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The purpose of this research is to develop a model and solution methodology for selecting and scheduling timely and cost-effective maintenance, rehabilitation, and reconstruction activities (M & R) for each pavement section in a highway network and allocating the funding levels through a finite multi-period horizon within the constraints imposed by budget availability in each period, frequency availability of activities, and specified minimum pavement quality requirements. M & R is defined as a chronological sequence of reconstruction, rehabilitation, and major/minor maintenance, including a "do nothing" activity. A procedure is developed for selecting an M & R activity for each pavement section in each period of a specified extended planning horizon. Each activity in the sequence consumes a known amount of capital and generates a known amount of effectiveness measured in pavement quality. The effectiveness of an activity is the expected value of the overall gains in pavement quality rating due to the activity performed on a highway network over an analysis period. It is assumed that the unused portion of the budget for one period can be carried over to subsequent periods. Dynamic Programming (DP) and Branch-and-Bound (B-and-B) approaches are combined to produce a hybrid algorithm for solving the problem under consideratioin. The algorithm is essentially a DP approach in the sense that the problem is divided into smaller subproblems corresponding to each single period problem. However, the idea of fathoming partial solutions that could not lead to an optimal solution is incorporated within the algorithm to reduce storage and computational requirements in the DP frame using the B-and-B approach. The imbedded-state approach is used to reduce a multi-dimensional DP to a one-dimensional DP. For bounding at each stage, the problem is relaxed in a Lagrangean fashion so that it separates into longest-path network model subproblems. The values of the Lagrangean multipliers are found by a subgradient optimization method, while the Ford-Bellman network algorithm is employed at each iteration of the subgradient optimization procedure to solve the longest-path network problem as well as to obtain an improved lower and upper bound. If the gap between lower and upper bound is sufficiently small, then we may choose to accept the best known solutions as being sufficiently close to optimal and terminate the algorithm rather than continue to the final stage.

Phosphorus (P) is an essential element for life on our planet. It is central in numerous biochemical processes in terrestrial and aqueous ecosystems including food production; and it is the primary growth-limiting nutrient in some of the world’s biomes. The main source of P for use as agricultural fertilizer is mining of non-renewable mineral phosphate. In terrestrial ecosystems the main source is soil P, where the largest fraction is organic P, composed of many species with widely differing properties. This fraction controls the utilization of P by plants and microorganisms and influences ecosystem development and productivity. However, there is only scarce knowledge about the molecular composition of the organic P pool, about the processes controlling its bioavailability, and about its changes as soils develop. Therefore, the aim of this thesis was to develop robust solution- and solid-state 31P nuclear magnetic resonance spectroscopy (NMR) methods to provide molecular information about speciation of the organic P pool, and to study its dynamics in boreal and tropical soils. By studying humus soils of a groundwater recharge/discharge productivity gradient in a Fennoscandian boreal forest by solution- and solid-state NMR, it was found that P speciation changed with productivity. In particular, the level of orthophosphate diesters decreased with increasing productivity while mono-esters such as inositol phosphates increased. Because the use of solution NMR on conventional NaOH/EDTA extracts of soils was limited due to severe line broadening caused by the presence of paramagnetic metal ions, a new extraction method was developed and validated. Based on the removal of these paramagnetic impurities by sulfide precipitation, a dramatic decrease in NMR linewidths was obtained, allowing for the first time to apply modern multi-dimensional solution NMR techniques to soil extracts. Identification of individual soil P-species, and tracking changes in the organic P pools during soil development provided information for connecting P-speciation to bioavailability and ecosystem properties. Using this NMR approach we studied the transformation of organic P in humus soils along a chronosequence (7800 years) in Northern Sweden. While total P varied little, the composition of the soil P pool changed particularly among young sites, where also the largest shift in the composition of the plant community and of soil microorganisms was observed. Very old soils, such as found Africa, are thought to strongly adsorb P, limiting plant productivity. I used NMR to study the effect of scattered agroforestry trees on P speciation in two semi-arid tropical woodlands with different soil mineralogy (Burkina Faso). While the total P concentration was low, under the tree canopies higher amounts of P and higher diversity of P-species were found, presumably reflecting higher microbial activity. / <p>I delarbete III har titel och författaruppgifter förändrats.</p>

As técnicas de projeção ou posicionamento de pontos no plano, que servem para mapear dados multi-dimensionais em espaços visuais, sempre despertaram grande interesse da comunidade de visualização e análise de dados por representarem uma forma útil de exploração baseada em relações de similaridade e correlação. Apesar disso, muitos problemas ainda são encontrados em tais técnicas, limitando suas aplicações. Em especial, as técnicas de projeção multi-dimensional de maior qualidade têm custo computacional proibitivo para grandes conjuntos de dados. Adicionalmente, problemas referentes à escalabilidade visual, isto é, à capacidade da metáfora visual empregada de representar dados de forma compacta e amigável, são recorrentes. Esta tese trata o problema da projeção multi-dimensional de vários pontos de vista, propondo técnicas que resolvem, até certo ponto, cada um dos problemas verificados. Também é fato que a complexidade e o tamanho dos conjuntos de dados indicam que a visualização deve trabalhar em conjunto com técnicas de mineração, tanto embutidas no processo de mapeamento, como por meio de ferramentas auxiliares de interpretação. Nesta tese incorporamos alguns aspectos de mineração integrados ao processo de visualização multi-dimensional, principalmente na aplicação de projeções para visualização de coleções de documentos, propondo uma estratégia de extração de tópicos. Como suporte ao desenvolvimento e teste dessas técnicas, foram criados diferentes sistemas de software. O principal inclui as técnicas desenvolvidas e muitas das técnicas clássicas de projeção, podendo ser usado para exploração de conjuntos de dados multi-dimensionais em geral, com funcionalidade adicional para mapeamento de coleções de documentos. Como principal contribuição desta tese propomos um entendimento mais profundo dos problemas encontrados nas técnicas de projeção vigentes e o desenvolvimento de técnicas de projeção (ou mapeamento) que são rápidas, tratam adequadamente a formação visual de grupos de dados altamente similares, separam satisfatoriamente esses grupos no layout, e permitem a exploração dos dados em vários níveis de detalhe / Projection or point placement techniques, useful for mapping multidimensional data into visual spaces, have always risen interest in the visualization and data analysis communities because they can support data exploration based on similarity or correlation relations. Regardless of that interest, various problems arise when dealing with such techniques, impairing their widespread application. In particularly the projections that yield highest quality layouts have prohibitive computational cost for large data sets. Additionally, there are issues regarding visual scalability, i.e., the capability of visually fit the individual points in the exploration space as the data set grows large. This thesis treats the problems of projections from various perspectives, presenting novel techniques that solve, to certain extent, several of the verified problems. It is also a fact that size and complexity of data sets suggest the integration of data mining capabilities into the visualization pipeline, both during the mapping process and as a tools to extract additional information after the data have been layed out. This thesis also add some aspects of mining to the multidimensional visualization process, mainly for the particular application of analysis of document collections, proposing and implementing an approach for topic extraction. As supporting tools for testing these techniques and comparing them to existing ones different software systems were written. The main one includes the techniques developed here as well as several of the classical projection and dimensional reduction techniques, and can be used for exploring various kinds of data sets, with addition functionality to support the mapping of document collections. This thesis contributes to the understanding of the projection or mapping problem and develops new techniques that are fast, treat adequately the visual formation of groups of highly related data items, separate those groups properly and allow exploration of data in various levels of detail

This study investigates expressions akin to ‘again’, which inhabit the semantic domain of repetition and restitution, from a cross-linguistic perspective. Using massively parallel corpora as the primary source of data the aim of this study is to investigate whether the encoding of repetitive and restitutive meaning is a cross-linguistically valid difference and if there are any patterns in the language specific variation of the repetitive and restitutive domain. By using Multi-Dimensional Scaling and Partitioning Around Medoids to investigate how the expressions ‘third time’, ‘second time’, ‘again’, ‘back’ and ‘return’ make up the semantic space of the domain, it was determined that the domain in question forms a continuum of meanings. This scale, named the TURN-hierarchy, is comprised of repetitive expressions like ‘third time’ to the far left, ambiguous expressions like ‘again’ in the intermediate section and restitutive expressions such as ‘return, back’ to the far right. Furthermore, the results show that repetitive and restitutive meaning is encoded differently in a majority of the sample languages, and that there is asymmetry in the encoding of repetition and restitution where repetitive meaning is privileged. Thus,it is proposed that all languages have at least one exclusively repetitive expression.

Performance analysis often considers numerous factors contributing to performance, and the relative importance of these factors is evolving based on dynamic conditions and requirements. Investigating large numbers of factors and understanding individual factors' predictability within the ultimate performance are challenging tasks. A visual analytics approach that integrates interactive analysis, novel visual representations, and predictive machine learning models can provide new capabilities to examine performance effectively and thoroughly. Currently, only limited research has been done on the possible applications of visual analytics for performance evaluation. In this dissertation, two specific types of performance analysis are presented: (1) organizational employee performance evaluation and (2) performance improvement of machine learning models with interactive feature selection. Both application scenarios leverage the human-in-the-loop approach to assist the identification of influential factors. For organizational employee performance evaluation, a novel visual analytics system, MetricsVis, is developed to support exploratory organizational performance analysis. MetricsVis incorporates hybrid evaluation metrics that integrate quantitative measurements of observed employee achievements and subjective feedback on the relative importance of these achievements to demonstrate employee performance at and between multiple levels regarding the organizational hierarchy. MetricsVis II extends the original system by including actual supervisor ratings and user-guided rankings to capture preferences from users through derived weights. Comparing user preferences with objective employee workload data enables users to relate user evaluation to historical observations and even discover potential bias. For interactive feature selection and model evaluation, a visual analytics system, FeatureExplorer, allows users to refine and diagnose a model iteratively by selecting features based on their domain knowledge, interchangeable features, feature importance, and the resulting model performance. FeatureExplorer enables users to identify stable, trustable, and credible predictive features that contribute significantly to a prediction model.

Systematic Literature Reviews (SLR) are a powerful research tool to identify and select literature to answer a certain question. However, an approach to extract inherent analytical data in Systematic Literature Reviews’ multi-dimensional datasets was lacking. Previous Systematic Literature Review tools do not incorporate the capability of providing said analytical insight. Therefore, this thesis aims to provide a useful approach comprehending various algorithms and data treatment techniques to provide the user with analytical insight on their data that is not evident in the bare execution of a Systematic Literature Review. For this goal, a literature review has been conducted to find the most relevant techniques to extract data from multi-dimensional data sets and the aforementioned approach has been tested on a survey regarding Self-Adaptive Systems (SAS) using a web-application. As a result, we find out what are the most adequate techniques to incorporate into the approach this thesis will provide.

Groundwater analyses have revealed high concentrations of the toxic element arsenic around Stockholm and Mälardalen, a problem that often is linked to high levels of arsenic in the bedrock and which could be escalated by the many construction projects in the same region. However, it is unknown what part of the bedrock is causing the contamination. The aim of this thesis is to identify the chemical elements that associate with arsenic and study how the rock types differ in their content of elements and compounds. The highest median concentration of arsenic is found in quartz-feltspar-rich sedimentary rock, while intrusive rock types reveal the lowest levels. Using cluster analysis, arsenic is placed in a group including nine other elements, to which the strongest correlations are found with antimony, bismuth and silver. A moderate correlation with sulphur is also observed. The associations between groupings of elements are analysed using measures of dependence, which reveal relatively strong associations. Dimension reduction and ordination techniques provide further insight to the typical appearances of elements and reveal two groups of similar rock types.

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