Numerical Simulation of the Performance of a Twin Scroll Radial Turbine at Different Operating Conditions

Twin scroll radial turbines are increasingly used for turbocharging applications, to take advantage of the pulsating exhaust gases. In spite of its relevance in turbocharging techniques, scientific literature about CFD applied to twin scroll turbines is limited, especially in case of partial admission. In the present paper a CFD complete model of a twin scroll radial turbine is developed in order to give a contribution to literature in understanding the capabilities of current industrial CFD approaches applied to these difficult cases and to develop performance index that can be used for turbine design optimization purposes. The flow solution is obtained by means of ANSYS CFX \uae in a wide range of operating conditions in full and partial admission cases. The total-to-static efficiency and the mass flow parameter (MFP) have been calculated and compared with the experimental database in order to validate the numerical model. The purpose of the developed procedure is also to generate a database for twin scroll turbines useful for future applications. A comparison between performances obtained in different admission conditions was performed. In particular the analysis focused on the characterization of the flow at volute outlet/rotor inlet section. A flow distortion index at rotor inlet was introduced to correlate the turbine performance and the flow nonuniformities generated by the volute. Finally the influence of the backside cavity on the performance parameters is also discussed. The introduction of these new nonuniformity indices is proposed for volute design and optimization procedures

Rasch analysis of the Fatigue Severity Scale in Italian subjects with multiple sclerosis.

To perform a psychometric analysis of the Fatigue Severity Scale (FSS) using Rasch analysis in a sample of Italian subjects with multiple sclerosis

TDEM for Martian in situ resource prospecting missions

This paper presents a TDEM (Time Domain Electromagnetic Methods) application, addressed to the search for water on Mars. In this context, the opportunities for a TDEM system as payload in a future mission are investigated for different in situ exploration scenarios. The TDEM sounding capability is evaluated with respect to the expected Martian environment, and some considerations are made about the many unknown variables (above all the background EM noise and the subsoil composition) altogether with the limited resources availability (mission constraints in mass, time and power) and the way they could represent an obstacle for operations and measurements

Hyperhidrosis treatment with bornaprine in the acute phase of spinal cord-injured patients

n/

Survival of patients with spinal muscular atrophy type 1

BACKGROUND: Spinal muscular atrophy type 1 (SMA1) is a progressive disease and is usually fatal in the first year of life. METHODS: A retrospective chart review was performed of SMA1 patients and their outcomes according to the following choices: letting nature take its course (NT); tracheostomy and invasive mechanical ventilation (TV); continuous noninvasive respiratory muscle aid (NRA), including noninvasive ventilation; and mechanically assisted cough. RESULTS: Of 194 consecutively referred patients enrolled in this study (103 males, 91 females), NT, TV, and NRA were chosen for 121 (62.3%), 42 (21.7%), and 31 (16%) patients, respectively. Survival at ages 24 and 48 months was higher in TV than NRA users: 95% (95% confidence interval: 81.8%-98.8%) and 67.7% (95% confidence interval: 46.7%-82%) at age 24 months (P < .001) and 89.43% and 45% at age 48 months in the TV and NRA groups, respectively (P < .001). The choice of TV decreased from 50% (1992-1998) to 12.7% (2005-2010) (P < .005) with a nonstatistically significant increase for NT from 50% to 65%. The choice of NRA increased from 8.1% (1999-2004) to 22.7% (2005-2010) (P < .001). CONCLUSIONS: Long-term survival outcome is determined by the choice of the treatment. NRA and TV can prolong survival, with NRA showing a lower survival probability at ages 24 and 48 months. Copyright © 2013 by the American Academy of Pediatrics

Urinary metabolomics (GC-MS) reveals that low and high birth weight infants share elevated inositol concentrations at birth

Objective: Metabolomics is a new ‘‘omics’’ platform aimed at high-throughput identification, quantification and characterization of small molecule metabolites. The metabolomics approach has been successfully applied to the classification different physiological states and identification of perturbed biochemical pathways. The purpose of the current investigation is the application of metabolomics to explore biological mechanisms which may lead to the onset of metabolic syndrome in adulthood. Methods: We evaluated differences in metabolites in the urine collected within 12 hours from 23 infants with IUGR (IntraUterine Growth Restriction), or LGA (Large for Gestational Age), compared to control infants (10 patients defined AGA: Appropriate for Gestational Age). Urinary metabolites were quantified by GC-MS and used to highlight similarities between the two metabolic diseases and identify metabolic markers for their predisposition. Quantified metabolites were analyzed using a multivariate statistics coupled with receiver operator characteristic curve (ROC) analysis of identified biomarkers. Results: Urinary myo-inositol was the most important discriminant between LGA + IUGR and control infants, and displayed an area under the ROC curve¼1. Conclusion: We postulate that the increase in plasma and consequently urinary inositol may constitute a marker of altered glucose metabolism during fetal development in both IUGR and LGA newborns

The Italian Journal of Geosciences is increasing its appeal among Geoscientists

This is an Editorial of the new Editorial Board of the Italian Journal of Geosciences, composed by Marco Balini (Milan), Hugo Bucher (Zürich), Simonetta Cirilli (Perugia), Laura Crispini (Genoa), Maurizio Mazzucchelli (Modena), and Giulio Ottonello (Genoa), which underlines the progress in the appeal of the journal among Geoscientists

Compton Scattering by the Proton using a Large-Acceptance Arrangement

Compton scattering by the proton has been measured using the tagged-photon facility at MAMI (Mainz) and the large-acceptance arrangement LARA. The new data are interpreted in terms of dispersion theory based on the SAID-SM99K parameterization of photo-meson amplitudes. It is found that two-pion exchange in the t-channel is needed for a description of the data in the second resonance region. The data are well represented if this channel is modeled by a single pole with mass parameter m(sigma)=600 MeV. The asymptotic part of the spin dependent amplitude is found to be well represented by pi-0-exchange in the t-channel. A backward spin-polarizability of gamma(pi)=(-37.1+-0.6(stat+syst)+-3.0(model))x10^{-4}fm^4 has been determined from data of the first resonance region below 455 MeV. This value is in a good agreement with predictions of dispersion relations and chiral pertubation theory. From a subset of data between 280 and 360 MeV the resonance pion-photoproduction amplitudes were evaluated leading to a E2/M1 multipole ratio of the p-to-Delta radiative transition of EMR(340 MeV)=(-1.7+-0.4(stat+syst)+-0.2(model))%. It was found that this number is dependent on the parameterization of photo-meson amplitudes. With the MAID2K parameterization an E2/M1 multipole ratio of EMR(340 MeV)=(-2.0+-0.4(stat+syst)+-0.2(model))% is obtained

A machine learning approach for knee injury detection from magnetic resonance imaging

The knee is an essential part of our body, and identifying its injuries is crucial since it can significantly affect quality of life. To date, the preferred way of evaluating knee injuries is through magnetic resonance imaging (MRI), which is an effective imaging technique that accurately identifies injuries. The issue with this method is that the high amount of detail that comes with MRIs is challenging to interpret and time consuming for radiologists to analyze. The issue becomes even more concerning when radiologists are required to analyze a significant number of MRIs in a short period. For this purpose, automated tools may become helpful to radiologists assisting them in the evaluation of these images. Machine learning methods, in being able to extract meaningful information from data, such as images or any other type of data, are promising for modeling the complex patterns of knee MRI and relating it to its interpretation. In this study, using a real-life imaging protocol, a machine-learning model based on convolutional neural networks used for detecting medial meniscus tears, bone marrow edema, and general abnormalities on knee MRI exams is presented. Furthermore, the model’s effectiveness in terms of accuracy, sensitivity, and specificity is evaluated. Based on this evaluation protocol, the explored models reach a maximum accuracy of 83.7%, a maximum sensitivity of 82.2%, and a maximum specificity of 87.99% for meniscus tears. For bone marrow edema, a maximum accuracy of 81.3%, a maximum sensitivity of 93.3%, and a maximum specificity of 78.6% is reached. Finally, for general abnormalities, the explored models reach 83.7%, 90.0% and 84.2% of maximum accuracy, sensitivity and specificity, respectively