Optimal fault resolution in geodetic inversion of coseismic data

With the continued growth in availability of DInSAR and GPS data, space based geodesy has been widely applied to image the coseismic displacement field and to retrieve the static dislocation over the fault plane for almost all the significant earthquakes of the past two decades. This is performed by linear data inversion over a set of subfaults, generally characterized by a constant and predefined or manually adjusted dimensions. In this paper we propose a new algorithm to automatically retrieve an optimized fault subdivision in the linear inversion of coseismic geodetic data. The code iteratively keeps the parameter resolution close to a predefined high value. We first discuss the rationale supporting our algorithm and, after a detailed description of its implementation, we analyze the advantages of its introduction in the data inversion. The algorithm was tested against an exhaustive range of synthetic and real datasets and fault mechanisms. Among them, we present the results for the Mw 6.2, 2009 L’Aquila (Central Italy) earthquake and compare the new and previously published slip distributions showing the disappearance of misleading slip pattern and the increased resolution for shallower zones

Towards the prediction of the quality of experience from facial expression and gaze direction

In this paper we investigate on the potentials to implicitly estimate the Quality of Experience (QoE) of a user of video streaming services by acquiring a video of her face and monitoring her facial expression and gaze direction. To this, we conducted a crowdsourcing test in which participants were asked to watch and rate the quality when watching 20 videos subject to different impairments, while their face was recorded with their PC's webcam. The following features were then considered: the Action Units (AU) that represent the facial expression, and the position of the eyes' pupil. These features were then used, together with the respective QoE values provided by the participants, to train three machine learning classifiers, namely, Support Vector Machine with quadratic kernel, RUSBoost trees and bagged trees. We considered two prediction models: only the AU features are considered or together with the position of the eyes' pupils. The RUSBoost trees achieved the best results in terms of accuracy, sensitivity and area under the curve scores. In particular, when all the features were considered, the achieved accuracy is of 44.7%, 59.4% and 75.3% when using the 5-level, 3level and 2-level quality scales, respectively. Whereas these results are not satisfactory yet, these represent a promising basis

Cross-Talk Effects in the Uncertainty Estimation of Multiplexed Data Acquisition Systems

This paper deals with the analysis of multi-channel data-acquisition systems with the aim of identifying and combining the main uncertainty contributions according to the GUM framework. Particular attention has been paid towards cross-talk effect, which could be an important uncertainty contribution in multiplexed data-acquisition systems. The uncertainty analysis is described for three commercial data acquisition devices highlighting that cross-talk specifications are often not suitable for a reliable uncertainty estimation in operating conditions. For this reason, an experimental set-up has been arranged to fully characterize the inter-channel effects of the investigated devices. The obtained results have highlighted that a proper characterization of a data-acquisition system is effective in estimating the actual performance at the frequency of interest and in the operating conditions for the source resistance and the input-channel configuration. Eventually, a customized procedure has been proposed that is effective in correcting the cross-talk effects also in very severe conditions of inter-channel disturbance

Bandwidth and accuracy-aware state estimation for smart grids using software defined networks

Smart grid (SG) will be one of the major application domains that will present severe pressures on future communication networks due to the expected huge number of devices that will be connected to it and that will impose stringent quality transmission requirements. To address this challenge, there is a need for a joint management of both monitoring and communication systems, so as to achieve a flexible and adaptive management of the SG services. This is the issue addressed in this paper, which provides the following major contributions. We define a new strategy to optimize the accuracy of the state estimation (SE) of the electric grid based on available network bandwidth resources and the sensing intelligent electronic devices (IEDs) installed in the field. In particular, we focus on phasor measurement units (PMUs) as measurement devices. We propose the use of the software defined networks (SDN) technologies to manage the available network bandwidth, which is then assigned by the controller to the forwarding devices to allow for the flowing of the data streams generated by the PMUs, by considering an optimization routine to maximize the accuracy of the resulting SE. Additionally, the use of SDN allows for adding and removing PMUs from the monitoring architecture without any manual intervention. We also provide the details of our implementation of the SDN solution, which is used to make simulations with an IEEE 14-bus test network in order to show performance in terms of bandwidth management and estimation accuracy

An iot-based smart building solution for indoor environment management and occupants prediction

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939)

The gustin (CA6) gene polymorphism, rs2274333 (A/G), as a mechanistic link between PROP tasting and fungiform taste papilla density and maintenance

Taste sensitivity to PROP varies greatly among individuals and is associated with polymorphisms in the bitter receptor gene TAS2R38, and with differences in fungiform papilla density on the anterior tongue surface. Recently we showed that the PROP non-taster phenotype is strongly associated with the G variant of polymorphism rs2274333 (A/G) of the gene that controls the salivary trophic factor, gustin. The aims of this study were 1) to investigate the role of gustin gene polymorphism rs2274333 (A/G), in PROP sensitivity and fungiform papilla density and morphology, and 2) to investigate the effect of this gustin gene polymorphism on cell proliferation and metabolic activity. Sixty-four subjects were genotyped for both genes by PCR techniques, their PROP sensitivity was assessed by scaling and threshold methods, and their fungiform papilla density, diameter and morphology were determined. In vitro experiments examined cell proliferation and metabolic activity, following treatment with saliva of individuals with and without the gustin gene mutation, and with isolated protein, in the two iso-forms. Gustin and TAS2R38 genotypes were associated with PROP threshold (p=0.0001 and p=0.0042), but bitterness intensity was mostly determined by TAS2R38 genotypes (p<0.000001). Fungiform papillae densities were associated with both genotypes (p<0.014) (with a stronger effect for gustin; p=0.0006), but papilla morphology was a function of gustin alone (p<0.0012). Treatment of isolated cells with saliva from individuals with the AA form of gustin or direct application of the active iso-form of gustin protein increased cell proliferation and metabolic activity (p<0.0135). These novel findings suggest that the rs2274333 polymorphism of the gustin gene affects PROP sensitivity by acting on fungiform papilla development and maintenance, and could provide the first mechanistic explanation for why PROP super-tasters are more responsive to a broad range of oral stimul

Spleen stiffness measurements using point shear wave elastography detects noncirrhotic portal hypertension in human immunodeficiency virus

Objectives:To assess the utility of spleen stiffness as a diagnostic tool inindividuals withHIV andnon-cirrhotic portal hypertension(NCPH).Design:The Philips EPIQ7TM, a newpoint shearwave elastography (pSWE) technique, was used to assess liver and spleen stiffnessin3 patient groups. Group1:HIV and NCPH(n=11); Group 2: HIV withpast didanosine(ddI) exposure without known liver disease or NCPH(n=5), Group 3: HIV without known liver disease or ddI exposure(n=9). Methods:Groups were matched for age, HIV chronicity and antiretroviral treatment (including cumulative ddI exposure in Groups 1 and 2). Differences in liver and spleen stiffness (in kPa) between groups were analysed using the Mann-Whiney U test.Results:Liver and spleen stiffness were both significantly higher in NCPH vs ddI-exposed (p=0.019 and p=0.006) and ddI-unexposed controls (p=0.038 and p<0.001). Spleen stiffness was more effective than liver stiffness at predicting NCPH, AUROC 0.812 vs 0.948. Combining the two variables improved the diagnostic performance, AUROC 0.961. The optimal cut-off for predicting NCPH using splenic stiffness was 25.4kPa, with sensitivity 91%, specificity 93%, PPV 91%, NPV 93%, positive likelihood ratio 12.73, negative likelihood ratio 0.10. Spleen and liver stiffness scores were strongly correlated (p=0.0004 95%CI 18,59). Conclusions:Elevated spleen stiffness is observed in HIV with NCPH and can be quantified easily using pSWE with high diagnostic accuracy. Novel strategies such as pSWE for longitudinal monitoring of patients with HIV and NCPH should be considered