A laranja é uma das frutas mais produzidas e consumidas no mundo, sendo o Brasil o maior produtor e exportador do seu suco concentrado. Entretanto, pragas e doenças comprometem consideravelmente sua produção. Atualmente, a doença mais preocupante é o Greening, também conhecida mundialmente como Huanglongbing (HLB). A doença não possui cura, apresenta longa fase assintomática e não possui um método eficiente de controle. Além disso, não existem métodos de diagnóstico aplicáveis em larga escala. Neste trabalho são propostas as técnicas fotônicas de fluorescência induzida por laser e de infravermelho por transformada de Fourier para o diagnóstico do HLB. Para a realização das medidas, foram coletadas folhas de árvores saudáveis, doentes com HLB e doentes com a clorose variegada dos citros, sendo esta incluída nos estudos para verificar a capacidade de diferenciação entre as doenças. Foram coletadas quatro classes de folhas nessas plantas: sadia, HLB-sintomática, HLB-assintomática e CVC-sintomática. As folhas foram medidas em laboratório e seus espectros foram pré-processados para indução de um classificador via regressão por mínimos quadrados parciais. Além das folhas, foram medidas amostras dos seguintes metabólitos primários e secundários para entendimento espectral: amido, glicose, sacarose, hesperidina, naringina e umbeliferona. Taxas de acerto de superiores a 89% foram obtidas na classificação das folhas nas técnicas de fluorescência e infravermelho, sendo superior às taxas dos métodos de manejo empregados atualmente no campo. A fluorescência induzida por laser apresenta um grande potencial para uso em campo devido a possibilidade de miniaturização de seus componentes. Os espectros dos metabólitos secundários apresentam fortes indícios de que a alteração de suas concentrações podem contribuir na detecção de doenças pelas técnicas fotônicas. / Sweet orange is one of the most produced and consumed fruit in the world, and Brazil is the largest producer and exporter of this fruit. However, pests and diseases significantly reduce the worldwide production. Currently, the most destructive disease in the field is called greening, also known as huanglongbing (HLB). There is no control for HLB. In addition, the disease presents a long asymptomatic phase. Furthermore, no diagnostic methods are available to use in large scale. In this study are proposed fluorescence and infrared spectroscopy for the HLB diagnosis. For the measurements were collected leaves from healthy, HLB- and citrus variegated chlorosis-infected plants, being the last one to comparison between the diseases. It were collected four classes of leaves: healthy, HLB-asymptomatic, HLB-symptomatic and CVC-symptomatic. The leaves were measured and their spectra were pre-processed for the induction of classifier via partial least squares regression. In addition, samples of plant metabolites were measured for leaves spectral interpretation: starch, glucose, sucrose, hesperidin, naringin and umbelliferone. Success rates above 89% were obtained through both photonic techniques, higher compared to the sucess rates of the actual management methods. The metabolites spectra have shown strong evidence that their concentrations changes could contribute to the diagnosis of the diseases by photonic techniques. Particularly, the fluorescence spectroscopy seems interesting because it has a great potential for field application due to the existence of portable photonic devices.

L'imagerie cérébrale connaît un intérêt grandissant, en tant que phénotype intermédiaire, dans la compréhension du chemin complexe qui relie les gènes à un phénotype comportemental ou clinique. Dans ce contexte, un premier objectif est de proposer des méthodes capables d'identifier la part de variabilité génétique qui explique une certaine part de la variabilité observée en neuroimagerie. Les approches univariées classiques ignorent les effets conjoints qui peuvent exister entre plusieurs gènes ou les covariations potentielles entre régions cérébrales.Notre première contribution a été de chercher à améliorer la sensibilité de l'approche univariée en tirant avantage de la nature multivariée des données génétiques, au niveau local. En effet, nous adaptons l'inférence au niveau du cluster en neuroimagerie à des données de polymorphismes d'un seul nucléotide (SNP), en cherchant des clusters 1D de SNPs adjacents associés à un même phénotype d'imagerie. Ensuite, nous prolongeons cette idée et combinons les clusters de voxels avec les clusters de SNPs, en utilisant un test simple au niveau du "cluster 4D", qui détecte conjointement des régions cérébrale et génomique fortement associées. Nous obtenons des résultats préliminaires prometteurs, tant sur données simulées que sur données réelles.Notre deuxième contribution a été d'utiliser des méthodes multivariées exploratoires pour améliorer la puissance de détection des études d'imagerie génétique, en modélisant la nature multivariée potentielle des associations, à plus longue échelle, tant du point de vue de l'imagerie que de la génétique. La régression Partial Least Squares et l'analyse canonique ont été récemment proposées pour l'analyse de données génétiques et transcriptomiques. Nous proposons ici de transposer cette idée à l'analyse de données de génétique et d'imagerie. De plus, nous étudions différentes stratégies de régularisation et de réduction de dimension, combinées avec la PLS ou l'analyse canonique, afin de faire face au phénomène de sur-apprentissage dû aux très grandes dimensions des données. Nous proposons une étude comparative de ces différentes stratégies, sur des données simulées et des données réelles d'IRM fonctionnelle et de SNPs. Le filtrage univarié semble nécessaire. Cependant, c'est la combinaison du filtrage univarié et de la PLS régularisée L1 qui permet de détecter une association généralisable et significative sur les données réelles, ce qui suggère que la découverte d'associations en imagerie génétique nécessite une approche multivariée.

The Sub-Saharan Africa (SSA) region is faced with limited medical personnel and healthcare services to address the many healthcare problems of the region. Poor health indicators reflect the overall decline in socio-economic development. Shortages of access to health services in the region is further complicated by the concentration of health services in urban areas, the region’s multiple medical problems (over 70% of HIV/AIDS cases in the world); and the brain drain phenomenon – it is estimated one-third of African physicians emigrate to North America and Europe. The result is that the SSA region is left with about 10 physicians, and 20 beds, per 100,000 patients. Telemedicine has been found to offer socio-economic benefits, reduce costs, and improve access to healthcare service providers by patients, but previous attempts to move various information technologies from developers in the industrial world to the developing world have failed because of a clear neglect of infrastructural and cultural factors that influence such transfers. The objective of this study is to address key factors that challenge the introduction of telemedicine technology into the health sector in SSA in particular, and by extension, other developing countries with similar socio-economic structures. This research offers a distinctive perspective, focusing on visually-based clinical applications in the SSA region, and considerable attention to the national infrastructure and cultural impact of telemedicine transfer (social and value) outcomes. Two research models and its associated hypotheses are proposed and empirically tested using quantitative data collected from SSA physicians and other health professionals. The study also contributes to the ongoing debate on the potential of telemedicine in improving access and reducing costs. This research can help to understand the socio-economic impact of telemedicine outcomes in a comprehensive way. The finding from the survey shows the rapid advances in telemedicine technology specifically, visual clinical applications may become an essential healthcare tool in the near future within SSA countries.

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.

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.

This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which affects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules individually. Support vector regression and partial least squares regression are used for prediction.

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.

Η φασματοσκοπία Raman είναι τεχνική ανελαστικής σκέδασης φωτός, ικανή να ανιχνεύει και να χαρακτηρίζει μόρια σε ποικιλία υδατικών διαλυμάτων. Σκοπός της εργασίας είναι η ανάπτυξη μιας μη επεμβατικής, μη καταστροφικής φασματοσκοπικής μεθόδου για την ανίχνευση και τον ποσοτικό προσδιορισμό τόσο φαρμακευτικών ουσιών (π.χ αντιβιοτικών) όσο και φυσιολογικών ουσιών (π.χ γλυκόζη) στο υδατοειδές υγρό οφθαλμού. Στο πλαίσιο της παρούσας εργασίας αναπτύχθηκε μια νέα γεωμετρική οπτική διάταξη για την καθοδήγηση της δέσμης του laser στον οφθαλμό, που προσαρμόζεται σε φασματοσκόπιο Raman με ανιχνευτή CCD, δίνει τη δυνατότητα επιλεκτικής συλλογής του σκεδαζόμενου φωτός, σαρώνοντας τον εμπρόσθιο θάλαμο, σε γεωμετρία σκέδασης 90 μοιρών. Τα πειράματα πραγματοποιήθηκαν (α) σε χοιρινούς οφθαλμούς in-vitro, max 24 ώρες μετά τη θανάτωση των ζώων και την αφαίρεση του βολβού, μετά την έγχυση στον εμπρόσθιο θάλαμο μορίων κεφταζιδίμης, αμφοτερισίνης Β και γλυκόζης και (β) σε μοντέλο πρόσθιου θαλάμου (AAC) σε συνδυασμό με κερατοειδή χιτώνα χοιρινών οφθαλμών μετά την έγχυση μορίων κεφταζιδίμης, αμφοτερισίνης, θειικής αμικασίνης και σιπροφλοξασίνης. Επιπλέον, χρησιμοποιήθηκε χημειομετρικός αλγόριθμος μερικών ελαχίστων τετραγώνων (PLS) για να προβλέψει τη συγκέντρωση των αντιβιοτικών στο μοντέλο του πρόσθιου θαλάμου. Με τον νεό αυτό σχεδιασμό αποφεύγεται η απευθείας έκθεση βασικών οφθαλμικών ιστών, όπως του φακού και του αμφιβληστροειδούς, στη δέσμη του laser, ενώ παράλληλα επιτυγχάνονται βέλτιστες συνθήκες συλλογής του σκεδαζόμενου φωτός βελτιώνοντας το λόγο σήματος/θορύβου των φασμάτων. Ανιχνεύτηκαν συγκεντρώσεις στην περιοχή της μέσης ανασταλτικής πυκνότητας για τα αντιβιοτικά τόσο στο υδατοειδές υγρό χοιρινών οφθαλμών όσο και στο μοντέλο του πρόσθιου θαλάμουֹ η γλυκόζη ανιχνεύτηκε σε συγκέντρωση κοντά στα παθολογικά επίπεδα των διαβητικών ασθενών. Με βάση και το σφάλμα RMS της ποσοτικής ανάλυσης PLS, προσδοκάται βάσιμα ότι η μέθοδος είναι δυνατό χρησιμοποιηθεί στον τομέα της οφθαλμολογίας για τη μελέτη της φαρμακοκινητικής καθώς και για την έγκαιρη διάγνωση ασθενειών (π.χ. σακχαρώδης διαβήτης). / Laser Raman spectroscopy is an inelastic light scattering technique able to characterize molecules in aqueous environments. The purpose of this work is to develop a non-contact and non-invasive spectroscopic method to identify and eventually quantify the presence of medicines (e.g. antibiotics) and physiological substances (e.g. glucose) in the aqueous humor of the eye. Α new laser light delivery probe has been developed and adapted to a Raman spectroscopic system with the ability of favorable collection of the Raman light at 90o scattering geometry while scanning the anterior chamber of the eye. The technique is applied both, to porcine eyes in-vitro, max. 24 hours after death and extraction, for ceftazidime, amphotericin B and glucose and to a commercially available artificial anterior chamber (AAC) fitted with corneas of porcine eyes for ceftazidime, amphotericin B, amikacin sulphate and ciprofloxacin. Finally, a PLS chemometric algorithm has been developed to predict the concentration of antibiotics in AAC. This special illumination design gives the opportunity of reducing the direct exposure of the basic cordial ocular tissues, like lens and retina, to the laser beam, while at the same time an optimum collection of scattered light is accomplished. Concentrations close to the minimum inhibitory concentration (MIC) have been detected for antibiotics both in porcine eyes and AAC; the detection of glucose has been realized at concentrations close to the early pathological levels of patients with diabetes. Furthermore, the quantification of concentration of antibiotics in AAC is accomplished by a partial least-squares (PLS) chemometric regression algorithm and the RMS error of the validation procedure further emphasize the promising prospect of the application of the Raman spectroscopy to the Ophthalmology.

Quantitative structure-activity relationships (OSAR) constitute empirical analogy models connecting chemical structure and biological activity. The analogy approach to QSAR assume that the factors important in the biological system also are contained in chemical model systems. The development of a QSAR can be divided into subproblems: 1. to quantify chemical structure in terms of latent variables expressing analogy, 2. to design test series of compounds, 3. to measure biological activity and 4. to construct a mathematical model connecting chemical structure and biological activity. In this thesis it is proposed that many possibly relevant descriptors should be considered simultaneously in order to efficiently capture the unknown factors inherent in the descriptors. The importance of multivariately and multipositionally varied test series is discussed. Multivariate projection methods such as PCA and PLS are shown to be appropriate far QSAR and to closely correspond to the analogy assumption. The multivariate analogy approach is applied to a beta- adrenergic agents, b haloalkanes, c halogenated ethyl methyl ethers and d four different families of peptides. / <p>Diss. (sammanfattning) Umeå : Umeå universitet, 1986, härtill 8 uppsatser</p> / digitalisering@umu