7,921 research outputs found

    Prediction of knot size in uneven-sized Norway spruce stands in Sweden

    Get PDF
    The size of knots is negatively correlated with bending strength in sawn timber and it is therefore used as a quality grading criterion in national roundwood grading standards. Some standards even use the size of the largest knot as the sole estimate for individual log knottiness. The size of knots is determined by crown horizontal extension, which in turn is dependent on the impact of competing trees. Thus, with knot size models that are competition-dependent, roundwood quality due to knottiness can be simulated for different management alternatives. However, these types of models, calibrated on uneven-sized Norway spruce in Fennoscandia, are currently not available. Therefore, the objective of this study is to develop a competition-dependent model framework for prediction of the largest knot size per stem height section, for application within uneven-sized Norway spruce stands. Data from terrestrial laser scanning of an uneven-sized stand in southern Sweden are used to calibrate a modular prediction framework, consisting of interlinked allometric statistical models. Alternative framework sub-models are presented and the preferred model combination can be selected according to context and available input data. The flexible modular format enables further development of separate sub-components for adaptation to growing conditions not covered by the current calibration range

    Visible and near infrared spectroscopy in soil science

    Get PDF
    This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

    Role based behavior analysis

    Get PDF
    Tese de mestrado, Segurança Informática, Universidade de Lisboa, Faculdade de Ciências, 2009Nos nossos dias, o sucesso de uma empresa depende da sua agilidade e capacidade de se adaptar a condições que se alteram rapidamente. Dois requisitos para esse sucesso são trabalhadores proactivos e uma infra-estrutura ágil de Tecnologias de Informacão/Sistemas de Informação (TI/SI) que os consiga suportar. No entanto, isto nem sempre sucede. Os requisitos dos utilizadores ao nível da rede podem nao ser completamente conhecidos, o que causa atrasos nas mudanças de local e reorganizações. Além disso, se não houver um conhecimento preciso dos requisitos, a infraestrutura de TI/SI poderá ser utilizada de forma ineficiente, com excessos em algumas áreas e deficiências noutras. Finalmente, incentivar a proactividade não implica acesso completo e sem restrições, uma vez que pode deixar os sistemas vulneráveis a ameaças externas e internas. O objectivo do trabalho descrito nesta tese é desenvolver um sistema que consiga caracterizar o comportamento dos utilizadores do ponto de vista da rede. Propomos uma arquitectura de sistema modular para extrair informação de fluxos de rede etiquetados. O processo é iniciado com a criação de perfis de utilizador a partir da sua informação de fluxos de rede. Depois, perfis com características semelhantes são agrupados automaticamente, originando perfis de grupo. Finalmente, os perfis individuais são comprados com os perfis de grupo, e os que diferem significativamente são marcados como anomalias para análise detalhada posterior. Considerando esta arquitectura, propomos um modelo para descrever o comportamento de rede dos utilizadores e dos grupos. Propomos ainda métodos de visualização que permitem inspeccionar rapidamente toda a informação contida no modelo. O sistema e modelo foram avaliados utilizando um conjunto de dados reais obtidos de um operador de telecomunicações. Os resultados confirmam que os grupos projectam com precisão comportamento semelhante. Além disso, as anomalias foram as esperadas, considerando a população subjacente. Com a informação que este sistema consegue extrair dos dados em bruto, as necessidades de rede dos utilizadores podem sem supridas mais eficazmente, os utilizadores suspeitos são assinalados para posterior análise, conferindo uma vantagem competitiva a qualquer empresa que use este sistema.In our days, the success of a corporation hinges on its agility and ability to adapt to fast changing conditions. Proactive workers and an agile IT/IS infrastructure that can support them is a requirement for this success. Unfortunately, this is not always the case. The user’s network requirements may not be fully understood, which slows down relocation and reorganization. Also, if there is no grasp on the real requirements, the IT/IS infrastructure may not be efficiently used, with waste in some areas and deficiencies in others. Finally, enabling proactivity does not mean full unrestricted access, since this may leave the systems vulnerable to outsider and insider threats. The purpose of the work described on this thesis is to develop a system that can characterize user network behavior. We propose a modular system architecture to extract information from tagged network flows. The system process begins by creating user profiles from their network flows’ information. Then, similar profiles are automatically grouped into clusters, creating role profiles. Finally, the individual profiles are compared against the roles, and the ones that differ significantly are flagged as anomalies for further inspection. Considering this architecture, we propose a model to describe user and role network behavior. We also propose visualization methods to quickly inspect all the information contained in the model. The system and model were evaluated using a real dataset from a large telecommunications operator. The results confirm that the roles accurately map similar behavior. The anomaly results were also expected, considering the underlying population. With the knowledge that the system can extract from the raw data, the users network needs can be better fulfilled, the anomalous users flagged for inspection, giving an edge in agility for any company that uses it

    Sensitivity of the Exporting Economy on the External Shocks: Evidence from Slovene Firms

    Full text link
    In this paper we investigate the export participation of Slovene firms. We first show that sunk costs are an important factor for explaining the export behavior of Slovene firms. Next we show that when the absorption power of the exporting market declines, firms still trade with their established buyers (hysteresis) despite the fact that due to lower prices their exporting revenues decline. We show that this can be explained with high exit costs, which consist of switching costs (costs of replacing stable buyers with new ones) and cost of reducing the production (compensation money for excess workers) and high re-entry costs.http://deepblue.lib.umich.edu/bitstream/2027.42/40020/3/wp634.pd

    Estimating Tsunami-Induced Building Damage through Fragility Functions: Critical Review and Research Needs

    Get PDF
    Tsunami damage, fragility, and vulnerability functions are statistical models that provide an estimate of expected damage or losses due to tsunami. They allow for quantification of risk, and so are a vital component of catastrophe models used for human and financial loss estimation, and for land-use and emergency planning. This paper collates and reviews the currently available tsunami fragility functions in order to highlight the current limitations, outline significant advances in this field, make recommendations for model derivation, and propose key areas for further research. Existing functions are first presented, and then key issues are identified in the current literature for each of the model components: building damage data (the response variable of the statistical model), tsunami intensity data (the explanatory variable), and the statistical model that links the two. Finally, recommendations are made regarding areas for future research and current best practices in deriving tsunami fragility functions (see Discussion, Recommendations, and Future Research). The information presented in this paper may be used to assess the quality of current estimations (both based on the quality of the data, and the quality of the models and methods adopted) and to adopt best practice when developing new fragility functions

    Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

    Get PDF
    This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

    RealROC: a shiny based application for ROC curve study with covariate adjustment

    Get PDF
    Dissertação de mestrado em BioinformaticsA curva ROC (Receiver operating characteristic) é uma ferramenta analítica eficaz para testes clínicos. A análise permite visualizar a variação de sensibilidade e especificidade para uma dada região de corte através de um simples, mas robusto gráfico bidimensional. Num contexto biológico, testes podem ser influenciados por múltiplas variáveis externas e como tal a análise ROC pode não ser a ideal ou gerar resultados incompletos. É então necessário saber que variáveis afetam determinado teste clínico de forma a determinar os melhores parâmetros para determinado teste ou até descartar determinada metodologia mediante a situação. O ajuste da curva ROC a covariáveis permite a normalização do efeito das mesmas ou diretamente ajustar a curva para os seus efeitos. Software direcionado ao ajuste da curva ROC é, infelizmente, escasso e muitas vezes difícil de manusear por utilizadores não especializados. Recentemente o pacote AROC foi lançando para R que disponibiliza vários recursos para estes ajustamentos, no entanto a dificuldade de utilização mantém-se. A combinação deste pacote com a estrutura Shiny, um pacote que permite o desenvolvimento de aplicações interativas, tem por objetivo a criação de um programa grátis e acessível que permita uma análise mais aprofundada disponível para todos os investigadores. RealROC foi capaz de replicar resultados de um caso de estudo que analisou a influência do sexo no sistema de pontuação CRIB e respetiva previsão de mortalidade, demonstrando a usabilidade e acessibilidade do programa que será disponibilizado online e potencialmente contribuir para novos desenvolvimentos na área.Receiver operating characteristic (ROC) curves are a powerful analytical tool for clinical tests. The analysis allows the visualization of varying sensitivity and specificity for a given threshold through a simple, yet robust, two-dimensional plot. In a biological framework, tests can be influenced by multiple external variables, as such, standard ROC analysis may not be suitable or may provide incomplete data. It is then necessary to know which variables influence clinical test results to determine optimal conditions for trials or even to disregard a given method of evaluation in certain contexts. Adjusting for covariates allows ROC analysis to normalize the effects of the variable in question or to directly adjust the curve for its effects. Unfortunately ROC software that is able to conduct such an adjustment is sparse and proven difficult to use for non technical users. Recently, the AROC package for R was released and provides a robust resource for such adjustments however with he same usability problems previously stated. By combining this package with the Shiny framework, an R package that allows the creation of interactive applications, we hope to provide an accessible and free software that allows this extra depth of analysis to be available for all researchers. RealROC was able to mimic the results of a case study analysing the affects of sex to the CRIB score and resulting mortality rates that proving its practicality and will be made available online and hopefully contribute to the advancement of software in this field

    Comfort in Urban Public Spaces

    Get PDF
    • …
    corecore