@phdthesis{Dietze2004,
  author    = {Dietze, Stefan},
  title     = {Modell und Optimierungsansatz f{\"u}r Open Source Softwareentwicklungsprozesse},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-0001594},
  school      = {Universit{\"a}t Potsdam},
  year      = {2004},
  abstract  = {Gerade in den letzten Jahren erfuhr Open Source Software (OSS) eine zunehmende Verbreitung und Popularit{\"a}t und hat sich in verschiedenen Anwendungsdom{\"a}nen etabliert. Die Prozesse, welche sich im Kontext der OSS-Entwicklung (auch: OSSD \– Open Source Software-Development) evolution{\"a}r herausgebildet haben, weisen in den verschiedenen OSS-Entwicklungsprojekten z.T. {\"a}hnliche Eigenschaften und Strukturen auf und auch die involvierten Entit{\"a}ten, wie z.B. Artefakte, Rollen oder Software-Werkzeuge sind weitgehend miteinander vergleichbar. Dies motiviert den Gedanken, ein verallgemeinerbares Modell zu entwickeln, welches die generalisierbaren Entwicklungsprozesse im Kontext von OSS zu einem {\"u}bertragbaren Modell abstrahiert. Auch in der Wissenschaftsdisziplin des Software Engineering (SE) wurde bereits erkannt, dass sich der OSSD-Ansatz in verschiedenen Aspekten erheblich von klassischen (propriet{\"a}ren) Modellen des SE unterscheidet und daher diese Methoden einer eigenen wissenschaftlichen Betrachtung bed{\"u}rfen. In verschiedenen Publikationen wurden zwar bereits einzelne Aspekte der OSS-Entwicklung analysiert und Theorien {\"u}ber die zugrundeliegenden Entwicklungsmethoden formuliert, aber es existiert noch keine umfassende Beschreibung der typischen Prozesse der OSSD-Methodik, die auf einer empirischen Untersuchung existierender OSS-Entwicklungsprojekte basiert. Da dies eine Voraussetzung f{\"u}r die weitere wissenschaftliche Auseinandersetzung mit OSSD-Prozessen darstellt, wird im Rahmen dieser Arbeit auf der Basis vergleichender Fallstudien ein deskriptives Modell der OSSD-Prozesse hergeleitet und mit Modellierungselementen der UML formalisiert beschrieben. Das Modell generalisiert die identifizierten Prozesse, Prozessentit{\"a}ten und Software-Infrastrukturen der untersuchten OSSD-Projekte. Es basiert auf einem eigens entwickelten Metamodell, welches die zu analysierenden Entit{\"a}ten identifiziert und die Modellierungssichten und -elemente beschreibt, die zur UML-basierten Beschreibung der Entwicklungsprozesse verwendet werden. In einem weiteren Arbeitsschritt wird eine weiterf{\"u}hrende Analyse des identifizierten Modells durchgef{\"u}hrt, um Implikationen, und Optimierungspotentiale aufzuzeigen. Diese umfassen beispielsweise die ungen{\"u}gende Plan- und Terminierbarkeit von Prozessen oder die beobachtete Tendenz von OSSD-Akteuren, verschiedene Aktivit{\"a}ten mit unterschiedlicher Intensit{\"a}t entsprechend der subjektiv wahrgenommenen Anreize auszu{\"u}ben, was zur Vernachl{\"a}ssigung einiger Prozesse f{\"u}hrt. Anschließend werden Optimierungszielstellungen dargestellt, die diese Unzul{\"a}nglichkeiten adressieren, und ein Optimierungsansatz zur Verbesserung des OSSD-Modells wird beschrieben. Dieser Ansatz umfasst die Erweiterung der identifizierten Rollen, die Einf{\"u}hrung neuer oder die Erweiterung bereits identifizierter Prozesse und die Modifikation oder Erweiterung der Artefakte des generalisierten OSS-Entwicklungsmodells. Die vorgestellten Modellerweiterungen dienen vor allem einer gesteigerten Qualit{\"a}tssicherung und der Kompensation von vernachl{\"a}ssigten Prozessen, um sowohl die entwickelte Software- als auch die Prozessqualit{\"a}t im OSSD-Kontext zu verbessern. Desweiteren werden Softwarefunktionalit{\"a}ten beschrieben, welche die identifizierte bestehende Software-Infrastruktur erweitern und eine gesamtheitlichere, softwaretechnische Unterst{\"u}tzung der OSSD-Prozesse erm{\"o}glichen sollen. Abschließend werden verschiedene Anwendungsszenarien der Methoden des OSS-Entwicklungsmodells, u.a. auch im kommerziellen SE, identifiziert und ein Implementierungsansatz basierend auf der OSS GENESIS vorgestellt, der zur Implementierung und Unterst{\"u}tzung des OSSD-Modells verwendet werden kann.},
  language  = {de}
}
@phdthesis{Karabudak2009,
  author    = {Karabudak, Engin},
  title     = {Development of MWL-AUC / CCD-C-AUC / SLS-AUC detectors for the analytical ultracentrifuge},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-39921},
  school      = {Universit{\"a}t Potsdam},
  year      = {2009},
  abstract  = {Analytical ultracentrifugation (AUC) has made an important contribution to polymer and particle characterization since its invention by Svedberg (Svedberg and Nichols 1923; Svedberg and Pederson 1940) in 1923. In 1926, Svedberg won the Nobel price for his scientific work on disperse systems including work with AUC. The first important discovery performed with AUC was to show the existence of macromolecules. Since that time AUC has become an important tool to study polymers in biophysics and biochemistry. AUC is an absolute technique that does not need any standard. Molar masses between 200 and 1014 g/mol and particle size between 1 and 5000 nm can be detected by AUC. Sample can be fractionated into its components due to its molar mass, particle size, structure or density without any stationary phase requirement as it is the case in chromatographic techniques. This very property of AUC earns it an important status in the analysis of polymers and particles. The distribution of molar mass, particle sizes and densities can be measured with the fractionation. Different types of experiments can give complementary physicochemical parameters. For example, sedimentation equilibrium experiments can lead to the study of pure thermodynamics. For complex mixtures, AUC is the main method that can analyze the system. Interactions between molecules can be studied at different concentrations without destroying the chemical equilibrium (Kim et al. 1977). Biologically relevant weak interactions can also be monitored (K ≈ 10-100 M-1). An analytical ultracentrifuge experiment can yield the following information: • Molecular weight of the sample • Number of the components in the sample if the sample is not a single component • Homogeneity of the sample • Molecular weight distribution if the sample is not a single component • Size and shape of macromolecules \& particles • Aggregation \& interaction of macromolecules • Conformational changes of macromolecules • Sedimentation coefficient and density distribution Such an extremely wide application area of AUC allows the investigation of all samples consisting of a solvent and a dispersed or dissolved substance including gels, micro gels, dispersions, emulsions and solutions. Another fact is that solvent or pH limitation does not exist for this method. A lot of new application areas are still flourishing, although the technique is 80 years old. In 1970s, 1500 AUC were operational throughout the world. At those times, due to the limitation in detection technologies, experimental results were obtained with photographic records. As time passed, faster techniques such as size exclusion chromatography (SEC), light scattering (LS) or SDS-gel electrophoresis occupied the same research fields with AUC. Due to these relatively new techniques, AUC began to loose its importance. In the 1980s, only a few AUC were in use throughout the world. In the beginning of the 1990s a modern AUC -the Optima XL-A - was released by Beckman Instruments (Giebeler 1992). The Optima XL-A was equipped with a modern computerized scanning absorption detector. The addition of Rayleigh Interference Optics is introduced which is called XL-I AUC. Furthermore, major development in computers made the analysis easier with the help of new analysis software. Today, about 400 XL-I AUC exist worldwide. It is usually applied in the industry of pharmacy, biopharmacy and polymer companies as well as in academic research fields such as biochemistry, biophysics, molecular biology and material science. About 350 core scientific publications which use analytical ultracentrifugation are published every year (source: SciFinder 2008 ) with an increasing number of references (436 reference in 2008). A tremendous progress has been made in method and analysis software after digitalization of experimental data with the release of XL-I. In comparison to the previous decade, data analysis became more efficient and reliable. Today, AUC labs can routinely use sophisticated data analysis methods for determination of sedimentation coefficient distributions (Demeler and van Holde 2004; Schuck 2000; Stafford 1992), molar mass distributions (Brookes and Demeler 2008; Brookes et al. 2006; Brown and Schuck 2006), interaction constants (Cao and Demeler 2008; Schuck 1998; Stafford and Sherwood 2004), particle size distributions with Angstrom resolution (C{\"o}lfen and Pauck 1997) and the simulations determination of size and shape distributions from sedimentation velocity experiments (Brookes and Demeler 2005; Brookes et al. 2006). These methods are also available in powerful software packages that combines various methods, such as, Ultrascan (Demeler 2005), Sedift/Sedphat (Schuck 1998; Vistica et al. 2004) and Sedanal (Stafford and Sherwood 2004). All these powerful packages are free of charge. Furthermore, Ultrascans source code is licensed under the GNU Public License (http://www.gnu.org/copyleft/gpl.html). Thus, Ultrascan can be further improved by any research group. Workshops are organized to support these software packages. Despite of the tremendous developments in data analysis, hardware for the system has not developed much. Although there are various user developed detectors in research laboratories, they are not commercially available. Since 1992, only one new optical system called "the fluorescence optics" (Schmidt and Reisner, 1992, MacGregor et al. 2004, MacGregor, 2006, Laue and Kroe, in press) has been commercialized. However, except that, there has been no commercially available improvement in the optical system. The interesting fact about the current hardware of the XL-I is that it is 20 years old, although there has been an enormous development in microelectronics, software and in optical systems in the last 20 years, which could be utilized for improved detectors. As examples of user developed detector, Bhattacharyya (Bhattacharyya 2006) described a Multiwavelength-Analytical Ultracentrifuge (MWL-AUC), a Raman detector and a small angle laser light scattering detector in his PhD thesis. MWL-AUC became operational, but a very high noise level prevented to work with real samples. Tests with the Raman detector were not successful due to the low light intensity and thus high integration time is required. The small angle laser light scattering detector could only detect latex particles but failed to detect smaller particles and molecules due to low sensitivity of the detector (a photodiode was used as detector). The primary motivation of this work is to construct a detector which can measure new physico-chemical properties with AUC with a nicely fractionated sample in the cell. The final goal is to obtain a multiwavelength detector for the AUC that measures complementary quantities. Instrument development is an option for a scientist only when there is a huge potential benefit but there is no available commercial enterprise developing appropriate equipment, or if there is not enough financial support to buy it. The first case was our motivation for developing detectors for AUC. Our aim is to use today's technological advances in microelectronics, programming, mechanics in order to develop new detectors for AUC and improve the existing MWL detector to routine operation mode. The project has multiple aspects which can be listed as mechanical, electronical, optical, software, hardware, chemical, industrial and biological. Hence, by its nature it is a multidisciplinary project. Again by its nature it contains the structural problem of its kind; the problem of determining the exact discipline to follow at each new step. It comprises the risk of becoming lost in some direction. Having that fact in mind, we have chosen the simplest possible solution to any optical, mechanical, electronic, software or hardware problem we have encountered and we have always tried to see the overall picture. In this research, we have designed CCD-C-AUC (CCD Camera UV/Vis absorption detector for AUC) and SLS-AUC (Static Light Scattering detector for AUC) and tested them. One of the SLS-AUC designs produced successful test results, but the design could not be brought to the operational stage. However, the operational state Multiwavelength Analytical Ultracentrifuge (MWL-AUC) AUC has been developed which is an important detector in the fields of chemistry, biology and industry. In this thesis, the operational state Multiwavelength Analytical Ultracentrifuge (MWL-AUC) AUC is to be introduced. Consequently, three different applications of MWL-AUC to the aforementioned disciplines shall be presented. First of all, application of MWL-AUC to a biological system which is a mixture of proteins lgG, aldolase and BSA is presented. An application of MWL-AUC to a mass-produced industrial sample (β-carotene gelatin composite particles) which is manufactured by BASF AG, is presented. Finally, it is shown how MWL-AUC will impact on nano-particle science by investigating the quantum size effect of CdTe and its growth mechanism. In this thesis, mainly the relation between new technological developments and detector development for AUC is investigated. Pioneering results are obtained that indicate the possible direction to be followed for the future of AUC. As an example, each MWL-AUC data contains thousands of wavelengths. MWL-AUC data also contains spectral information at each radial point. Data can be separated to its single wavelength files and can be analyzed classically with existing software packages. All the existing software packages including Ultrascan, Sedfit, Sedanal can analyze only single wavelength data, so new extraordinary software developments are needed. As a first attempt, Emre Brookes and Borries Demeler have developed mutliwavelength module in order to analyze the MWL-AUC data. This module analyzes each wavelength separately and independently. We appreciate Emre Brookes and Borries Demeler for their important contribution to the development of the software. Unfortunately, this module requires huge amount of computer power and does not take into account the spectral information during the analysis. New software algorithms are needed which take into account the spectral information and analyze all wavelengths accordingly. We would like also invite the programmers of Ultrascan, Sedfit, Sedanal and the other programs, to develop new algorithms in this direction.},
  language  = {en}
}