Aims The aim of this study was to investigate the effect of a posterior malleolar fragment (PMF), with < 25% ankle joint surface, on pressure distribution and joint-stability. There is still little scientific evidence available to advise on the size of PMF, which is essential to provide treatment. To date, studies show inconsistent results and recommendations for surgical treatment date from 1940. Materials and Methods A total of 12 cadaveric ankles were assigned to two study groups. A trimalleolar fracture was created, followed by open reduction and internal fixation. PMF was fixed in Group I, but not in Group II. Intra-articular pressure was measured and cyclic loading was performed. Results Contact area decreased following each fracture, while anatomical fixation restored it nearly to its intact level. Contact pressure decreased significantly with fixation of the PMF. In plantarflexion, the centre of force shifted significantly posteriorly in Group II and anteriorly in Group I. Load to failure testing showed no difference between the groups. Conclusion Surgical reduction of a small PMF with less than 25% ankle joint surface improves pressure distribution but does not affect ankle joint stability. Cite this article: Bone Joint J 2018;100-B:95–100.
This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009-2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the mark et potential variable indicating that spatial spillovers do not matter. (authors' abstract)
This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency suprises around policy announcements as an external instrument. Impulse reponse functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states.
The focus of this paper is on the neural network modelling approach that has gained increasing recognition in GIScience in recent years. The novelty about neural networks lies in their ability to model non-linear processes with few, if any, a priori assumptions about the nature of the data-generating process. The paper discusses some important issues that are central for successful application development. The scope is limited to feedforward neural networks, the leading example of neural networks. It is argued that failures in applications can usually be attributed to inadequate learning and/or inadequate complexity of the network model. Parameter estimation and a suitably chosen number of hidden units are, thus, of crucial importance for the success of real world neural network applications. The paper views network learning as an optimization problem, reviews two alternative approaches to network learning, and provides insights into current best practice to optimize complexity so to perform well on generalization tasks.
The paper provides an explanation of the mechanisms underlying trade roots of the contagion effects emanating from the recent turmoils. It is argued that under demand uncertainty risk averse behavior of firms provides a basis for international trade. The paper shows by means of a simple two-country model that risk averse firms operating in perfectly competitive markets with uncertainty of demand tend to diversify markets what gives a basis for international trade in identical commodities even between identical countries. It is shown that such trade may be welfare improving despite efficiency losses due to cross-hauling and transportation costs. The analysis reveals that change of the expectations concerning market conditions caused by the turmoil in the neighbor country (i.e., shift in the perception of market conditions) may lead to macroeconomic destabilization (increase in price level and unemployment, worsening of terms of trade, and deterioration of trade balance).
Two toxic principles of Kalanchoe daigremontiana were isolated and structurally elucidated as bufadienolides with an unusual substitution pattern. Daigremontianin, a novel compound, and bersaldegenin-1,3,5-orthoacetate which was also found in Kalanchoe tubiflora Hamet, show a pronounced sedative, positive inotropic and CNS-activity.
No abstract is provided for this article.
This paper attempts to develop a mathematically rigid and unified framework for neural spatial interaction modeling. Families of classical neural network models, but also less classical ones such as product unit neural network ones are considered for the cases of unconstrained and singly constrained spatial interaction flows. Current practice appears to suffer from least squares and normality assumptions that ignore the true integer nature of the flows and approximate a discrete-valued process by an almost certainly misrepresentative continuous distribution. To overcome this deficiency we suggest a more suitable estimation approach, maximum likelihood estimation under more realistic distributional assumptions of Poisson processes, and utilize a global search procedure, called Alopex, to solve the maximum likelihood estimation problem. To identify the transition from underfitting to overfitting we split the data into training, internal validation, and test sets. The bootstrapping pairs approach with replacement is adopted to combine the purity of data splitting with the power of a resampling procedure to overcome the generally neglected issue of fixed data splitting and the problem of scarce data. In addition, the approach has power to provide a better statistical picture of the prediction variability. Finally, a benchmark comparison against the classical gravity models illustrates the superiority of both, the unconstrained and the origin constrained neural network model versions in terms of generalization performance measured by Kullback and Leibler's information criterion.
Safflower is a promising oilseed crop for the production of specialty oils in the Mediterranean area. Oil quality traits such as high oleic acid content, high linoleic acid content, high saturated fatty acid content, or high gamma-tocopherol content have been developed in this crop. The traits are controlled by the genotype of the developing embryo and therefore they are influenced by the presence of foreign pollen. The objective of this research was to study the rate of cross-fertilization in safflower using the high oleic acid trait as a biochemical marker. An experiment in which each high oleic plant was surrounded by 24 low oleic acid plants was conducted over three environments in the same location at Cordoba, Spain. The average rate of cross-fertilization in the three environments was 5.7, 12.1, and 13.2%, though higher frequencies up to 35.9% were detected at the single-plant level and up to 58.3% at the single-head level. The low average outcrossing frequencies identified in this research indicate no need for large isolation distances between conventional cultivars and cultivars with special oil characteristics. However, the occurrence of a significant outcrossing rate should be taken into consideration if transgenic safflower is to be cultivated close to conventional safflower or in areas of distribution of wild Carthamus species.
Both geographic information systems (GIS) and network analysis are burgeoning fields, characterised by rapid methodological and scientific advances in recent years. A geographic information system (GIS) is a digital computer application designed for the capture, storage, manipulation, analysis and display of geographic information. Geographic location is the element that distinguishes geographic information from all other types of information. Without location, data are termed to be non-spatial and would have little value within a GIS. Location is, thus, the basis for many benefits of GIS: the ability to map, the ability to measure distances and the ability to tie different kinds of information together because they refer to the same place (Longley et al., 2001). GIS-T, the application of geographic information science and systems to transportation problems, represents one of the most important application areas of GIS-technology today. While traditional GIS formulation's strengths are in mapping display and geodata processing, GIS-T requires new data structures to represent the complexities of transportation networks and to perform different network algorithms in order to fulfil its potential in the field of logistics and distribution logistics. This paper addresses these issues as follows. The section that follows discusses data models and design issues which are specifically oriented to GIS-T, and identifies several improvements of the traditional network data model that are needed to support advanced network analysis in a ground transportation context. These improvements include turn-tables, dynamic segmentation, linear referencing, traffic lines and non-planar networks. Most commercial GIS software vendors have extended their basic GIS data model during the past two decades to incorporate these innovations (Goodchild, 1998). The third section shifts attention to network routing problems that have become prominent in GIS-T: the travelling salesman problem, the vehicle routing problem and the shortest path problem with time windows, a problem that occurs as a subproblem in many time constrained routing and scheduling issues of practical importance. Such problems are conceptually simple, but mathematically complex and challenging. The focus is on theory and algorithms for solving these problems. The paper concludes with some final remarks.
No abstract is provided for this article.
No abstract is provided for this article.
The focus of this paper is on pre-competitive R&D cooperation across Europe, as captured by R&D joint ventures funded by the European Commission in the time period 1998-2002, within the 5th Framework Program. The cooperations in this Framework Program give rise to a bipartite network with 72,745 network edges between 25,839 actors (representing organizations that include firms, universities, research organizations and public agencies) and 9,490 R&D projects. With this construction, participating actors are linked only through joint projects. We formally describe and analyze the network from a social network perspective that shifts attention to the detection and analysis of the community structure within the network. Distinct communities within networks may be loosely defined as groups of actors such that there is a higher density of relations within groups than between them. In this study, we attempt to detect communities of actors solely on the basis of the relational structure within the network, and to characterize and differentiate the identified network communities by means of information-theoretic methods, community-specific profiles and the location of their major actors. We expect the results to enrich our picture of the European Research Area by providing new insights into the global and local structures of R&D cooperation across Europe.
The focus is on cross-sectional dependence in panel trade flow models. We propose alternative specifications for modeling time-invariant factors such as so
Für die Therapie mit offenen Radionukliden oder Radiopharmaka ist grundsätzlich eine spezifische intratumorale Speicherung oder ein hoher Tumor/Background/Quotient erforderlich. Für Nebennierenrinden-Tumoren gibt es keine spezielle nuklearmedizinische...
The focus in this article is on knowledge spillovers between high-technology firms in Europe, as captured by patent citations. The European coverage is given by patent applications at the European Patent Office that are assigned to high technology firms located in the EU-25 member states (except Cyprus and Malta), the two accession countries Bulgaria and Romania, and Norway and Switzerland. By following the paper trail left by citations between these high-technology patents we adopt a Poisson spatial interaction modeling perspective to identify and measure spatial separation effects to interregional knowledge spillovers. In doing so we control for technological proximity between the regions, as geographical distance could be just proxying for technological proximity. The study produces prima facie evidence that geography matters. First, geographical distance has a significant impact on knowledge spillovers, and this effect is substantial. Second, national border effects are important and dominate geographical distance effects. Knowledge flows within European countries more easily than across. Not only geography, but also technological proximity matters. Interregional knowledge flows are industry specific and occur most often between regions located close to each other in technological space.
This article investigates the impact of knowledge capital stocks on total factor productivity (TFP) 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 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 labor 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 annual regional knowledge capital stocks for N =203 regions during 1997–2002. In estimating the effects, we implement a spatial panel data model that controls for spatial autocorrelation as well as 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 discussions in the literature by showing that productivity effects of knowledge spillovers increase with geographic proximity.
