56 research outputs found
MOSE': A grid-enabled software platform to solve geoprocessing problems
Grid computing has emerged as an important new field in the distributed computing arena. It focus on intensive resource sharing, innovative applications, and in some cases,
high-performance orientation. This paper describes how
grids technologies can be used to develop an infrastructure for developing geoprocessing applications. We present the MOS`E system, a grid-enabled problem solving environment (PSE) able to support the activities that concern the modelling and simulation of spatio-temporal phenomena for analyzing and managing the identification and the mitigation of natural disasters like floods, wildfires, landslides, etc. MOSE' takes advantages of the standardized resource access and workflow support for loosely coupled software components provided by the web/grid services technologies
A review on data stream classification
At this present time, the significance of data streams cannot be denied as many researchers have placed their focus on the research areas of databases, statistics, and computer science. In fact, data streams refer to some data points sequences that are found in order with the potential to be non-binding, which is generated from the process of generating information in a manner that is not stationary. As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. This paper presents several data stream clustering approaches, which are based on density, besides attempting to comprehend the function of the related algorithms; both semi-supervised and active learning, along with reviews of a number of recent studies
Prediction of hyperaldosteronism subtypes when adrenal vein sampling is unilaterally successful
Objective: Adrenal venous sampling (AVS) is the gold standard to discriminate patients with unilateral primary aldosteronism (UPA) from bilateral disease (BPA). AVS is technically demanding and in cases of unsuccessful cannulation of adrenal veins, the results may not always be interpreted. The aim of our study was to develop diagnostic models to distinguish UPA from BPA, in cases of unilateral successful AVS and the presence of contralateral suppression of aldosterone secretion.Design: Retrospective evaluation of 158 patients referred to a tertiary hypertension unit who underwent AVS. We randomly assigned 110 patients to a training cohort and 48 patients to a validation cohort to develop and test the diagnostic models.Methods: Supervised machine learning algorithms and regression models were used to develop and validate two prediction models and a simple 19-point score system to stratify patients according to their subtype diagnosis.Results: Aldosterone levels at screening and after confirmatory testing, lowest potassium, ipsilateral and contralateral imaging findings at CT scanning, and contralateral ratio at AVS, were associated with a diagnosis of UPA and were included in the diagnostic models. Machine learning algorithms correctly classified the majority of patients both at training and validation (accuracy: 82.9-95.7%). The score system displayed a sensitivity/specificity of 95.2/96.9%, with an AUC of 0.971. A flow-chart integrating our score correctly managed all patients except 3 (98.1% accuracy), avoiding the potential repetition of 77.2% of AVS procedures.Conclusions: Our score could be integrated in clinical practice and guide surgical decision-making in patients with unilateral successful AVS and contralateral suppression
Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension
Development of a Prediction Score to Avoid Confirmatory Testing in Patients With Suspected Primary Aldosteronism
Nuovi dati sulle comunità di Lepidotteri notturni del Parco Nazionale del Circeo
The moth communites of the Circeo National Park have been studied for the first time in 1986 with a monthly sample of specimens attracted by light traps in four habitat types (holm oak evergreen forest, evergreen scrub, lowland deciduous oak forest, coastal marshland). Since 2004 a team of entomologists is carrying out a new research in the same study sites with the same methods, in order to point out eventual changes in the community. The preliminary results show a marked difference in species diversity and abundance among the four habitat types: the forest environmental patches show a higher value of species richness. On the whole 190 species have been captured during the first year
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