Monitoring urban heat island through google earth engine. Potentialities and difficulties in different cities of the United States

The aim of this work is to exploit the large-scale analysis capabilities of the innovative Google Earth Engine platform in order to investigate the temporal variations of the Urban Heat Island phenomenon as a whole. A intuitive methodology implementing a large-scale correlation analysis between the Land Surface Temperature and Land Cover alterations was thus developed. The results obtained for the Phoenix MA are promising and show how the urbanization heavily affects the magnitude of the UHI effects with significant increases in LST. The proposed methodology is therefore able to efficiently monitor the UHI phenomenon

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring

Coherent Change Detection for repeated-pass interferometric SAR images: An application to earthquake damage assessment on buildings

During disaster response, the availability of relevant information, delivered in a proper format enabling its use among the different actors involved in response efforts, is key to lessen the impact of the disaster itself. Focusing on the contribution of geospatial information, meaningful advances have been achieved through the adoption of satellite earth observations within emergency management practices. Among these technologies, the Synthetic Aperture Radar (SAR) imaging has been extensively employed for large-scale applications such as flood areas delineation and terrain deformation analysis after earthquakes. However, the emerging availability of higher spatial and temporal resolution data has uncovered the potential contribution of SAR to applications at a finer scale. This paper proposes an approach to enable pixel-wise earthquake damage assessments based on Coherent Change Detection methods applied to a stack of repeated-pass interferometric SAR images. A preliminary performance assessment of the procedure is provided by processing Sentinel-1 data stack related to the 2016 central Italy earthquake for the towns of Amatrice and Accumoli. Damage assessment maps from photo-interpretation of high-resolution airborne imagery, produced in the framework of Copernicus EMS (Emergency Management Service - European Commission) and cross-checked with field survey, is used as ground truth for the performance assessment. Results show the ability of the proposed approach to automatically identify changes at an almost individual building level, thus enabling the possibility to empower traditional damage assessment procedures from optical imagery with the centimetric change detection sensitivity characterizing SAR. The possibility of disseminating outputs in a GIS-like format represents an asset for an effective and cross-cutting information sharing among decision makers and analysts

Eddy diffusivity derived from drifter data for dispersion model applications

Ocean transport and dispersion processes are at the present time simulated using Lagrangian stochastic models coupled with Eulerian circulation models that are supplying analyses and forecasts of the ocean currents at unprecedented time and space resolution. Using the Lagrangian approach, each particle displacement is described by an average motion and a fluctuating part. The first one represents the advection associated with the Eulerian current field of the circulation models while the second one describes the sub-grid scale diffusion. The focus of this study is to quantify the sub-grid scale diffusion of the Lagrangian models written in terms of a horizontal eddy diffusivity. Using a large database of drifters released in different regions of the Mediterranean Sea, the Lagrangian sub-grid scale diffusion has been computed, by considering different regimes when averaging statistical quantities. In addition, the real drifters have been simulated using a trajectory model forced by OGCM currents, focusing on how the Lagrangian properties are reproduced by the simulated trajectories

Fluorimetric methods for the measurement of intermediate metabolites (lactate, pyruvate, alanine, β-hydroxybutyrate, glycerol) using a COBAS FARA centrifugal analyser

Intermediate products of the metabolism of glucose, fat and amino-acid are important in the evaluation of such metabolic disorders as diabetes mellitus, liver disease and metabolic acidosis. In the present study, methods for the measurement of intermediate metabolites (lactate, pyruvate, alanine, β-hydroxybutyrate and glycerol) have been adapted to a fast centrifugal analyzer: the COBAS FARA. Correlation coeffcients rangedfrom 0.90 to 0.99, compared to established manual spectrophotometric methods. Within-run coeffcients of variation (CVs) ranged between 2.9 and 8.8% at low levels, between 1.5 and 5.7% at medium levels and between 1.2 and 5.6% at high levels. Between-run CVs were between 4.0 and 15.0% at low levels, between 1.7 and 7.0% at medium levels and between 1.3 and 2.7% at high levels. These fluorimetric assays for the determination of intermediate metabolites on COBAS FARA (Roche) have a good sensitivity and precision, are less costly than manual methods and can be used on a routine basis

Intraerythrocytic pH variations during hemodialysis: A 31P NMR study

Intraerythrocytic pH variations during hemodialysis: A 31P NMR study. Before hemodialysis, patients have an intraerythrocytic pH (pHi) and an extracellular pH, measured in whole blood (pHo), which are lower than those of healthy controls. During bicarbonate hemodialysis, pHi values continuously increase, approaching a normal value at the end of the session. Concomitantly, pHo values follow similar variations. During acetate hemodialysis, pHi values exhibit a steep initial decrease, reaching a minimum after about 15 minutes. Concurrently, however, pHo values decrease only slightly. This phenomenon seems to originate in the intraerythrocytic medium and might be due to a shift in intracellular CO2/bicarbonate equilibrium. This drop in pHi exhibits interpatient variability, suggesting that the magnitude of pH decrease would be correlated with the degree of the problems observed in some patients undergoing acetate hemodialysis

Ghigliottin-AI @ EVALITA2020: Evaluating artificial players for the language game “La Ghigliottina”

Evaluating Artificial Players for the Language Game “La Ghigliottina” (Ghigliottin-AI) task is one of the tasks organized in the context of the 2020 EVALITA edition, a periodic evaluation campaign of Natural Language Processing (NLP) and speech tools for the Italian language. Ghigliottin-AI participants are asked to build an artificial player able to solve “La Ghigliottina”, namely the final game of an Italian TV show called “L'Eredità”. The game involves a single player who is given a set of five words unrelated to each other, but related with a sixth word that represents the solution to the game. Fourteen teams registered to Ghigliottin-AI. Nevertheless, only two teams submitted their run. In order to evaluate the submitted systems, we rely on an API base methodology, via a Remote Evaluation Server (RES). In this report we describe the Ghigliottin-AI task, the data, the evaluation and we discuss results

LivHeart: A Multi Organ-on-Chip Platform to Study Off-Target Cardiotoxicity of Drugs Upon Liver Metabolism

The drug discovery and development process is still long, costly, and highly risky. The principal attrition factor is undetected toxicity, with hepatic and cardiac toxicities playing a critical role and being the main responsible of safety-related drug withdrawals from the market. Multi Organs-on-Chip (MOoC) represent a disruptive solution to study drug-related effects on several organs simultaneously and to efficiently predict drug toxicity in preclinical trials. Specifically focusing on drug safety, different technological features are applied here to develop versatile MOoC platforms encompassing two culture chambers for generating and controlling the type of communication between a metabolically competent liver model and a functional 3D heart model. The administration of the drug Terfenadine, a cardiotoxic compound liver-metabolized into the noncardiotoxic Fexofenadine, proved that liver metabolism and a fine control over drug diffusion are fundamental to elicit a physio-pathological cardiac response. From these results, an optimized LivHeart platform is developed to house a liver model and a cardiac construct that can be mechanically trained to achieve a beating microtissue, whose electrophysiology can be directly recorded in vitro. The platform is proved able to predict off-target cardiotoxicity of Terfenadine after liver metabolism both in terms of cell viability and functionality