Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland, link to shapefiles

The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, is presented. Using the topography, thickness and distribution of sediments is calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling.

Regularization for sparsity in statistical analysis and machine learning

Pragmatism is the leading motivation of regularization. We can understand regularization as a modification of the maximum-likelihood estimator so that a reasonable answer could be given in an unstable or ill-posed situation. To mention some typical examples, this happens when fitting parametric or non-parametric models with more parameters than data or when estimating large covariance matrices. Regularization is usually used, in addition, to improve the bias-variance tradeoff of an estimation. Then, the definition of regularization is quite general, and, although the introduction of a penalty is probably the most popular type, it is just one out of multiple forms of regularization. In this dissertation, we focus on the applications of regularization for obtaining sparse or parsimonious representations, where only a subset of the inputs is used. A particular form of regularization, L1-regularization, plays a key role for reaching sparsity. Most of the contributions presented here revolve around L1-regularization, although other forms of regularization are explored (also pursuing sparsity in some sense). In addition to present a compact review of L1-regularization and its applications in statistical and machine learning, we devise methodology for regression, supervised classification and structure induction of graphical models. Within the regression paradigm, we focus on kernel smoothing learning, proposing techniques for kernel design that are suitable for high dimensional settings and sparse regression functions. We also present an application of regularized regression techniques for modeling the response of biological neurons. Supervised classification advances deal, on the one hand, with the application of regularization for obtaining a na¨ıve Bayes classifier and, on the other hand, with a novel algorithm for brain-computer interface design that uses group regularization in an efficient manner. Finally, we present a heuristic for inducing structures of Gaussian Bayesian networks using L1-regularization as a filter. El pragmatismo es la principal motivación de la regularización. Podemos entender la regularización como una modificación del estimador de máxima verosimilitud, de tal manera que se pueda dar una respuesta cuando la configuración del problema es inestable. A modo de ejemplo, podemos mencionar el ajuste de modelos paramétricos o no paramétricos cuando hay más parámetros que casos en el conjunto de datos, o la estimación de grandes matrices de covarianzas. Se suele recurrir a la regularización, además, para mejorar el compromiso sesgo-varianza en una estimación. Por tanto, la definición de regularización es muy general y, aunque la introducción de una función de penalización es probablemente el método más popular, éste es sólo uno de entre varias posibilidades. En esta tesis se ha trabajado en aplicaciones de regularización para obtener representaciones dispersas, donde sólo se usa un subconjunto de las entradas. En particular, la regularización L1 juega un papel clave en la búsqueda de dicha dispersión. La mayor parte de las contribuciones presentadas en la tesis giran alrededor de la regularización L1, aunque también se exploran otras formas de regularización (que igualmente persiguen un modelo disperso). Además de presentar una revisión de la regularización L1 y sus aplicaciones en estadística y aprendizaje de máquina, se ha desarrollado metodología para regresión, clasificación supervisada y aprendizaje de estructura en modelos gráficos. Dentro de la regresión, se ha trabajado principalmente en métodos de regresión local, proponiendo técnicas de diseño del kernel que sean adecuadas a configuraciones de alta dimensionalidad y funciones de regresión dispersas. También se presenta una aplicación de las técnicas de regresión regularizada para modelar la respuesta de neuronas reales. Los avances en clasificación supervisada tratan, por una parte, con el uso de regularización para obtener un clasificador naive Bayes y, por otra parte, con el desarrollo de un algoritmo que usa regularización por grupos de una manera eficiente y que se ha aplicado al diseño de interfaces cerebromáquina. Finalmente, se presenta una heurística para inducir la estructura de redes Bayesianas Gaussianas usando regularización L1 a modo de filtro.

Pulmonary Artery-Vein Segmentation in Real and Synthetic CT Images = Segmentación Arteria-Vena Pulmonares en Imágenes de CT Reales y Sintéticas

La tomografía axial computerizada (TAC) es la modalidad de imagen médica preferente para el estudio de enfermedades pulmonares y el análisis de su vasculatura. La segmentación general de vasos en pulmón ha sido abordada en profundidad a lo largo de los últimos años por la comunidad científica que trabaja en el campo de procesamiento de imagen; sin embargo, la diferenciación entre irrigaciones arterial y venosa es aún un problema abierto. De hecho, la separación automática de arterias y venas está considerado como uno de los grandes retos futuros del procesamiento de imágenes biomédicas. La segmentación arteria-vena (AV) permitiría el estudio de ambas irrigaciones por separado, lo cual tendría importantes consecuencias en diferentes escenarios médicos y múltiples enfermedades pulmonares o estados patológicos. Características como la densidad, geometría, topología y tamaño de los vasos sanguíneos podrían ser analizados en enfermedades que conllevan remodelación de la vasculatura pulmonar, haciendo incluso posible el descubrimiento de nuevos biomarcadores específicos que aún hoy en dípermanecen ocultos. Esta diferenciación entre arterias y venas también podría ayudar a la mejora y el desarrollo de métodos de procesamiento de las distintas estructuras pulmonares. Sin embargo, el estudio del efecto de las enfermedades en los árboles arterial y venoso ha sido inviable hasta ahora a pesar de su indudable utilidad. La extrema complejidad de los árboles vasculares del pulmón hace inabordable una separación manual de ambas estructuras en un tiempo realista, fomentando aún más la necesidad de diseñar herramientas automáticas o semiautomáticas para tal objetivo. Pero la ausencia de casos correctamente segmentados y etiquetados conlleva múltiples limitaciones en el desarrollo de sistemas de separación AV, en los cuales son necesarias imágenes de referencia tanto para entrenar como para validar los algoritmos. Por ello, el diseño de imágenes sintéticas de TAC pulmonar podría superar estas dificultades ofreciendo la posibilidad de acceso a una base de datos de casos pseudoreales bajo un entorno restringido y controlado donde cada parte de la imagen (incluyendo arterias y venas) está unívocamente diferenciada. En esta Tesis Doctoral abordamos ambos problemas, los cuales están fuertemente interrelacionados. Primero se describe el diseño de una estrategia para generar, automáticamente, fantomas computacionales de TAC de pulmón en humanos. Partiendo de conocimientos a priori, tanto biológicos como de características de imagen de CT, acerca de la topología y relación entre las distintas estructuras pulmonares, el sistema desarrollado es capaz de generar vías aéreas, arterias y venas pulmonares sintéticas usando métodos de crecimiento iterativo, que posteriormente se unen para formar un pulmón simulado con características realistas. Estos casos sintéticos, junto a imágenes reales de TAC sin contraste, han sido usados en el desarrollo de un método completamente automático de segmentación/separación AV. La estrategia comprende una primera extracción genérica de vasos pulmonares usando partículas espacio-escala, y una posterior clasificación AV de tales partículas mediante el uso de Graph-Cuts (GC) basados en la similitud con arteria o vena (obtenida con algoritmos de aprendizaje automático) y la inclusión de información de conectividad entre partículas. La validación de los fantomas pulmonares se ha llevado a cabo mediante inspección visual y medidas cuantitativas relacionadas con las distribuciones de intensidad, dispersión de estructuras y relación entre arterias y vías aéreas, los cuales muestran una buena correspondencia entre los pulmones reales y los generados sintéticamente. La evaluación del algoritmo de segmentación AV está basada en distintas estrategias de comprobación de la exactitud en la clasificación de vasos, las cuales revelan una adecuada diferenciación entre arterias y venas tanto en los casos reales como en los sintéticos, abriendo así un amplio abanico de posibilidades en el estudio clínico de enfermedades cardiopulmonares y en el desarrollo de metodologías y nuevos algoritmos para el análisis de imágenes pulmonares. ABSTRACT Computed tomography (CT) is the reference image modality for the study of lung diseases and pulmonary vasculature. Lung vessel segmentation has been widely explored by the biomedical image processing community, however, differentiation of arterial from venous irrigations is still an open problem. Indeed, automatic separation of arterial and venous trees has been considered during last years as one of the main future challenges in the field. Artery-Vein (AV) segmentation would be useful in different medical scenarios and multiple pulmonary diseases or pathological states, allowing the study of arterial and venous irrigations separately. Features such as density, geometry, topology and size of vessels could be analyzed in diseases that imply vasculature remodeling, making even possible the discovery of new specific biomarkers that remain hidden nowadays. Differentiation between arteries and veins could also enhance or improve methods processing pulmonary structures. Nevertheless, AV segmentation has been unfeasible until now in clinical routine despite its objective usefulness. The huge complexity of pulmonary vascular trees makes a manual segmentation of both structures unfeasible in realistic time, encouraging the design of automatic or semiautomatic tools to perform the task. However, this lack of proper labeled cases seriously limits in the development of AV segmentation systems, where reference standards are necessary in both algorithm training and validation stages. For that reason, the design of synthetic CT images of the lung could overcome these difficulties by providing a database of pseudorealistic cases in a constrained and controlled scenario where each part of the image (including arteries and veins) is differentiated unequivocally. In this Ph.D. Thesis we address both interrelated problems. First, the design of a complete framework to automatically generate computational CT phantoms of the human lung is described. Starting from biological and imagebased knowledge about the topology and relationships between structures, the system is able to generate synthetic pulmonary arteries, veins, and airways using iterative growth methods that can be merged into a final simulated lung with realistic features. These synthetic cases, together with labeled real CT datasets, have been used as reference for the development of a fully automatic pulmonary AV segmentation/separation method. The approach comprises a vessel extraction stage using scale-space particles and their posterior artery-vein classification using Graph-Cuts (GC) based on arterial/venous similarity scores obtained with a Machine Learning (ML) pre-classification step and particle connectivity information. Validation of pulmonary phantoms from visual examination and quantitative measurements of intensity distributions, dispersion of structures and relationships between pulmonary air and blood flow systems, show good correspondence between real and synthetic lungs. The evaluation of the Artery-Vein (AV) segmentation algorithm, based on different strategies to assess the accuracy of vessel particles classification, reveal accurate differentiation between arteries and vein in both real and synthetic cases that open a huge range of possibilities in the clinical study of cardiopulmonary diseases and the development of methodological approaches for the analysis of pulmonary images.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Report on the classification of positions and schedules of compensation, City of Minneapolis ... /

Contains tables.

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Herbal medicines:physician's recommendation and clinical evaluation of St.John's Wort for depression

Why some physicians recommend herbal medicines while others do not is not well understood. We undertook a survey designed to identify factors, which predict recommendation of herbal medicines by physicians in Malaysia. About a third (206 out of 626) of the physicians working at the University of Malaya Medical Centre ' were interviewed face-to-face, using a structured questionnaire. Physicians were asked about their personal use of, recommendation of, perceived interest in and, usefulness and safety of herbal medicines. Using logistic regression modelling we identified personal use, general interest, interest in receiving training, race and higher level of medical training as significant predictors of recommendation. St. John's wort is one of the most widely used herbal remedies. It is also probably the most widely evaluated herbal remedy with no fewer than 57 randomised controlled trials. Evidence from the depression trials suggests that St. John's wort is more effective than placebo while its comparative efficacy to conventional antidepressants is not well established. We updated previous meta-analyses of St. John's wort, described the characteristics of the included trials, applied methods of data imputation and transformation for incomplete trial data and examined sources of heterogeneity in the design and results of those trials. Thirty randomised controlled trials, which were heterogeneous in design, were identified. Our meta-analysis showed that St. John's wort was significantly more effective than placebo [pooled RR 1.90 (1.54-2.35)] and [Pooled WMD 4.09 (2.33 to 5.84)]. However, the remedy was similar to conventional antidepressant in its efficacy [Pooled RR I. 0 I (0.93 -1.10)] and [Pooled WMD 0.18 (- 0.66 to 1.02). Subgroup analyses of the placebo-controlled trials suggested that use of different diagnostic classifications at the inclusion stage led to different estimates of effect. Similarly a significant difference in the estimates of efficacy was observed when trials were categorised according to length of follow-up. Confounding between the variables, diagnostic classification and length of trial was shown by loglinear analysis. Despite extensive study, there is still no consensus on how effective St. lohn's wort is in depression. However, most experts would agree that it has some effect. Our meta-analysis highlights the problems associated with the clinical evaluation of herbal medicines when the active ingredients are poorly defined or unknown. The problem is compounded when the target disease (e.g. depression) is also difficult to define and different instruments are available to diagnose and evaluate it.

The application of artificial neural networks to the interpretation and classification of freshwater benthic invertebrate communities

This thesis presents a thorough and principled investigation into the application of artificial neural networks to the biological monitoring of freshwater. It contains original ideas on the classification and interpretation of benthic macroinvertebrates, and aims to demonstrate their superiority over the biotic systems currently used in the UK to report river water quality. The conceptual basis of a new biological classification system is described, and a full review and analysis of a number of river data sets is presented. The biological classification is compared to the common biotic systems using data from the Upper Trent catchment. This data contained 292 expertly classified invertebrate samples identified to mixed taxonomic levels. The neural network experimental work concentrates on the classification of the invertebrate samples into biological class, where only a subset of the sample is used to form the classification. Other experimentation is conducted into the identification of novel input samples, the classification of samples from different biotopes and the use of prior information in the neural network models. The biological classification is shown to provide an intuitive interpretation of a graphical representation, generated without reference to the class labels, of the Upper Trent data. The selection of key indicator taxa is considered using three different approaches; one novel, one from information theory and one from classical statistical methods. Good indicators of quality class based on these analyses are found to be in good agreement with those chosen by a domain expert. The change in information associated with different levels of identification and enumeration of taxa is quantified. The feasibility of using neural network classifiers and predictors to develop numeric criteria for the biological assessment of sediment contamination in the Great Lakes is also investigated.

The relationship between vegetation and modern pollen assemblages on Mount Paggeo (NE Greece)

In this study, we investigated the relationship between vegetation and modern-pollen rain along the elevational gradient of Mount Paggeo. We apply multivariate data analysis to assess the relationship between vegetation and modern-pollen rain and quantify the representativeness of forest zones. This study represents the first statistical analysis of pollen-vegetation relationship along an elevational gradient in Greece. Hence, this paper improves confidence in interpretation of palynological records from north-eastern Greece and may refine past climate reconstructions for a more accurate comparison of data and modelling. Numerical classification and ordination were performed on pollen data to assess differences among plant communities that beech (Fagus sylvatica) dominates or co-dominates. The results show a strong relationship between altitude, arboreal cover, human impact and variations in pollen and nonpollen palynomorph taxa percentages.

Beyond the Acronyms: Sport Diplomacy and the Classification of the International Olympic Committee

Diplomacy often finds itself reduced to actions centred on states. However, after the Cold War, international relations and diplomacy have expanded with different actors growing into significant roles, particularly in the increase of diplomatic relations in the context of sport. The classification and significance of other actors remains under-researched in relation to sport, with literature focusing more on the growth of new and varying practices of diplomacy. This analysis contends that there is a need to interrogate fundamental components of modern diplomacy—with the actor being the focus—more specifically the classification of sports organisations in diplomacy. It is relevant as a more accurate understanding of sports organisations will contribute to how diplomatic studies can analyse and evaluate modern diplomacy within the context of sport. The International Olympic Committee is the actor used to illustrate how problematic classifications currently in the academic literature translate into weak and reduced analysis and evaluation of its role and significance in diplomacy. As counterpoint, this analysis proposes an analytical framework of socio-legal theory that harnesses legal regulation as a benchmark to classify an actor’s capacity within a society. In consequence, the IOC is as an active and significant contributor to the ever expanding and complex diplomatic environment and wider society.

Impact of oral diseases and disorders on oral health-related quality of life of preschool children

BACKGROUND The presence of oral diseases and disorders can produce an impact on the quality of life of preschool children and their parents, affecting their oral health and well-being. However, socioeconomic factors could confound this association, but it has not been yet tested at this age. OBJECTIVE To assess the impact of early childhood caries (ECC), traumatic dental injuries (TDI) and malocclusions on the oral health-related quality of life (OHRQoL) of children between 2 and 5 years of age adjusted by socioeconomic factors. METHODS Parents of 260 children answered the Early Childhood Oral Health Impact Scale (ECOHIS) (six domains) on their perception of the children's OHRQoL and socioeconomic conditions. Two calibrated dentists (κ>0.8) examined the severity of ECC according to dmft index, and children were categorized into: 0=caries free; 1-5=low severity; ≥6=high severity. TDI and malocclusions were examined according to Andreasen & Andreasen (1994) classification and for the presence or absence of three anterior malocclusion traits (AMT), respectively. OHRQoL was measured through ECOHIS domain and total scores, and poisson regression was used to associate the different factors with the outcome. RESULTS In each domain and overall ECOHIS scores, the severity of ECC showed a negative impact on OHRQoL (P<0.001). TDI and AMT did not show a negative impact on OHRQoL nor in each domain (P>0.05). The increase in the child's age, higher household crowding, lower family income and mother working out of home were significantly associated with OHRQoL (P<0.05). The multivariate adjusted model showed that the high severity of ECC (RR=3.81; 95% CI=2.66, 5.46; P<0.001) was associated with greater negative impact on OHRQoL, while high family income was a protective factor for OHRQoL (RR=0.93; 95% CI=0.87, 0.99; P<0.001). CONCLUSIONS The severity of ECC and a lower family income had a negative impact on the OHRQoL of preschool children and their parents.

Terminal defect analysis and classification methodology for automotive industry

The impact of end customer quality complaints with direct relationship with automotive components has presented negative trend at European level for the entire automotive industry. Thus, this research proposal is to concentrate efforts on the most important items of Pareto chart and understand the failure type and the mechanism involved, link and impact of the project and parameters on the process, ending it with the development of one of the company’s most desired tool, that hosted this project – European methodology of terminals defects classification, and listing real opportunities for improvement based on measurement and analysis of actual data. Through the development of terminals defects classification methodology, which is considered a valuable asset to the company, all the other companies of the YAZAKI’s group will be able to characterize terminals as brittle or ductile, in order to put in motion, more efficiently, all the other different existing internal procedures for the safeguarding of the components, improving manufacturing efficiency. Based on a brief observation, nothing can be said in absolute sense, concerning the failure causes. Base materials, project, handling during manufacture and storage, as well as the cold work performed by plastic deformation, all play an important role. However, it was expected that this failure has been due to a combination of factors, in detriment of the existence of a single cause. In order to acquire greater knowledge about this problem, unexplored by the company up to the date of commencement of this study, was conducted a thorough review of existing literature on the subject, real production sites were visited and, of course, the actual parts were tested in lab environment. To answer to many of the major issues raised throughout the investigation, were used extensively some theoretical concepts focused on the literature review, with a view to realizing the relationship existing between the different parameters concerned. Should here be stated that finding technical studies on copper and its alloys is really hard, not being given all the desirable information. This investigation has been performed as a YAZAKI Europe Limited Company project and as a Master Thesis for Instituto Superior de Engenharia do Porto, conducted during 9 months between 2012/2013.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.

AAGL 2021 classification system and risk of surgical complications in women submitted to surgery for endometriosis

Introduction: Recently, the American Association of Gynecologic Laparoscopists proposed a new classification and scoring system with the specific aim to assess surgical complexity. This study sought to assess if a higher AAGL score correlates with an increased risk of peri-operative complications in women submitted to surgery for endometriosis. Methods: This is a retrospective cohort study conducted in a third level referral center. We collected data from women with endometriosis submitted to complete surgical removal of endometriosis from January 2019 to December 2021. ENZIAN, r-ASRM classifications and AAGL total score was calculated for each patient. Population was divided in two groups according to the occurrence or not of at least one peri-operative complication. Our primary outcome was to evaluate the correlation between AAGL score and occurrence of complications. Results: During the study period we analyzed data from 282 eligible patients. Among them, 80 (28.4%) experienced peri-operative complications. No statistically significant difference was found between the two groups in terms of baseline characteristics, except for pre-operative hemoglobin (Hb), which was lower in patients with complications (p=0.001). Surgical variables associated with the occurrence of complications were recto-sigmoid surgery (p=0.003), ileocecal resection (0.034), and longer operative time (p=0.007). Furthermore, a higher ENZIAN B score (p=0.006), AAGL score (p=0.045) and stage (p=0.022) were found in the group of patients with complications. The multivariate analysis only confirmed the significant association between the occurrence of peri-operative complications and lower pre-operative Hb level (OR 0.74; 95% CI, 0.59 - 0.94; p=0.014), longer operative time (OR 1.00; 95% CI, 1.00 – 1.01; p=0.013), recto-sigmoid surgery - especially discoid resection (OR 8.73; 95% CI, 2.18 – 35; p=0.016) and ENZIAN B3 (OR 3.62; 95% CI, 1.46 – 8.99; p= 0.006). Conclusion: According to our findings, high AAGL scores or stages do not seem to increase the risk of peri-operative complications.