Structural studies of mesoporous ZrO2_{2}-CeO2_{2} and ZrO2_{2}-CeO2_{2}/SiO2_{2} mixed oxides for catalytical applications

In this work the synthesis of ZrO$_{2}$-CeO$_{2}$ and ZrO$_{2}$-CeO$_{2}$/SiO$_{2}$ were developed, based on the process to form ordered mesoporous materials such as SBA-15 silica. The triblock copolymer Pluronic P-123 was used as template, aiming to obtain crystalline single phase walls and larger specific surface area, for future applications in catalysis. SAXS and XRD results showed a relationship between ordered pores and the material crystallization. 90% of CeO$_{2}$ leaded to single phase homogeneous ceria-zirconia solid solution of cubic fluorite structure (Fm$\bar{3}$m). The SiO$_{2}$ addition improved structural and textural properties as well as the reduction behavior at lower temperatures, investigated by XANES measurements under H$_{2}$ atmosphere

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel

Raman excitation spectroscopy of carbon nanotubes: effects of pressure medium and pressure

Raman excitation and emission spectra for the radial breathing mode (RBM) are reported, together with a preliminary analysis. From the position of the peaks on the two-dimensional plot of excitation resonance energy against Raman shift, the chiral indices (m, n) for each peak are identified. Peaks shift from their positions in air when different pressure media are added - water, hexane, sulphuric acid - and when the nanotubes are unbundled in water with surfactant and sonication. The shift is about 2 - 3 cm-1 in RBM frequency, but unexpectedly large in resonance energy, being spread over up to 100meV for a given peak. This contrasts with the effect of pressure. The shift of the peaks of semiconducting nanotubes in water under pressure is orthogonal to the shift from air to water. This permits the separation of the effects of the pressure medium and the pressure, and will enable the true pressure coefficients of the RBM and the other Raman peaks for each (m, n) to be established unambiguously.Comment: 6 pages, 3 Figures, Proceedings of EHPRG 2011 (Paris

Enabling Cloud-based Computational Fluid Dynamics with a Platform-as-a-Service Solution

Computational Fluid Dynamics (CFD) is widely used in manufacturing and engineering from product design to testing. CFD requires intensive computational power and typically needs high performance computing to reduce potentially long experimentation times. Dedicated high performance computing systems are often expensive for small-to-medium enterprises (SMEs). Cloud computing claims to enable low cost access to high performance computing without the need for capital investment. The CloudSME Simulation Platform aims to provide a flexible and easy to use cloud-based Platform-as-a-Service (PaaS) technology that can enable SMEs to realize the benefits of high performance computing. Our Platform incorporates workflow management and multi-cloud implementation across various cloud resources. Here we present the components of our technology and experiences in using it to create a cloud-based version of the TransAT CFD software. Three case studies favourably compare the performance of a local cluster and two different clouds and demonstrate the viability of our cloud-based approach

Energy efficiency in LTE-Advanced networks with relay nodes

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An E 3F based assessment of energy efficiency of relay nodes in LTE-advanced networks

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Sociodemographic and health service organizational factors associated with the choice of the private versus public sector for specialty visits: Evidence from a national survey in Italy

Introduction Although Italy\u2019s NHS is funded through general taxation, the private sector plays an important role in health service provision and financing. The aim of this paper was to identify the sociodemographic and health service organizational factors associated with the propensity to seek specialist care in the private sector. Materials and methods Data were retrieved from the national Istat survey \u201cHealth conditions and use of health services\u201d carried out in 2012\u20132013. We selected adults with a specialty visit in the previous 12 months in the four most frequent medical specialties: ophthalmology, cardiology, obstetrics/ gynecology and orthopedics. The study outcome was the choice to use a private service. In order to investigate the determinants of private use, we adopted the socio-behavioral model by Andersen and Newman, making a distinction between sociodemographic and healthcare organizational factors. The associations with the outcome were analyzed using chi-squared test, t-test and multivariable logistic regression analysis. Results and discussion Use of private care varied widely, from 26.3% for cardiology to 53.6% for obstetrics/gynecology. Females, patients with higher educational levels and patients with higher self-reported economic resources sought more frequently private healthcare for all specialties; younger patients and employed patients were more likely to seek private care for ophthalmic conditions. Exemption from copayment for public services reduced more than half the propensity to seek private care. Trust in this healthcare service was the main reason for private users (52.5%) followed by waiting time (26.7%) and physician choice (20.1%). Conclusion The attitude of the population to use private services for specialist visits is linked both to sociodemographic and health services organizational factors: the former are unmodifiable while the latter are susceptible to managerial and health policy actions. In a public-financed, universal coverage system, policy makers may act upon the organizational factors that make private health facilities more attractive in order to reduce private care use

Risk adjustment models for interhospital comparison of CS rates using Robson's ten group classification system and other socio-demographic and clinical variables

BACKGROUND: Caesarean section (CS) rate is a quality of health care indicator frequently used at national and international level . The aim of this study was to assess whether adjustment for Robson's Ten Group Classification System (TGCS), and clinical and socio-demographic of the mother and the fetus is necessary for inter-hospital comparisons of CS rates. METHODS: The study population includes 64,423 deliveries in Emilia-Romagna between January 1, 2003 and December 31, 2004, classified according to theTGCS. Poisson regression was used to estimate crude and adjusted hospital relative risks of CS compared to a reference category. Analyses were carried out in the overall population and separately according to the Robson groups (groups I, II, III, IV and V-X combined). Adjusted relative risks (RR) of CS were estimated using two risk-adjustment models; the first (M1) including the TGCS group as the only adjustment factor; the second (M2) including in addition demographic and clinical confounders identified using a stepwise selection procedure. Percentage variations between crude and adjusted RRs by hospital were calculated to evaluate the confounding effect of covariates. RESULTS: The percentage variations from crude to adjusted RR proved to be similar in M1 and M2 model. However, stratified analyses by Robson's classification groups showed that residual confounding for clinical and demographic variables was present in groups I (nulliparous, single, cephalic, [greater than or equal to]37 weeks, spontaneous labour) and III (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, spontaneous labour) and IV (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour) and to a minor extent in groups II (nulliparous, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour) and IV (multiparous, excluding previous CS, single, cephalic, [greater than or equal to]37 weeks, induced or CS before labour). CONCLUSIONS: The TGCS classification is useful for inter-hospital comparison of CS section rates, but residual confounding is present in the TGCS strata