Search

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ET a , the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm. • GPR algorithm is suitable to fill gaps in daily ET a time series. • The best m(x) and k(x,x') functions required by the GPR algorithm were identified. • The best results were obtained when the dataset included climate data, SWC and VIs. • The use of ERA5-L data and VIs retrieved by Sentinel 2 or MODIS is a good alternative. • GPR algorithm was tested for different crops in continental and temperate zones. [ABSTRACT FROM AUTHOR]

The paper presents a machine-learning based framework, named VULMA (VULnerability analysis using MAchine-learning), for vulnerability analysis of existing buildings. The underlying idea is to provide an indication of the seismic vulnerability by exploiting available photographs, which can be properly processed to provide some input data for empirical vulnerability algorithms. To this scope, a complete processing pipeline has been defined, which consists in four consecutive modules offering different and specific services. The first module, Street VULMA , performs the image gathering starting from the raw data; the second module, Data VULMA , provides a mean for the data labelling and storage; the third module, Bi VULMA , uses the collected data to train several machine-learning models for image classification; the fourth module, In VULMA , performs a ranking of the images, their analysis and consequently assigns the vulnerability index. The proposed procedure has been employed on the existing building portfolio in an extended area of the municipality of Bisceglie, Puglia, Southern Italy, for which all the modules have been tested and, above all, the machine-learning models of Bi VULMA have been trained. After, in order to test the efficiency and the reliability of the proposed tools, the entire procedure has been applied on five case study buildings. The results in terms of vulnerability index have been compared with the manual computations performed by the authors applying the same algorithm. Despite the proposed tool could be improved or modified in some of its modules, the obtained results show a good effectiveness of VULMA , which opens new scenarios in the field of vulnerability assessment procedures and risk mitigation strategies. • Proposal of a framework for the vulnerability analysis of existing building starting from a photo: VULMA ; • Definition of the four modules characterizing VULMA: Street VULMA , Data VULMA , Bi VULMA and In VULMA ; • Application and training of the proposed procedure to a dataset extracted from a municipality of Southern Italy and testing and validation of the tool; • Assessment and proposal of VULMA as new instrument for the definition of the vulnerability response of individual buildings and for the seismic risk estimate at large-scale. [ABSTRACT FROM AUTHOR]

This paper considers the modernization of an existing non-commercial Pulse Duplicator in use at the Healing Research Laboratory at the University of Padova, Italy. The system reproduces human systemic circulation and it is used to test heart medical devices. The focus of this study is the full automation of a crucial system component that is the peripheral resistance manual valve that is replaced by a motorized one. First, under certain technological constraints, the problem of the automatic setting adjustment of the valve is tackled by using a Sliding Mode Extremum Seeking Control (ESC) method. This approach guarantees the system fundamental pressure drop to simulate the peripheral resistance to flow in the human systemic circulation in various system configurations and operating conditions. Then, the Sliding Mode ESC algorithm is embedded in an Arduino board driving the motorized valve. Finally, experimental tests are performed to assess the effectiveness of the motorized valve. [ABSTRACT FROM AUTHOR]