Publication Server of Constructor University Library
Not a member yet
858 research outputs found
Sort by
Integration of Knocked-Down Supply Chains and Global Manufacturing Networks
No full textGlobal manufacturing networks and the underlying global supply chains form the centerpiece of automotive production. Over the last decades, original equipment manufacturers established overseas plants in the course of their expansion strategy and employed so-called knocked-down supply chains to ship all parts pre-assembled and arranged in kits to them. The overseas plants have matured into fully-equipped plants by taking over value-adding processes. As a consequence, the global manufacturing networks have shifted their focus away from simplification toward performance. The underlying knocked-down supply chains, however, have not adapted and still feature high inventories, lead times and costs.
Even though knocked-down supply chains play a key role in global manufacturing networks, they have not been integrated. It is not possible to evaluate their fit and to derive the requirements. Despite the intense effort to improve the performance in the factories, there is little research on improvement levers in the context of knocked-down supply chains.
This Thesis intends to explore how knocked-down supply chains can be aligned with global manufacturing networks. It conducts a cross-case study to provide an overview of current knocked-down supply chains and global manufacturing networks. The Thesis develops an integrated framework that matches knocked-down supply chains and global manufacturing networks and identifies weak spots in supply chain performance. The Thesis then applies a two-fold approach. It explores the general working principle of knocked-down supply chains by means of intermodal transportation. Gaining impetus from lean management, the Thesis then identifies improvement levers and subsequently evaluates their effect on knocked-down supply chains. The Thesis shows that the supply chain performance of knocked-down supply chains and thus the fit with the global manufacturing network can be improved
Transient Dynamics in Heteroclinic Networks
No full textHistorically, the study of dynamical systems was primarily focussed on attractors, describing the dynamics in the large-time limit. However, many dynamical processes are intrinsically transient. Dynamics comprising long-lasting transients are relevant to applications in ecological systems or cognitive processes, for example. There are different ways in which transient dynamics are realized. One of them is via heteroclinic cycles or heteroclinic networks, which consist of saddles connected by heteroclinic orbits. In these systems, the dynamics continuously switches between different metastable states. When realized in the framework of evolutionary game theory, metastable states correspond to states with one dominating population, which is the winner only temporarily. Accordingly, these games are also termed winnerless competition. This thesis focusses on two aspects of heteroclinic dynamics. Firstly, we construct and analyse a hierarchically structured heteroclinic network. It constitutes a hierarchical attractor composed of multiple levels of winnerless competition. We demonstrate that this hierarchy in phase space translates to a hierarchy in timescales, whereby the heteroclinic switching on the slower timescale modulates the dynamics on the faster timescale. Our system thus presents a realization of dynamics that are nested, and in addition also self-similar by construction. We derive analytically the dependence of the ratio of timescales on model parameters and initial conditions via a Poincaré map approach. Moreover, we show how the hierarchy transfers to spatial patterns when multiple units of the system are assigned to a spatial lattice and coupled by diffusion. In this spatial system, we observe a large dimensional reduction when a multitude of hierarchical heteroclinic networks synchronizes to the dynamics of a single such network. In the broader context of evolutionary game theory, our construction method allows selecting in advance which [...
The World Bank and Agricultural and Rural Development in the 1960s and 1970s
No full textThis dissertation analyzes the history of the World Bank in the 1960s and 1970s, the crucial period in which the Bank transitioned from being a rather small, specialized investment bank for development into becoming a powerful development finance organization. The analysis has a specific focus on the Bank’s ‘discovery’ and adoption of agricultural and rural development.
The thesis draws on a wide array of historical material from different archives, including some newly declassified sources from the World Bank Group Archive. The thesis approaches development as a contested field that involved debates about meanings and struggles over financial resources, which it analyzes with a focus on the World Bank.
Analysis in chapter two and three demonstrates that the Bank’s understanding of an ‘agrarian reform’ in the 1960s relied on the ideas of others, and it differed between world regions. In East Africa the Bank was influenced by British colonial land settlement schemes and contained a large element of so-called ‘technical assistance’. For India, in contrast, the World Bank shared the notion of the U.S. Government, that focused on making capital investments into agriculture and on establishing linkages with industry.
Chapter four interprets the World Bank’s embrace of a mission for poverty alleviation and rural development, under the presidency of Robert McNamara, as a response to a specific analysis of crisis with established models of development. Chapter five demonstrates that there was a quick disillusionment with rural development. The two chapters shed light on the huge gap between rhetoric and practice with regards to poverty alleviation at the Bank.
The final chapter analyzes the World Bank’s adoption of structural adjustment lending in 1980. It argues that the debate about this new lending instrument was entangled with the larger North-South conflict of the 1970s, and with the struggle over the access to international financial resources
Performance of a Stirred Catalytic Basket Reactor for the production of Bio-ethanol
No full textCurrent environmental and health problems are sourced from the usage of fossil fuel. This issue has encouraged the scientific community to find environmentally friendly alternatives, such as biofuel Ethanol. The Stirrer Catalytic Basket Reactor (SCBR) presents some of the advantages of a Packed bed reactor (PBR). From all the results, our two hypotheses were evaluated: 1). DEAE sponges are better than Alginate beads. 2). SCBR performs better than PBR. In the case of the first hypothesis, from results, it has been observed that even at 200 rpm with a 100 g/l glucose concentration, the sponges consume glucose in 9 h, while alginate beads and free cells consume it in 19 h and 20 h, respectively. Alginate beads take a longer time to consume than polymers. At a higher glucose concentration of 200 g/l, the volumetric productivity of polymer was higher as compare to alginate beads. These polymers are presumed as the best immobilizing materials than alginate beads with higher ethanol productivity and lower glucose consumption time. This might be due to a lower difference in concentration between bulk and inside polymer and lower film diffusion barrier in sponges. The second hypothesis is also proved correct as in SCBR complete conversion of 200g/l glucose, has been observed in a short time (5h) as compare to Packed Bed. In SCBR, no lag phase was observed. It is depicted that sponges have no external or internal mass transfer limitations or due to macro-pores fluid flow is improved therefore there is no difference in consumption time at a higher speed. SCBR provides an efficient radial & axial mixing system. The SCBR with cell immobilized in sponges is more attractive and economical, especially on an industrial scale
Robust and Reliable Multiscale Modeling of Molecular Photoinduced Processes
No full textThe light-harvesting protein-pigment complexes of plants, bacteria and algae play a major role in the conversion of solar energy into sustainable forms of chemical energy during photosynthesis. Chlorophyll, bacterio-chlorophyll and bilin molecules are the key pigments present in those complexes which mainly control the excitation energy transfer processes. However, due to the large size and the multiscale quantum-classical methods are often required to study the dynamical processes within protein environments.
In this direction, the present thesis aims to formulate an efficient strategy to describe the underlying foundation of energy transfer process in these complexes and provides an outlook for the modeling of artificial LH complexes. To this end, the density functional based tight-binding (DFTB) method has been applied to perform ground state molecular dynamics simulations coupled to a classical environment within a quantum mechanics/molecular mechanics (QM/MM) fashion. In a subsequent step, the time-dependent extension of the long-range-corrected DFTB formalism has been applied to calculate the excitation energies of each pigment molecules also in a QM/MM setting. This provides the basis of the multiscale scheme which is then applied to various bacterial and plant LH complexes to extract the major excitonic parameters like site energies, couplings and spectral densities. Moreover, based on these input parameters, exciton dynamics and transfer rate calculations have been performed for some of these systems. Furthermore, the calculated results were compared with the experimental counterparts resulting remarkable agreement for these large bio-molecular systems. These findings motivated further investigations on an artificially designed light-harvesting system by employing porphyrin molecules attached to a clay surface which is experimentally found to exhibit excellent energy transfer properties
Analysis by constant electric field with linear increasing current (CELIC) model and degradation processes in organic polymer solar cells.
No full textThis thesis covers four topics about organic solar cells to improve the understanding of cell behaviour with models based on physical processes and the influence of degradation.
First, this thesis presents a new model to describe the electrical behavior of solar cells based on physical parameters. This model is based on the assumption of a constant electric field and a linear increasing current in the intrinsic layer (CELIC). As a result, the model describes nicely the currentvoltage (JV) behavior and thereby covers a large range of charge carrier mobilities and illumination intensities. In addition, the crossing point of JV-curves under different illumination intensities can be modeled and correlated to the built-in potential for ohmic contact properties at the interface to the intrinsic layer.
Second, organic solar cell behavior under different illumination levels was tested with the JscVoc method, which allows an investigation of the JV curve without a series resistance. An expression for the series and shunt resistance was developed combining JscVoc and JV analysis. It is shown that the conductivity of the intrinsic layer is associated with recombination processes and is dependent on illumination power. From the findings, a model is presented which splits the recombination and extraction into four illumination dependent regimes.
Third, the influence of the electron and hole blocking layers on the current-voltage characteristics was tested. It was found that the forward current of the solar cells is originating from an exclusive surface recombination current at the semiconductor-ZnO interface.
Forth, the degradation processes in organic solar cells were tested under different external conditions. It was found that the materials and the solar cell do degrade under these conditions by phase separation of the semiconductor mixture, p-doping of the semiconductor, photo-bleaching, and dedoping of the zinc oxide layer
Zinc oxide thin-films by spray pyrolysis with low deposition temperature
No full textMetal oxides such as zinc oxide have good electrical properties but processing those from solution requires relatively high temperatures (e.g. spray pyrolysis of zinc acetate at ~ 360 °C). This thesis pursues three fundamentally different approaches to lower the process temperature.
Tailored organic molecules are used as a post-deposition treatment to passivate surface traps, i.e. hydroxy groups or chemisorbed water, as the first approach. A successful passivation of surface defects improve the electrical properties of the zinc oxide that occur at low process temperatures. Therefore tailored 1,3-diketones, are presented. Their binding towards zinc is studied and their passivating properties of zinc oxide thin-film transistors analyzed.
Fluorinated zinc carboxylate derivates are analyzed as novel potential zinc oxide precursors with focus on lower deposition temperatures as a second approach. FTIR (Fourier transform infrared spectroscopy) and TGA (Thermogravimetric analysis) reveal whether a precursor thermally decomposes to zinc oxide and identifies the decomposition temperature.
The third approach: High-speed picoliter droplet analysis gives deeper understanding of droplet interactions with the substrate depending on the temperature under real deposition conditions. A novel model for top-view analysis of dynamic and static advancing contact angles and a comprehensive determination of thermodynamic properties like Leidenfrost point, critical heat flux and thermodynamic boiling regimes is presened. Additionally, a novel hovering state of very small droplets above the substrate at room temperature is presented. This state is similar to the Leidenfrost point and enables the deposition of smooth layers at low temperatures (T < 100 °C). Overall, this analysis allows a fast screening for suitable solvent and substrate combinations for the deposition of precursors that are not processable with standard solvents to find beneficial deposition conditions at low temperatures
Implications of Dataset Heterogeneity on Deep Learning Performance in Medical Image Segmentation
No full textThis thesis is about medical image segmentation using deep learning, with a particular focus on the influence of the training data. The performance of deep learning algorithms is impacted by the training set quality and heterogeneity, here grouped into three categories: technical image quality, reference segmentations and study populations.
Different training strategies are compared for parotid gland segmentation in CT data. All yield robust segmentation results, also in the presence of artifacts, and outperform non-deep learning methods on public data. Typical errors coincide with regions of high inter-observer variability. Training on contours from clinical routine, and on curated contours yield similar accuracy and results.
Bladder, rectum and uterus are segmented in cone-beam CT data, that is noisier and less well-calibrated than CT data. Using CT data for augmenting the anatomical variability is proposed and found to improve the performance. Prior knowledge about the presence of typical artifacts is integrated into the data sampling. Curriculum learning seems promising to increase the robustness to the particular artifact.
The hippocampus is segmented in CT data. A CT-only approach for generating the training contours could facilitate the data collection, but it is found that MRI-based training contours yield significantly higher performance and lower uncertainty.
White matter hyperintensity lesions are an imaging biomarker linked to stroke and cognitive decline. It is shown that a single neural network can segment these lesions in heterogeneous MRI data with varying image quality and lesion loads, and for a wide range of training set compositions, generated by pooling and systematic sampling. A challenge is the co-occurrence of stroke lesions. An approach that uses stroke segmentations for guiding the sampling, but not for optimizing the training loss, is proposed and found to outperform other sampling approaches with respect to false positive detections
Guidance of medical instruments based on tracking systems
No full textCurrently, two-dimensional (2D) fluoroscopy and conventional digital subtraction angiography are the gold standard for the navigation of medical instruments in many minimal-invasive interventions like the endovascular aneurysm repair (EVAR) procedures. However, this requires X-rays, contrast agent is used, and the depth information is missing. A three-dimensional (3D) guidance based on tracking systems does no have these disadvantages. The key hypothesis of the PhD work is that a tracking-based guidance of medical instruments is possible and that it facilitates the navigation in minimal invasive interventions. The evaluated use case will be the navigation of a stent graft during an EVAR procedure.
First, an analysis and optimization of a fiber optical shape sensing (FOSS) model is conducted: The usage of an optical fiber with fiber Bragg gratings (FBGs) allows measuring the shape of a medical tool. Here, methods from literature are analyzed and evaluated in different experiments. The accuracy of the obtained optimized shape sensing model is evaluated with different 3D measurements.
Then, novel tracking-based guidance methods are introduced: The combination of FOSS and EM sensors allows determining the located shape of medical instruments. For this purpose, a spatial calibration method for an optical fiber and EM sensors is introduced. Moreover, the methods for obtaining the located shape using three, two or only one EM sensor (together with preoperative data). The guidance methods have been evaluated in different experiments and compared with an image-based 3D shape localization approach.
In addition, the developed approaches are applied in order to guide a stent graft in EVAR procedures. A spatial calibration between stent graft and tracking systems and a suitable visualization of the guidance information are described. This stent graft guidance method was evaluated by conducting an EVAR procedure on a torso phantom
Theoretical study of phonons in large-scale and layered materials: method development and applications
No full textExfoliation of graphene in 2004 initiated a tremendous interest (both theoretical as well as experimental) in the field of the two-dimensional (2D) materials. Such materials have typically layered structures, so the field of 2D materials is naturally related to the more general field of layered materials. A special (and relatively unexplored) class of such materials are the so-called misfit layered compounds (MLCs), which are made up of layers of 2D crystals whose lattice constant ratio is an irrational number.
Moreover, with the ever growing interest in the chemistry and physics of new materials, there is a high demand for novel and improved theoretical and computational methods, especially for ones which offer accurate, ab-initio theoretical description of large systems (such as the aforementioned MLCs) with low computational costs. One of the methods that seems to meet these demands well is the density-functional based tight-binding.
This thesis is a summary of my work on the above mentioned topics, including:
i) development of new phonon calculation methods in the SCC-DFTB formalism
ii) investigation of layered, intercalated PbNbS2 and misfit (PbS)1.14NbS2 compounds through Raman spectroscopy
iii) investigation of a new family of layered materials, with the formula XY3 (X = group 14, Y = group 15 element