Modeling The Complex Land Administration in Brazil

Land administration is one of the pillars of economic development and poverty reduction. Land registration and cadastres make up an important part of land administration. In Brazil, recent initiatives attempt to build an efficient land administration to overcome its deficiencies built from a history of disorderly occupation and with many specificities of a colonial past. The objective of this paper is to present the land registration process in urban areas in Brazil using modeling of land administration. The purpose is to present in a model the procedures for three scenarios of the registration and transfer of urban properties: 1. Procedures for transfer a formally registered; 2. Procedures for registration of a semi-formal property (individual proceeding); and 3. Procedures for the registration of an informal settlement (collective proceeding). From the models it was possible to visualize the complexity of the procedures of registration and transfer of a property in the urban area. The procedures usually have many steps, many actors involved, it requires a lot of time and it has high costs. In addition, the procedures show the absence of an urban cadastre that supports land registration. In conclusion, in Brazil, despite recent developments of legal framework and practices related to land, does not have a complete land administration system. The legal framework is extensive and often contradictory, processes are complex, expensive and take long, and there is still a long way until land and the information about land may be effectively managed

De Landelijke Bestuurskunde Dag

Donderdag 19 maart werd in Nijmegen de zesde Landelijke Bestuurskunde Dag georganiseerd. Het thema van deze dag was: "De overheid: Vernieuwer of Vernieler?" Met een redelijk grote groep enthousiastelingen vertrokken we om half acht naar het Oosten. Dit in tegenstelling tot de Leidse docenten. Er waren er slechts nul aanwezig

Modelling the spatial distribution of DEM Error

Assessment of a DEM’s quality is usually undertaken by deriving a measure of DEM accuracy – how close the DEM’s elevation values are to the true elevation. Measures such as Root Mean Squared Error and standard deviation of the error are frequently used. These measures summarise elevation errors in a DEM as a single value. A more detailed description of DEM accuracy would allow better understanding of DEM quality and the consequent uncertainty associated with using DEMs in analytical applications. The research presented addresses the limitations of using a single root mean squared error (RMSE) value to represent the uncertainty associated with a DEM by developing a new technique for creating a spatially distributed model of DEM quality – an accuracy surface. The technique is based on the hypothesis that the distribution and scale of elevation error within a DEM are at least partly related to morphometric characteristics of the terrain. The technique involves generating a set of terrain parameters to characterise terrain morphometry and developing regression models to define the relationship between DEM error and morphometric character. The regression models form the basis for creating standard deviation surfaces to represent DEM accuracy. The hypothesis is shown to be true and reliable accuracy surfaces are successfully created. These accuracy surfaces provide more detailed information about DEM accuracy than a single global estimate of RMSE

A probabilistic model of the economic risk to Britain’s railway network from bridge scour during floods

Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs

Multi-scale digital soil mapping with deep learning

We compared different methods of multi-scale terrain feature construction and their relative effectiveness for digital soil mapping with a Deep Learning algorithm. The most common approach for multi-scale feature construction in DSM is to filter terrain attributes based on different neighborhood sizes, however results can be difficult to interpret because the approach is affected by outliers. Alternatively, one can derive the terrain attributes on decomposed elevation data, but the resulting maps can have artefacts rendering the approach undesirable. Here, we introduce ‘mixed scaling’ a new method that overcomes these issues and preserves the landscape features that are identifiable at different scales. The new method also extends the Gaussian pyramid by introducing additional intermediate scales. This minimizes the risk that the scales that are important for soil formation are not available in the model. In our extended implementation of the Gaussian pyramid, we tested four intermediate scales between any two consecutive octaves of the Gaussian pyramid and modelled the data with Deep Learning and Random Forests. We performed the experiments using three different datasets and show that mixed scaling with the extended Gaussian pyramid produced the best performing set of covariates and that modelling with Deep Learning produced the most accurate predictions, which on average were 4–7% more accurate compared to modelling with Random Forests

Choosing mathematics: the narrative of the self as a site of agency

In this paper, we discuss the theoretical and methodological issues in exploring identity and agency within a narrative of choosing mathematics. Taking as our starting point Bakhtin’s emphasis on the dialogic space between interlocutors, we explore how an awareness of the addressivity and otherness of utterances, and of the role of genre and heteroglossia in self-authoring, can be used in the analysis of an interview to gain insight into one student’s narrative of choosing mathematics despite the fear that it held for her. We consider how our own research preoccupations with the role of gender and family discourses in learners’ relationships with mathematics played a part in the interview, and how the interviewee’s appropriation of, and resistance to, these and other genres can be understood as an assertion of agency within her particular narrative of choice

Alternative Ii-independent antigen-processing pathway in leukemic blasts involves TAP-dependent peptide loading of HLA class II complexes

During HLA class II synthesis in antigen-presenting cells, the invariant chain (Ii) not only stabilizes HLA class II complexes in the endoplasmic reticulum, but also mediates their transport to specialized lysosomal antigen-loading compartments termed MIICs. This study explores an alternative HLA class II presentation pathway in leukemic blasts that involves proteasome and transporter associated with antigen processing (TAP)-dependent peptide loading. Although HLA-DR did associate with Ii, Ii silencing in the human class II-associated invariant chain peptide (CLIP)-negative KG-1 myeloid leukemic cell line did not affect total and plasma membrane expression levels of HLA-DR, as determined by western blotting and flow cytometry. Since HLA-DR expression does require peptide binding, we examined the role of endogenous antigen-processing machinery in HLA-DR presentation by CLIP− leukemic blasts. The suppression of proteasome and TAP function using various inhibitors resulted in decreased HLA-DR levels in both CLIP− KG-1 and ME-1 blasts. Simultaneous inhibition of TAP and Ii completely down-modulated the expression of HLA-DR, demonstrating that together these molecules form the key mediators of HLA class II antigen presentation in leukemic blasts. By the use of a proteasome- and TAP-dependent pathway for HLA class II antigen presentation, CLIP− leukemic blasts might be able to present a broad range of endogenous leukemia-associated peptides via HLA class II to activate leukemia-specific CD4+ T cells