Silicon Quasi‐One‐Dimensional Nanostructures for Photovoltaic Applications

Thanks to the silicon abundance, stability, non-toxicity and well known electronic properties, Si based solar cells have represented the leading actors in the photovoltaic market and future projections confirm this predominance. However, half of the module cost is due to the material consumption and processing. In order to decrease the costs, a cut in the Si consumption must be operated, with consequent decrement in the optical absorption, generated current and device efficiency. To keep the performance level, a proper Si surface design with the objective to trap the light, has been developed. One of the most popular approaches is to use silicon nanowires embedded in the solar cell emitter where they play the role of optically and electrically active layer, thanks to their excellent optical absorption properties. However, also another material has been the terminus of the light-trapping materials, the silicon nanoholes. Their mechanical robustness is superior, making their integration inside the cell easier and cost-effective. The review will bring about all of the most common methods to fabricate these two types of nanostructures when used for solar cells applications, their optical properties and some critical aspects related to their high surface to volume ratio which modify the recombination processes

Deep Audio Analyzer: a Framework to Industrialize the Research on Audio Forensics

Deep Audio Analyzer is an open source speech framework that aims to simplify the research and the development process of neural speech processing pipelines, allowing users to conceive, compare and share results in a fast and reproducible way. This paper describes the core architecture designed to support several tasks of common interest in the audio forensics field, showing possibility of creating new tasks thus customizing the framework. By means of Deep Audio Analyzer, forensics examiners (i.e. from Law Enforcement Agencies) and researchers will be able to visualize audio features, easily evaluate performances on pretrained models, to create, export and share new audio analysis workflows by combining deep neural network models with few clicks. One of the advantages of this tool is to speed up research and practical experimentation, in the field of audio forensics analysis thus also improving experimental reproducibility by exporting and sharing pipelines. All features are developed in modules accessible by the user through a Graphic User Interface. Index Terms: Speech Processing, Deep Learning Audio, Deep Learning Audio Pipeline creation, Audio Forensics

Human papillomavirus vaccination coverage among adolescents living in southern Italy

Objective: The aim of this study was to estimate HPV vaccination coverage in the target population residing in Sicily, five years after launch of the vaccination campaign, and to analyze its organization in this region. Methods: Regional data as at 31 December 2013, grouped by province, issued by the Regional Health Authority were used. The organization and information materials of the campaign were also assessed (letters, posters, brochures, etc.). Results: The results for Sicily show uptake rates for three doses of HPV vaccination of 56.5%, 55.8%, 58.2%, 55.3% for cohorts born in 1997, 1998, 1999, 2000 respectively, and 56.4% for cohorts born in 1996. These figures highlight the problems encountered during the promotion campaign and vaccination provision. Conclusions: Vaccine uptake in Sicily was lower than national figures for Italy as a whole for all cohorts and both fall far short of the targets set by the National Immunization Prevention Plan 2012-2014. In order to promote vaccination uptake and improve coverage, at both regional and local level, the quality of information should be improved and more communication campaigns be instigated to increase the involvement of professionals

Objective estimation of body condition score by modeling cow body shape from digital images.

Body condition score (BCS) is considered an important tool for management of dairy cattle. The feasibility of estimating the BCS from digital images has been demonstrated in recent work. Regression machines have been successfully employed for automatic BCS estimation, taking into account information of the overall shape or information extracted on anatomical points of the shape. Despite the progress in this research area, such studies have not addressed the problem of modeling the shape of cows to build a robust descriptor for automatic BCS estimation. Moreover, a benchmark data set of images meant as a point of reference for quantitative evaluation and comparison of different automatic estimation methods for BCS is lacking. The main objective of this study was to develop a technique that was able to describe the body shape of cows in a reconstructive way. Images, used to build a benchmark data set for developing an automatic system for BCS, were taken using a camera placed above an exit gate from the milking robot. The camera was positioned at 3 m from the ground and in such a position to capture images of the rear, dorsal pelvic, and loin area of cows. The BCS of each cow was estimated on site by 2 technicians and associated to the cow images. The benchmark data set contained 286 images with associated BCS, anatomical points, and shapes. It was used for quantitative evaluation. A set of example cow body shapes was created. Linear and polynomial kernel principal component analysis was used to reconstruct shapes of cows using a linear combination of basic shapes constructed from the example database. In this manner, a cow's body shape was described by considering her variability from the average shape. The method produced a compact description of the shape to be used for automatic estimation of BCS. Model validation showed that the polynomial model proposed in this study performs better (error=0.31) than other state-of-the-art methods in estimating BCS even at the extreme values of BCS scale

Mindfulness-Based Interventions for Physical and Psychological Wellbeing in Cardiovascular Diseases: A Systematic Review and Meta-Analysis

Background: Recently, there has been an increased interest in the efficacy of mindfulness-based interventions (MBI) for people with cardiovascular diseases (CVD), although the exact beneficial effects remain unclear. Methods: This review aims to establish the role of MBI in the management of wellbeing for patients with CVD. Seventeen articles have been included in this systematic synthesis of the literature and eleven in the meta-analysis. Results: Considering physical (i.e., heart rate, blood pressure) and psychological outcomes (i.e., depression, anxiety, stress, styles of coping), the vast majority of studies confirmed that MBI has a positive influence on coping with psychological risk factors, also improving physiological fitness. Random-effects meta-analysis models suggested a moderate-to-large effect size in reducing anxiety, depression, stress, and systolic blood pressure. Conclusions: Although a high heterogeneity was observed in the methodological approaches, scientific literature confirmed that MBI can now be translated into a first-line intervention tool for improving physical and psychological wellbeing in CVD patients

ROBUST VIDEO STABILIZATION APPROACH BASED ON A VOTING STRATEGY

Today many people in the world without any (or with little) knowledge about video recording, thanks to the widespread use of mobile devices (PDAs, mobile phones, etc.) take videos. However the unwanted movements of their hands typically blur and introduce disturbing jerkiness in the recorded sequences. A fundamental issue is the overall robustness with respect to different scene contents (indoor, outdoor, etc.) and conditions (illumination changes, moving objects, etc.). In this paper we propose an accurate and robust image alignment algorithm for video stabilization purposes based on a voting strategy. Experimental results confirm the effectiveness of the proposed approach

Artificial Mosaic Generation: a Survey

Abstract The focus of the chapter is related to review techniques for the automatic generation of good quality digital mosaics from raster images. Mosaics, in the digital realm, are illustrations composed by a set of small images called ”tiles”. The tiles tessellate an input image with the aim of reproducing the original visual information rendered into a mosaic-like style. This chapter will review the major different approaches for digital mosaic generation reporting a short description and a discussion about the most relevant and recent issues. Particular emphasis will be devoted to techniques able to generate artificial mosaics that emulates in some way ancient mosaics both in terms of tile positioning and tile cutting procedures. Visual comparisons among different approaches together with suggestions for future work will be also provided.