Dropshaft cascades in urban drainage systems.

Dropshaft cascades are typical elements of sewer systems in steep urban catchment basins. The design of a dropshaft cascade, which is generally addressed as an optimization problem, also needs to consider the subsequent effects induced on the flow by the different elements of the cascade. Experimental research has been performed at the hydraulic engineering laboratory of the University of Cassino and Southern Lazio in order to investigate the basic flow patterns in a dropshaft cascade, with particular reference to energy dissipation and air entrainment. This research has shown that, regarding these aspects, a dropshaft cascade proved to be a more efficient solution of the single drop manhole with the same total drop height

Support Vector Regression for Rainfall-Runoff Modeling in Urban Drainage: A Comparison with the EPA's Storm Water Management Model

Rainfall-runoff models can be classified into three types: physically based models, conceptual models, and empirical models. In this latter class of models, the catchment is considered as a black box, without any reference to the internal processes that control the transformation of rainfall to runoff. In recent years, some models derived from studies on artificial intelligence have found increasing use. Among these, particular attention should be paid to Support Vector Machines (SVMs). This paper shows a comparative study of rainfall-runoff modeling between a SVM-based approach and the EPA's Storm Water Management Model (SWMM). The SVM is applied in the variant called Support Vector regression (SVR). Two different experimental basins located in the north of Italy have been considered as case studies. Two criteria have been chosen to assess the consistency between the recorded and predicted flow rates: the root-mean square error (RMSE) and the coefficient of determination. The two models showed comparable performance. In particular, both models can properly model the hydrograph shape, the time to peak and the total runoff. The SVR algorithm tends to underestimate the peak discharge, while SWMM tends to overestimate it. SVR shows great potential for applications in the field of urban hydrology, but currently it also has significant limitations regarding the model calibration

Phenotypic plasticity of two invasive alien plant species inside a deciduous forest in a strict nature reserve in Italy

ABSTRACTInvasive alien plant species (IAPs) represent one of the main biological threats to biodiversity worldwide. Information about their phenotypic plasticity are needed to increase awareness ab..

TRADES: A new software to derive orbital parameters from observed transit times and radial velocities. Revisiting Kepler-11 and Kepler-9

Aims. With the purpose of determining the orbital parameters of exoplanetary systems from observational data, we have developed a software, named TRADES (TRAnsits and Dynamics of Exoplanetary Systems), to simultaneously fit observed radial velocities and transit times data. Methods. We implemented a dynamical simulator for N-body systems, which also fits the available data during the orbital integration and determines the best combination of the orbital parameters using grid search, $\chi^2$ minimization, genetic algorithms, particle swarm optimization, and bootstrap analysis. Results. To validate TRADES, we tested the code on a synthetic three-body system and on two real systems discovered by the Kepler mission: Kepler-9 and Kepler-11. These systems are good benchmarks to test multiple exoplanet systems showing transit time variations (TTVs) due to the gravitational interaction among planets. We have found that orbital parameters of Kepler-11 planets agree well with the values proposed in the discovery paper and with a a recent work from the same authors. We analyzed the first three quarters of Kepler-9 system and found parameters in partial agreement with discovery paper. Analyzing transit times (T0s) covering 12 quarters of Kepler data, that we have found a new best-fit solution. This solution outputs masses that are about 55% of the values proposed in the discovery paper; this leads to a reduced semi-amplitude of the radial velocities of about 12.80 m/s.Comment: 14 pages, 13 figures, 6 tables; accepted for publication in Astronomy & Astrophysics, and corrected by the Language Edito

machine learning models for spring discharge forecasting

Nowadays, drought phenomena increasingly affect large areas of the globe; therefore, the need for a careful and rational management of water resources is becoming more pressing. Considering that most of the world's unfrozen freshwater reserves are stored in aquifers, the capability of prediction of spring discharges is a crucial issue. An approach based on water balance is often extremely complicated or ineffective. A promising alternative is represented by data-driven approaches. Recently, many hydraulic engineering problems have been addressed by means of advanced models derived from artificial intelligence studies. Three different machine learning algorithms were used for spring discharge forecasting in this comparative study: M5P regression tree, random forest, and support vector regression. The spring of Rasiglia Alzabove, Umbria, Central Italy, was selected as a case study. The machine learning models have proven to be able to provide very encouraging results. M5P provides good short-term predictions of monthly average flow rates (e.g., in predicting average discharge of the spring after 1 month, R2=0.991, RAE=14.97%, if a 4-month input is considered), while RF is able to provide accurate medium-term forecasts (e.g., in forecasting average discharge of the spring after 3 months, R2=0.964, RAE=43.12%, if a 4-month input is considered). As the time of forecasting advances, the models generally provide less accurate predictions. Moreover, the effectiveness of the models significantly depends on the duration of the period considered for input data. This duration should be close to the aquifer response time, approximately estimated by cross-correlation analysis

Comparative analysis of digital models from 3D photogrammetry and structured light scanning for the study of tetrapod tracks

The present work aims at analyzing the acquisition capacity of different digital reconstruction techniques for three-dimensional models, in the frame of the study of the remarkable Middle Triassic (Ladinic) tetrapod ichnoassemblage from the Quarziti del Monte Serra Formation (Monti Pisani, Tuscany, central Italy). Tracks stored in different Italian museum collections were processed and analyzed through two different digital acquisition methodologies, namely, digital photogrammetry and structured light scanning (with the EinScan Pro HD scanner model, capable of a maximum resolution of 0.2 mm) to evaluate which of these techniques is most suitable for the study of small- to medium-sized tetrapod tracks. Two models were created for each sample, one for each acquisition methodology. These models were processed using the software Meshmixer, Meshlab and CloudCompare, to locate any possible error in the mesh, correct them and compare the models with each other in terms of quality and graphical rendering, respectively. The RStudio software was also used to verify and control, by using statistical tests, the normal distribution of the data, as well as to further process them. We noticed that the average number of triangles is higher for the meshes obtained via photogrammetry; likewise, the values of the metric “Per Face Quality according to triangle shape and aspect ratio – Mean ratio of triangle”, available on Meshlab and used here to evaluate the quality of a mesh, is higher. Photogrammetry is thus preferable in the study of centimetric tracks as it allows for very high levels of mesh detail. That said, more experience and a deeper understanding of the acquisition process by the operator are needed for fruitfully exploiting the full potentialities of photogrammetr

Validation of the Italian version of Behavioral Pain Scale in sedated, intubated, and mechanically ventilated pediatric patients

Background and aim: Pain assessment in pediatric intensive care unit (PICU) is a demanding challenge. The COMFORT-B scale is recognized as the gold standard in such patients. However, the use of this instrument in PICU setting is disputed. The Behavioral Pain Scale (BPS), instead, is considered to be the gold standard for pain assessment in deeply sedated, mechanically ventilated adult patients. The BPS has been validated in Italian, requires a short observation time compared to the COMFORT-B and does not increase workloads. A first evaluation of BPS was made in PICU with good results regarding face validity and content validity, however suggesting further studies given the small sample on which it was tested. The aim of this study was the validation of the BPS in sedated, intubated, and mechanically ventilated pediatric patients. Methods: A descriptive, comparative design was used. A convenience sample of 84 non-verbal, sedated and mechanically ventilated critical care pediatric patients was included. Patient pain was assessed concurrently with three observational scales (BPS, COMFORT-B, NRS) before, during and after routine procedures that are considered painful and non-painful. Results: Internal consistency was α = .86. Correlations between BPS and the other instruments were high, demonstrating a good concurrent validity of the BPS. T test and assessment of ROC curves demonstrated a good discriminant validity of the BPS. Conclusions: The BPS proved to be valid and reliable for the assessment of pain also in the use with pediatric patients

Post-Treatment Edema after Meningioma Radiosurgery is a Predictable Complication

Symptomatic post-treatment edema (PTE) causing seizures, focal deficits, and intracranial hypertension is a rather common complication of meningioma radiosurgery. Factors associated to the occurrence of PTE still needs to be clarified. We retrospectively analyzed our patients' data to identify factors associated with the development of symptomatic PTE. Supposed risk factors were systematically analyzed. Between July 2007 and March 2014, 245 meningiomas in 229 patients were treated by a single fraction or multisession radiosurgery (2-5 fractions) or hypofractionated stereotactic radiotherapy (6-15 fractions) using the CyberKnife system (Accuray Inc., Sunnyvale, CA) at the University Hospital of Messina, Italy. Local tumor control was achieved in 200 of 212 patients with World Health Organization (WHO) Grade I meningiomas (94%) at a mean follow-up of 62 months. Symptomatic PTE on MRI was diagnosed in 19 patients (8.3%) causing seizure (n=17, 89%), aggravating headache (n=12, 63%), or focal deficits (n=13, 68%). Four variables were found to be associated with the likelihood of edema development, including tumor volume > 4.5 mL, non-basal tumor location, tight brain/tumor interface, and atypical histology. Nonetheless, when multivariate logistic regression analysis was performed, only tumor volume and brain-tumor interface turned out to be independent predictors of PTE development. Our results suggest that the factor associated with the risk of developing PTE is associated to characteristics of meningioma rather than to the treatment modality used. Accordingly, an appropriate patient selection is the way to achieve safe treatment and long-term disease control