Metasurface dome for above-the-horizon grating lobes reduction in 5G-NR systems

The use of fifth-generation (5G) new radio (NR) spectrum around 26 GHz is currently raising the quest on its compatibility with the well-established Earth exploration-satellite service, which may be blinded by the spurious radiation emitted above the horizon (AtH) by base station (BS) antennas. Indeed, AtH grating lobes are often present during cell scanning due to the large interelement spacing in BS array antennas for achieving higher gains with a reduced number of RF chains. In this letter, we propose an approach based on an electrically thin metasurface-based dome for the reduction of AtH grating lobes in 5G-NR BS antennas. The proposed scanning range shifting approach exploits the natural lower amplitude of the grating lobes when the antenna array scans in an angular region closer to the broadside direction. The grating lobe reduction is here demonstrated considering a 1x4 phased linear antenna array operating under dual-liner +/- 45 degrees-slant polarization. A simple design procedure for designing the metasurface dome is reported, together with the antenna performances, evaluated through a proper set of numerical experiments. It is shown that the grating lobe radiation toward the satellite region is significantly reduced, whereas the overall insertion loss is moderate

The obstetric syndromes: Clinical relevance of placental hormones

Preterm delivery, preeclampsia and intrauterine growth restriction are the major diseases of pregnancy. A key role in their pathogenesis is played by the placenta, which is the source of hormones and other important regulatory molecules providing the metabolic and endocrine homeostasis of the fetal-placental unit. Since obstetric syndromes are characterized by important maternal and neonatal morbidity and mortality worldwide, numerous efforts have been made over the years to prevent and treat them. Due to their complex pathogenesis, however, the therapy is poor and not very effective. Therefore, great emphasis is currently given to the prevention of these diseases through the identification of biochemical and biophysical markers, among which placental factors play a crucial role. The increasing knowledge of the role of placental molecules can indeed lead to the development of new therapeutic and diagnostic tools. © 2013 Expert Reviews Ltd

Long-term efficacy and tolerability of intranasal fentanyl in the treatment of breakthrough cancer pain

Purpose: The aim of the present study was to assess the long-term tolerability and efficacy of intranasal fentanyl (INFS) in opioid-tolerant patients with breakthrough cancer pain (BTP).Patients and methods: A 6 months, observational, prospective, cohort study design was employed to follow advanced cancer patients with BTP receiving INFS under routine clinical practice. Eligible adult cancer patients suffering from BTP had been prescribed INFS at effective doses. Data were collected at T0 and at month intervals for six months. The principal outcomes were the evaluation of possible serious adverse effects with prolonged use of INFS, the efficacy of BTP treatment with INFS, the quality of sleep, the rate of INFS discontinuation, and reasons for that.Results: Seventy-five patients were surveyed. Thirty-four patients (45.3 %) had a follow-up at 3 months, and twelve patients (16 %) were followed up at 6 months. The mean opioid doses, expressed as oral morphine equivalents, ranged 111\u2013180 mg/day, while the mean INFS doses were 87\u2013119 \u3bcg. Adverse effects were reported in a minority of patients and were considered to be associated with opioid therapy used for background pain. The quality of sleep significantly improved during the first 3\u20134 months. Finally, efficacy based on a general impression regarding the efficacy of INFS was good-excellent in most patients and statistically improved in time up to the third month.Conclusion: The long-term use of INFS in advanced cancer patients is effective and safe. No serious adverse effects were found up to six months of assessment. The level of quality of sleep and patients\u2019 satisfaction was relatively good, considering the advanced stage of disease

Guselkumab for treatment of moderate-to-severe plaque psoriasis: real-life effectiveness and drug-survival for up to 148 weeks

Background: Real-world data are useful to guide the management of psoriasis. Here, we present data on the effectiveness and survival of guselkumab in moderate-to-severe chronic plaque psoriasis for up to 148 weeks. Research design and methods: Cross-sectional study of 122 patients receiving guselkumab (100 mg at weeks 0 and 4, and then every 8 weeks thereafter) for>12 weeks, from November 2018 to April 2022. Main outcome measures: Clinical features and drug survival were analyzed up to 148 weeks. Results: Obese patients (32.8%) and those receiving prior biologics (64.8%) were included. Guselkumab treatment was associated with a rapid decrease in PASI, from 16.2 to 3.2 at week 12, and long-term improvements in all subgroups (97.6%, 82.9%, and 63.4% of patients, respectively, achieved PASI 75, 90, and 100 after 148 weeks). More non-obese than obese patients achieved PASI 100 at week 148 (86.4% vs 38.9%), as did bio-naïve vs bio-experienced patients (86.7% vs 50.0%). Previous biologic therapy was a negative prognostic factor for achieving PASI 100 over the long-term by multivariate analysis (p = 0.005). Overall, 96% of patients were on treatment after 2 years. Conclusions: Real-world data confirm the long-term effectiveness of guselkumab in patients with psoriasis

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms

Response to the 2009 influenza A(H1N1) pandemic in Italy

n/

Hierarchical large-scale elastic metamaterials for passive seismic wave mitigation

Large scale elastic metamaterials have recently attracted increasing interest in the scientific community for their potential as passive isolation structures for seismic waves. In particular, so-called "seismic shields"have been proposed for the protection of large areas where other isolation strategies (e.g. dampers) are not workable solutions. In this work, we investigate the feasibility of an innovative design based on hierarchical design of the unit cell, i.e. a structure with a self-similar geometry repeated at different scales. Results show how the introduction of hierarchy allows the conception of unit cells exhibiting reduced size with respect to the wavelength while maintaining the same or improved isolation efficiency at frequencies of interest for earthquake engineering. This allows to move closer to the practical realization of such seismic shields, where low-frequency operation and acceptable size are both essential characteristics for feasibility

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end of r<20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared bands are added. While the fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter $\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 $\mu$, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives $\delta z<4e-5$ and $\sigma_z<0.019$. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.Comment: A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz

METAPHOR: a machine-learning-based method for the probability density estimation of photometric redshifts

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo-z. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the LE PHARE spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, I.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models