A System to Filter Unwanted Messages from OSN User Walls

This paper proposes a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in support of content-based filtering

Snow cover thickness estimation using radial basis function networks

Abstract. This paper reports an experimental study designed for the in-depth investigation of how the radial basis function network (RBFN) estimates snow cover thickness as a function of climate and topographic parameters. The estimation problem is modeled in terms of both function regression and classification, obtaining continuous and discrete thickness values, respectively. The model is based on a minimal set of climatic and topographic data collected from a limited number of stations located in the Italian Central Alps. Several experiments have been conceived and conducted adopting different evaluation indexes. A comparison analysis was also developed for a quantitative evaluation of the advantages of the RBFN method over to conventional widely used spatial interpolation techniques when dealing with critical situations originated by lack of data and limited n-homogeneously distributed instrumented sites. The RBFN model proved competitive behavior and a valuable tool in critical situations in which conventional techniques suffer from a lack of representative data

Prediction of Displacements in Unstable Areas Using a Neural Model

In pipeline management the accurate prediction of weak displacements is a crucial factor in drawing up a prevention policy since the accumulation of these displacements over a period of several years can lead to situations of high risk. This work addresses the specific problem related to the prediction of displacements induced by rainfall in unstable areas, of known geology, and crossed by underground pipelines. A neural model has been configured which learns of displacements from instrumented sites (where inclinometric measurements are available) and is able to generalise to other sites not equipped with inclinometers

Contractile reserve in systemic sclerosis patients as a major predictor of global cardiac impairment and exercise tolerance

Several studies have evidenced high prevalence of myocardial systolic and diastolic dysfunction among patients with systemic sclerosis (SSc). Exercise echocardiography has shown a diagnostic and prognostic role in identifying early left ventricular (LV) dysfunction in several myocardial pathological settings. The aim of our study was to evaluate early signs of LV impairment under exercise and their correlation to patient's exercise tolerance. Forty-five patients (age 60.4 ± 10.3 years) with SSc and 20 age and sex comparable controls were enrolled in the study. All patients underwent clinical evaluation, 2D echocardiography associated with Tissue Doppler and speckle tracking to evaluate LV deformation indexes, and an exercise echocardiography to evaluate left ventricle contractile reserve (LVCR) and exercise pulmonary pressures. Finally, a 6-minute walking test (6MWT) to evaluate exercise tolerance was also performed. Compared to controls, SSc patients showed an impaired diastolic function (E/E' 10.9 ± 3.7 vs 8.36 ± 2.01; p < 0.01) associated with larger left atrial dimensions (LAVI 28.4 ± 8.7 vs 19.3 ± 4.6 mL/m2; p < 0.01). During exercise echocardiography, a reduced global longitudinal strain at peak exercise (S-GLS) was highlighted compared to controls (15.7 ± 3.6 vs 18.2 ± 2.2; p = 0.001). A S-GLS cutoff <18 %, identified by ROC analysis, identified SSc patients with a reduced diastolic function, exercise tolerance at the 6MWT and higher pulmonary pressures. Our data show that in SSc patients a reduced LVCR characterizes the patients with a more extensive cardiovascular impairment in terms of LV diastolic function, pulmonary pressures and exercise tolerance. These data underline the importance of exercise echocardiography for the preclinical screening of the LV impairment in this population

Waiting times for diagnosis of attention-deficit hyperactivity disorder in children and adolescents referred to Italian ADHD centers must be reduced

BACKGROUND: To investigate timely access to and the time needed to complete the diagnostic path of children and adolescents with suspected attention deficit hyperactivity disorder (ADHD) in the 18 Italian Lombardy Region ADHD reference centers. METHODS: Data of children and adolescents enrolled in the Regional ADHD disease-oriented Registry for suspected ADHD who requested their first visit in 2013-2017 were analyzed. RESULTS: The sample comprised 2262 children and adolescents aged 5-17\u2009years who accessed the ADHD centers for diagnostic classification and management. The median waiting time was of 177\u2009days (range 66-375) from the request for the initial appointment to the completion of the diagnostic path, with a three - fold difference between centers. In addition to the center, the strongest significant predictors of long waiting times were age comorbidities, the severity of the disorder, and having already completed some diagnostic procedures provided by the common standard path. CONCLUSIONS: To guarantee an equal standard of care in ADHD centers for all children and adolescents there is a pressing need to reduce the times to complete the diagnostic path. It is the task of both policymakers and each center to optimize the quality of the service and of the care delivered

Peripheral artery disease assessed by ankle-brachial index in patients with established cardiovascular disease or at least one risk factor for atherothrombosis - CAREFUL Study: A national, multi-center, cross-sectional observational study

<p>Abstract</p> <p>Background</p> <p>To investigate the presence of peripheral artery disease (PAD) via the ankle brachial index (ABI) in patients with known cardiovascular and/or cerebrovascular diseases or with at least one risk factor for atherothrombosis.</p> <p>Methods</p> <p>Patients with a history of atherothrombotic events, or aged 50-69 years with at least one cardiovascular risk factor, or > = 70 years of age were included in this multicenter, cross-sectional, non-interventional study (DIREGL04074). Demographics, medical history, physical examination findings, and physician awareness of PAD were analyzed. The number of patients with low ABI (< = 0.90) was analyzed.</p> <p>Results</p> <p>A total of 530 patients (mean age, 63.4 ± 8.7 years; 50.2% female) were enrolled. Hypertension and dyslipidemia were present in 88.7% and 65.5% of patients, respectively. PAD-related symptoms were evident in about one-third of the patients, and at least one of the pedal pulses was negative in 6.5% of patients. The frequency of low ABI was 20.0% in the whole study population and 30% for patients older than 70 years. Older age, greater number of total risk factors, and presence of PAD-related physical findings were associated with increased likelihood of low ABI (<it>p </it>< 0.001). There was no gender difference in the prevalence of low ABI, PAD symptoms, or total number of risk factors. Exercise (33.6%) was the most common non-pharmacological option recommended by physicians, and acetylsalicylic acid (ASA) (45.4%) was the most frequently prescribed medication for PAD.</p> <p>Conclusion</p> <p>Our results indicate that advanced age, greater number of total risk factors and presence of PAD-related physical findings were associated with increased likelihood of low ABI. These findings are similar to those reported in similar studies of different populations, and document a fairly high prevalence of PAD in a Mediterranean country.</p

Using mixed objects in the training of object-based image classifications

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification