Savannah ecosystems play a prominent role in the global carbon (C) cycle, yet fluxes are poorly quantified, and the key processes regulating vegetation dynamics are uncertain. Insight is particularly deficient in southern Africa’s miombo woodlands, a woody savannah that is home to over 100 million people. This biome is heavily disturbed, with widespread deforestation and degradation associated with agriculture, charcoal and timber extraction, and frequent fires from anthropogenic sources. In this thesis I combine plot inventory data with remote sensing and modelling techniques to improve our understanding of the miombo woodland C cycle. Using a network of forest inventory plots, I characterise floristic and functional diversity in a savannah-forest mosaic in southeastern Tanzania. Divergent vegetation structures are associated with variation in fire frequency, water supply, and soil chemo-physical properties. Corresponding differences are noted in fire resilience, water-use, and nutrient acquisition plant functional traits, suggesting that multiple interrelated environmental filters act to assemble heterogeneous tree communities. Re-inventory of forest plots was used to quantify key aspects of the woody C cycle. Tree growth rates are slow, calling for careful management of woodland resources, and significantly reduced where stems were damaged. Stem mortality is rare, though elevated in the smallest trees and where damage was recorded. Contemporary strategies to incentivise the conservation of miombo woodland ecosystems, such as the REDD+ programme of the United Nations, advocate payments for sustaining ecosystem services such as C sequestration. I report on a pilot REDD+ project aiming to reduce woodland degradation from frequent high intensity fires in southeastern Tanzania. Model simulations suggest that woody biomass is being gradually lost from the region, and that setting early season fires has the potential to reverse this trend. Realising substantial changes in C storage requires a demanding reduction to late fire frequency, and uncertainty in model predictions remains high. I quantify the C cycle of southern African woodlands by combining observational data with a diagnostic C cycle model under a model-data fusion framework. Model outputs show substantial variation in primary production, C allocation patterns, and foliar and canopy traits, which are associated with differences in woody cover, fire, and precipitation properties. C cycle dynamics correspond poorly to conventional land cover maps, indicating they may be unsuited to upscaling measurements and models of the terrestrial C cycle.

The diploma thesis focuses on the issue of knowledge management, its principles and its application. The first part focuses on theoretical foundations. These are then used for analytical and practical part, which is dedicated to a proposal of own solution, where I propose practices to eliminate the identified weaknesses.

No / The recent development of information techniques, especially the state-of-the-art “big data” solutions, enables the extracting, gathering, and processing large amount of toxicity information from multiple sources. Facilitated by this technology advance, a framework named integrated testing strategies (ITS) has been proposed in the predictive toxicology domain, in an effort to intelligently jointly use multiple heterogeneous toxicity data records (through data fusion, grouping, interpolation/extrapolation etc.) for toxicity assessment. This will ultimately contribute to accelerating the development cycle of chemical products, reducing animal use, and decreasing development costs. Most of the current study in ITS is based on a group of consensus processes, termed weight of evidence (WoE), which quantitatively integrate all the relevant data instances towards the same endpoint into an integrated decision supported by data quality. Several WoE implementations for the particular case of toxicity data fusion have been presented in the literature, which are collectively studied in this paper. Noting that these uncertainty handling methodologies are usually not simply developed from conventional probability theory due to the unavailability of big datasets, this paper first investigates the mathematical foundations of these approaches. Then, the investigated data integration models are applied to a representative case in the predictive toxicology domain, with the experimental results compared and analysed.

Understanding and managing change is a strategic objective for many organisations to successfully compete in a market place; as a result, organisations are leveraging their data asset and implementing data warehouses to gain business intelligence necessary to improve their businesses. Data warehouses are expensive initiatives, one-half to two-thirds of most data warehousing efforts end in failure. In the absence of well-formalised design methodology in the industry and in the context of the debate on data architecture in data warehousing, this thesis examines why multidimensional and relational data models define the data architecture landscape in the industry. The study develops a number of propositions from the literature and empirical data to understand the factors impacting the choice of logical data model in data warehousing. Using a comparative case study method as the mean of collecting empirical data from the case organisations, the research proposes a conceptual model for logical data model adoption. The model provides a framework that guides decision making for adopting a logical data model for a data warehouse. The research conceptual model identifies the characteristics of business requirements and decision pathways for multidimensional and relational data warehouses. The conceptual model adds value by identifying the business requirements which a multidimensional and relational logical data model is empirically applicable.

The Arctic archipelago of Svalbard has experienced among the greatest increases in temperature on Earth in the last few decades. The changing climatic conditions have a large impact on the glacier mass balance. This study makes use of a highresolution model dataset with data on climatic and glacier conditions on Svalbard from 1957 to 2018. The model dataset is used to analyse the spatiotemporal variability in glacier mass balance across Svalbard and linking those changes to long-term trends in meteorological conditions. The study is focused on the spatial gradients in trends between two regions in Svalbard, the coldest part of the archipelago, Nordaustlandet and the milder southern part of the main island Spitsbergen. The north eastern (NE) region is found to have a greater increase in annual air temperature over the simulation period with 5.5 °C compared to 3.5 °C for the south western (SW) region. The increase in annual summer temperatures is much smaller with a total increase of 1 °C for the NW and 1.5 °C for the SW. Both regions show a small, but significant, increase of precipitation. Relative humidity and cloud cover in the NE are increasing slightly over the time period, probably due to retreating sea ice cover. Glacier melt and runoff are increasing in both regions, which is contributing to significant negative trends in the mass balance. The increase in melt and run off is stronger in the SW than in the NE. There’s a strong correlation between summer air temperature and glacier mass balance, melt and runoff. Refreezing in the NE is decreasing much faster than in the SW. Refreezing is strongly correlated with annual air temperatures in the NE and not in the SW, probably due to lower temperatures in the NE region.

In the presented work, we study a derivation of an optimal communication XML schemas for a given conceptual model of a business process, complemented with a conceptual model of exchanged data and influence of a change in user requirements on derived XML schema. The work contains a review of important problems related to the topic. It also describes existing approaches of these problems. The work analyses a derivation of an optimal XML schema in detail. The approach presented in this thesis shows an ability to model exchanged data with a conceptual model of a business process. The thesis describes a solution, how to derive a concrete XML schema automaticly from a given conceptual model of exchanged data. Finally, the work contains a prototype implementation of the presented solution.

Numerical models are powerful and widely used tools for environmental prediction; however, any model prediction contains errors due to imperfect model parameterizations, insufficient model resolution, numerical errors, imperfect initial and boundary conditions etc. A variety of approaches is applied to quantify, correct and minimize these errors including skill assessments, bias correction and formal data assimilation. All of these require observations and benefit from comprehensive data sets. In this thesis, two aspects related to the quantification and correction of errors in biological ocean models are addressed: (i) A new bias correction method for a biological ocean model is evaluated, and (ii) a novel approach for expanding the set of typically available phytoplankton observations is assessed. The bias correction method, referred to as frequency-dependent nudging, was proposed by Thompson et al. (Ocean Modelling, 2006, 13:109-125) and is used to nudge a model only in prescribed frequencies. A desirable feature of this method is that it can preserve high frequency variability that would be dampened with conventional nudging. The method is first applied to an idealized signal consisting of a seasonal cycle and high frequency variability. In this example, frequency-dependent nudging corrected for the imposed seasonal bias without affecting the high-frequency variability. The method is then applied to a non-linear, 1 dimensional (1D) biogeochemical ocean model. Results showed that application of frequency-dependent nudging leads to better biogeochemical estimates than conventional nudging. In order to expand the set of available phytoplankton observations, light measurements from sensors attached on grey seals where assessed to determine if they provide a useful proxy of phytoplankton biomass. A controlled experiment at Bedford Basin showed that attenuation coefficient estimates from light attenuation measurements from seal tags were found to correlate significantly with chlorophyll. On the Scotian Shelf, results of the assessment indicate that seal tags can uncover spatio-temporal patterns related to phytoplankton biomass; however, more research is needed to derive absolute biomass estimates in the region.

Thesis (Ed. S.)--Miami University, Dept. of Educational Psychology, 2008. / Title from first page of PDF document. Includes bibliographical references (p. 15-18).

3-D computational modelling of the human spine provides a sophisticated and cost-effective medium for bioengineers, researchers, and ergonomics designers in order to study the biomechanical behaviour of the human spine under different loading conditions. Developing a generic parametric computational human spine model to be employed in biomechanical modelling introduces a considerable potential to reduce the complexity of implementing and amending the intricate spinal geometry. The main objective of this research is to develop a 3-D parametric human spine model generation framework based on a command file system, by which the parameters of each vertebra are read from the database system, and then modelled within commercial 3-D CAD software. A novel data acquisition and generation system was developed as a part of the framework for determining the unknown vertebral dimensions, depending on the correlations between the parameters estimated from existing anthropometrical studies in the literature. The data acquisition system embodies a predictive methodology that comprehends the relations between the features of the vertebrae by employing statistical and geometrical techniques. Relations amongst vertebral parameters such as golden ratio were investigated and successfully implemented into the algorithms. The validation of the framework was carried out by comparing the developed 3-D computational human spine models against various real life human spine data, where good agreements were achieved. The constructed versatile framework possesses the capability to be utilised as a basis for quickly and effectively developing biomechanical models of the human spine such as finite element models.

A software component model is the cornerstone of any Component-based Software Development (CBSD) methodology. Such a model defines the modelling elements for constructing software systems. In software system modelling, it is necessary to capture the three elements of a system's behaviour: (i) control (ii) computation and (iii) data. Within a system, computations are performed according to the flow of control or the flow of data, depending on whether computations are control-driven or data-driven. Computations are function evaluations, assignments, etc., which transform data when invoked by control or data flow. Therefore a component model should be able to model control flow, data flow as well as computations. Current component models all model computations, but beside computations tend to model either control flow only or data flow only, but not both. In this thesis, we present a new component model which can model both control flow and data flow. It contains modelling elements that capture control flow and data flow explicitly. Furthermore, the modelling of control flow is separate from that of data flow; this enables the modelling of both control-driven and data-driven computations. The feasibility of the model is shown by means of an implementation of the model, in the form of a prototype tool. The usefulness of the model is then demonstrated for a specific domain, the embedded systems domain, as well as a generic domain. For the embedded systems domain, unlike current models, our model can be used to construct systems that are both control-driven and data-driven. In a generic domain, our model can be used to construct domain models, by constructing control flows and data flows which together define a domain model.