inside the staples. Variable height design with higher staples in outer rows significantly reduced the stresses and strains in outer rows when compared to the same configuration with flat cartridge.
Temperature-dependent ranges of coexistence in a model of a two-prey-one-predator microbial food web
(2012)
The objective of our study was to analyze the effects of temperature on the population dynamics of a three-species food web consisting of two prey bacteria (Pedobacter sp. and Acinetobacter johnsonii) and a protozoan predator (Tetrahymena pyriformis) as model organisms. We assessed the effects of temperature on the growth rates of all three species with the objective of developing a model with four differential equations based on the experimental data. The following hypotheses were tested at a theoretical level: Firstly, temperature changes can affect the dynamic behavior of a system by temperature-dependent parameters and interactions and secondly, food web response to temperature cannot be derived from the single species temperature response. The main outcome of the study is that temperature changes affect the parameter range where coexistence is possible within all three species. This has significant consequences on our ideas regarding the evaluation of effects of global warming.
Synthesis of derivatives of the peptide sequence L-pyroglutamyl-L-phenylalanyl-L-aspartyl-glycyl-L-lysyl-glycyl-glycyl-glycine as the antigenic determinant representing the N-terminal non-helical region of the α-2-chain of rabbit skin collagen, and conjugation to two different polypeptide carriers, are described.
Software Stories Guide
(2017)
The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.
[Skripte]
(2008)
In this paper we investigate the use of deep neural networks for 3D object detection in uncommon, unstructured environments such as in an open-pit mine. While neural nets are frequently used for object detection in regular autonomous driving applications, more unusual driving scenarios aside street traffic pose additional challenges. For one, the collection of appropriate data sets to train the networks is an issue. For another, testing the performance of trained networks often requires tailored integration with the particular domain as well. While there exist different solutions for these problems in regular autonomous driving, there are only very few approaches that work for special domains just as well. We address both the challenges above in this work. First, we discuss two possible ways of acquiring data for training and evaluation. That is, we evaluate a semi-automated annotation of recorded LIDAR data and we examine synthetic data generation. Using these datasets we train and test different deep neural network for the task of object detection. Second, we propose a possible integration of a ROS2 detector module for an autonomous driving platform. Finally, we present the performance of three state-of-the-art deep neural networks in the domain of 3D object detection on a synthetic dataset and a smaller one containing a characteristic object from an open-pit mine.
The work in modern open-pit and underground mines requires the transportation of large amounts of resources between fixed points. The navigation to these fixed points is a repetitive task that can be automated. The challenge in automating the navigation of vehicles commonly used in mines is the systemic properties of such vehicles. Many mining vehicles, such as the one we have used in the research for this paper, use steering systems with an articulated joint bending the vehicle’s drive axis to change its course and a hydraulic drive system to actuate axial drive components or the movements of tippers if available. To address the difficulties of controlling such a vehicle, we present a model-predictive approach for controlling the vehicle. While the control optimisation based on a parallel error minimisation of the predicted state has already been established in the past, we provide insight into the design and implementation of an MPC for an articulated mining vehicle and show the results of real-world experiments in an open-pit mine environment.
In times of social climate protection movements, such as Fridays for Future, the priorities of society, industry and higher education are currently changing. The consideration of sustainability challenges is increasing. In the context of sustainable development, social skills are crucial to achieving the United Nations Sustainable Development Goals (SDGs). In particular, the impact that educational activities have on people, communities and society is therefore coming to the fore. Research has shown that people with high levels of social competence are better able to manage stressful situations, maintain positive relationships and communicate effectively. They are also associated with better academic performance and career success. However, especially in engineering programs, the social pillar is underrepresented compared to the environmental and economic pillars.
In response to these changes, higher education institutions should be more aware of their social impact - from individual forms of teaching to entire modules and degree programs. To specifically determine the potential for improvement and derive resulting change for further development, we present an initial framework for social impact measurement by transferring already established approaches from the business sector to the education sector. To demonstrate the applicability, we measure the key competencies taught in undergraduate engineering programs in Germany.
The aim is to prepare the students for success in the modern world of work and their future contribution to sustainable development. Additionally, the university can include the results in its sustainability report. Our method can be applied to different teaching methods and enables their comparison.
A new trend in automation is to deploy so-called cyber-physical systems (CPS) which combine computation with physical processes. The novel RoboCup Logistics League Sponsored by Festo (LLSF) aims at such CPS logistic scenarios in an automation setting. A team of robots has to produce products from a number of semi-finished products which they have to machine during the game. Different production plans are possible and the robots need to recycle scrap byproducts. This way, the LLSF is a very interesting league offering a number of challenging research questions for planning, coordination, or communication in an application-driven scenario. In this paper, we outline the objectives of the LLSF and present steps for developing the league further towards a benchmark for logistics scenarios for CPS. As a major milestone we present the new automated referee system which helps in governing the game play as well as keeping track of the scored points in a very complex factory scenario.
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.