967 resultados para Distributed virtual machine


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INTRODUCTION: Partial splenectomy in children is a good surgical option for hematological diseases and focal splenic tumors because it allows the preservation of the spleen's immunological function. Furthermore, it can be performed by laparoscopy in children as it is a safe procedure, offering the benefits of a minimally invasive approach. MATERIALS AND METHODS: The software VR-render LE version 0.81 is a system that enables the visualization of bidimentional 3D images with magnification of anatomical details. We have applied this system to five cases of non-parasitic splenic cysts before laparoscopic partial splenectomy. RESULTS: The images obtained with VR rendering software permitted the preoperative reconstruction of the vascularization of the splenic hilum, allowing the surgeon safe vessel control during laparoscopic procedures. All five partial splenectomies were carried out with no complications or major blood loss. CONCLUSIONS: Laparoscopic partial splenectomy should be a first choice procedure because it is feasible, reproducible, and safe for children; furthermore, it preserves enough splenic tissue thereby preventing post-splenectomy infections. Volume rendering provides high anatomical resolution and can be useful in guiding the surgical procedure.

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Intensification of agricultural production without a sound management and regulations can lead to severe environmental problems, as in Western Santa Catarina State, Brazil, where intensive swine production has caused large accumulations of manure and consequently water pollution. Natural resource scientists are asked by decision-makers for advice on management and regulatory decisions. Distributed environmental models are useful tools, since they can be used to explore consequences of various management practices. However, in many areas of the world, quantitative data for model calibration and validation are lacking. The data-intensive distributed environmental model AgNPS was applied in a data-poor environment, the upper catchment (2,520 ha) of the Ariranhazinho River, near the city of Seara, in Santa Catarina State. Steps included data preparation, cell size selection, sensitivity analysis, model calibration and application to different management scenarios. The model was calibrated based on a best guess for model parameters and on a pragmatic sensitivity analysis. The parameters were adjusted to match model outputs (runoff volume, peak runoff rate and sediment concentration) closely with the sparse observed data. A modelling grid cell resolution of 150 m adduced appropriate and computer-fit results. The rainfall runoff response of the AgNPS model was calibrated using three separate rainfall ranges (< 25, 25-60, > 60 mm). Predicted sediment concentrations were consistently six to ten times higher than observed, probably due to sediment trapping along vegetated channel banks. Predicted N and P concentrations in stream water ranged from just below to well above regulatory norms. Expert knowledge of the area, in addition to experience reported in the literature, was able to compensate in part for limited calibration data. Several scenarios (actual, recommended and excessive manure applications, and point source pollution from swine operations) could be compared by the model, using a relative ranking rather than quantitative predictions.

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The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

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Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. In this paper, we focus on the prediction of drug concentrations using Support Vector Machines (S VM) and the analysis of the influence of each feature to the prediction results. Our study shows that SVM-based approaches achieve similar prediction results compared with pharmacokinetic model. The two proposed example-based SVM methods demonstrate that the individual features help to increase the accuracy in the predictions of drug concentration with a reduced library of training data.

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We examined sequence variation in the mitochondrial cytochrome b gene (1140 bp, n = 73) and control region (842-851 bp, n = 74) in the Eurasian harvest mouse (Micromys minutus (Pallas, 1771)), with samples drawn from across its range, from Western Europe to Japan. Phylogeographic analyses revealed region-specific haplotype groupings combined with overall low levels of inter-regional genetic divergence. Despite the enormous intervening distance, European and East Asian samples showed a net nucleotide divergence of only 0.36%. Based on an evolutionary rate for the cytochrome b gene of 2.4%(.)(site(.)lineage(.)million years)(-1), the initial divergence time of these populations is estimated at around 80 000 years before present. Our findings are consistent with available fossil evidence that has recorded repeated cycles of extinction and recolonization of Europe by M. minutus through the Quaternary. The molecular data further suggest that recolonization occurred from refugia in the Central to East Asian region. Japanese haplotypes of M. minutus, with the exception of those from Tsushima Is., show limited nucleotide diversity (0.15%) compared with those found on the adjacent Korean Peninsula. This finding suggests recent colonization of the Japanese Archipelago, probably around the last glacial period, followed by rapid population growth.

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Recently, modern cross-sectional imaging techniques such as multi-detector computed tomography (MDCT) have pioneered post mortem investigations, especially in forensic medicine. Such approaches can also be used to investigate bones non-invasively for anthropological purposes. Long bones are often examined in forensic cases because they are frequently discovered and transferred to medico-legal departments for investigation. To estimate their age, the trabecular structure must be examined. This study aimed to compare the performance of MDCT with conventional X-rays to investigate the trabecular structure of long bones. Fifty-two dry bones (24 humeri and 28 femora) from anthropological collections were first examined by conventional X-ray, and then by MDCT. Trabecular structure was evaluated by seven observers (two experienced and five inexperienced in anthropology) who analyzed images obtained by radiological methods. Analyses contained the measurement of one quantitative parameter (caput diameter of humerus and femur) and staging the trabecular structure of each bone. Preciseness of each technique was indicated by describing areas of trabecular destruction and particularities of the bones, such as pathological changes. Concerning quantitative parameters, the measurements demonstrate comparable results for the MDCT and conventional X-ray techniques. In contrast, the overall inter-observer reliability of the staging was low with MDCT and conventional X-ray. Reliability increased significantly when only the results of the staging performed by the two experienced observers were compared, particularly regarding the MDCT analysis. Our results also indicate that MDCT appears to be better suited to a detailed examination of the trabecular structure. In our opinion, MDCT is an adequate tool with which to examine the trabecular structure of long bones. However, adequate methods should be developed or existing methods should be adapted to MDCT.

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Games are powerful and engaging. On average, one billion people spend at least 1 hour a day playing computer and videogames. This is even more true with the younger generations. Our students have become the < digital natives >, the < gamers >, the < virtual generation >. Research shows that those who are most at risk for failure in the traditional classroom setting, also spend more time than their counterparts, using video games. They might strive, given a different learning environment. Educators have the responsibility to align their teaching style to these younger generation learning styles. However, many academics resist the use of computer-assisted learning that has been "created elsewhere". This can be extrapolated to game-based teaching: even if educational games were more widely authored, their adoption would still be limited to the educators who feel a match between the authored games and their own beliefs and practices. Consequently, game-based teaching would be much more widespread if teachers could develop their own games, or at least customize them. Yet, the development and customization of teaching games are complex and costly. This research uses a design science methodology, leveraging gamification techniques, active and cooperative learning theories, as well as immersive sandbox 3D virtual worlds, to develop a method which allows management instructors to transform any off-the-shelf case study into an engaging collaborative gamified experience. This method is applied to marketing case studies, and uses the sandbox virtual world of Second Life. -- Les jeux sont puissants et motivants, En moyenne, un milliard de personnes passent au moins 1 heure par jour jouer à des jeux vidéo sur ordinateur. Ceci se vérifie encore plus avec les jeunes générations, Nos étudiants sont nés à l'ère du numérique, certains les appellent des < gamers >, d'autres la < génération virtuelle >. Les études montrent que les élèves qui se trouvent en échec scolaire dans les salles de classes traditionnelles, passent aussi plus de temps que leurs homologues à jouer à des jeux vidéo. lls pourraient potentiellement briller, si on leur proposait un autre environnement d'apprentissage. Les enseignants ont la responsabilité d'adapter leur style d'enseignement aux styles d'apprentissage de ces jeunes générations. Toutefois, de nombreux professeurs résistent lorsqu'il s'agit d'utiliser des contenus d'apprentissage assisté par ordinateur, développés par d'autres. Ceci peut être extrapolé à l'enseignement par les jeux : même si un plus grand nombre de jeux éducatifs était créé, leur adoption se limiterait tout de même aux éducateurs qui perçoivent une bonne adéquation entre ces jeux et leurs propres convictions et pratiques. Par conséquent, I'enseignement par les jeux serait bien plus répandu si les enseignants pouvaient développer leurs propres jeux, ou au moins les customiser. Mais le développement de jeux pédagogiques est complexe et coûteux. Cette recherche utilise une méthodologie Design Science pour développer, en s'appuyant sur des techniques de ludification, sur les théories de pédagogie active et d'apprentissage coopératif, ainsi que sur les mondes virtuels immersifs < bac à sable > en 3D, une méthode qui permet aux enseignants et formateurs de management, de transformer n'importe quelle étude de cas, provenant par exemple d'une centrale de cas, en une expérience ludique, collaborative et motivante. Cette méthode est appliquée aux études de cas Marketing dans le monde virtuel de Second Life.