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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]

This paper investigates the possibility of the implementation of Genetic Programming (GP) algorithm on a publicly available COVID-19 data set, in order to obtain mathematical models which could be used for estimation of confirmed, deceased, and recovered cases and the estimation of epidemiology curve for specific countries, with a high number of cases, such as China, Italy, Spain, and USA and as well as on the global scale. The conducted investigation shows that the best mathematical models produced for estimating confirmed and deceased cases achieved R2 scores of 0.999, while the models developed for estimation of recovered cases achieved the R2 score of 0.998. The equations generated for confirmed, deceased, and recovered cases were combined in order to estimate the epidemiology curve of specific countries and on the global scale. The estimated epidemiology curve for each country obtained from these equations is almost identical to the real data contained within the data set [ABSTRACT FROM AUTHOR]

Abstract: The paper deals with the design and realization of a hexapod rover prototype completely manufactured by students and researchers of the Space Robotics Group of the II Faculty of Engineering of the University of Bologna “ALMA MATER”. The rover project has been developed for didactical purposes, with the aim of involving students in practical, hands-on education, pushing them to face real problems and to put in practice what they have learnt in theory during regular courses. The work done is described in the paper, highlighting its potential to test different solutions in autonomous navigation systems: low-cost sensors, innovative algorithms and different step procedures. Moreover, the mechanical and electronic solutions adopted for leg design, main controller, and remote control are discussed and depicted in the paper. [Copyright &y& Elsevier]

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]

A simplified version of TOMGRO, a well-known model for tomato growth simulation, was delivered by its authors in 1999, with the aim to adapt the program to operational exigencies. This model version was chosen to integrate the functions of a decision support tool, oriented to support Italian tomato growers in soilless crop fertigation management. During the preliminary evaluation phase, its application on data collected in three greenhouse experiments conducted on tomato cv. Jama in Pisa (central Italy) in 2004 and 2005, showed an insufficient accuracy in the Leaf Area Index (LAI) estimation of tomato crops. Consequently, node number and LAI computational algorithms were modified. This paper discusses model modifications, analysing their effects on tomato growth simulation. In comparison with the observed data, the modified model showed a good enhancement of estimation accuracy of node and LAI, with significantly positive effects also on plant and fruit biomass estimation. [ABSTRACT FROM AUTHOR]

In this paper is presented a methodological approach which integrates statistic modelling and 2-D cellular automata (CA) in order to describe tree species shifts responding to the climate changes foreseen for Italy in the 21st century. Five Italian tree species populations of Abies alba, Pinus sylvestris, Fagus sylvatica, Acer campestris and Quercus suber and their actual potential distributions (PDs) – represented by Importance Value (IV), have been considered. Environmental and climatic relationships have been modelled through application of a new statistical methodology called extreme discretization, where the PD of a species was considered as a random field. The IV-based PD has been spatialized through a probability function π(A,S), which represented the spatio-temporal relationships between IV values and climatic (A) and geo-morphological (S) variables. For each tree species π =(A,S) has been estimated and inserted as rule in the 2-D cellular automata. The latter, acting by a Moore neighbouring, took in consideration also the suitability map for tree species, which has been obtained by land cover map. Two time frames (2050 and 2080) and two climatic scenarios (A2 and B1) have been considered. Results described a general reduction of the IV values and their distribution for A. alba, P. sylvestris and F. sylvatica, in both climatic scenarios, whereas an increase of IVs and distribution for Q. suber and only a slight increment of distribution for A. campestris was mainly observed under the B1 scenario, but not for the more limiting A2 scenario. Convergent results have been obtained with respect to other simulation systems concerning the shift of tree species responding to different climatic change scenarios but lacking of the description of dynamical paths. Our approach seems natural and practical to describe such phenomena. The transition rules for the CA and the parameters taken into account for the construction of the probabilistic models can be surely improved to obtain a more realistic pattern of tree species shifts. Future efforts should be made to take in account the inter-specific relationships inside the Italian forest ecosystems, in order to also consider the competiveness for resources that exert some effects on the plant distribution both in time and space. [Copyright &y& Elsevier]

Abstract: The Medicina Radioastronomical Station is located nearby Bologna, in Italy. It consists of two receiving antennas currently dedicated to the astronomical research at radio frequencies. The 32m diameter parabolic dish performs observations from 1.4 to 22GHz whereas the Northern Cross (a 30.000m2 wide T-shaped array transit antenna) works at 408MHz. So far SETI observations have been performed using a SERENDIP IV high resolution spectrometer connected to the parabolic antenna. Data acquisition were performed meanwhile the antenna was employed in ordinary astronomical observations (piggy-back mode). An innovative method to search for possible extraterrestrial signals could be provided by using the UHF Northern Cross transit telescope. In this paper observational modalities and the required technological set-up are investigated. [Copyright &y& Elsevier]

This paper deals with the problem of supervising and monitoring a photovoltaic (PV) plant. First, an offline descriptive and inferential statistical procedure for evaluating the goodness of system performance is presented. Then, an online inferential algorithm for real-time monitoring and fault detection is introduced. The two methodologies utilize the energy output of inverters as input data and are valid for both Gaussian and non-normal distribution of data. The procedures have been tested on a real PV installation, and results are reported for the case of a grid-connected PV plant in Italy for which one PV module over 132 resulted in being badly connected. [ABSTRACT FROM AUTHOR]

Dealing with the evaluation of the risk connected to the formation of landslide dams at regional scale, it is important to estimate the volume of the depleted material that can reach the riverbed. This information, combined with other elements (river dimensions, valley width, landslide velocity, etc.) allows making predictions on the possibility of river blockage. One of the problems of this approach is the lack of data concerning the shape and position of the sliding surface; this does not permit us to estimate the volume of the landslide material. The IFFI (Inventario dei Fenomeni Franosi in Italia, i.e. Landslide Inventory in Italy) project furnishes information, at different levels of precision, on nearly totality of the landslides existing in Italy. The first level of the IFFI (compiled for all slides) does not contain information on the depth of the sliding surface but contains data regarding the type and the activity of the slope movement. Along with this information the IFFI project also furnishes vector maps containing the boundary of each landslide and the main sliding direction. This paper describes the implementation of an algorithm aimed to define, with an adequate approximation, the 3D geometry of the sliding surface of rotational slides for which, on the basis of geologic maps available at regional scale, some geotechnical parameters can be known or estimated. The work also required the creation of a computer code useful for the 3D analysis of slope stability (3D safety factor) using the simplified Janbu method. All computer code has been created on a GNU-Linux OS and using shell scripting, based on GRASS GIS and R statistical software. [ABSTRACT FROM AUTHOR]

Abstract: Since climate trends are getting considerable attention in recent years, we aimed in this study to compare trends and rhythms of complexity (fractal dimension, FD) of rainfall data series between two continents: Latin America and Europe. Two parallel nonlinear methods for calculating FD of a temporal data series, Higuchi''s and consecutive differences, were combined with Fast Fourier Transform (FFT) to obtain FD oscillations of monthly accumulated rainfall. The data were collected for the last thirty years in Pastaza province, Ecuador and Veneto province, Italy. In order to calculate their FD time dependence, FD(t), moving windows of different lengths (short, 10–20 and long 21–350 samples), were applied. Both methods, each combined with FFT, detected identical (or very similar) rhythms of detrended FD(t) in the two data series, but frequencies with dominant amplitudes differed (4.4 years in Ecuador, 10.3 years in Italy). Long-term FD(t) trends were also studied using optimized long window lengths (~200 samples). A linear positive trend was obtained for the Ecuadorian rainfall data over the whole recorded period. Italian fractal trend profile was, however, characterized by two periods: a constant high value for years 1974–1993, followed by a linear decrease for 1993–2005. Trend results, obtained with two different methods, were also similar. Accordance of the results, reported in the present paper by applying these two methods, validates their use as a tool in future fractal meteorological measurements. As well, these results indicate that positive FD trend obtained for Pastaza (Ecuador) and negative trend computed for Veneto (Italy) account for a local or regional phenomenon, most probably caused by extensive deforestation and land use change (Ecuador) and continental or global atmospheric pattern variability (Italy). [Copyright &y& Elsevier]

In this paper, we propose an analysis on the joint effect of hyperspectral and light detection and ranging (LIDAR) data for the classification of complex forest areas. In greater detail, we present: 1) an advanced system for the joint use of hyperspectral and LIDAR data in complex classification problems; 2) an investigation on the effectiveness of the very promising support vector machines (SVMs) and Gaussian maximum likelihood with leave-one-out-covariance algorithm classifiers for the analysis of complex forest scenarios characterized from a high number of species in a multisource framework; and 3) an analysis on the effectiveness of different LIDAR returns and channels (elevation and intensity) for increasing the classification accuracy obtained with hyperspectral images, particularly in relation to the discrimination of very similar classes. Several experiments carried out on a complex forest area in Italy provide interesting conclusions on the effectiveness and potentialities of the joint use of hyperspectral and LIDAR data and on the accuracy of the different classification techniques analyzed in the proposed system. In particular, the elevation channel of the first LIDAR return was very effective for the separation of species with similar spectral signatures but different mean heights, and the SVM classifier proved to be very robust and accurate in the exploitation of the considered multisource data. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we introduce the concept of a priority curve associated with a video. We then provide an algorithm that can use the priority curve to create a summary (of a desired length) of any video. The summary thus created exhibits nice continuity properties and also avoids repetition. We have implemented the priority curve algorithm (PriCA) and compared it with other summarization algorithms in the literature with respect to both performance and the output quality. The quality of summaries was evaluated by a group of 200 students in Naples, Italy, who watched soccer videos. We show that PriCA is faster than existing algorithms and also produces better quality summaries. We also briefly describe a soccer video summarization system we have built on using the PriCA architecture and various (classical) image processing algorithms. [Copyright &y& Elsevier]

This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100 x 100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements. [ABSTRACT FROM AUTHOR]

The article presents computing survey's electronic symposium on the theory of computation. The 25th anniversary of the European Association for the Theoretical Computing Science (EATCS) was celebrated during the 24th session of the "International Colloquium on Automata, Languages and Programming (ICALP) Bologna, Italy, from July 7-11, 1997. More than 300 scientists participated in the conference and its satellite events. There were many stimulating discussing about the role of theory in computer science. Papers of the symposium are divided into subareas such as algorithms, analysis, concurrency, formal methods, syntax and semantics and complexity of cognitive models. Algorithms area includes papers on approximate algorithms, algorithm engineering and online algorithms. The analysis area includes papers on program analysis based on game semantics and on abstract interpretation. The two papers on the complexity of cognitive models deal with simulating free will and simulating the mind.

Background: Case-finding tools, such as the Identify Chronic Migraine (ID-CM) questionnaire, can improve detection of CM and alleviate its significant societal burden. We aimed to develop and validate the Italian version of the ID-CM (ID-EC) in paper and as a smart app version in a headache clinic-based setting.Methods: The study investigators translated and adapted to the Italian language the original ID-CM questionnaire (ID-EC) and further implemented it as a smart app. The ID-EC was tested in its paper and electronic version in consecutive patients referring to 9 Italian tertiary headache centers for their first in-person visit. The scoring algorithm of the ID-EC paper version was applied by the study investigators (case-finding) and by patients (self-diagnosis), while the smart app provided to patients automatically the diagnosis. Diagnostic accuracy of the ID-EC was assessed by matching the questionnaire results with the interview-based diagnoses performed by the headache specialists during the visit according to the criteria of International Classification of Headache Disorders, III edition, beta version.Results: We enrolled 531 patients in the test of the paper version of ID-EC and 427 in the validation study of the smart app. According to the clinical diagnosis 209 patients had CM in the paper version study and 202 had CM in the smart app study. 79.5% of patients returned valid paper questionnaires, while 100% of patients returned valid and complete smart app questionnaires. The paper questionnaire had a 81.5% sensitivity and a 81.1% specificity for case-finding and a 30.7% sensitivity and 90.7% specificity for self-diagnosis, while the smart app had a 64.9% sensitivity and 90.2% specificity.Conclusions: Our data suggest that the ID-EC, developed and validated in tertiary headache centers, is a valid case-finding tool for CM, with sensitivity and specificity values above 80% in paper form, while the ID-EC smart app is more useful to exclude CM diagnosis in case of a negative result. Further studies are warranted to assess the diagnostic accuracy of the ID-EC in general practice and population-based settings. [ABSTRACT FROM AUTHOR]

This paper describes a new rainfall rate retrieval algorithm, developed within the EUMETSAT H SAF program, based on the Passive microwave Neural network Precipitation Retrieval approach (PNPR v3), designed to work with the conically scanning Global Precipitation Measurement (GPM) Microwave Imager (GMI). A new rain/no-rain classification scheme, also based on the NN approach, which provides different rainfall masks for different minimum thresholds and degree of reliability, is also described. The algorithm is trained on an extremely large observational database, built from GPM global observations between 2014 and 2016, where the NASA 2B-CMB (V04) rainfall rate product is used as reference. In order to assess the performance of PNPR v3 over the globe, an independent part of the observational database is used in a verification study. The good results found over all surface types (CC > 0.90, ME < −0.22 mm h−1, RMSE < 2.75 mm h−1 and FSE% < 100% for rainfall rates lower than 1 mm h−1 and around 30–50% for moderate to high rainfall rates), demonstrate the good outcome of the input selection procedure, as well as of the training and design phase of the neural network. For further verification, two case studies over Italy are also analysed and a good consistency of PNPR v3 retrievals with simultaneous ground radar observations and with the GMI GPROF V05 estimates is found. PNPR v3 is a global rainfall retrieval algorithm, able to optimally exploit the GMI multi-channel response to different surface types and precipitation structures, that provide global rainfall retrieval in a computationally very efficient way, making the product suitable for near-real time operational applications. [ABSTRACT FROM AUTHOR]

Standard chlorophyll-a (chl-a) algorithms, which rely on Moderate Resolution Imaging Spectro-radiometer (MODIS) data aboard the Aqua satellite, usually show different performances depending on the area under consideration. In this paper, we assessed their accuracy in retrieving the chl-a concentration in the Basilicata Ionian Coastal waters (Ionian Sea, South of Italy). The outputs of one empirical (Med-OC3) and two semi-analytical algorithms, the Garver–Siegel–Maritorena (GSM) and the Generalized Inherent Optical Properties (GIOP) model, have been compared with ground measurements acquired during three different measurement campaigns. The achieved results prove the poor accuracy (adjusted R2 value of 0.12) of the investigated empirical algorithm and, conversely, the good performance of semi-analytical algorithms (adjusted R2 ranging from 0.74 to 0.79). The co-existence of Coloured Dissolved Organic Matter (CDOM) and Non-Algal Particles (NAP) has likely determined large errors in the reflectance ratios used in the OCx form algorithms. Finally, a local scale assessment of the bio-optical properties, on the basis of the in situ dataset, allowed for the definition of an operational local scale-tuned version of the MODIS chl-a algorithm, which assured increased accuracy (adjusted R2 value of 0.86). Such a tuned algorithm version can provide useful information which can be used by local authorities within regional management systems. [ABSTRACT FROM AUTHOR]