Modeling dynamic behavior in tilting pad gas journal bearings

Thisresearch deals with the dynamic modeling of gas lubricated tilting pad journal bearings provided with spring supported pads, including experimental verification of the computation. On the basis of a mathematical model of a film bearing, a computer program has been developed, which can be used for the simulation of a special type of tilting pad gas journal bearing supported by a rotary spring under different loading conditions time dependently (transient running conditions due to geometry variations in time externally imposed). On the basis of literature, different transformations have been used in the model to achieve simpler calculation. The numerical simulation is used to solve a non-stationary case of a gasfilm. The simulation results were compared with literature results in a stationary case (steady running conditions) and they were found to be equal. In addition to this, comparisons were made with a number of stationary and non-stationary bearing tests, which were performed at Lappeenranta University of Technology using bearings designed with the simulation program. A study was also made using numerical simulation and literature to establish the influence of the different bearing parameters on the stability of the bearing. Comparison work was done with literature on tilting pad gas bearings. This bearing type is rarely used. One literature reference has studied the same bearing type as that used in LUT. A new design of tilting pad gas bearing is introduced. It is based on a stainless steel body and electron beam welding of the bearing parts. It has good operation characteristics and is easier to tune and faster to manufacture than traditional constructions. It is also suitable for large serial production.

Applying the principles of life cycle assessment and costing in process modeling to examine profit-making capability

Static process simulation has traditionally been used to model complex processes for various purposes. However, the use of static processsimulators for the preparation of holistic examinations aiming at improving profit-making capability requires a lot of work because the production of results requires the assessment of the applicability of detailed data which may be irrelevant to the objective. The relevant data for the total assessment gets buried byirrelevant data. Furthermore, the models do not include an examination of the maintenance or risk management, and economic examination is often an extra property added to them which can be performed with a spreadsheet program. A process model applicable to holistic economic examinations has been developed in this work. The model is based on the life cycle profit philosophy developed by Hagberg and Henriksson in 1996. The construction of the model has utilized life cycle assessment and life cycle costing methodologies with a view to developing, above all, a model which would be applicable to the economic examinations of complete wholes and which would require the need for information focusing on aspects essential to the objectives. Life cycle assessment and costing differ from each other in terms of the modeling principles, but the features of bothmethodologies can be used in the development of economic process modeling. Methods applicable to the modeling of complex processes can be examined from the viewpoint of life cycle methodologies, because they involve the collection and management of large corpuses of information and the production of information for the needs of decision-makers as well. The results of the study shows that on the basis of the principles of life cycle modeling, a process model can be created which may be used to produce holistic efficiency examinations on the profit-making capability of the production line, with fewer resources thanwith traditional methods. The calculations of the model are based to the maximum extent on the information system of the factory, which means that the accuracyof the results can be improved by developing information systems so that they can provide the best information for this kind of examinations.

Modeling and measuring organizational renewal capability

Globalization and new information technologies mean that organizations have to face world-wide competition in rapidly transforming, unpredictable environments, and thus the ability to constantly generate novel and improved products, services and processes has become quintessential for organizational success. Performance in turbulent environments is, above all, influenced by the organization's capability for renewal. Renewal capability consists of the ability of the organization to replicate, adapt, develop and change its assets, capabilities and strategies. An organization with a high renewal capability can sustain its current success factors while at the same time building new strengths for the future. This capability does not only mean that the organization is able to respond to today's challenges and to keep up with the changes in its environment, but also that it can actas a forerunner by creating innovations, both at the tactical and strategic levels of operation and thereby change the rules of the market. However, even though it is widely agreed that the dynamic capability for continuous learning, development and renewal is a major source of competitive advantage, there is no widely shared view on how organizational renewal capability should be defined, and the field is characterized by a plethora of concepts and definitions. Furthermore,there is a lack of methods for systematically assessing organizational renewal capability. The dissertation aims to bridge these gaps in the existing research by constructing an integrative theoretical framework for organizational renewal capability and by presenting a method for modeling and measuring this capability. The viability of the measurement tool is demonstrated in several contexts, andthe framework is also applied to assess renewal in inter-organizational networks. In this dissertation, organizational renewal capability is examined by drawing on three complimentary theoretical perspectives: knowledge management, strategic management and intellectual capital. The knowledge management perspective considers knowledge as inherently social and activity-based, and focuses on the organizational processes associated with its application and development. Within this framework, organizational renewal capability is understood as the capacity for flexible knowledge integration and creation. The strategic management perspective, on the other hand, approaches knowledge in organizations from the standpoint of its implications for the creation of competitive advantage. In this approach, organizational renewal is framed as the dynamic capability of firms. The intellectual capital perspective is focused on exploring how intangible assets can be measured, reported and communicated. From this vantage point, renewal capability is comprehended as the dynamic dimension of intellectual capital, which consists of the capability to maintain, modify and create knowledge assets. Each of the perspectives significantly contributes to the understanding of organizationalrenewal capability, and the integrative approach presented in this dissertationcontributes to the individual perspectives as well as to the understanding of organizational renewal capability as a whole.

In silico pathway reconstruction: Iron-sulfur cluster biogenesis in Saccharomyces cerevisiae

Background: Current advances in genomics, proteomics and other areas of molecular biology make the identification and reconstruction of novel pathways an emerging area of great interest. One such class of pathways is involved in the biogenesis of Iron-Sulfur Clusters (ISC). Results: Our goal is the development of a new approach based on the use and combination of mathematical, theoretical and computational methods to identify the topology of a target network. In this approach, mathematical models play a central role for the evaluation of the alternative network structures that arise from literature data-mining, phylogenetic profiling, structural methods, and human curation. As a test case, we reconstruct the topology of the reaction and regulatory network for the mitochondrial ISC biogenesis pathway in S. cerevisiae. Predictions regarding how proteins act in ISC biogenesis are validated by comparison with published experimental results. For example, the predicted role of Arh1 and Yah1 and some of the interactions we predict for Grx5 both matches experimental evidence. A putative role for frataxin in directly regulating mitochondrial iron import is discarded from our analysis, which agrees with also published experimental results. Additionally, we propose a number of experiments for testing other predictions and further improve the identification of the network structure. Conclusion: We propose and apply an iterative in silico procedure for predictive reconstruction of the network topology of metabolic pathways. The procedure combines structural bioinformatics tools and mathematical modeling techniques that allow the reconstruction of biochemical networks. Using the Iron Sulfur cluster biogenesis in S. cerevisiae as a test case we indicate how this procedure can be used to analyze and validate the network model against experimental results. Critical evaluation of the obtained results through this procedure allows devising new wet lab experiments to confirm its predictions or provide alternative explanations for further improving the models.

Reliability of the density functional approximation to describe the charge transfer and electrostatic complexes involved in the modeling of organic conducting polymers

Both the intermolecular interaction energies and the geometries for M ̄ thiophene, M ̄ pyrrole, M n+ ̄ thiophene, and M n+ ̄ pyrrole ͑with M = Li, Na, K, Ca, and Mg; and M n+ = Li+ , Na+ , K+ , Ca2+, and Mg2+͒ have been estimated using four commonly used density functional theory ͑DFT͒ methods: B3LYP, B3PW91, PBE, and MPW1PW91. Results have been compared to those provided by HF, MP2, and MP4 conventional ab initio methods. The PBE and MPW1PW91 are the only DFT methods able to provide a reasonable description of the M ̄ complexes. Regarding M n+ ̄ ␲ complexes, the four DFT methods have been proven to be adequate in the prediction of these electrostatically stabilized systems, even though they tend to overestimate the interaction energies.

Dynamic drag modeling of submerged aquatic vegetation canopy flows

Vegetation has a profound effect on flow and sediment transport processes in natural rivers, by increasing both skin friction and form drag. The increase in drag introduces a drag discontinuity between the in-canopy flow and the flow above, which leads to the development of an inflection point in the velocity profile, resembling a free shear layer. Therefore, drag acts as the primary driver for the entire canopy system. Most current numerical hydraulic models which incorporate vegetation rely either on simple, static plant forms, or canopy-scaled drag terms. However, it is suggested that these are insufficient as vegetation canopies represent complex, dynamic, porous blockages within the flow, which are subject to spatially and temporally dynamic drag forces. Here we present a dynamic drag methodology within a CFD framework. Preliminary results for a benchmark cylinder case highlight the accuracy of the method, and suggest its applicability to more complex cases.

Predictive Factors for Intraocular Pressure Reduction after Phacoemulsification in Swiss Patients.

Background: Phacoemulsification is known to induce postoperative intraocular pressure (IOP) reduction, the degree of which varies according to glaucoma subtype and race. The authors set out to investigate the effect of cataract surgery on IOP, in a Swiss Caucasian population, and identify ocular predictive factors. Patients and Methods: 234 consecutive cases of 188 patients undergoing phacoemulsification between January 2011 and December 2012 were retrospectively reviewed and data collected. Exclusion criteria included acute angle closure, malignant glaucoma and pre-existing or subsequent glaucoma surgery. Pre- and post-operative visual acuity, IOP, gonioscopic findings, glaucoma medications, and laser treatments were recorded for eligible eyes. All eyes received the same postoperative regimen. Using multivariate analysis the predictive power of preoperative IOP, iridocorneal angle width, axial length on IOP reduction following phacoemulsification at months 3, 6 and 12 postoperatively were assessed. Eyes with narrow angles were compared against those with open angles. Results: 172 eyes of 121 patients met the inclusion criteria; mean age was 70.3 years (SD ± 10.7 years), with 77 males. Preoperatively median IOP was 16 mmHg (range 9-32 mmHg), mean number of glaucoma medications was 1.2 (SD ± 1.1), median visual acuity was 0.28 LogMAR (range 0-2.3LogMar). At 3 months post-operatively mean IOP decreased to 14 mmHg (p < 0.01) and remained statistically significantly reduced until 12 months, mean number of glaucoma medications was reduced to 1.0 and mean Snellen visual acuity increased to 0.8. Multivariate analysis revealed that pre-operative IOP and iridocorneal angle width (at 3 months) were significant predictive indicators of IOP reduction. At 12 months, IOP reduction was similar between open and narrow angle groups and total IOP reduction was no longer statistically significant. No intraoperative complications were recorded. Conclusions: Intraocular pressure reduction following phacoemulsification was greatest during the very early post-operative period, particularly in narrow angle patients. By one year, angle size was no longer predictive of IOP lowering, however pre-operative IOP and number of anti-glaucoma medications remained correlated with total IOP reduction.

Development and application of computational methodologies in qualitative modeling

La présente thèse s'intitule "Développent et Application des Méthodologies Computationnelles pour la Modélisation Qualitative". Elle comprend tous les différents projets que j'ai entrepris en tant que doctorante. Plutôt qu'une mise en oeuvre systématique d'un cadre défini a priori, cette thèse devrait être considérée comme une exploration des méthodes qui peuvent nous aider à déduire le plan de processus regulatoires et de signalisation. Cette exploration a été mue par des questions biologiques concrètes, plutôt que par des investigations théoriques. Bien que tous les projets aient inclus des systèmes divergents (réseaux régulateurs de gènes du cycle cellulaire, réseaux de signalisation de cellules pulmonaires) ainsi que des organismes (levure à fission, levure bourgeonnante, rat, humain), nos objectifs étaient complémentaires et cohérents. Le projet principal de la thèse est la modélisation du réseau de l'initiation de septation (SIN) du S.pombe. La cytokinèse dans la levure à fission est contrôlée par le SIN, un réseau signalant de protéines kinases qui utilise le corps à pôle-fuseau comme échafaudage. Afin de décrire le comportement qualitatif du système et prédire des comportements mutants inconnus, nous avons décidé d'adopter l'approche de la modélisation booléenne. Dans cette thèse, nous présentons la construction d'un modèle booléen étendu du SIN, comprenant la plupart des composantes et des régulateurs du SIN en tant que noeuds individuels et testable expérimentalement. Ce modèle utilise des niveaux d'activité du CDK comme noeuds de contrôle pour la simulation d'évènements du SIN à différents stades du cycle cellulaire. Ce modèle a été optimisé en utilisant des expériences d'un seul "knock-out" avec des effets phénotypiques connus comme set d'entraînement. Il a permis de prédire correctement un set d'évaluation de "knock-out" doubles. De plus, le modèle a fait des prédictions in silico qui ont été validées in vivo, permettant d'obtenir de nouvelles idées de la régulation et l'organisation hiérarchique du SIN. Un autre projet concernant le cycle cellulaire qui fait partie de cette thèse a été la construction d'un modèle qualitatif et minimal de la réciprocité des cyclines dans la S.cerevisiae. Les protéines Clb dans la levure bourgeonnante présentent une activation et une dégradation caractéristique et séquentielle durant le cycle cellulaire, qu'on appelle communément les vagues des Clbs. Cet évènement est coordonné avec la courbe d'activation inverse du Sic1, qui a un rôle inhibitoire dans le système. Pour l'identification des modèles qualitatifs minimaux qui peuvent expliquer ce phénomène, nous avons sélectionné des expériences bien définies et construit tous les modèles minimaux possibles qui, une fois simulés, reproduisent les résultats attendus. Les modèles ont été filtrés en utilisant des simulations ODE qualitatives et standardisées; seules celles qui reproduisaient le phénotype des vagues ont été gardées. L'ensemble des modèles minimaux peut être utilisé pour suggérer des relations regulatoires entre les molécules participant qui peuvent ensuite être testées expérimentalement. Enfin, durant mon doctorat, j'ai participé au SBV Improver Challenge. Le but était de déduire des réseaux spécifiques à des espèces (humain et rat) en utilisant des données de phosphoprotéines, d'expressions des gènes et des cytokines, ainsi qu'un réseau de référence, qui était mis à disposition comme donnée préalable. Notre solution pour ce concours a pris la troisième place. L'approche utilisée est expliquée en détail dans le dernier chapitre de la thèse. -- The present dissertation is entitled "Development and Application of Computational Methodologies in Qualitative Modeling". It encompasses the diverse projects that were undertaken during my time as a PhD student. Instead of a systematic implementation of a framework defined a priori, this thesis should be considered as an exploration of the methods that can help us infer the blueprint of regulatory and signaling processes. This exploration was driven by concrete biological questions, rather than theoretical investigation. Even though the projects involved divergent systems (gene regulatory networks of cell cycle, signaling networks in lung cells), as well as organisms (fission yeast, budding yeast, rat, human), our goals were complementary and coherent. The main project of the thesis is the modeling of the Septation Initiation Network (SIN) in S.pombe. Cytokinesis in fission yeast is controlled by the SIN, a protein kinase signaling network that uses the spindle pole body as scaffold. In order to describe the qualitative behavior of the system and predict unknown mutant behaviors we decided to adopt a Boolean modeling approach. In this thesis, we report the construction of an extended, Boolean model of the SIN, comprising most SIN components and regulators as individual, experimentally testable nodes. The model uses CDK activity levels as control nodes for the simulation of SIN related events in different stages of the cell cycle. The model was optimized using single knock-out experiments of known phenotypic effect as a training set, and was able to correctly predict a double knock-out test set. Moreover, the model has made in silico predictions that have been validated in vivo, providing new insights into the regulation and hierarchical organization of the SIN. Another cell cycle related project that is part of this thesis was to create a qualitative, minimal model of cyclin interplay in S.cerevisiae. CLB proteins in budding yeast present a characteristic, sequential activation and decay during the cell cycle, commonly referred to as Clb waves. This event is coordinated with the inverse activation curve of Sic1, which has an inhibitory role in the system. To generate minimal qualitative models that can explain this phenomenon, we selected well-defined experiments and constructed all possible minimal models that, when simulated, reproduce the expected results. The models were filtered using standardized qualitative ODE simulations; only the ones reproducing the wave-like phenotype were kept. The set of minimal models can be used to suggest regulatory relations among the participating molecules, which will subsequently be tested experimentally. Finally, during my PhD I participated in the SBV Improver Challenge. The goal was to infer species-specific (human and rat) networks, using phosphoprotein, gene expression and cytokine data and a reference network provided as prior knowledge. Our solution to the challenge was selected as in the final chapter of the thesis.

Establishment and characterization of models of chemotherapy resistance in colorectal cancer: Towards a predictive signature of chemoresistance.

Current standard treatments for metastatic colorectal cancer (CRC) are based on combination regimens with one of the two chemotherapeutic drugs, irinotecan or oxaliplatin. However, drug resistance frequently limits the clinical efficacy of these therapies. In order to gain new insights into mechanisms associated with chemoresistance, and departing from three distinct CRC cell models, we generated a panel of human colorectal cancer cell lines with acquired resistance to either oxaliplatin or irinotecan. We characterized the resistant cell line variants with regards to their drug resistance profile and transcriptome, and matched our results with datasets generated from relevant clinical material to derive putative resistance biomarkers. We found that the chemoresistant cell line variants had distinctive irinotecan- or oxaliplatin-specific resistance profiles, with non-reciprocal cross-resistance. Furthermore, we could identify several new, as well as some previously described, drug resistance-associated genes for each resistant cell line variant. Each chemoresistant cell line variant acquired a unique set of changes that may represent distinct functional subtypes of chemotherapy resistance. In addition, and given the potential implications for selection of subsequent treatment, we also performed an exploratory analysis, in relevant patient cohorts, of the predictive value of each of the specific genes identified in our cellular models.

Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Elevated hypertension risk for African-origin populations in biracial societies : Modeling the Epidemiologic Transition Study.

OBJECTIVES: Blood pressures in persons of African descent exceed those of other racial/ethnic groups in the United States. Whether this trait is attributable to the genetic factors in African-origin populations, or a result of inadequately measured environmental exposures, such as racial discrimination, is not known. To study this question, we conducted a multisite comparative study of communities in the African diaspora, drawn from metropolitan Chicago, Kingston, Jamaica, rural Ghana, Cape Town, South Africa, and the Seychelles. METHODS: At each site, 500 participants between the age of 25 and 49 years, with approximately equal sex balance, were enrolled for a longitudinal study of energy expenditure and weight gain. In this study, we describe the patterns of blood pressure and hypertension observed at baseline among the sites. RESULTS: Mean SBP and DBP were very similar in the United States and South Africa in both men and women, although among women, the prevalence of hypertension was higher in the United States (24 vs. 17%, respectively). After adjustment for multiple covariates, relative to participants in the United States, SBP was significantly higher among the South Africans by 9.7 mmHg (P < 0.05) and significantly lower for each of the other sites: for example, Jamaica: -7.9 mmHg (P = 0.06), Ghana: -12.8 mmHg (P < 0.01) and Seychelles: -11.1 mmHg (P = 0.01). CONCLUSION: These data are consistent with prior findings of a blood pressure gradient in societies of the African diaspora and confirm that African-origin populations with lower social status in multiracial societies, such as the United States and South Africa, experience more hypertension than anticipated based on anthropometric and measurable socioeconomic risk factors.

Minimal requirements for parameters to be used for the development of predictive models for microbial source tracking. The example of the pair somatic coliphages and phages infecting Bacteroides

Several methods and approaches for measuring parameters to determine fecal sources of pollution in water have been developed in recent years. No single microbial or chemical parameter has proved sufficient to determine the source of fecal pollution. Combinations of parameters involving at least one discriminating indicator and one universal fecal indicator offer the most promising solutions for qualitative and quantitative analyses. The universal (nondiscriminating) fecal indicator provides quantitative information regarding the fecal load. The discriminating indicator contributes to the identification of a specific source. The relative values of the parameters derived from both kinds of indicators could provide information regarding the contribution to the total fecal load from each origin. It is also essential that both parameters characteristically persist in the environment for similar periods. Numerical analysis, such as inductive learning methods, could be used to select the most suitable and the lowest number of parameters to develop predictive models. These combinations of parameters provide information on factors affecting the models, such as dilution, specific types of animal source, persistence of microbial tracers, and complex mixtures from different sources. The combined use of the enumeration of somatic coliphages and the enumeration of Bacteroides-phages using different host specific strains (one from humans and another from pigs), both selected using the suggested approach, provides a feasible model for quantitative and qualitative analyses of fecal source identification.