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

The usefulness of c-Kit in the immunohistochemical assessment of melanocytic lesions

C-Kit (CD117), the receptor for the stem cell factor, a growth factor for melanocyte migra- tion and proliferation, has shown differential immunostaining in various benign and malig- nant melanocytic lesions. The purpose of this study is to compare c-Kit immunostaining in benign nevi and in primary and metastatic malignant melanomas, to determine whether c-Kit can aid in the differential diagnosis of these lesions. c-Kit immunostaining was per- formed in 60 cases of pigmented lesions, including 39 benign nevi (5 blue nevi, 5 intra- dermal nevi, 3 junctional nevi, 15 cases of pri- mary compound nevus, 11 cases of Spitz nevus), 18 cases of primary malignant melanoma and 3 cases of metastatic melanoma. The vast majority of nevi and melanomas examined in this study were posi- tive for c-Kit, with minimal differences between benign and malignant lesions. C-Kit cytoplasmatic immunoreactivity in the intraepidermal proliferating nevus cells, was detected in benign pigmented lesions as well as in malignant melanoma, increasing with the age of patients (P=0.007) in both groups. The patient’s age at presentation appeared to be the variable able to cluster benign and malignant pigmented lesions. The percentage of c-Kit positive intraepidermal nevus cells was better associated with age despite other vari- ables (P=0.014). The intensity and percentage of c-Kit positivity in the proliferating nevus cells in the dermis was significantly increased in malignant melanocytic lesions (P=0.015 and P=0.008) compared to benign lesions (compound melanocytic nevi, Spitz nevi, intradermal nevi, blue nevi). Immunostaning for c-Kit in metastatic melanomas was nega- tive. Interestingly in two cases of melanoma occurring on a pre-existent nevus, the melanoma tumor cells showed strong cytoplas- matic and membranous positivity for c-kit, in contrast with the absence of any immunoreac- tivity in pre-existent intradermal nevus cells. C-Kit does not appear to be a strong immuno- histochemical marker for distinguishing melanoma from melanocytic nevi, if we consid- er c-Kit expression in intraepidermal prolifer- ating cells. The c-Kit expression in proliferat- ing melanocytes in the dermis could help in the differential diagnosis between a superfi- cial spreading melanoma (with dermis inva- sion) and a compound nevus or an intradermal nevus. Finally, c-Kit could be a good diagnostic tool for distinguishing benign compound nevi from malignant melanocytic lesions with der- mis invasion and to differentiate metastatic melanoma from primary melanoma

Subsidence due to crack closure and depressurization of hydrothermal systems: a case study from Mt Epomeo (Ischia Island, Italy)

Levelling surveys carried out between 1990 and 2003 on the Mt Epomeo resurgent block (Ischia Island) record negative dislocations on its northern and southern flanks with a maximum subsidence rate of 1.27 cm yr)1. This deformation is not associated with the cooling, crystallization or lateral drainage of magma and cannot be explained by a pressure point or prolate ellipsoid source. Results from dislocation models and the available structural and geochemical information indicate that the subsidence is due to crack closure processes along two main ENE–WSW and E–W preexisting faults, which represent the preferred pathway of CO2 degassing from the hydrothermal system located beneath Mt Epomeo. The monitoring of the dislocations and CO2 flux along these faults could give useful information on the dynamics of the hydrothermal system

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

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

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)