Medical management of exchange students traveling to an isolated tropical milieu: an experience in western Ivory Coast

According to some ultrastructural studies, the pericapillary axon terminals in the central nervous system (CNS) are functionally connected with the capillary vessel wall. Thus, it may be expected that the population of pericapillary axon terminals will be morphologically distinct from the terminals at a further distance from the capillary walls. To test this hypothesis, morphometrical analysis of 3,048 axon terminals was performed, comparing terminals situated in the close vicinity of the capillary vessel with those at a distance from the vessels in the lateral, basal, medial, central and cortical nuclei of the amygdaloid body of eight cats. The cross-sectional area and circumference of each identified axon terminal profile were measured, and the shape of synaptic vesicles and the presence of synaptic contacts and granular vesicles were recorded. The statistical evaluation of results was performed by means of the Newman-Keuls' test, Wilcoxon's test, Fisher's contingency-table test and the test for two coefficients of structure. The morphometric examination revealed two ultrastructurally distinct groups of axon terminals, pericapillary and distant terminals, in all the nuclei of the amygdaloid body. The differentiating features were the shape of the synaptic vesicles, the number of synaptic contacts, and the size of the axon terminals. These results further support the hypothesis of a functional connection between axon terminals and the capillary vessel wall in the CNS.     

Decision by the Principle Board of the Polish Allergy Society

A review of isolates of Neisseria meningitidis and other laboratory evidence of meningococcal infection was carried out for the period 1990-1995. Thirty-three patients with evidence of meningococcal disease were identified over the six year period. Group B organisms were the most commonly identified, but an increase in Group C has occurred and is almost equal in number. Data from clinical, laboratory and public health sources should be centralized in a National Surveillance Centre.     

Strengthening of concrete prisms using the plate-bonding technique

This paper presents the use of fracture mechanics for the plate bonding technique. Plates of steel or carbon-fibre reinforced plastic are bonded with an epoxy adhesive to rectangular concrete prisms and loaded in shear up to failure, what is normally known in fracture mechanics as mode II failure. In this special application a linear and a nonlinear approach are presented. The nonlinear equation derived for a realistic shear-deformation curve can only be used for numerical calculations. However, for simplified shear-deformation curves, the derived formula can be solved analytically. Results from tests, which are compared with the theory, are also presented.     

Experimental estimate of self-irradiation of building bricks due to incorporated radionuclides

A method is described for experimentally determining the dose rate created in building bricks by incorporated natural radionuclides. It was established using the thermoluminescence dosimetry method that the measured dose rate depends on the detector thickness, the mass of the ceramic product investigated, and the irradiation geometry. The contributions to the measured dose of weakly penetrating α and β radiation and hard ψ radiation are separated, the ratio between them depending on the experimental conditions. Translated from Izmeritel'naya Tekhnika, No. 7, pp. 62–66, July, 1996.     

A PAC-Bayesian margin bound for linear classifiers

We present a bound on the generalization error of linear classifiers in terms of a refined margin quantity on the training sample. The result is obtained in a probably approximately correct (PAC)-Bayesian framework and is based on geometrical arguments in the space of linear classifiers. The new bound constitutes an exponential improvement of the so far tightest margin bound, which was developed in the luckiness framework, and scales logarithmically in the inverse margin. Even in the case of less training examples than input dimensions sufficiently large margins lead to nontrivial bound values and-for maximum margins-to a vanishing complexity term. In contrast to previous results, however, the new bound does depend on the dimensionality of feature space. The analysis shows that the classical margin is too coarse a measure for the essential quantity that controls the generalization error: the fraction of hypothesis space consistent with the training sample. The practical relevance of the result lies in the fact that the well-known support vector machine is optimal with respect to the new bound only if the feature vectors in the training sample are all of the same length. As a consequence, we recommend to use support vector machines (SVMs) on normalized feature vectors only. Numerical simulations support this recommendation and demonstrate that the new error bound can be used for the purpose of model selection.     

Test Case Generation as an AI Planning Problem

While Artificial Intelligence techniques have been applied to a variety of software engineering applications, the area of automated software testing remains largely unexplored. Yet, test cases for certain types of systems (e.g., those with command language interfaces and transaction based systems) are similar to plans. We have exploited this similarity by constructing an automated test case generator with an AI planning system at its core. We compared the functionality and output of two systems, one based on Software Engineering techniques and the other on planning, for a real application: the StorageTek robot tape library command language. From this, we showed that AI planning is a viable technique for test case generation and that the two approaches are complementary in their capabilities.     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.