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

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

Serum pro-inflammatory biomarkers associated with improvement in quality of life in pulmonary tuberculosis

IntroductionPulmonary dysfunction is an underestimated complication in tuberculosis (TB) infection, affecting quality of life (QoL). Although respiratory function tests objectively reflect lung disturbances in a specific moment, predictors of illness severity at the time of diagnosis are still lacking.MethodsWe measured serum pro-inflammatory cytokines (TNF-α and IL-8), eicosanoids (PGE2, LTB4, RvD1, Mar1, and LXA4), a marker of tissue damage (cell-free nucleosomes), and indicators of redox status (malonaldehyde, 8-isoprostane, total oxidants, and antioxidants), as well as a score of radiological abnormalities (SRA) and a QoL questionnaire, in 25 patients with pulmonary TB at the time of diagnosis (t0) and two months after the initiation of treatment (t2).ResultsWe found higher antioxidant levels in the patients with the worst QoL at t0, and all the indicators of the prooxidant state were significantly reduced at t2, while the total antioxidant levels increased. LTB4, a pro-inflammatory eicosanoid, was diminished at t2, while all the pro-resolutory lipids decreased substantially. Significant correlations between the SRA and the QoL scores were observed, the latter showing a substantial reduction at t2, ranking it as a reliable tool for monitoring disease evolution during TB treatment.DiscussionThese results suggest that evaluating a combination of these markers might be a valuable predictor of QoL improvement and a treatment response indicator; in particular, the oxidation metabolites and eicosanoid ratios could also be proposed as a future target for adjuvant therapies to reduce inflammation-associated lung injury in TB disease

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

Good practices for estimating area and assessing accuracy of land change

The remote sensing science and application communities have developed increasingly reliable, consistent, and robust approaches for capturing land dynamics to meet a range of information needs. Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. We provide practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data. The good practice recommendations address the three major components: sampling design, response design and analysis. The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results. An example application is provided to illustrate the recommended process

Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning