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Empirischen Studien zufolge können Patente sich positiv auf den Unternehmenserfolg auswirken. Allerdings wirkt dieser Effekt nicht automatisch, sondern Unternehmen müssen sich um den Aufbau und die gesteuerte Weiterentwicklung eines nachhaltigen und wertvollen Patentportfolios bemühen. Bisher ist jedoch nicht wissenschaftlich untersucht worden, welche Maßnahmen Unternehmen ergreifen können, um die unternehmensinternen Vorraussetzungen für eine erfolgreiche Steuerung von Patentaktivitäten zu schaffen. Um diese betrieblichen Faktoren zu identifizieren und deren Relevanz zu quantifizieren, wurden 2005 in einer breiten empirischen Untersuchung die aktiven Patentanmelder im deutschsprachigen Raum (über 1.000 Unternehmen) mit Hilfe eines standardisierten Fragebogens befragt. Auf der Basis von 325 auswertbaren Fragebögen (Ausschöpfungsquote 36,8 %) konnten zum einen Ergebnisse zum aktuellen Aufgabenspektrum der Patentabteilungen sowie zu deren organisatorischen und personellen Strukturen gewonnen werden. Ebenfalls wurde in dieser Status quo-Analyse der Bekanntheits- und Implementierungsgrad von Methoden und Systemen (z. B. Patentbewertungsmethoden, Patent-IT-Systeme) beleuchtet. Zum anderen wurden die betrieblichen Faktoren herausgestellt, auf die technologieorientierte Unternehmen achten sollten, um das Fundament für eine erfolgreiche Patentsteuerung zu legen. / Empirical studies have shown that patents can have a positive effect on corporate success. However, this effect does not occur by itself. Companies have to make an effort to create and to develop a sustainable patent portfolio. So far, no academic studies have investigated into which actions a company can take to establish the internal conditions for successful patent management. To identify and to quantify the relevance of these internal factors, a study was conducted using a standardized written questionnaire with more than 1,000 patent-oriented companies in the German-speaking countries (Germany, Austria, Switzerland, Liechtenstein). In total, 325 valid questionnaires were included in the analyses; this corresponds to an above-average response rate of 36.8 %. These analyses revealed insights into the current task profile of patent departments and their organizational and personnel structures. This status quo analysis also included the investigation into the awareness and implementation level of used methods and systems (e. g. patent evaluation methods, patent IT systems). Furthermore, the study could expose the internal determinants, which technology-oriented companies should focus on to ensure a successful patent management.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.

Today¡¦s motorcycle business has come to the saturation point in the market of Tawian; consequently, the major motorcycle companies recently competed with each other in building the DMS (Dealer Management System) by using the e-solutions. Through the deployment of an e-DMS (e-solutions of Dealer Management System) for shops of motorcycle, the manufacturers hope that all the channels could be more competitive. The purpose of this research is to explore the influence factors concerning the adoption of e-DMS of motorcycle¡¦s shops. After the studies of literature and empiric, the research is based on ¡§Decomposed Theory of Planned Behavior¡¨ (Taylor and Todd, 1995b) to establish the research model.This resrerch suveryed 250 samples of motorcycle¡¦s shops for study cases The result of the research indicated that factors influenced the adoption of e-DMS for motorcycle shops as follows: (1).¡§Behavioral Intention¡¨ was principally influenced by ¡§Attitude¡¨ and ¡§Perceived Behavioral Control¡¨. The later was less important than the former. ¡§Subject Norms¡¨ showed no obvious influence. (2).¡§Attitude¡¨ was mainly influenced by ¡§Perceived Usefulness¡¨, ¡§Perceived Ease of Use¡¨ and ¡§Compatibility¡¨. The first two factors were more important than the last one. (3).¡§Perceived Behavioral Control¡¨ was chiefly influenced by ¡§Self-efficacy¡¨ and ¡§Technology Facilitating Conditions¡¨. The later was less essential than the former. ¡§Resource Facilitating Conditions¡¨ showed no apparent influence. In the end, this research checks explanation by using three acceptance models, TAM (Davis, 1989), TPB (Ajzen, 1985) and D-TPB (Taylor and Todd, 1995b) to evaluate. All the explanations were nearly close. Because D-TPB considered the contruct of society psychology, it shows better explanation than the others.

Diss. (sammanfattning) Umeå : Umeå universitet, 2010. / Härtill 4 uppsatser.

Manufacturing equipments in semiconductor factories (fabs) provide abundant data and opportunities for data-driven process monitoring and modeling. In particular, virtual metrology (VM) is an active area of research. Traditional monitoring techniques using univariate statistical process control charts do not provide immediate feedback to quality excursions, hindering the implementation of fab-wide advanced process control initiatives. VM models or inferential sensors aim to bridge this gap by predicting of quality measurements instantaneously using tool fault detection and classification (FDC) sensor measurements. The existing research in the field of inferential sensor and VM has focused on comparing regressions algorithms to demonstrate their feasibility in various applications. However, two important areas, data pretreatment and post-deployment model maintenance, are usually neglected in these discussions. Since it is well known that the industrial data collected is of poor quality, and that the semiconductor processes undergo drifts and periodic disturbances, these two issues are the roadblocks in furthering the adoption of inferential sensors and VM models. In data pretreatment, batch data collected from FDC systems usually contain inconsistent trajectories of various durations. Most analysis techniques requires the data from all batches to be of same duration with similar trajectory patterns. These inconsistencies, if unresolved, will propagate into the developed model and cause challenges in interpreting the modeling results and degrade model performance. To address this issue, a Constrained selective Derivative Dynamic Time Warping (CsDTW) method was developed to perform automatic alignment of trajectories. CsDTW is designed to preserve the key features that characterizes each batch and can be solved efficiently in polynomial time. Variable selection after trajectory alignment is another topic that requires improvement. To this end, the proposed Moving Window Variable Importance in Projection (MW-VIP) method yields a more robust set of variables with demonstrably more long-term correlation with the predicted output. In model maintenance, model adaptation has been the standard solution for dealing with drifting processes. However, most case studies have already preprocessed the model update data offline. This is an implicit assumption that the adaptation data is free of faults and outliers, which is often not true for practical implementations. To this end, a moving window scheme using Total Projection to Latent Structure (T-PLS) decomposition screens incoming updates to separate the harmless process noise from the outliers that negatively affects the model. The integrated approach was demonstrated to be more robust. In addition, model adaptation is very inefficient when there are multiplicities in the process, multiplicities could occur due to process nonlinearity, switches in product grade, or different operating conditions. A growing structure multiple model system using local PLS and PCA models have been proposed to improve model performance around process conditions with multiplicity. The use of local PLS and PCA models allows the method to handle a much larger set of inputs and overcome several challenges in mixture model systems. In addition, fault detection sensitivities are also improved by using the multivariate monitoring statistics of these local PLS/PCA models. These proposed methods are tested on two plasma etch data sets provided by Texas Instruments. In addition, a proof of concept using virtual metrology in a controller performance assessment application was also tested.

This thesis describes the application of multivariate methods in a novel approach to the formulation of tablets for direct compression. It begins with a brief historical review, followed by a basic introduction to key aspects of tablet formulation and multivariate data analysis. The bulk of the thesis is concerned with the novel approach, in which excipients were characterised in terms of multiple physical or (in most cases) spectral variables. By applying Principal Component Analysis (PCA) the descriptive variables are summarized into a few latent variables, usually termed scores or principal properties (PP’s). In this way the number of descriptive variables is dramatically reduced and the excipients are described by orthogonal continuous variables. This means that the PP’s can be used as ordinary variables in a statistical experimental design. The combination of latent variables and experimental design is termed multivariate design or experimental design in PP’s. Using multivariate design many excipients can be included in screening experiments with relatively few experiments. The outcome of experiments designed to evaluate the effects of differences in excipient composition of formulations for direct compression is, of course, tablets with various properties. Once these properties, e.g. disintegration time and tensile strength, have been determined with standardised tests, quantitative relationships between descriptive variables and tablet properties can be established using Partial Least Squares Projections to Latent Structures (PLS) analysis. The obtained models can then be used for different purposes, depending on the objective of the research, such as evaluating the influence of the constituents of the formulation or optimisation of a certain tablet property. Several examples of applications of the described methods are presented. Except in the first study, in which the feasibility of this approach was first tested, the disintegration time of the tablets has been studied more carefully than other responses. Additional experiments have been performed in order to obtain a specific disintegration time. Studies of mixtures of excipients with the same primary function have also been performed to obtain certain PP’s. Such mixture experiments also provide a straightforward approach to additional experiments where an interesting area of the PP space can be studied in more detail. The robustness of a formulation with respect to normal batch-to-batch variability has also been studied. The presented approach to tablet formulation offers several interesting alternatives, for both planning and evaluating experiments.

This thesis presents a number of studies, where a multivariate approach was taken to construct models that predict wood species and thermo mechanical pulp brightness from roundwood of Norway spruce and Scots pine. The first and second studies produced multivariate prediction models for wood species from the bark of spruce and pine. These models can be used for wood species classification and would replace the manual log assessment that takes place today. Principal Component Analysis, PCA, and Partial least squares projections to Latent Structures, PLS, were used to predict the wood species from multivariate measurements recorded from the bark of spruce and pine. Two different kinds of measurements were employed, near-infrared spectroscopy and digital imaging. Both methods showed that it was possible to predict the wood species with a high accuracy. The third and fourth studies of the thesis are related to the wood storage of roundwood and the deterioration of wood that occurs during the storage. The third study used an experimental design with five storage factors that provided different conditions for the analysed wood. The experimental design made it possible to identify the factors and the interaction between factors, which were important for the ISO brightness of peroxide and dithionite bleached thermo mechanical pulp, TMP. The final study of the thesis used NIR spectroscopy for predicting the ISO brightness of bleached TMP. Spectra recorded from stored wood were used to construct PLS prediction models.

Rheumatoid arthritis (RA) is an autoimmune disease affecting approximately 1% of the population in the western world. It is characterised by a tissue specific attack of cartilage in peripheral joints. Collagen induced arthritis (CIA) is one of the most commonly used animal models for (RA), with similar symptoms and histopathology. CIA is induced by immunisation of mice with type II collagen (CII), and the immunodominant part was previously found to be located between residues 256-270. This thesis describes the interaction between the MHC molecule, glycopeptide antigens from CII and the T cells that is essential in development of CIA. The glycopeptide properties for binding to the mouse MHC molecule Aq have been studied, as well as interaction points in the glycopeptide that are critical for stimulation of a T-cell response. The thesis is based on five studies. In the first paper the minimal glycopeptide core, that is required for binding to the Aq molecule while still giving a full T cell response was determined. The second paper studied the roles of amino acid side-chains and a backbone amide bond as T-cell contact points. In the third paper the hydrogen bond donor-acceptor characteristics of the 4-OH galactose hydroxyl group of the glycopeptide was studied in detail. In the fourth paper we established a structure activity relationship (QSAR model) for (glyco)peptide binding to the Aq molecule. Finally, the stereochemical requirements for glycopeptide binding to the Aq molecule and for T-cell recognition was studied in the fifth paper. The study was performed using collagen glycopeptide analogues, which were synthesised on solid phase. Amide bond and hydroxyl group isosteres were introduced for study of hydrogen bond donor-acceptor characteristics. Statistical methods were used to design a representative peptide test set and in establishing a QSAR model. The results give a deeper understanding of the interactions involved in the ternary MHC-glycopeptide-T cell complex. This information contributes to research directed towards finding new treatments for RA.

In our society in the 21st century one of the greatest challenges is to provide raw materials to the industry in a sustainable way. This requires increased use of renewable raw materials such as wood. Wood is widely used in pulp, paper and textile industries and ongoing research efforts aim to extend the use of wood in e.g. liquid biofuels and green chemicals. At Umeå Plant Science Center (UPSC) poplar trees are used as model systems to study wood formation. The objective is to understand the function of the genes underlying the wood forming process. This knowledge could result in improved chemical and physical wood properties suitable for different industrial processes. This will in turn meet the demands for a sustainable development. This thesis presents tools and strategies to unravel information regarding genetic perturbations in poplar trees by the use of nuclear magnetic resonance (NMR) spectroscopy and multivariate analysis (MVA). Furthermore, gas chromatography/mass spectrometry (GC/MS) is briefly discussed in this context. Multivariate methods to find patterns and trends in NMR data have been used for more than 30 years. In the beginning MVA was applied in pattern recognition studies in order to characterize chemical structures with different ligands and in different solvents. Today, the multivariate methods have developed and the research have changed focus towards the study of biofluids from plant extracts, urine, blood plasma, saliva etc. NMR spectra of biofluids can contain thousands of resonances, originating from hundreds of different compounds. This type of complex data can be hard to summarize and interpret without appropriate tools and require sophisticated strategies for data evaluation. Related fields of research are rapidly growing and are here referred to as metabolomics. Five different research projects are presented which includes analysis of poplar samples where macromolecules such as pectin and also small molecules such as metabolites were analysed by high resolution magic angle spinning (HR/MAS) NMR spectroscopy, 1H-13C HSQC NMR and GC/MS. The discussion topics include modelling of metabolomic time dependencies in combination with genetic variation, validation of orthogonal projections to latent structures (OPLS) models, selection of putative biomarkers related to genetic modification from OPLS-discriminant analysis (DA) models, measuring one of the main components found in the primary cell-walls of poplar i.e. pectin, the use of Fourier transformed two-dimensional (2D) NMR data in OPLS modelling and model complexity in a PLS model.