A NEURAL NETWORK REGRESSION MODEL FOR ESTIMATING MAXIMUM DAILY AIR TEMPERATURE USING LANDSAT-8 DATA

Abstract. Urban Heat Islands (UHI) phenomenon is a pressing problem for highly industrialized areas with serious risks for public health. Weather stations guarantee long-term accurate observations of weather parameters, such Air Temperature (AT), but lack appropriate spatial coverage. Numerous studies have argued that satellite Land Surface Temperature (LST) is a relevant parameter for estimating AT maps, exploring both linear regression and Machine Learning algorithms. This study proposes a Neural Network (NN) regression model for estimating the maximum AT from Landsat-8 data. The approach has been tested in a variegated morphological region (Puglia, Italy) using a large stack of data acquired from 2018 to 2020. The algorithm uses the median values of LST and Normalized Difference Vegetation Index (NDVI) computed using different buffer radius around the location of each reference weather station (250 m, 1000 m, and 2000 m) to train the NN model with a K-fold cross-validation strategy. The reference dataset was split into three sets using a stratified sampling approach considering the different station categories: rural, High- and Low-density Urban areas respectively. The algorithm was tested with different learning rates (LR) (0.001 and 0.005). The results show that our NN model accuracy improves with the increase of the buffer radius, minimizing the difference in terms of R^2 between training and evaluation data, with an overall accuracy consistently higher than 0.84. Future research could investigate more input variables in the NN model such as morphology or climate variables and test the algorithm on larger areas

Indoor height determination of the new absolute gravimetric station of L'Aquila

In this paper we describe all the field operations and the robust post-processing proceduresto determine the height of the new absolute gravimetric station purposely selected to belong to a new absolute gravimetric network and located in the Science Faculty of the L’Aquila University. This site has been realized indoor in the Geomagnetism laboratory, so that the height cannot be measured directly, but linking it to the GNSS antenna of AQUI benchmark located on the roof of the same building, by a classical topographic survey. After the topographic survey, the estimated height difference between AQUI and the absolute gravimetric site AQUIgis 14.970±0.003 m. At the epoch of the 2018 gravimetric measures, the height of AQUI GNSS station was 712.974±0.003 m, therefore the estimated ellipsoidalheight of the gravimetric site at the epoch of gravity measurements is 698.004±0.005 m. Absolute gravity measurements are referred to the equipotential surface of gravity field, so that the knowledge of the geoidal undulation at AQUIg allows us to infer the orthometric height as 649.32 m

Data-Driven Epidemic Intelligence Strategies Based on Digital Proximity Tracing Technologies in the Fight against COVID-19 in Cities

In a modern pandemic outbreak, where collective threats require global strategies and local operational defence applications, data-driven solutions for infection tracing and forecasting epidemic trends are crucial to achieve sustainable and socially resilient cities. Indeed, the need for monitoring, containing, and mitigating the ongoing COVID-19 pandemic has generated a great deal of interest in Digital Proximity Tracing Technology (DPTT) on smartphones, as well as their function and effectiveness and insights of population acceptance. This paper introduces and compares different Data-Driven Epidemic Intelligence Strategies (DDEIS) developed on DPTTs. It aims to clarify to what extent DDEIS could be effective and both technologically and socially suitable in reaching the objective of a swift return to normality for cities, guaranteeing public health safety and minimizing the risk of epidemic resurgence. It assesses key advantages and limits in supporting both individual decision-making and policy-making, considering the role of human behaviour. Specifically, an online survey carried out in Italy revealed user preferences for DPTTs and provided preliminary data for an SEIR (Susceptible–Exposed–Infectious–Recovered) epidemiological model. This was developed to evaluate the impact of DDEIS on COVID-19 spread dynamics, and results are presented together with an evaluation of potential drawbacks

APPLICATION OF LASER SCANNING SURVEYING TO ROCK SLOPES RISK ASSESSMENT ANALYSIS

The methods for understanding rock instability mechanisms and for evaluating potential destructive scenarios are of great importance in risk assessment analysis dedicated to the establishment of appropriate prevention and mitigation actions. When the portion of the unstable rock mass is very large, effective actions to counteract the risks are complex and expensive. In these conditions, an optimal risk management cannot ignore procedures able to faster and accurately acquire i) geometrical data for modeling the geometry of the rock walls and implementing reliable forecasting models and ii) monitoring data able to describe the magnitude and the direction of deformation processes. These data contributes to the prediction of the behavior of a landslide if the measurements are acquired frequently and reliable numerical models can be implemented. Innovative geomatic techniques, based on GPS, Terrestrial Laser Scanning Surveying (TLS), automated total station and satellite and ground SAR Interferometry, have been recently applied to define the geometry and monitoring the displacements of unstable slopes. Among these, TLS is mainly adopted to generate detailed 3D models useful to reconstruct rock wall geometry by contributing to the estimation of geo-mechanical parameters, that is orientation, persistence and apparent spacing of rock discontinuities. Two examples of applications of TLS technique to the analysis of a large front in a quarry and of a rock shoulder of a dam are presented

NLRP3 Inflammasome From Bench to Bedside: New Perspectives for Triple Negative Breast Cancer

The tumor microenvironment (TME) is crucial in cancer onset, progression and response to treatment. It is characterized by an intricate interaction of immune cells and cytokines involved in tumor development. Among these, inflammasomes are oligomeric molecular platforms and play a key role in inflammatory response and immunity. Inflammasome activation is initiated upon triggering of pattern recognition receptors (Toll-like receptors, NOD-like receptors, and Absent in melanoma like receptors), on the surface of immune cells with the recruitment of caspase-1 by an adaptor apoptosis-associated speck-like protein. This structure leads to the activation of the pro-inflammatory cytokines interleukin (IL)-1β and IL-18 and participates in different biological processes exerting its effects. To date, the Nod–Like Receptor Protein 3 (NLRP3) inflammasome has been well studied and its involvement has been established in different cancer diseases. In this review, we discuss the structure, biology and mechanisms of inflammasomes with a special focus on the specific role of NLRP3 in breast cancer (BC) and in the sub-group of triple negative BC. The NLRP3 inflammasome and its down-stream pathways could be considered novel potential tumor biomarkers and could open new frontiers in BC treatment

The Biological Relevance of NHERF1 Protein in Gynecological Tumors

Gynecological cancer management remains challenging and a better understanding of molecular mechanisms that lead to carcinogenesis and development of these diseases is needed to improve the therapeutic approaches. The Na+/H+ exchanger regulatory factor 1 (NHERF1) is a scaffold protein that contains modular protein-interaction domains able to interact with molecules with an impact on carcinogenesis and cancer progression. During recent years, its involvement in gynecological cancers has been explored, suggesting that NHERF1 could be a potential biomarker for the development of new targeted therapies suitable to the management of these tumors. This comprehensive review provides an update on the recent study on NHERF1 activity and its pathological role in cervical and ovarian cancer, as well as on its probable involvement in the therapeutic landscape of these cancer types

A tool for mapping the evolution of a lava field through the Etna video-surveillance camera network

In active volcanic areas it is often difficult carry out direct surveys during an eruption, remote sensing techniques based on airborne/satellite platforms and ground-based sensors have remarkable monitoring potentialities in terms of safety and observation capability. In addition, the recent development of high resolution digital cameras, laser scanners and SAR instruments have improved the ability to obtain reliable measurements for modelling the evolution of effusive and explosive eruptions by following the rate of advancement of a lava flow or the dispersal of a volcanic plume. In order to collect data at an adequate level of accuracy and frequency it is not possible to exclusively rely on airborne or satellite methods and it is necessary to carry out measurements using also remote sensing instruments operating on the ground. Among the other techniques, the use of a simplified photogrammetric approach based a video-surveillance camera network represents a straightforward alternative for rapid mapping in active volcanic areas. Therefore a procedure for optimizing and extending the observational capability of the Etna NEtwork of Thermal and VIsible cameras (NETVIS) for systematically monitoring and quantifying surface sin-eruptive processes was implemented. The activity included also the extension of the permanent video-surveillance network by installing additional mobile stations. A dedicated tool for automatic processing of image datasets was developed and tested in both simulated and real scenarios to obtain a time series of digital orthophotos for tracking the evolution of a lava flow emplacement. The developed tool was tested by processing images acquired by the Etna_NETVIS sensors, in particular from Monte Cagliato thermal camera, during the 2011 paroxysmal episodes of the New South East Crater that poured lava flows in the Valle del Bove.PublishedRoma, Italia5V. Sorveglianza vulcanica ed emergenzeope

Terrestrial laser scanning survey in support of unstable slopes analysis. The case of Vulcano Island (Italy)

The capability to measure at distance dense cloud of 3D point has improved the relevance of geomatic techniques to support risk assessment analysis related to slope instability. This work focuses on quantitative analyses carried out to evaluate the effects of potential failures in the Vulcano Island (Italy). Terrestrial laser scanning was adopted to reconstruct the geometry of investigated slopes that is required for the implementation of numerical modeling adopted to simulate runout areas. Structural and morphological elements, which influenced past instabilities or may be linked to new events, were identified on surface models based on ground surveying. Terrestrial laser scanning was adopted to generate detailed 3D models of subvertical slopes allowing to characterize the distribution and orientation of the rock discontinuities that affect instability mechanism caused by critical geometry. Methods for obtaining and analyzing 3D topographic data and to implement simulation analyses contributing to hazard and risk assessment are discussed for two case studies (Forgia Vecchia slope and Lentia rock walls)

Assessing and improving the measuring capability of the Etna_NETVIS camera network for lava flow rapid mapping

This work is aimed at improving the performance of the ground NEtwork of Thermal and VIsible and cameras located on Mt. Etna volcano (Etna_NETVIS) by optimizing its observational capability on lava flows evolution and by developing dedicated tools for systematically measuring quantitative parameters of known accuracy. The first goal will be achieved through the analysis of the geometrical configuration and its improvement by means of the establishment of additional observation sites to be equipped with mobile stations, depending on the area of interest. This will increment the spatial coverage and improve the observation of the most active areas for surface sin-eruptive processes. For the second objective we will implement new processing tools to permit a reliable quantitative use of the data collected by the surveillance sensors of NETVIS, extending their capability in monitor the lava flow thermal and spatial evolution and by providing georeferenced data for rapid mapping scope. The tool will be used to automatically pre-process multitemporal datasets and will be tested on both simulated and real scenarios. Thanks to data collected and archive by the NETVIS INGV team, we will have the opportunity to develop and test the procedure in different operational conditions selected among the large number of lava flows coupled to lava fountan events occurred between 2011 and 2013. Additionally, Etna_NETVIS data can be used to downscale the information derived from satellite data and/or to integrate the satellite datasets in case of incomplete coverage or missing acquisitions (both due to low revisiting time or bad geometrical conditions). Therefore an additional goal is that of comparing/integrating quantitative data derived from visible and radar satellite sensors with the maps obtained using Etna_NETVIS. The procedure will take into account the discrepancy among the different datasets in terms of accuracy and resolution and will attempt to provide a combined approach (based on error analysis and data weighting) to evaluate the final results reliability. Preliminary results on the procedure and algorithm adopted for geometric and radiometric sensor calibration, definition of optimized configurations through simulation and for extracting updated mapping data from multi-temporal dataset will be presented. This work is developed in the framework of the EU-FP7 project “MED-SUV” (MEDiterranean SUpersite Volcanoes)

An agent-based simulation model to evaluate alternative co-payment scenarios for contributing to health systems financing

Ageing populations, rapid technological progress and recent public budget cuts currently threaten the sustainability of public health systems. To meet growing needs with declining resources, decision-makers must identify new ways to avoid reducing the quality of services offered to citizens. This paper focuses on the so-called "co-payment" tools aimed to obtain additional resources for the public health budget directly from citizens. Whereas certain forms of co-payments have always been introduced within health systems to prevent moral hazard behaviours, other co-payment mechanisms are explicitly intended to help finance public healthcare systems. Literature and empirical findings do not agree about the final impact of such co-payment tools, particularly whether they can attain system sustainability and guarantee equitably delivered services. In this paper, we develop an agent-based simulation model which can be used by decision-makers as a decision support tool to compare different co-payment rules and evaluate their impact on the public budget and the health expense of different groups of citizens