No abstract

No abstract

No abstract

Numerical dating methods in Quaternary science are faced with the need to adequately visualise data consisting of estimates that have differing standard errors. Recent approaches either focus on the display of age frequency distributions that ignore the standard errors or on radial plots, that allow comparisons between estimates allowing for their differing precisions, but without giving an explicit picture of the age frequency distribution. Hence, visualising both aspects requires at least two plots. Here, an alternative is introduced: The abanico plot. It combines both aspects and therefore allows comprehensive presentation of chronometric data with individual standard errors. It extends the radial plot by a kernel density estimate plot, histogram or dot plot and contains elements that link both plot types. As part of the R package 'Luminescence' (version 0.4.5), the abanico plot is designed as the final part of a comprehensive analysis chain of luminescence data but is open to a wide range of other Quaternary dating communities, as illustrated by several examples.

The Aufsess River catchment (97 km2) in northern Bavaria, Germany, is studied to establish a Holocene sediment budget and to investigate the sediment dynamics since the early times of farming in the third millennium BCE. The temporal characterization of the sediment dynamics is based on an intensive dating program with 73 OSL and 14 14C ages. To estimate soil erosion and deposition, colluvial and alluvial archives are investigated in the field by piling and trenching, supported by laboratory analyses. The sediment budget shows that 58% of these sediments are stored as colluvium in on- and foot-slope positions, 9% are stored as alluvium in the floodplains and 33% are exported from the Aufsess River catchment. Colluviation starts in the end-Neolithic ( c. 3100 BCE), while first indicators of soil erosion-derived alluviation is recorded c. 2–3 ka later. The pattern of sedimentation rates also displays differences between the colluvial and alluvial system, with a distinct increase in the Middle Ages ( c. 1000 CE) for the alluvial system, while the colluvial system records low sedimentation rates for this period. A contrast is also observed since Modern times ( c. 1500 CE), with increasing sedimentation rates for the colluvial system, whereas the alluvial system records decreasing rates. The different behavior of the colluvial and alluvial systems clearly shows the non-linear behavior of the catchment’s fluvial system. The results further suggest that human impact is most probably the dominant factor influencing the sediment dynamics of the catchment since the introduction of farming.

Original Citation: Fischer, Manfred M. and Scherngell, Thomas and Reismann, Martin (2009) Abstract. This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge, the latter capturing the contribution of cross-region knowledge spillovers. We construct patent stocks to proxy regional knowledge capital stocks for N=203 regions over the 1997-2002 time period. In estimating the effects we implement a spatial panel data model that controls for the spatial autocorrelation due to neighbouring regions and the individual heterogeneity across regions. The findings provide a fairly remarkable confirmation of the role of knowledge capital contributing to productivity differences among regions, and add an important spatial dimension to the discussion, by showing that productivity effects of knowledge spillovers increase with geographic proximity.

A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables.The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

No abstract