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In the time of increased awareness about the environment problems by the public opinion and also intensive international efforts to reduce emissions of greenhouse gases, as well increase of the generation of electrical energy to facilitate industrial growth, the conference offers broad contribution towards achieving the goals of diversification and sustainable development.
Focus of the student conference is to promote the discussion of views from scientists and students from Wroclaw University of Technology, Technical University of Ostrava and Brandenburg University of Technology Cottbus-Senftenberg.
The conference offers prominent academics and industrial practitioners from all over the world the forum for discussion about the future of electrical energy and environmental issues and presents a base for identifying directions for continuation of research.
In the time of increased awareness about the environment problems by the public opinion and also intensive international efforts to reduce emissions of greenhouse gases, as well increase of the generation of electrical energy to facilitate industrial growth, the conference offers broad contribution towards achieving the goals of diversification and sustainable development.
Focus of the student conference is to promote the discussion of views from scientists and students from Wroclaw University of Technology, Technical University of Ostrava and Brandenburg University of Technology Cottbus-Senftenberg.
The conference offers prominent academics and industrial practitioners from all over the world the forum for discussion about the future of electrical energy and environmental issues and presents a base for identifying directions for continuation of research.
Tax effects on asset prices
(2018)
In this dissertation I treat questions of asset pricing under the presence of taxes. I present four published articles. The first three articles are concerned with topics of company valuation when debt is risky. The first article analyzes the applicable discount rate for the valuation of tax savings in a simple setting without taxes on cancelled debt. The article concludes that the discount rate of tax savings is the same as the one for interest payments. Other than frequently assumed, this discount rate is not necessarily the same as the discount rate for debt as a whole. With the prioritization of interest or principal payments in case of losses on debt payments, the discount rates on interest payments and of tax savings are regularly different from the one for the overall debt issue. Only a pro rata distribution of losses on principal and interest payments generally leads to equal discount rates for interest payments, principal payments, and, therefore, also for debt payments as a whole. The second article continues to look at the valuation of tax savings. It differentiates the case with and the one without the taxation of cancelled debt. For both cases, the article derives equations for the value of tax savings as well as for risk-adjusted discount rates and WACC-like equations. A major finding is that the previous corporate finance literature on this topic usually makes the implicit assumption that cancelled debt is taxed. In this case valuation equations have a simple form since they are independent from the distribution of losses between interest and principal payments. Without the taxation of cancelled debt, the distribution of losses on interest and principal payments becomes important for the valuation procedures and a differentiation between cases such as interest prioritization, principal prioritization and pro rata loss distribution is necessary. The third article uses the findings of the first two articles and constructs equations for a de- and re-levering procedure using the expected return equations from the mean-variance CAPM. It extends the regularly used procedure that uses the assumption of risk-free debt to simple settings with risky debt. The forth article looks at two economies, which differ only with respect to taxation - one features taxes on capital gains and one does not. The analysis leads to several extensions on prior findings on the question of the conditions under which asset prices are the same in both economies. The article provides sufficient conditions for unchanged prices for the case of a zero risk-free rate, which entails that all agents consume exactly the same in each state. Without a zero risk-free rate, prices are the same in both economies with exponential utility and normal returns and with linear marginal utility.
Cameroon’s forest is one of the richest ecosystems in the Congo Basin and in Africa as a whole in terms of its biodiversity. These forest types are currently subjected to multiple categories of threats. By enacting a new forestry law in 1994, the government of Cameroon intended to intensify efforts toward the protection and conservation of this rich biodiversity. This study aimed to compare two forest management systems, a state management system (the case of Takamanda National Park) and a community-based management system (the case of Bimbia- Bonadikombo Community Forest), to determine which management system better conserves and protects the forest against biodiversity loss. The study applied a methodological framework that made use of selected indicators and criteria to evaluate the extent of sustainability of the two forest management systems and challenges faced in implementing them. Both quantitative and qualitative results were realized through the administration of questionnaires, semi-structure interview and in-depth contents analysis of Law No.94-1 of 20th January 1994 that lays down forestry, wildlife and fisheries regulations and the 1996 Environmental Management Law that directs Cameroon’s compliance to the international standard of protecting the environment.Results indicated that community-based system of forest management is a much more sustainable approach of forest management than a state management system. Based on the criterion of cultural values attributed to natural resources in the community-based management zone (Bimbia- Bonadikombo Community Forest), 78.2% of the local communities were more willing to protect biodiversity as opposed to 48.3% from state management zone (Takamanda National Park). This study found that the level of participation in the community-based management system was much more inclusive and transparent. On the other hand, in the state management system, a high level of corruption, lack of transparency, delayed and irregular salaries of forest guards and minimal participation of local communities in forest management decision-making was noted and likely responsible for the ineffectiveness and unsustainable management efforts in this system. The study recommends the adoption and implementation of a more inclusive, transparent and accountable management in the state managed system (Takamada national park), particularly the full involvement of respected Elites, Chiefs and Traditional Councils.
Sophisticated engine knock modeling supports the optimization of the thermal efficiency of spark ignition engines. For this purpose the presented work introduces the resonance theory (Bradley and co-workers, 2002) for three-dimensional Reynolds-Averaged Navier-Stokes (RANS) and for the zero-dimensional Spark Ignition Stochastic Reactor Model (SI-SRM) simulations. Hereby, the auto-ignition in the unburnt gases is investigated directly instead of the resulting pressure fluctuations. Based on the detonation diagram auto-ignition events can be classified to be in acceptable deflagration regime or possibly turn to a harmful developing detonation.
Combustion is modeled using detailed chemistry and formulations for turbulent flame propagation. The use of detailed chemistry caters for the prediction of physical and chemical properties, such as the octane rating, C:H:O-ratio or dilution. For both models, the laminar flame speed is retrieved from surrogate specific look-up tables compiled using the reaction mechanism for Ethanol containing Toluene Reference Fuels by Seidel (2017). In the fresh gas zone, the scheme is used for auto-ignition prediction. For this purpose, the G-equation coupled with a Well-Stirred-Reactor model is applied in RANS. In analogy, in the SI-SRM the combustion is modeled using a two zone model with stochastic mixing between the particles.
RANS is used to develop the knock classification methodology and to analyze in detail location, size and shape of the auto-ignition kernels. RANS estimates the ensemble average of the process and therefore cannot reproduce a developing detonation. Hence, Large Eddy Simulation (LES) is used to verify the methodology. Studies using wide ranges of surrogates with different octane rating and cycle-to-cycle variations are carried out using the computationally efficient SI-SRM. Cyclic variations are predicted based on stochastic mixing, stochastic heat transfer to the wall, varying exhaust gas recirculation composition and imposed probability density functions for the inflammation time and the scaling of the mixing time retrieved from RANS.
The methodology is verified for spark timing and octane rating. It is shown that the surrogate formulation has an important impact on knock prediction.
RANS is suitable to predict the mean strength of auto-ignition in the unburnt gas if the thermodynamic and chemical state of the ignition kernel is analyzed instead of the pressure gradients. The probability of the transition to knocking combustion can be determined. Good agreement between RANS and SI-SRM are obtained. The combination of both tools gives insights of local effects using RANS and the distribution of auto-ignition in the whole pressure range of an operating point using SI-SRM with reasonable computationally cost for development purposes.
This thesis is a combined work of understanding the high temperature oxidation chemistry of cycloalkanes viz. methylcyclohexane based on previously developed cyclohexane and extending it to generate the larger n-propylcyclohexane chemical kinetic mechanism. The detailed kinetic reaction mechanism model for the oxidation of 1-hexene previously developed has been added to account for the ring opening of cyclohexane forming 1-hexene. As an update to the publication, preference of allylic H-abstractions from 1-hexene has been taken into account and retro-ene reaction producing propene has been added. The complete model is composed of 329 species and 2065 reactions with 3796 reversible elementary reactions. Further, these models have been validated against different experiments such as shock tubes, jet stirred reactors and laminar flames to cover full range of temperatures, pressures and equivalence ratios making the models comprehensive and was found to be adequate to satisfactorily reproduce the experimental data. The allylic radicals (C₆H₁₁-D1R3) preferred abstractions from 1-hexene improves the C₆H₁₁ profiles in the 1-hexene model. But it also influences the otherwise isomerization path of C₆H1₁₁-D1R6 to CYC₆H₁₁ (Cyclohexyl radical) which would further form cyclohexene (CYC₆H₁₀). It is observed that CYC₆H₁₀ profiles in 1-hexene flames and cyclohexane speciation are over-predicted. The major decomposition pathway of the cycloalkanes is through H-abstractions on the ring. The path which leads towards ring opening to form olefin is observed for cyclohexane and methylcyclohexane but is very low. The fulvene pathway influence on benzene profiles of 1-hexene is obvious but do not seem to affect the cycloalkanes. This infers there are other benzene formation pathways in cycloalkanes. Some possible pathways would be the dehydrogenation of dienes and dehydrogenation of cyclo-olefins.
The provision of nutrients and organic matter to arable soils is critical to facilitate an intensive agriculture and secure long-term soil functionality. Municipal organic waste (MOW) is rich in nutrients and organic matter and its recycling onto agricultural lands presents a promising alternative to conventional fertilizers. In comparison to common aerobic treatment, the application of the biogas technology enables the recovery of both energy and soil amendments. The introduction of a mandatory separate collection in Germany in 2015 reflects the political will to increase MOW recovery rates and facilitates its utilization as feedstock in biogas plants. However, MOW is a challenging feedstock as its composition varies and it is often contaminated with impurities such as plastics and metals. The two-stage anaerobic digestion process with dry fermentation is very robust and offers possibilities for process control so that no extensive MOW pretreatment is required. To close nutrient circles, remaining digestates shall be processed to soil amendments, which can be redistributed to arable land. However, less is known about digestate properties from two-stage digestion of MOW and how they are influenced during the treatment process. Furthermore, only scarce information on nutrient recovery rates and the accumulation of elements during processing is available. Therefore, this thesis investigates the development of digestate properties during anaerobic and subsequent aerobic treatment at laboratory and semi-industrial scale. During a first experiment, changes in nutrient and heavy metal concentration in the solid digestate were monitored during anaerobic treatment of MOW in a two-stage laboratory biogas plant. A second investigation related amendment properties of MOW digestate of one origin to different post-treatment procedures. The impact of drying, composting and sieving on final digestate properties and specifically nutrient availability and heavy metal and carbon elution was evaluated. A third experimental approach investigated total material and substance flows during treatment of source-separated MOW in a semi-industrial scale two-stage biogas plant and subsequent digestate composting including impurities removal.
The main part of the research outlined in this thesis is to develop Deep Learning models for the linguistic interpretation of the visual contents. This part is split into two research problems: interactive region segmentation and captioning, and selective texture labeling. In the first attempt, we proposed a novel hybrid Deep Learning architecture whereby the user is able to specify an arbitrary region of the image that should be highlighted and described. The proposed model alternates the bounding box indications of the standard object localization process with the output of a deep interactive segmentation module to achieve a better understanding of the dense image captioning and improve the object localization accuracy. The idea of the next part is to establish a bidirectional correlation between deep texture representation and its linguistic description via a hybrid CNN-RNN model that enables end-to-end learning of the selective texture labeling. This novel architecture provides new opportunities to describe, search, and also retrieve texture images from their linguistic descriptions. To be able to train such a model, we generated a multi-label texture dataset that covers color, material, and pattern labeling simultaneously. Our contribution to the automatic generation of texture descriptions provides an excellent opportunity to enrich the existing vocabulary of the image captioning. Such a conceptual extension can be used for fine-grained captioning applicable in geology, meteorology and other natural sciences where fine-grained image structures are of importance to understand complicated patterns. Apart from Deep Learning technologies, in the final section of the thesis, we proposed a novel approach to define mathematical morphology on color images. To this end, we converted common RGB-values of the color images into a new biconal color space and then combined two approaches of mathematical morphology to give meaning to the maximum and the minimum of the matrix field data and formulate our novel strategy.
Reconstruction of the 3D shape information is a fundamental problem in computer vision. Among different shape recovering technologies, photometric stereo is highlighted for its capability to produce high quality 3D reconstruction. This dissertation generalizes photometric stereo in different aspects towards creating a practical 3D reconstruction. The proposed techniques can be considered as a fundamental support to develop future cameras offering 3D shapes for various applications such as movie and video game industry, medical sciences, virtual reality, automotive driving and etc. The first generalization is developed for addressing specularities in 3D reconstructions and also involving the perspective projection. These attempts lead to remove the limitation of working with diffuse materials and confined projected scenes. We will prove the applicability of our approach using complex scenes like endoscopy images. In the second proposed approach, we will offer a real-time 3D reconstruction of micro-details with a more generalized reflectance model. Moreover, a recurrent optimization network will be provided. These innovations lead to presenting the 3D reconstruction of details which are even invisible to human eyes like micro-prints on the banknote. This information recovery can be used in various areas such as detecting security items on financial documents for fraud detection and also the quality control of any industrial productions including delicate details such as printed circuits. In the third proposed model, we develop a PS reconstruction technique using neural networks for the uncalibrated PS where the light direction is not available. Finally, for the first time, benefiting from deep neural networks and meta heuristic algorithms, we will devise an approach which can deliver high qualified 3D shape from the internet and out-door images, without any pre-necessary knowledge.
For many countries, gasturbine technology is one of the key technologies for the reduction of climate-damaging pollutant emissions. The profitability of such facilities, however, is highly dependent on the price for the utilized fossil fuel, which is why there is a constant need for increased efficiency. The potential of increasing the efficiency of the individual components is basically limited by factors which will reduce operating life. The goal of this thesis is to develop methods for improved automated structural design optimization, which shall be developed on the basis of compressor airfoils. Special attention is payed to avoid the excitation of failure critical eigenmodes by detecting them automatically. This is achieved by introducing a method based on self-organizing neural networks which enables the projection of eigenmodes of arbitrary airfoil geometries onto standard surfaces, thereby making them comparable. Another neural network is applied to identify eigenmodes which have been defined as critical for operating life. The failure rate of such classifiers is significantly reduced by introducing a newly developed initialization method based on principle components. A structural optimization is set up which shifts the eigenfrequency bands of critical modes in such a way that the risk of resonance with engine orders is minimized. In order to ensure practical relevance of optimization results, the structural optimization is coupled with an aerodynamic optimization in a combined process. Conformity between the loaded hot-geometry utilized by the aerodynamic design assessment and the unloaded cold-geometry utilized by the structural design assessment is ensured by using loaded-to-unloaded geometry transformation. Therefor an innovative method is introduced which, other than the established time-consuming iterative approach, uses negative density for a direct transformation taking only a few seconds, hence, making it applicable to optimization. Additionally, in order for the optimal designs to be robust against manufacturing variations, a method is developed which allows to assess the maximum production tolerance of a design from which onwards possible design variations are likely to violate design constraints. In contrast to the usually applied failure rate, the production tolerance is a valid requirement for suppliers w.r.t.~expensive parts produced in low-quantity, and therefore is a more suitable optimization objective.