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The aim of this study was to determine the examination of the researches about the use of technology by fine arts teachers. The study was conducted according to the content and citation analysis model. In this context, Web of Science (WOS) Core Collection indexes were included. In the document scanning in the WOS environment, the keywords 'Fine arts', 'Teachers' and 'Technology' were searched. In total, 169 documents were examined and analysed one by one. They were analysed according to year, document type, WOS content category, country, source title, organisation and citation, authors, publication language and categories. As a result of this research, the first study was conducted in 2004, while the most studies were conducted in 2016. It was concluded that the published studies had the most Proceedings papers as the document type. The area where the studies of fine arts teachers on the use of technology are mostly carried out is Education Educational Research, according to the Web of Science content category. The most researched title in the distribution according to the Source Title field is 'International Multidisciplinary Scientific Conferences on Social Sciences and Arts.' The university with the most studies is Kazan Federal University. The 19 authors who conducted the studies have a large number of studies in this field. It was concluded that other authors had only one study in the field. Again, when we look at the distribution of the countries and documents according to the language of writing, the country with the most studies is China and the language of the documents is English. The area continues to evolve.

The problem of taking a data set and separating it into subgroups, where the members of each subgroup are more similar to each other than they are to members outside the subgroup, has been extensively studied in science and mathematics education research. Student responses to written questions and multiple-choice tests have been characterised and studied using several qualitative and/or quantitative analysis methods. However, there are inherent difficulties in the categorisation of student responses in the case of open-ended questionnaires. Very often, researcher bias means that the categories picked out tend to find the groups of students that the researcher is seeking out. In this paper, we discuss an example of application of a quantitative, non-hierarchical analysis method to interpret the answers given by 118 Tenth Grade students in Palermo (Italy), to six open-ended questions about algebraic thinking. We show that the use of non-hierarchical analysis allows us to interpret the reasoning of students solving different mathematical problems using Algebra, and to separate them into different groups, that can be recognised and characterised by common traits in their answers, without any prior knowledge on the part of the researcher of what form those groups would take (unbiased classification).

In the last years the quality of Higher Education (HE) system and its evaluation have been key issues of the political and scientific debate on education policies all over Europe. In the wide landscape that involves the entire HE system we draw attention on the third level of its organization, i.e. the Ph.D. In particular, this paper discusses the necessity of monitoring the recruitment process of Ph.D. system because it represents a fundamental aspect of the Ph.D. system as a whole. We introduce a set of concepts related to the recruitment process and then we make them computable with synthetic indicators. The study provides an empirical analysis based on doctoral schools of four academic years at the University of Siena. Proposed indicators are finally used for detecting weakness and strength of each Ph.D. school. (Contains 5 tables and 9 endnotes.)

Since 1999, Italian Universities have been monitoring teaching using students opinions to improve the quality of the education process. The acceleration of the evaluation has been promoted by ministerial laws, that have obliged Universities to write reports containing the main results. In this paper, the aim is to find a method to classify the Universities based on teaching evaluation.

Despite their relevance in neurorehabilitation, physical therapy (PT) goals and interventions are poorly described, compromising a proper understanding of PT effectiveness in everyday clinical practice. Thus, this paper aims to describe the prevalence of PT goals and interventions in people with neurological disorders, along with the participants' clinical features, setting characteristics of the clinical units involved, and PT impact on outcome measures. A multicenter longitudinal observational study involving hospitals and rehabilitation centers across Italy has been conducted. We recruited people with stroke (n = 119), multiple sclerosis (n = 48), and Parkinson's disease (n = 35) who underwent the PT sessions foreseen by the National Healthcare System. Clinical outcomes were administered before and after the intervention, and for each participant the physical therapists completed a semi-structured interview to report the goals and interventions of the PT sessions. Results showed that the most relevant PT goals were related to the ICF activities with "walking" showing the highest prevalence. The most used interventions aimed at improving walking performance, followed by those aimed at improving organ/body system functioning, while interventions targeting the cognitive–affective and educational aspects have been poorly considered. Considering PT effectiveness, 83 participants experienced a clinically significant improvement in the outcome measures assessing gait and balance functions. [ABSTRACT FROM AUTHOR]

Since the 1970s, the development of archaeological databases has characterised the history of archaeological computing. The paper presents a summary of the pivotal early projects, with a particular focus on Italy and France, up to the current projects shared online. They are constantly monitored by the international journal «Archeologia e Calcolatori», that since 1990 is an observatory of theoretical and methodological aspects of computing and information technology applied to archaeology. [ABSTRACT FROM AUTHOR]

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban–rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies. [ABSTRACT FROM AUTHOR]

This research aims to propose a classification of masonry typologies in Central Italy after the earthquakes that in 2016 involved about 120 municipalities. This territory, since several decades, presents high fragility features due to the depopulation process that increase the vulnerability and risk degree. This condition affects even the maintenance practices of traditional buildings by the inhabitants and the extraordinary post-traumatic situation acts as an accelerating factor of the abandonment. In this article we will explain deeply the first part of the research, focusing in particular on methods and tools that were defined and used to carry out this study. The research highlights the need of a specific comparative tool for masonry facades classification. This was elaborated through the comparison of existed bibliography as the EMS-98, the Aedes schedules and the local classification by Umbria Region and the De Meo book. The result then is the production of another synoptic map that would simplifies the correlation between different approaches to classification and the censed facades. Moreover, it was elaborated a second synoptic map moving from the analyses of many survey forms already discuss in literature. The result of this comparison is a new survey form to carry out the field research on vernacular masonry buildings. This new form focuses on the characteristics of the buildings that the research aims to study in Central Italy. The two tools that are illustrate in the following paper were applied on one hundred survey of masonry buildings carried out during August 2018. The last part of this paper proposes a statistical analysis of the results of the field research. [ABSTRACT FROM AUTHOR]

Heritage maps represent fundamental information for the study of the evolution of a region, especially in terms of landscape and ecologic features. Historical maps present two kinds of hurdle before they can be used in a modern GIS: they must be geometrically corrected to correspond to the datum in use and they must be classified to exploit the information they contain. This study deals the latter problem: the Historical Cadaster Map, created between 1851 and 1861, for the Trentino region in the North of Italy is available as a collection of maps in the ETRS89/UTM 32N datum. The map is a high resolution scan (230 DPI, 24 bit) of the original map and has been used in several ecological studies, since it provides detailed information not only about land property but also about land use. In the past the cadaster map has been manually digitized and for each area a set of attributes has been recorded. Since this approach is time consuming and prone to errors, automatic and semi-automatic procedures have been tested. Traditional image classification techniques, such as maximum likelihood classification, supervised or un-supervised, pixelwise and contextual, do not provide satisfactory results for many reasons: map colors are very variable within the same area, symbols and characters are used to identify cadaster parcels and locations, lines, drawn by hand on the original map, have variable thickness and colors. The availability of FOSS tools for the Object-based Image Analysis (OBIA) has made possible the application of this technique to the cadaster map. This paper describes the use of GRASS GIS and R for the implementation of the OBIA approach for the supervised classification of the historic cadaster map. It describes the determination of the optimal segments, the choice of their attributes and relevant statistics, and their classification. The result has been evaluated with respect to a manually digitized map using Cohens Kappa and the analysis of the confusion matrix. The result of the OBIA classification has also been compared to the classification of the same map using maximum likelihood classification, un-supervised and supervised, both pixelwise and contextual. The OBIA approach has provided very satisfactory results with the ability to automatically remove the background and symbols and characters, creating a ready to be used classified map. This study highlights the effectiveness of the OBIA processing chain available in the FOSS4G ecosystem, and in particular the added value of the interoperability between GRASS GIS and R. [ABSTRACT FROM AUTHOR]

Open data initiatives are characterized, in several countries, by a great extension of the number of data sets made available for access by public administrations, constituencies, businesses and other actors, such as journalists, international institutions and academics, to mention a few. However, most of the open data sets rely on selection criteria, based on a technology-driven perspective, rather than a focus on the potential public and social value of data to be published. Several experiences and reports confirm this issue, such as those of the Open Data Census. However, there are also relevant best practices. The goal of this paper is to investigate the different dimensions of a framework suitable to support public administrations, as well as constituencies, in assessing and benchmarking the social value of open data initiatives. The framework is tested on three initiatives, referring to three different countries, Italy, the United Kingdom and Tunisia. The countries have been selected to provide a focus on European and Mediterranean countries, considering also the difference in legal frameworks (civic law vs. common law countries). [ABSTRACT FROM AUTHOR]

Brassicaceae is a family of vegetables found all over the world that has been attracting the attention of researchers due to its rich chemical composition and potential health benefits (antioxidant and anti-inflammatory, as well as antimutagenic activity and potential anticarcinogenic effects). In Italy, various Brassicaceae varieties are commercially available, including traditional local cultivars, which have unique features and genetic diversity. As a result, there is a growing need to protect and recognize these landraces to preserve biodiversity. In this study, non-destructive tools such as Attenuated Total Reflectance-Fourier Transform-Infrared Spectroscopy (ATR-FT-IR) and chemometrics were employed to investigate eight distinct Brassicaceae landraces. The collected data were analyzed using a class modeling approach (Soft Independent Modeling of Class Analogy) and a discriminant classification method (Partial Least Squares Discriminant Analysis) to assess similarities and dissimilarities among the samples, all cultivated in an experimental field under the same pedoclimatic conditions. Remarkably, the combination of IR spectra and chemometric tools allowed accurate classification of the samples according only to their genetic background and despite their inclination to hybridization. The study highlights and demonstrates the importance and applicability of this specific non-destructive method for assisting the management and preservation of the genetic resources related to the local varieties of Brassicaceae. [ABSTRACT FROM AUTHOR]

According to the published literature, for Strategic environment assessment (SEA) to make a meaningful difference, it should be integrated into planning processes. Yet, in situations and contexts where the two processes appear to be disjointed, SEA could be applied upon conclusion of the plan-making process justifying decisions already made, or just before a decision can be made to comply with existing legal requirements. Where this is the case, then what is SEA really for? This paper aims to reflect on the usefulness of SEA, and explore whether a shift of focus from actual effectiveness to perceived effectiveness could help further legitimise the need for SEA. This is done by looking at SEA practice in Italy as a case study, as the country's diverse regional setup presents situations in which SEA may appear to be avoided because being perceived as not very useful or where SEA may appear to be functioning and actually effective. [ABSTRACT FROM AUTHOR]

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems. • We quantify economic resilience of Italian municipalities through bank transaction data. • We model territories with a set of socioeconomic and network-based covariates. • Machine learning classifiers accurately predict economic resilience. • We use SHAP values to identify most relevant predictors. [ABSTRACT FROM AUTHOR]

"The paper presents recent changes in the Italian settlement system and variations in urban hierarchical relations. Functional classification of towns and their new position compared with the situation at the beginning of [the] 1980s is...proposed in terms of three categories of cities, medium-sized towns and small towns. The analysis and verification attempted in this study have revealed a marked reduction in the number of small towns as well as shifts in functional characteristics within urban areas in Italy.", (excerpt)

Monitoring infrastructures is becoming an important and challenging issue. In Italy, the heritage consists of more than 60,000 bridges, which need to be inspected and detected in order to guarantee their strength and durability function during nominal lifespan. In this paper, a non-destructive survey methodology for study concrete bridges surface deterioration and viaducts is presented. Terrestrial and unmanned aerial vehicle (UAV) photogrammetry has been used for visual inspection of a standard concrete overpass in L'Aquila (Italy). The obtained orthomosaic has been processed by means of Object-Based Image Analysis (OBIA) to identify and classify deteriorated areas and decay forms. The results show a satisfactory identification and survey of deteriorated areas. It has also been possible to quantify metric information, such as width and length of cracks and extension of weathered areas. This allows to perform easy and fast periodic inspections over time in order to evaluate the evolution of deterioration and plan urgency of preservation or maintenance measures. [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]

Large-scale gridded climatic data can be useful for the assessment of climate variability and change as a basis for understanding and monitoring natural hazards, as well as for determining appropriate coping strategies. However, an evaluation of the accuracy of these data products against local observational measurements over the different regions of the globe is always required, as these large-scale data may be affected by systematic errors, which can affect the results of downstream applications. Therefore, this study was carried out to evaluate the performances of two long-term gridded datasets in reproducing station-based precipitation and temperature data over the Apulia region (southern Italy) for the period 1956–2019, with a particular focus on the effect of using the different data sources on the results of trend analyses and aridity classification. The results revealed that the considered gridded data products allow only general indications on the spatial and temporal behavior of climatic variables over the Apulia region, especially in regard to precipitation data. [ABSTRACT FROM AUTHOR]

Background: Persistent Idiopathic Facial Pain (PIFP), previously named Atypical Facial Pain (AFP) is a poorly understood condition, often diagnosed after several inconclusive investigations. The aim of this retrospective study was to evaluate the demographic and clinical characteristics of patients with PIFP referred to a Facial Pain Center. Methods: Between May 2011 and September 2014, data on 41 PIFP patients were analyzed regarding temporal, topographical and descriptive pain features, including onset, localization, pain descriptors and intensity. Pharmacological pain treatments were also registered. Finally, the presence and type of previous minor oro-surgery procedures in the painful area were investigated. Results: Demographic and clinical characterization were similar to PIFP patients reported in literature. The presence of previous minor oro-surgery procedures in the painful area was reported in most of these patients, in particular endodontic treatments and tooth extractions. Conclusions: This retrospective analysis showed a high prevalence of minor oro-surgery procedures in our population, while its role in PIFP pathophysiology remains unknown. A new classification of PIFP built around the main discriminant factor of presence of these procedures in the painful area could be considered while available data were still insufficient to define specific diagnostic criteria. [ABSTRACT FROM AUTHOR]

Background: The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models. Methodology: Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists. Results: The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings. Conclusions: The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans. [ABSTRACT FROM AUTHOR]

Abstract: The measurement and management of positional accuracy and positional uncertainty is especially problematic in historical cartography and Historical GIS applications, for at least two reasons: first, historical sources, and especially historical maps, generally carry a higher degree of positional inaccuracy and uncertainty compared to contemporary geographic databases; second, it is always difficult and often impossible to reliably measure the positional accuracy and positional uncertainty of the spatial attribute of historical data. As an added complication, the terms “inaccuracy” and “uncertainty” are often used as synonyms in the literature, with relatively little attention given to issues of uncertainty. In this article we propose a methodology for detecting the positional inaccuracy and positional uncertainty of measurements of urban change using historical maps at a very high spatial resolution (the building). A widely accepted and routinely employed method for detecting urban change, and spatial change in general, consists in overlaying two or more maps created at different dates, but the technique can lead to the formation of spurious changes—typically, sliver polygons—that are the product of misclassification error or map misalignment rather than actual modifications in land cover. In this paper we develop an algorithm to detect such spurious changes. More in general, we extend the discussion to examine the effects of positional uncertainty and positional inaccuracy in feature change detection analysis. The case-study is the city of Milan, Italy. [Copyright &y& Elsevier]

One of the main challenges in river management is the setting of nutrient thresholds that support good ecological status, which is the main objective to achieve for the European member states. This is a complex process, which needs an accurate analysis of the data collected so far for the ecological classification of rivers belonging to different typologies. We analysed the data of the multiannual monitoring concerning diatoms and nutrients in the upper Po River (NW Italy) with the aim of exploring the response of diatom community in terms of species composition, ecological guilds and indices. We considered data of 390 samples, of which 2/3 belonging to the "Central macrotype" (i.e., lowland stretches) and 1/3 to "Alpine siliceous". We performed a Principal Coordinate Analysis to detect community patterns with respect to water chemical classification and macrotypes highlighting species and ecological guilds characteristic of samples along a water quality gradient. We then performed a partial RDA to focus on the role of environmental and spatial factors in shaping the diatom community in each of the two macrotypes. Finally, we investigated the concordance between the Italian normative indices ICMi (for diatoms) and LIMECO (a chemical index of water quality). We found significant differences in the diatom communities of the two macrotypes and in their response to water quality and to spatial factors. Communities resulted as much more uniform in sites with a low water quality, with characteristic species such as Navicula gregaria, Nitzschia palea and Sellaphora nigri. On the other hands, moderately disturbed sites (in terms of trophic level) were characterised by the highest guild diversity. The RDA confirmed the importance of spatial factors in shaping the diatom assemblages, especially in Alpine streams where the physical barriers may condition species dispersion. The comparison between the two normative indices highlights that the correspondence in the classification is achieved in the 57% (Alpine macrotype) and 43% (Central macrotype) of samples. According to our findings, we suggest the revision of the ICMi, both class boundaries and reference value. In addition, we recommend to lower LIMECO threshold for total phosphorus: indeed, several studies have shown significant changes in the diatom community composition starting from very low values (below the current LIMECO threshold, i.e. 50 µgL--1). Moreover, the extension of our study to the whole Po River basin will complete our knowledge of species not yet included in the diatom indices and of the community response to nutrient levels also in other macrotypes. [ABSTRACT FROM AUTHOR]

The present work analyses the main weather patterns over the period 1981–2010 in the Central Apennines (Italy), drawing upon data from 23 monitoring stations spanning a wide elevation range (260–1750 m asl). Cluster analysis was used to identify homogeneous units and to verify the effectiveness of the bioclimatic classification by crossing the results derived from the application of hierarchical and non-hierarchical classification techniques. The results reveal a diversified picture of five clusters that depends on several factors as elevation, the geographic position within or outside the mountainous range, and the regional morphological traits. Although Mediterranean and Temperate climatic features coexist, the Mediterranean pattern in the southern areas and internal valleys better expresses the overall mixed characteristics of Central Italy. The use of a mixed methodology of hierarchic and partitioning methods of cluster analysis improves the bioclimatic classification, especially to quantify the level of humidity and the mediterraneity degree. [ABSTRACT FROM AUTHOR]

A rockfall (RF) is a ubiquitous geohazard that is difficult to monitor or predict and poses a significant risk for people and transportation in several hilly and mountainous environments. The seismic signal generated by RF carries abundant physical and mechanical information. Thus, signals can be used by researchers to reconstruct the event location, onset time, volume and trajectory, and develop an efficient early warning system. Therefore, the precise automatic detection and classification of RF events are important objectives for scientists, especially in seismic monitoring arrays. An algorithm called DESTRO (DEtection and STorage of ROckfalls) aimed at combining seismic event automatic detection and classification was implemented ad hoc within the MATLAB environment. In event detection, the STA/LTA (short-time-average through long-time-average) method combined with other parameters, such as the minimum duration of an RF and the minimum interval time between two continuous seismic events is used. Furthermore, nine significant features based on the frequency, amplitude, seismic waveform, duration and multiple station attributes are newly proposed to classify seismic events in a RF environment. In particular, a three-step classification method is proposed for the discrimination of five different source types: RFs, earthquakes (EQs), tremors, multispike events (MSs) and subordinate MS events. Each component (vertical, east–west and north–south) at each station within the monitoring network is analysed, and a three-step classification is performed. At a given time, the event series detected from each component are integrated and reclassified component by component and station by station into a final event-type series as an output result. By this algorithm, a case study of the seven-month-long seismic monitoring of a former quarry in Central Italy was investigated by means of four triaxial velocimeters with continuous acquisition at a sampling rate of 200 Hz. During this monitoring period, a human-induced RF simulation was performed, releasing 95 blocks (in which 90 blocks validated) of different sizes from the benches of the quarry. Consequently, 64.9 per cent of EQs within 100 km were confirmed in a one-month monitoring period, 88 blocks in the RF simulation were classified correctly as RF events and 2 blocks were classified as MSs given their small energy. Finally, an ad hoc section of the algorithm was designed specifically for RF classification combined with EQ recognition. The algorithm could be applied in slope seismic monitoring to monitor the dynamic states of rock masses, as well as in slope instability forecasting and risk evaluation in EQ-prone areas. [ABSTRACT FROM AUTHOR]

In this paper, multifrequency synthetic aperture radar (SAR) images from ALOS/PALSAR, ENVISAT/ASAR and Cosmo-SkyMed sensors were studied for forest classification in a test area in Central Italy (San Rossore), where detailed in-situ measurements were available. A preliminary discrimination of the main land cover classes and forest types was carried out by exploiting the synergy among L-, C- and X-bands and different polarizations. SAR data were preliminarily inspected to assess the capabilities of discriminating forest from non-forest and separating broadleaf from coniferous forests. The temporal average backscattering coefficient ( σ ¯ °) was computed for each sensor-polarization pair and labeled on a pixel basis according to the reference map. Several classification methods based on the machine learning framework were applied and validated considering different features, in order to highlight the contribution of bands and polarizations, as well as to assess the classifiers' performance. The experimental results indicate that the different surface types are best identified by using all bands, followed by joint L- and X-bands. In the former case, the best overall average accuracy (83.1%) is achieved by random forest classification. Finally, the classification maps on class edges are discussed to highlight the misclassification errors. [ABSTRACT FROM AUTHOR]

Background: Body Mass Index Italian reference data are available for clinical and/or epidemiological use, but no study compared the ability of this system to classify overweight and obesity and detect subjects with clustered cardiometabolic risk factors with international standards. Therefore our aim was to assess 1) the agreement among the Italian Society for Pediatric Endocrinology and Diabetology (ISPED), the World Health Organisation (WHO) and the International Obesity Task Force (IOTF) Body Mass Index cut-offs in estimating overweight or obesity in children and adolescents; 2) the ability of each above-mentioned set of cut-points to detect subjects with cardiometabolic risk factors. Methods: Data of 6070 Italian subjects aged 5-17 years were collected. Prevalence of normal-weight, overweight and obesity was determined using three classification systems: ISPED, WHO and IOTF. High blood pressure, hypertriglyceridemia, low high density lipoprotein-cholesterol and impaired fasting glucose were considered as cardiometabolic risk factors. Results: ISPED and IOTF classified more subjects as normal-weight or overweight and less subjects as obese as compared to WHO (p <0.0001) in the whole sample and in groups divided by gender and age. The strength of agreement between the three methods compared to each other was excellent for overweight (including obesity) definition (k > 0.900), while it differed for obesity definition, ranging from the highest agreement between ISPED and IOTF (k 0.875) to the lowest between ISPED and WHO (k 0.664). WHO had the highest sensitivity, while ISPED and IOTF systems had the highest specificity, in identifying obese subjects with clustered cardiometabolic risk factors. Analogous results were found in subjects stratified by gender or age. Conclusions: ISPED and IOTF systems performed similarly in assessing overweight and obesity, and were more specific in identifying obese children/adolescents with clustered cardiometabolic risk factors; on the contrary, the WHO system was more sensitive. Given the seriousness of the obesity epidemic, we wonder whether the WHO system should be preferable to the national standards for clinical practice and/or obesity screening. [ABSTRACT FROM AUTHOR]

Automatic procedures for landform extraction is a growing research field but extensive quantitative studies of the prediction accuracy of Automatic Landform Classification (ACL) based on a direct comparison with geomorphological maps are rather limited. In this work, we test the accuracy of an algorithm of automatic landform classification on a large sector of the Ionian coast of the southern Italian belt through a quantitative comparison with a detailed geomorphological map. Automatic landform classification was performed by using an algorithm based on the individuation of basic landform classes named geomorphons. Spatial overlay between the main mapped landforms deriving from traditional geomorphological analysis and the automatic landform classification results highlighted a satisfactory percentage of accuracy (higher than 70%) of the geomorphon-based method for the coastal plain area and drainage network. The percentage of accuracy decreased by about 20–30% for marine and fluvial terraces, while the overall accuracy of the ACL map is 69%. Our results suggest that geomorphon-based classification could represent a basic and robust tool to recognize the main geomorphological elements of landscape at a large scale, which can be useful for the advanced steps of geomorphological mapping such as genetic interpretation of landforms and detailed delineation of complex and composite geomorphic elements. [ABSTRACT FROM AUTHOR]

In a previous work, the hailpad data collected over the plain of the Friuli Venezia Giulia region in northeast Italy during the April-September 1992-2009 period were studied through a bivariate analysis with 52 sounding-derived indices from the Udine-Campoformido station (WMO code 16044). The results showed statistically significant relations but, nevertheless, were not completely satisfactory from a practical point of view. In the current work, a prognostic multivariate analysis is performed, using linear and nonlinear approaches, finding the best results with an ensemble of neural networks. For the hail occurrence-classification problem, a novel method for combining binary classifiers (a variant of the Mojirsheibani major voting algorithm) is introduced. For the hail extension-regression problem the ensemble is built by choosing the members with a bagging algorithm, but combining them with a linear multiregression, in order to increase the forecast variability. [ABSTRACT FROM AUTHOR]

Remote Sensing (RS) data and techniques, in combination with GIS and landscape metrics, are fundamental to analyse and characterise Land Cover (LC) and its changes. The case study here described, has been conducted in the area of Avellino (Southern Italy). To characterise the dynamics of changes during a fifty year period (1954/2004), a multi-temporal set of images has been processed: aerial photos (1954), and Landsat scenes (MSS 1975, TM 1985 and 1993, ETM+ 2004). LC pattern and its changes are linked to both natural and social processes whose driving role has been clearly demonstrated in the case study: after the disastrous Irpinia earthquake (1980), specific zoning laws and urban plans have significantly addressed landscape changes. [ABSTRACT FROM AUTHOR]

Active volcanoes generate sonic and infrasonic signals, whose investigation provides useful information for both monitoring purposes and the study of the dynamics of explosive phenomena. At Mt. Etna volcano (Italy), a pattern recognition system based on infrasonic waveform features has been developed. First, by a parametric power spectrum method, the features describing and characterizing the infrasound events were extracted: peak frequency and quality factor. Then, together with the peak-to-peak amplitude, these features constituted a 3-D 'feature space'; by Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) three clusters were recognized inside it. After the clustering process, by using a common location method (semblance method) and additional volcanological information concerning the intensity of the explosive activity, we were able to associate each cluster to a particular source vent and/or a kind of volcanic activity. Finally, for automatic event location, clusters were used to train a model based on Support Vector Machine, calculating optimal hyperplanes able to maximize the margins of separation among the clusters. After the training phase this system automatically allows recognizing the active vent with no location algorithm and by using only a single station. [ABSTRACT FROM AUTHOR]

Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of the structural information. According to the type of the attributes considered in the morphological attribute transformation, different parametric features can be modeled. The generation of APs, thanks to an efficient implementation, strongly reduces the computational load required for the computation of conventional MPs. Moreover, the characterization of the image with different attributes leads to a more complete description of the scene and to a more accurate modeling of the spatial information than with the use of conventional morphological filters based on a predefined structuring element. Here, the features extracted by the proposed operators were used for the classification of two very high resolution panchromatic images acquired by Quickbird on the city of Trento, Italy. The experimental analysis proved the usefulness of APs in modeling the spatial information present in the images. The classification maps obtained by considering different APs result in a better description of the scene (both in terms of thematic and geometric accuracy) than those obtained with an MP. [ABSTRACT FROM AUTHOR]

Background: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. Results: We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. Conclusions: the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/ [ABSTRACT FROM AUTHOR]

One-hundred and fourteen samples of saffron harvested in four different Italian areas (three in Central Italy and one in the South) were investigated by IR and UV-Vis spectroscopies. Two different multi-block strategies, Sequential and Orthogonalized Partial Least Squares Linear Discriminant Analysis (SO-PLS-LDA) and Sequential and Orthogonalized Covariance Selection Linear Discriminant Analysis (SO-CovSel-LDA), were used to simultaneously handle the two data blocks and classify samples according to their geographical origin. Both multi-block approaches provided very satisfying results. Each model was investigated in order to understand which spectral variables contribute the most to the discrimination of samples, i.e., to the characterization of saffron harvested in the four different areas. The most accurate solution was provided by SO-PLS-LDA, which only misclassified three test samples over 31 (in external validation). [ABSTRACT FROM AUTHOR]

Background: To best of our knowledge, this is the first quantitative ethnobotanical study with the aim of documenting the local knowledge and practices of using plants for curing diseases in the Cava de' Tirreni area, Salerno Province, Campania Region, Italy. The present ethnobotanical field study, carried out during 2016–2017, documents the local uses of 119 plant species for medicinal, food and domestic purposes. Methods: Ethnobotanical data were documented from 70 informants: field data were collected and information on the uses of plants was gathered through semi-structured and structured interviews with persons who still retain traditional ethnobotanical knowledge. Documented data were evaluated using the quantitative ethnobotanical index of use value (UV). Results: Overall, the informants native of the area were interviewed and 277 use-reports have been recorded. The scientific names, local names, plant part used, preparation and administration processes are reported and compared with practices in other Southern Italian regions. In total, 101 species are documented as medicinal, 36 as food or food aromatizer, 29 for domestic and handicraft uses, 10 in veterinary medicine. More or less 64% of all species have more uses and over half of the food plants (23 species) are also used for medicinal purposes. Conclusions: The comparison of the documented species and their uses with ethnobotanical literature of other Italian regions reveals that the traditional plant knowledge in this area shows strong similarities with adjacent Southern Italian areas. Some of the recorded species and administration processes however seem to be unique for the zone. [ABSTRACT FROM AUTHOR]

Background: The World Society of Emergency Surgery (WSES) spleen trauma classification meets the need of an evolution of the current anatomical spleen injury scale considering both the anatomical lesions and their physiologic effect. The aim of the present study is to evaluate the efficacy and trustfulness of the WSES classification as a tool in the decision-making process during spleen trauma management. Methods: Multicenter prospective observational study on adult patients with blunt splenic trauma managed between 2014 and 2016 in two Italian trauma centers (ASST Papa Giovanni XXIII in Bergamo and Sant'Anna University Hospital in Ferrara). Risk factors for operative management at the arrival of the patient and as a definitive treatment were analyzed. Moreover, the association between the different WSES grades of injury and the definitive management was analyzed. Results: One hundred twenty-four patients were included. At multivariate analysis, a WSES splenic injury grade IV is a risk factor for the operative management both at the arrival of the patients and as a definitive treatment. WSES splenic injury grade III is a risk factor for angioembolization. Conclusions: The WSES classification is a good and reliable tool in the decision-making process in splenic trauma management. [ABSTRACT FROM AUTHOR]