Search

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]

All societies should carefully address the child abuse and neglect phenomenon due to its acute and chronic sequelae. Even if artificial intelligence (AI) implementation in this field could be helpful, the state of the art of this implementation is not known. No studies have comprehensively reviewed the types of AI models that have been developed/validated. Furthermore, no indications about the risk of bias in these studies are available. For these reasons, the authors conducted a systematic review of the PubMed database to answer the following questions: "what is the state of the art about the development and/or validation of AI predictive models useful to contrast child abuse and neglect phenomenon?"; "which is the risk of bias of the included articles?". The inclusion criteria were: articles written in English and dated from January 1985 to 31 March 2023; publications that used a medical and/or protective service dataset to develop and/or validate AI prediction models. The reviewers screened 413 articles. Among them, seven papers were included. Their analysis showed that: the types of input data were heterogeneous; artificial neural networks, convolutional neural networks, and natural language processing were used; the datasets had a median size of 2600 cases; the risk of bias was high for all studies. The results of the review pointed out that the implementation of AI in the child abuse and neglect field lagged compared to other medical fields. Furthermore, the evaluation of the risk of bias suggested that future studies should provide an appropriate choice of sample size, validation, and management of overfitting, optimism, and missing data. [ABSTRACT FROM AUTHOR]

Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model's accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously. [ABSTRACT FROM AUTHOR]

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy. [ABSTRACT FROM AUTHOR]

• SeSAME system for collecting and analysing the behaviours of visitors inside a museum environment. • SeSAME is based on a multi-modal deep neural network architecture. • SeSAME uses re-identification techniques to perform visual profiling of visitor interest. • SeSAME aims at developing a tailored user experience in museum environment. Nowadays, understanding and analysing visitors activities and behaviours is becoming imperative for personalising and improving the user experience in a museum environment. Users' behaviour can provide important statistics, insights and objective information about their interactions, such as attraction, attention and action. These data represent a precious value for the museum curators, and they are one of the parameters that need to be assessed. These information are collected through manual approaches based on questionnaires or visual observations. This procedure is time consuming and can be affected by the subjective interpretation of the evaluator. From such premises, SeSAME (Senseable Self Adapting Museum Environment) a novel system for collecting and analysing the behaviours of visitors inside a museum environment is presented in this paper. SeSAME is based on a multi-modal deep neural network architecture able to extract anthropometric and appearance features from RGB-D videos acquired in crowded environments. Our approach has been tested on four different temporal modelling methods to aggregate a sequence of image-level features into clip-level features. This paper uses as a benchmark TVPR2, a public dataset of acquired videos with an RGB-D camera in a top-view configuration, in the presence of persistent and temporarily heavy occlusion. Moreover, a dataset specifically collected for this work has been acquired in a real museum environment, which is Palazzo Buonaccorsi, an important historical building in Macerata, in Marche Region in the center of Italy. During the experimental phase, the evaluation metrics show the effectiveness and the suitability of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Valori e Valutazioni is the property of Societa Italiana di Estimo e Valutazione (SIEV) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

COVID-19 is likely to be the 2020s' deadliest pandemic according to the World Health Organization (WHO). There have been more than 3.7 million confirmed deaths after 15 months spread. Besides the loss of human lives, COVID-19 has other unfavorable impacts on society, education, and the economy. Due to successive lockdowns and the continuous quarantines, the demands on power resources have reduced. Therefore, there is a need to investigate the impacts of the COVID-19 pandemic on electricity prices (EP). In this paper, a set of six economic factors that are affected by COVID-19 and affect EP are considered. These factors were fed into a functional link artificial neural network (FLANN) to model the relationships between them and the EP. An empirical equation was formulated to help decision makers and strategic developers in the electricity markets come up with more appropriate plans. Italy, the Czech Republic, and China were used as case studies in this research. [ABSTRACT FROM AUTHOR]

At the outlet of steep catchments, depositional processes ranging from stream flow to debris flow usually lead to alluvial fan development. Apart from geological and tectonic factors controlling basin sediment availability, several authors highlighted the control role of basin/fan morphometry on fan sedimentary processes. In this framework, the paper was aimed to identify the feeder basin variables that can best differentiate fan processes in Southern Italy. To evaluate the effect of the statistical method on variable selection, we compared logistic regression (LR) and artificial neural network (ANN), the latter not commonly used in fan studies. Alluvial fans were mapped at the mouth of steep V-shaped valleys dissecting the Tyrrhenian coast of northern Calabria, where crystalline-metamorphic and subordinate carbonate rocks crop out. Fans were classified through field survey into two groups: those with (F1), and those without (F0) any debris-flow evidence. Morphometric variables were derived for each basin/fan system. Percentage of lithological units cropping out in the catchments was also considered. Non-parametric statistics revealed that F0 and F1 significantly differ in fan size (area, perimeter and length), main channel slope, lowermost valley width, Melton's number and geologic index. The relationships between morphometric variables were stronger for F0 than F1. The LR and ANN highlighted the primary control of basin lithology on fan dynamics, followed by basin mean slope. Although ANN outperformed LR in model calibration, both the approaches correctly classified most of the validation samples (> 87%). Alluvial fans with unknown depositional process were classified as belonging to the same group. [ABSTRACT FROM AUTHOR]

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks. [ABSTRACT FROM AUTHOR]

In this paper, a method is presented to classify volcano activity into three classes, namely quiet, strombolian, and paroxysm. The method is based on training a six-layered deep neural network (DNN) model using these signals as inputs (features): time series of the number of distances of infrasonic events, radar backscatter power, RMS of tremor in five stations close to craters of the volcano, tilt derivative, and seismic tremor source depth. The method was tested on the data related to a period of five years, and the results were concluded using indexes of precision, recall, F1 score, and Cohen's Kappa coefficient were calculated to evaluate the qualification of the classification. Also, the results were compared to Bayesian network (BN), K-nearest neighbors (KNN), and decision tree (DT) methods. Decision learning trees and KNN are popular machine learning algorithms belonging to the class of supervised learning algorithms. They mimic the human level thinking and, differing from neural networks, are not black box models. The comparisons reveal the proposed method, especially in classifying both strombolian and paroxysm classes. This advantage makes the presented method a more reliable tool for practical use in the volcano monitoring control rooms. [ABSTRACT FROM AUTHOR]

This paper presents the results obtained by the elaboration of an artificial neuronal network for the creation of a rockfall susceptibility map. The analysis was carried out by analysing the predisposing and triggering factors of the rockfall phenomenon. The parameters considered for this study and representing the input data of the artificial neural network are factors such as: gradient, soil use, lithology, rockfall source areas and kinetic energy values obtained by considering the probable pathways of the blocks through simulations with dedicated softwares, DEMs and niches of the rockfalls that have already occurred in the past. The processing of this data (required in a versatile dedicated software for the realization of the artificial neural network in ASCII format) is done using GIS softwares, useful tools for the creation of hazard maps. An important step is the realization of the rockfall inventory map: it allows to identify the training set (consisting of 50% of the pixels relative to the rockfall niches) for the network training and the testing set (considering the remaining 50% of the pixels relative to the rockfall niches) to assess the network accuracy by overlaying the rockfall niches belonging to the testing set with the obtained susceptibility map. [ABSTRACT FROM AUTHOR]

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach's effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy. • We propose a multi-source time-series fusion methodology relying on Deep Learning. • We design a robust framework for daily 7-days-horizon predictions. • We assess the proposed approach on a real medical bookings dataset. [ABSTRACT FROM AUTHOR]

Variations in the CO2 dissolved in water springs have long been observed near the epicenters of moderate and strong earthquakes. In a recent work focused on data collected during the 2017–2021 period from a monitoring site in the Northern Apennines, Italy, we noticed a significant correlation between CO2 anomalies and moderate-to-weak seismic activity. Here, we extended this analysis by focusing on data collected from the same site during a different period (2010–2013) and by integrating the CENSUS method with an artificial neural network (ANN) in the already-tested protocol. As in our previous work, a fit of the computed residual CO2 distributions allowed us to evidence statistically relevant CO2 anomalies. Thus, we extended a test of the linear dependence of these anomalies to seismic events over a longer period by means of binary correlations. This new analysis also included strong seismic events. Depending on the method applied, we observed different time lags. Specifically, using the CENSUS methodology, we detected a CO2 anomaly one day ahead of the earthquake and another anomaly eleven days ahead. However, no anomaly was observed with the ANN methodology. We also investigated possible correlations between CO2 concentrations and rain events and between rain events and earthquakes, highlighting the occurrence of a CO2 anomaly one day after a rain event of at least 10 mm and no linear dependence of seismic and rain events. Similar to our previous work, we achieved a probability gain of around 4, which is the probably of earthquake increases after CO2 anomaly observations. [ABSTRACT FROM AUTHOR]

Monitoring soil organic carbon (SOC) typically assumes conducting a labor-intensive soil sampling campaign, followed by laboratory testing, which is both expensive and impractical for generating useful, spatially continuous data products. The present study leverages the power of machine learning (ML) and, in particular, deep neural networks (DNNs) for segmentation, as well as satellite imagery, to estimate the SOC remotely. We propose a new two-stage pipeline for remote SOC estimation, which relies on using a DNN trained to classify land cover to perform feature extraction, while the SOC estimation is performed by a different ML model. The first stage is an image segmentation DNN with the U-Net architecture, which is trained to estimate the land cover for an observed geographical region, based on multi-spectral images taken by the Sentinel-2 satellite constellation. This estimator is subsequently used to extract the latent feature vector for each of the output pixels, by rolling back from the output (dense) layer of the U-Net and accessing the last available convolutional layer of the same dimension as our desired output. The second stage is trained on a set of feature vectors extracted at the coordinates for which manual SOC measurements exist. We tested a variety of ML models and report on their performance. Using the best extremely randomized trees model, we generated a spatially continuous map of SOC estimations for the region of Tuscany, in Italy, with a resolution of 10 m, to share with the researchers as a means of validating the results and to demonstrate the efficiency of the proposed approach, which can can easily be scaled to create a global continuous SOC map. [ABSTRACT FROM AUTHOR]

This paper focuses on whether the on-going dramatic decrease in alcohol consumption in Italy, especially of wine, during 1961-2008, was associated with which parallel sociodemographic and economic changes and with alcohol control policies. The study, using both time series (TS) and artificial neural network (ANN)-based analyses documents that its selected sociodemographic and economic factors, and particularly urbanization, had a definite connection with wine consumption decrease, spirits decrease, and the increase in beer consumption over time. On the other hand, control policies showed no effect on the decline in alcohol consumption, since no alcohol control policy existed in Italy between 1960 and 1987. A few policies introduced since 1988 (BAC and sale restrictions during mass events) may have contributed to reducing or to maintaining the on-going reduction. Study limitations are noted and future needed research is suggested. [ABSTRACT FROM AUTHOR]

This study introduces a novel hybrid forecasting model for wind power generation. It integrates Artificial Neural Networks, data clustering, and Particle Swarm Optimization algorithms. The methodology employs a systematic framework: initial clustering of weather data via the k-means algorithm, followed by Pearson's analysis to pinpoint pivotal elements in each cluster. Subsequently, a Multi-Layer Perceptron Artificial Neural Network undergoes training with a Particle Swarm Optimization algorithm, enhancing convergence and minimizing prediction discrepancies. An important focus of this study is to streamline wind forecasting. By judiciously utilizing only sixteen observation points near a wind farm plant, in contrast to the complex global numerical weather prediction systems employed by the European Center Medium Weather Forecast, which rely on thousands of data points, this approach not only enhances forecast accuracy but also significantly simplifies the modeling process. Validation is performed using data from the Italian National Meteorological Centre. Comparative assessments against both a persistence model and actual wind farm data from Southern Italy substantiate the superior performance of the proposed hybrid model. Specifically, the clustered Particle Swarm Optimization-Artificial Neural Network-Wind Forecasting Method demonstrates a noteworthy improvement, with a reduction in mean absolute percentage error of up to 59.47% and a decrease in root mean square error of up to 52.27% when compared to the persistence model. [ABSTRACT FROM AUTHOR]

In this paper was used the data set collected in a research project between private companies from Romania and Italy, for the development of a basic approach of artificial neural network techniques, as an application in Matlab, aiming to detect the degree of degradation of oil, an automated installation, measuring online the physicochemical properties of the oil. Physical-chemical parameters measured lead to the creation of generous time series, but accessible by numerical and statistical calculation, for the application of artificial intelligence techniques. Applying neural network techniques to parameters that measure oil degradation, oxidation and humidity have generated the results of this work. The main function of monitoring the state of operation of a mechanical system, machine, or plant is to provide the almost correct diagnosis of the machine's state and rate of change so that preventive measures can be taken at a given time. [ABSTRACT FROM AUTHOR]

School buildings in Italy are outdated, in critical maintenance conditions and they often perform below acceptable service levels and quality standards. Nevertheless, data supporting renovation policies are missing or very expensive to be obtained. The paper presents a method for evaluating building's energy savings potential, using the Building Energy Certification (Certificazione Energetica degli Edifici - CENED) open database. The aim of the research concerns the development of a data-driven set of methods, based on the use of open data, machine learning (ML) and Geographic Information Systems (GIS) to support regional energy retrofit policies on school buildings. The main advantage concerns the possibility to predict the post-retrofit energy savings, avoiding the expensive on-site Condition Assessment (CA) phase. Data have been first clustered to identify the most common thermo-physical properties of the envelope, then three retrofit scenarios have been defined, to allow the retrofit of homogeneous types of buildings. The energy saving potentials have been evaluated through the implementation of eight Artificial Neural Networks. Ultimately, data have been geolocated and further processed to support the definition of the energy retrofit policies for the most critical regional areas. The Lombardy region has been chosen as case study to test the robustness of the proposed methods. The results of the case study proved that school buildings energy retrofit policies can be supported and defined using available open data, ML and GIS. The future developments of the research concern the further integration of GIS for retrofit cost assessment and scenario analysis. • Open-data, machine learning and spatial analyses support regional energy policies. • Eight Neural Networks are used to compute energy savings in three retrofit scenarios. • Data are geolocated and processed to guide the regional retrofit policy. • The retrofit policy is defined avoiding expensive on-site Condition Assessment. [ABSTRACT FROM AUTHOR]

One of the problems of wound specialists is that they may not have enough time to assess the length and width of the wound. This problem is aggravated when they have to visit the wounds virtually. This paper presents a novel AI-based method for evaluation of the wound healing process. In this algorithm, a neural network is used to detect a combination of color spectra next to each other and a neural network is used to detect the distance of the wound from the camera via hand detection. Finally, the dimensions of the wound are obtained along with the analysis of its texture. The neural networks used are a deep networks and use intensive learning algorithms with less computational complexity than deep networks, which can be implemented on mobile processors as well. Networks are modified in a way that the additional connections between different nodes are removed without any change in performance. In this way, time and computational complexity are reduced. Then through the server, images of the wound and its analysis can be sent to different users, including doctors and medical centers. [ABSTRACT FROM AUTHOR]

Insect pollinators provide an important ecosystem service that supports global biodiversity and environmental health. The study investigates the effects of the environmental matrix on six oxidative stress biomarkers in the honey bee Apis mellifera. Thirty-five apiaries located in urban, forested, and agricultural areas in Central Italy were sampled during the summer season. Enzyme activities in forager bees were analyzed using an artificial neural network, allowing the identification and representation of the apiary patterns in a Self-Organizing Map. The SOM nodes were correlated with the environmental parameters and tissue levels of eight heavy metals. The results indicated that the apiaries were not clustered according to their spatial distribution. Superoxide dismutase expressed a positive correlation with Cr and Mn concentrations; catalase with Zn, Mn, Fe, and daily maximum air temperature; glutathione S-transferase with Cr, Fe, and daily maximal air temperature; and glutathione reductase showed a negative correlation to Ni and Fe exposure. This study highlights the importance of exploring how environmental stressors affect these insects and the role of oxidative stress biomarkers. Artificial neural networks proved to be a powerful approach to untangle the complex relationships between the environment and oxidative stress biomarkers in honey bees. The application of SOM modeling offers a valuable means of assessing the potential effects of environmental pressures on honey bee populations. [ABSTRACT FROM AUTHOR]

Wind energy has shown significant growth in terms of installed power in the last decade. However, one of the most critical problems for a wind farm is represented by Operation and Maintenance (O&M) costs, which can represent 20–30% of the total costs related to power generation. Various monitoring methodologies targeted to the identification of faults, such as vibration analysis or analysis of oils, are often used. However, they have the main disadvantage of involving additional costs as they usually entail the installation of other sensors to provide real-time control of the system. In this paper, we propose a methodology based on machine learning techniques using data from SCADA systems (Supervisory Control and Data Acquisition). Since these systems are generally already implemented on most wind turbines, they provide a large amount of data without requiring extra sensors. In particular, we developed models using Artificial Neural Networks (ANN) to characterize the behavior of some of the main components of the wind turbine, such as gearbox and generator, and predict operating anomalies. The proposed method is tested on real wind turbines in Italy to verify its effectiveness and applicability, and it was demonstrated to be able to provide significant help for the maintenance of a wind farm. [ABSTRACT FROM AUTHOR]

One way to reduce road crashes is to determine the main influential factors among a long list that are attributable to driver behavior, environmental conditions, vehicle features, road type, and traffic signs. Hence, selecting the best modelling tool for extracting the relations between crash factors and their outcomes is a crucial task. To analyze the road crash data of Milan City, Italy, gathered between 2014–2017, this study used artificial neural networks (ANNs), generalized linear mixed-effects (GLME), multinomial regression (MNR), and general nonlinear regression (NLM), as the modelling tools. The data set contained 35,182 records of road crashes with injuries or fatalities. The findings showed that unbalanced and incomplete data sets had an impact on outcome performance, and data treatment methods could help overcome this problem. Age and gender were the most influential recurrent factors in crashes. Additionally, ANNs demonstrated a superior capability to approximate complicated relationships between an input and output better than the other regression models. However, they cannot provide an analytical formulation, but can be used as a baseline for other regression models. Due to this, GLME and MNR were utilized to gather information regarding the analytical framework of the model, that aimed to construct a particular NLM. [ABSTRACT FROM AUTHOR]

Accurate rainfall estimation is crucial to adequately assess the risk associated with extreme events capable of triggering floods and landslides. Data gathered from Rain Gauges (RGs), sensors devoted to measuring the intensity of the rain at individual points, are commonly used to feed interpolation methods (e.g., the Kriging geostatistical approach) and estimate the precipitation field over an area of interest. However, the information provided by RGs could be insufficient to model complex phenomena, and computationally expensive interpolation methods could not be used in real-time environments. Integrating additional data sources (e.g., radar and geostationary satellites) is an effective solution for improving the quality of the estimate, but it needs to cope with Big Data issues. To overcome all these issues, we propose a Rainfall Estimation Model (REM) based on an Ensemble of Deep Neural Networks (DeepEns-REM) that can automatically fuse heterogeneous data sources. The usage of Residual Blocks in the base models and the adoption of a Snapshot procedure to build the ensemble guarantees a fast convergence and scalability. Experimental results, conducted on a real dataset concerning a southern region in Italy, demonstrate the quality of the proposal in comparison with the Kriging interpolation technique and other machine learning techniques, especially in the case of exceptional rainfall events. [ABSTRACT FROM AUTHOR]

In the hydraulic construction field, approximated formulations have been widely used for calculating tank volumes. Identifying the proper water reservoir volumes is of crucial importance in order to not only satisfy water demand but also to avoid unnecessary waste in the construction phase. In this perspective, the planning and management of small reservoirs may have a positive impact on their spatial distribution and storage capacities. The purpose of this study is, therefore, to suggest an alternative approach to estimate the optimal volume of small urban reservoirs. In particular, an artificial neural network (ANN) is proposed to predict future water consumption as a function of certain environmental parameters, such as rainy days, temperature and the number of inhabitants. As the water demand is strongly influenced by such quantities, their future trend is recovered by means of the Copernicus Climate Change Service (C3S) over the next 10 years. Finally, based on ANN prediction of the future consumption requirements, the continuity equation applied to tanks was resolved through integral-discretization obtaining the time-series volume variation and the total number of crisis events. [ABSTRACT FROM AUTHOR]

The new European targets of achieving net zero emissions by 2050 have spurred Italy to aim for a 30% reduction in emissions by 2030, compared with 2005 levels. This goal will be achieved through the promotion of renewable energy sources and energy savings in the residential sector, which remains one of the main sectors accountable for total energy consumption, mainly for heating. This study aims at investigating the potential of some retrofit measures implemented in the Umbria Region, chosen as a case study, to reach the goal by 2030. Using parametric energy simulations with the standard calculation method and artificial neural networks (ANN), the energy consumption of Umbria's building stock and potential CO2 reductions were assessed. Results showed that with current energy policies, a reduction of 28% could be achieved, which is below the goal by 2030, while ANN integration within energy strategies could allow reaching it as early as 2025 or 2029, depending on the restriction set to the ANN and the extent of current energy policies. This study confirmed the potential benefits of using advanced technology in achieving national environmental goals, highlighting that they could be essential tools to be integrated into energy policies to accelerate progress towards ambitious climate goals. [ABSTRACT FROM AUTHOR]

The investigation of the determinants of public health expenditure is the focus of a vivid debate among health economists whereas the actual crisis of the welfare systems calls for the adoption of innovative tools to inform rational decisions, in the light of stringent budget constraints. The purpose of this paper is to show the potentialities of Artificial Neural Networks (ANNs) in investigating whether healthcare providers belonging to the same jurisdiction show similarities in their health care expenditure determinants. Similarities are reproduced in terms of fuzzy dependencies between health budgetary data of the healthcare providers belonging to five Italian regions. The analysis carried out sees the application of Auto Contractive Maps (AutoCM) model. The methodology is effective in illustrating regional patterns of expenditure and similarities across Local Health Units (Aziende Sanitarie Locali—ASLs). The results give interesting insights on the presence of notable regional models for health expenditure. [ABSTRACT FROM AUTHOR]

Artificial intelligence is demonstrated by machines, unlike the natural intelligence displayed by animals, including humans. Artificial intelligence research has been defined as the field of study of intelligent agents, which refers to any system that perceives its environment and takes actions that maximize its chance of achieving its goals. The techniques of intelligent computing solve many applications of mathematical modeling. The research work was designed via a particular method of artificial neural networks to solve the mathematical model of coronavirus. The representation of the mathematical model is made via systems of nonlinear ordinary differential equations. These differential equations are established by collecting the susceptible, the exposed, the symptomatic, super spreaders, infection with asymptomatic, hospitalized, recovery, and fatality classes. The generation of the coronavirus model’s dataset is exploited by the strength of the explicit Runge Kutta method for different countries like India, Pakistan, Italy, and many more. The generated dataset is approximately used for training, validation, and testing processes for each cyclic update in Bayesian Regularization Backpropagation for the numerical treatment of the dynamics of the desired model. The performance and effectiveness of the designed methodology are checked through mean squared error, error histograms, numerical solutions, absolute error, and regression analysis. [ABSTRACT FROM AUTHOR]

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant's productivity by not exceeding the process constraints. [ABSTRACT FROM AUTHOR]

Quantification of root-zone soil moisture (RZSM) is crucial for agricultural applications and the soil sciences. RZSM impacts processes such as vegetation transpiration and water percolation. Surface soil moisture (SSM) can be assessed through active and passive microwave remote-sensing methods, but no current sensor enables direct RZSM retrieval. Spatial maps of RZSM can be retrieved via proxy observations (vegetation stress, water storage change and surface soil moisture) or via land surface model predictions. In this study, we investigated the combination of surface soil moisture information with process-related inferred features involving artificial neural networks (ANNs). We considered the infiltration process through the soil water index (SWI) computed with a recursive exponential filter and the evaporation process through the evaporation efficiency computed based on a Moderate Resolution Imaging Spectroradiometer (MODIS) remote-sensing dataset and a simplified analytical model, while vegetation growth was not modeled and was only inferred through normalized difference vegetation index (NDVI) time series. Several ANN models with different sets of features were developed. Training was conducted considering in situ stations distributed in several areas worldwide characterized by different soil and climate patterns of the International Soil Moisture Network (ISMN), and testing was applied to stations of the same data-hosting facility. The results indicate that the integration of process-related features into ANN models increased the overall performance over the reference model level in which only SSM features were considered. In arid and semiarid areas, for instance, performance enhancement was observed when the evaporation efficiency was integrated into the ANN models. To assess the robustness of the approach, the trained models were applied to observation sites in Tunisia, Italy and southern India that are not part of the ISMN. The results reveal that joint use of surface soil moisture, evaporation efficiency, NDVI and recursive exponential filter represented the best alternative for more accurate predictions in the case of Tunisia, where the mean correlation of the predicted RZSM based on SSM only sharply increased from 0.443 to 0.801 when process-related features were integrated into the ANN models in addition to SSM. However, process-related features have no to little added value in temperate to tropical conditions. [ABSTRACT FROM AUTHOR]

Background: Handwriting is an acquired complex cognitive and motor skill resulting from the activation of a widespread brain network. Handwriting therefore may provide biologically relevant information on health status. Also, handwriting can be collected easily in an ecological scenario, through safe, cheap, and largely available tools. Hence, objective handwriting analysis through artificial intelligence would represent an innovative strategy for telemedicine purposes in healthy subjects and people affected by neurological disorders. Materials and Methods: One-hundred and fifty-six healthy subjects (61 males; 49.6 ± 20.4 years) were enrolled and divided according to age into three subgroups: Younger adults (YA), middle-aged adults (MA), and older adults (OA). Participants performed an ecological handwriting task that was digitalized through smartphones. Data underwent the DBNet algorithm for measuring and comparing the average stroke sizes in the three groups. A convolutional neural network (CNN) was also used to classify handwriting samples. Lastly, receiver operating characteristic (ROC) curves and sensitivity, specificity, positive, negative predictive values (PPV, NPV), accuracy and area under the curve (AUC) were calculated to report the performance of the algorithm. Results: Stroke sizes were significantly smaller in OA than in MA and YA. The CNN classifier objectively discriminated YA vs. OA (sensitivity = 82%, specificity = 80%, PPV = 78%, NPV = 79%, accuracy = 77%, and AUC = 0.84), MA vs. OA (sensitivity = 84%, specificity = 56%, PPV = 78%, NPV = 73%, accuracy = 74%, and AUC = 0.7), and YA vs. MA (sensitivity = 75%, specificity = 82%, PPV = 79%, NPV = 83%, accuracy = 79%, and AUC = 0.83). Discussion: Handwriting progressively declines with human aging. The effect of physiological aging on handwriting abilities can be detected remotely and objectively by using machine learning algorithms. [ABSTRACT FROM AUTHOR]

Background: After 2008 global economic crisis, Italian governments progressively reduced public healthcare financing. Describing the time trend of health outcomes and health expenditure may be helpful for policy makers during the resources' allocation decision making process. The aim of this paper is to analyze the trend of mortality and health spending in Italy and to investigate their correlation in consideration of the funding constraints experienced by the Italian national health system (SSN).Methods: We conducted a 20-year time-series study. Secondary data has been extracted from a national, institution based and publicly accessible retrospective database periodically released by the Italian Institute of Statistics. Age standardized all-cause mortality rate (MR) and health spending (Directly Provided Services - DPS, Agreed-Upon Services - TAUS, and private expenditure) were reviewed. Time trend analysis (1995-2014) through OLS and Multilayer Feed-forward Neural Networks (MFNN) models to forecast mortality and spending trend was performed. The association between healthcare expenditure and MR was analyzed through a fixed effect regression model. We then repeated MFNN time trend forecasting analyses on mortality by adding the spending item resulted significantly related with MR in the fixed effect analyses.Results: DPS and TAUS decreased since 2011. There was a mismatch in mortality rates between real and predicted values. DPS resulted significantly associated to mortality (p < 0.05). In repeated mortality forecasting analysis, predicted MR was found to be lower when considering the pre-constraints health spending trend.Conclusions: Between 2011 and 2014, Italian public health spending items showed a reduction when compared to prior years. Spending on services directly provided free of charge appears to be the financial driving force of the Italian public health system. The overall mortality was found to be higher than the predicted trend and this scenario may be partially attributable to the healthcare funding constraints experienced by the SSN. [ABSTRACT FROM AUTHOR]

Since December 2019, the novel coronavirus disease (COVID-19) has had a considerable impact on the health and socio-economic fabric of Italy. The effective reproduction number Rt is one of the most representative indicators of the contagion status as it reports the number of new infections caused by an infected subject in a partially immunized population. The task of predicting Rt values forward in time is challenging and, historically, it has been addressed by exploiting compartmental models or statistical frameworks. The present study proposes an Artificial Neural Networks-based approach to predict the Rt temporal trend at a daily resolution. For each Italian region and autonomous province, 21 daily COVID-19 indicators were exploited for the 7-day ahead prediction of the Rt trend by means of different neural network architectures, i.e., Feed Forward, Mono-Dimensional Convolutional, and Long Short-Term Memory. Focusing on Lombardy, which is one of the most affected regions, the predictions proved to be very accurate, with a minimum Root Mean Squared Error (RMSE) ranging from 0.035 at day t + 1 to 0.106 at day t + 7. Overall, the results show that it is possible to obtain accurate forecasts in Italy at a daily temporal resolution instead of the weekly resolution characterizing the official Rt data. [ABSTRACT FROM AUTHOR]

Huge quantities of pollutants are released into the atmosphere of many cities every day. These emissions, due to physicochemical conditions, can interact with each other, resulting in additional pollutants such as ozone. The resulting accumulation of pollutants can be dangerous for human health. To date, urban pollution is recognized as one of the main environmental risk factors. This research aims to correlate, through soft computing techniques, namely Artificial Neural Networks and Genetic Programming, the data of the tumours recorded by the Local Health Authority of the city of Benevento, in Italy, with those of the pollutants detected in the air monitoring stations. Such stations can monitor many pollutants, i.e. NO2, CO, PM10, PM2.5, O3 and Benzene (C6H6). Assuming possible effects on human health in the medium term, in this work we treat the data relating to pollutants from the 2012–2014 period while, the tumour data, provided by local hospitals, refer to the time interval 2016–2018. The results show a high correlation between the cases of lung tumours and the exceedance of atmospheric particulate matter and ozone. The explicit genetic programming knowledge representation allows also to measure the relevance of each considered pollutant on human health, evidencing the major role of PM10, NO2 and O3. [ABSTRACT FROM AUTHOR]

[Display omitted] • Mapping and monitoring of slow-moving landslides. • Remote Sensing, statistical and ground-based measurements have been combined. • Integration of Machine Learning and InSAR techniques for landslide risk assessment. This paper describes a novel methodology where Machine Learning Algorithms (MLAs) have been integrated to assess the landslide risk for slow moving mass movements, processes whose intermittent activity makes challenging any risk analysis worldwide. MLAs has been trained on datasets including Interferometric Synthetic Aperture Radar (InSAR) and additional remote sensing datasets such as aerial stereo photographs and LiDAR and tested in the Termini-Nerano landslides system (southern Apennines, Italy). The availability of such a wealth of materials allows also an unprecedented spatio-temporal reconstruction of the volume and the kinematic of the landslides system through which we could generate and validate the hazard map. Our analysis identifies fifteen slow-moving phenomena, traceable since 1955, whose total area amounts to 4.1 × 105 m2 and volume to ~1.4 × 106 m3. InSAR results prove that seven out of the fifteen slow-moving landslides are currently active and characterized by seasonal velocity patterns. These new insights on the dynamic of the landslides system have been selected as the main independent variables to train three MLAs (Artificial Neural Network, Generalized Boosting Model and Maximum Entropy) and derive the landslide hazard for the area. Finally, official population and buildings census data have been used to assess the landslide risk whose highest values are located in the crown area, south of Termini village, and nearby Nerano. This new methodology provides a different landslide risk scenario compared to the existing official documents for the study area and overall new insights on how to develop landslide risk management strategies worldwide based on a better understanding of slope processes thanks to the latest satellite technologies available. [ABSTRACT FROM AUTHOR]

Computer Tomography (CT) is currently being adapted for visualization of COVID-19 lung damage. Manual classification and characterization of COVID-19 may be biased depending on the expert's opinion. Artificial Intelligence has recently penetrated COVID-19, especially deep learning paradigms. There are nine kinds of classification systems in this study, namely one deep learning-based CNN, five kinds of transfer learning (TL) systems namely VGG16, DenseNet121, DenseNet169, DenseNet201 and MobileNet, three kinds of machine-learning (ML) systems, namely artificial neural network (ANN), decision tree (DT), and random forest (RF) that have been designed for classification of COVID-19 segmented CT lung against Controls. Three kinds of characterization systems were developed namely (a) Block imaging for COVID-19 severity index (CSI); (b) Bispectrum analysis; and (c) Block Entropy. A cohort of Italian patients with 30 controls (990 slices) and 30 COVID-19 patients (705 slices) was used to test the performance of three types of classifiers. Using K10 protocol (90% training and 10% testing), the best accuracy and AUC was for DCNN and RF pairs were 99.41 ± 5.12%, 0.991 (p < 0.0001), and 99.41 ± 0.62%, 0.988 (p < 0.0001), respectively, followed by other ML and TL classifiers. We show that diagnostics odds ratio (DOR) was higher for DL compared to ML, and both, Bispecturm and Block Entropy shows higher values for COVID-19 patients. CSI shows an association with Ground Glass Opacities (0.9146, p < 0.0001). Our hypothesis holds true that deep learning shows superior performance compared to machine learning models. Block imaging is a powerful novel approach for pinpointing COVID-19 severity and is clinically validated. [ABSTRACT FROM AUTHOR]

• A multicriteria-based approach is used to analyse Risk of Covid-19 contagion. • Data of 257 Apulian urban districts (Italy) support an optimization-based calibration. • The parameters involved in the spread of the Covid-19 in urban districts are quantified. • Artificial Neural Networks are used to study the non-linearity of the phenomenon. • Three different forecasting scenarios are evaluated for the Apulian Covid-19 risk. At the beginning of 2020, the spread of a new strand of Coronavirus named SARS-CoV-2 (COVID-19) raised the interest of the scientific community about the risk assessment related to the viral infection. The contagion became pandemic in few months forcing many Countries to declare lockdown status. In this context of quarantine, all commercial and productive activities are suspended, and many Countries are experiencing a serious crisis. To this aim, the understanding of risk of contagion in every urban district is fundamental for governments and administrations to establish reopening strategies. This paper proposes the calibration of an index able to predict the risk of contagion in urban districts in order to support the administrations in identifying the best strategies to reduce or restart the local activities during lockdown conditions. The objective regards the achievement of a useful tool to predict the risk of contagion by considering socio-economic data such as the presence of activities, companies, institutions and number of infections in urban districts. The proposed index is based on a factorial formula, simple and easy to be applied by practitioners, calibrated by using an optimization-based procedure and exploiting data of 257 urban districts of Apulian region (Italy). Moreover, a comparison with a more refined analysis, based on the training of Artificial Neural Networks , is performed in order to take into account the non-linearity of the phenomenon. The investigation quantifies the influence of each considered parameter in the risk of contagion useful to obtain risk analysis and forecast scenarios. [ABSTRACT FROM AUTHOR]

In this century, one of the main objectives of agriculture is sustainability addressed to achieve food security, based on the improvement of use efficiency of farm resources, the increasing of crop yield and quality, under climate change conditions. The optimization of farm resources, as well as the control of soil degradation processes (e.g., soil erosion), can be realized through crop monitoring in the field, aiming to manage the local spatial variability (time and space) with a high resolution. In the case of high profitability crops, as the case of vineyards for high-quality wines, the capability to manage and follow spatial behavior of plants during the season represents an opportunity to improve farmer incomes and preserve the environmental health. However, any field monitoring represents an additional cost for the farmer, which slows down the objective of a diffuse sustainable agriculture. Satellite multispectral images have been widely used for production management in large areas. However, their observation is limited by the pre-defined and fixed scale with relatively coarse spatial resolution, resulting in limitations in their application. In this paper, encouraged by recent achievements in convolutional neural network (CNN), a multiscale full-connected CNN is constructed for the pan-sharpening of Sentinel-2A images by UAV images. The reconstructed data are validated by independent multispectral UAV images and in-situ spectral measurements. The reconstructed Sentinel-2A images provide a temporal evaluation of plant responses using selected vegetation indices. The proposed methodology has been tested on plant measurements taken either in-vivo and through the retrospective reconstruction of the eco-physiological vine behavior, by the evaluation of water conductivity and water use efficiency indexes from anatomical and isotopic traits recorded in vine trunk wood. In this study, the use of such a methodology able to combine the pro and cons of space-borne and UAVs data to evaluate plant responses, with high spatial and temporal resolution, has been applied in a vineyard of southern Italy by analyzing the period from 2015 to 2018. The obtained results have shown a good correspondence between the vegetation indexes obtained from reconstructed Sentinel-2A data and plant hydraulic traits obtained from tree-ring based retrospective reconstruction of vine eco-physiological behavior. • CNN is constructed for pan-sharpening of Sentinel-2A images by UAV images. • Reconstructed data are validated on MS UAV images and in-situ spectral measures. • Reconstructed VIs from Sentinel-2A data agreed with dendroecological plant traits. [ABSTRACT FROM AUTHOR]

More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging. [ABSTRACT FROM AUTHOR]

Flood risk management generally relies on economic assessments performed by using flood loss models of different complexity, ranging from simple univariable models to more complex multivariable models. The latter account for a large number of hazard, exposure and vulnerability factors, being potentially more robust when extensive input information is available. We collected a comprehensive data set related to three recent major flood events in northern Italy (Adda 2002, Bacchiglione 2010 and Secchia 2014), including flood hazard features (depth, velocity and duration), building characteristics (size, type, quality, economic value) and reported losses. The objective of this study is to compare the performances of expert-based and empirical (both uni- and multivariable) damage models for estimating the potential economic costs of flood events to residential buildings. The performances of four literature flood damage models of different natures and complexities are compared with those of univariable, bivariable and multivariable models trained and tested by using empirical records from Italy. The uni- and bivariable models are developed by using linear, logarithmic and square root regression, whereas multivariable models are based on two machine-learning techniques: random forest and artificial neural networks. Results provide important insights about the choice of the damage modelling approach for operational disaster risk management. Our findings suggest that multivariable models have better potential for producing reliable damage estimates when extensive ancillary data for flood event characterisation are available, while univariable models can be adequate if data are scarce. The analysis also highlights that expert-based synthetic models are likely better suited for transferability to other areas compared to empirically based flood damage models. [ABSTRACT FROM AUTHOR]

Abstract: In liberalized markets, there usually exists a day‐ahead session where energy is sold and acquired for the following production day. Owing to the high uncertainty of its production, renewable energy (wind in particular) can significantly influence the network imbalance of the following day. In this work, we consider the problem of predicting the sum of the bid volumes for wind energy of all the producers inside the day‐ahead energy market. This is a valuable tool to be used by an energy provider in order to determine the imbalance of a market zone and, thus, properly size its bids. In particular, we focus on the estimation of the possible relationship between the meteorological forecasts and the wind power offered on the market by the companies for a market zone. We propose a machine learning model which is used to compute a 1‐day‐ahead forecast. The input‐output mapping is obtained by support vector regression. The input feature vector is defined by a suitable feature extraction technique since the meteorological forecasts are given on a lattice of thousands of geographical points. The computational experiments are performed considering the Italian market as a case study (years 2012‐2016). The results show that the proposed feature extraction technique, selecting only some geographical zones, manages to reduce the error attained using all the features. Moreover, classical statistical methods are shown to be outperformed by machine learning models. The analysis reveals also some weaknesses of the model, which may be due to other nonmeteorological factors at play. [ABSTRACT FROM AUTHOR]

Background This study describes the sociodemographic, clinical, and functional characteristics of a representative sample of service users in Italy. The supports provided by formal agencies, natural networks, and actual levels of quality of life (QOL) were assessed. Methods The participants were 1,285 individuals with intellectual and developmental disability served by 23 different services. The influence of availability of support strategies, environmental factors, client characteristics, personal desires and goals, and support needs on current QOL status was investigated using multiple regression. Results QOL outcomes were significantly explained by support needs, client characteristics, personal goals and desires, and marginally by the presence of support strategies and environmental factors. Further, only a minor effect was found from support activities for general QOL outcomes. Conclusions The results confirmed that the personal outcomes could be predicted providing support activities aligned to the specific personal needs and goals, confirming the importance of a person-centred planning process. [ABSTRACT FROM AUTHOR]

Aims To describe the utility of artificial neural networks in predicting communication risks. Background In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. Design A multicentre observational study. Methods Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. Results A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. Conclusion The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. [ABSTRACT FROM AUTHOR]

Most of the hydrological models developed and used previously in sediment yield modeling are complex and lack general applicability. Moreover, the availability of sediment data for the development and calibration of such models is very scarce. Therefore, there is a need to study and develop models that can handle easily measurable parameters of a particular site, even with short length. In recent years, multidisciplinary artificial intelligence techniques--namely, artificial neural networks (ANNs)--have shown the capability to solve such complex nonlinear systems. This study investigates the suitability of an inductive group method of data handling polynomial neural network (GMDH-NN) technique in estimating sediment yield. The data on various meteorological and geomorphological features--namely, river length, watershed area, erodible area, average slope of watershed, annual average rainfall, and drainage density--from 20 subwatersheds of the Arno River Basin in Italy were used for model development. The results of this study show that the inductive GMDH-NN can efficiently capture the trend of sediment yield with a coefficient of correlation of 0.975, even with this small data set. [ABSTRACT FROM AUTHOR]

Purpose (a) A psychometric validation of an Italian version of the Alzheimer's Functional Assessment Tool scale (AFAST-I), designed for informant-based assessment of the degree of impairment and of assistance required in seven basic daily activities in adult/elderly people with intellectual disabilities (ID) and (suspected) dementia; (b) a pilot analysis of its clinical significance with traditional statistical procedures and with an artificial neural network. Methods AFAST-I was administered to the professional caregivers of 61 adults/seniors with ID with a mean age (±) SD) of 53.4 (± 7.7) years (36% with Down syndrome). Internal consistency (Cronbach's α coefficient), inter/intra-rater reliabilities (intraclass coefficients, ICC) and concurrent, convergent and discriminant validity (Pearson's r coefficients) were computed. Clinical significance was probed by analysing the relationships among AFAST-I scores and the Sum of Cognitive Scores (SCS) and the Sum of Social Scores (SOS) of the Dementia Questionnaire for Persons with Intellectual Disabilities (DMR-I) after standardisation of their raw scores in equivalent scores (ES). An adaptive artificial system (AutoContractive Maps, AutoCM) was applied to all the variables recorded in the study sample, aimed at uncovering which variable occupies a central position and supports the entire network made up of the remaining variables interconnected among themselves with different weights. Results AFAST-I shows a high level of internal homogeneity with a Cronbach's α coefficient of 0.92. Inter-rater and intra-rater reliabilities were also excellent with ICC correlations of 0.96 and 0.93, respectively. The results of the analyses of the different AFAST-I validities all go in the expected direction: concurrent validity (r = -0.87 with ADL); convergent validity (r = 0.63 with SCS; r = 0.61 with SOS); discriminant validity (r = 0.21 with the frequency of occurrence of dementia-related Behavioral Excesses of the Assessment for Adults with Developmental Disabilities, AADS-I). In our sample age and gender do not correlate with the scale and comparing the distribution of the AFAST-I and DMR-SCS and DMR-SOS expressed as ES, it appears that memory disorders and temporal and spatial disorientation (SCS) precede the loss of functional abilities, whereas changes in social behaviour (SOS) are less specific in detecting cognitive deterioration sufficient to provoke functional disability and vice versa. The results of AutoCM analysis reveal that the hub (core) of the entire network is represented by the functional domain 'personal/oral hygiene' in the entire study sample and 'use of toilet' in a subgroup of subjects who obtained an ES equal to 0 at DMR-SCS. Conclusions These results confirm the reliability and validity of AFAST-I and emphasise the complexity of the relationship among functional status, cognitive functioning and behaviour also in adults/ seniors with ID. [ABSTRACT FROM AUTHOR]

Plants need a specific environment to grow and reproduce in fine fettle. Nevertheless, climatic conditions are not stable and can impact their well-being and, consequently, harvest quality. Thus, greenhouse cultivation is one of the suitable agricultural techniques for creating and controlling the inside microclimate to be adequate for plant growth. The relevance of greenhouse control is widely recognized. The prediction of greenhouse variables using artificial intelligence methods is of great interest for intelligent control and the potential reduction in energetic and financial losses. However, the studies carried out in this context are still more or less limited and several machine learning methods have not been sufficiently exploited. The aim of this study is to predict the air conditioning electrical consumption and photovoltaic module electrical production at the smart Agro-Manufacturing Laboratory (SamLab) greenhouse, located in Albenga, north-western Italy. Different supervised machine learning methods were compared, namely, Artificial Neural Networks (ANNs), Gaussian Process Regression (GPR), Support Vector Machine (SVM) and Boosting trees. We evaluated the performance of the models based on three statistical indicators: the coefficient of correlation (R), the normalized root mean square error (nRMSE) and the normalized mean absolute error (nMAE). The results show good agreement between the measured and predicted values for all models, with a correlation coefficient R > 0.9, considering the validation set. The good performance of the models affirms the importance of this approach and that it can be used to further improve greenhouse efficiency through its intelligent control. [ABSTRACT FROM AUTHOR]

The heating, ventilation, and air conditioning (HVAC) system serving the test room of the SENS i-Lab of the Department of Architecture and Industrial Design of the University of Campania Luigi Vanvitelli (Aversa, south of Italy) has been experimentally investigated through a series of tests performed during both summer and winter under both normal and faulty scenarios. In particular, five distinct typical faults have been artificially implemented in the HVAC system and analyzed during transient and steady-state operation. An optimal artificial neural network-based system model has been created in the MATLAB platform and verified by contrasting the experimental data with the predictions of twenty-two different neural network architectures. The selected artificial neural network architecture has been coupled with a dynamic simulation model developed by using the TRaNsient SYStems (TRNSYS) software platform with the main aims of (i) making available an experimental dataset characterized by labeled normal and faulty data covering a wide range of operating and climatic conditions; (ii) providing an accurate simulation tool able to generate operation data for assisting further research in fault detection and diagnosis of HVAC units; and (iii) evaluating the impact of selected faults on occupant indoor thermo-hygrometric comfort, temporal trends of key operating system parameters, and electric energy consumptions. [ABSTRACT FROM AUTHOR]

Real-time river flood forecasting models can be useful for issuing flood alerts and reducing or preventing inundations. To this end, machine-learning (ML) methods are becoming increasingly popular thanks to their low computational requirements and to their reliance on observed data only. This work aimed to evaluate the ML models' capability of predicting flood stages at a critical gauge station, using mainly upstream stage observations, though downstream levels should also be included to consider backwater, if present. The case study selected for this analysis was the lower stretch of the Parma River (Italy), and the forecast horizon was extended up to 9 h. The performances of three ML algorithms, namely Support Vector Regression (SVR), MultiLayer Perceptron (MLP), and Long Short-term Memory (LSTM), were compared herein in terms of accuracy and computational time. Up to 6 h ahead, all models provided sufficiently accurate predictions for practical purposes (e.g., Root Mean Square Error < 15 cm, and Nash-Sutcliffe Efficiency coefficient > 0.99), while peak levels were poorly predicted for longer lead times. Moreover, the results suggest that the LSTM model, despite requiring the longest training time, is the most robust and accurate in predicting peak values, and it should be preferred for setting up an operational forecasting system. [ABSTRACT FROM AUTHOR]