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

In this paper we report the observations of HD189733b, Kepler-41b, Kepler-42b, GJ 436b, WASP-77ab, HAT-P-32b and EPIC 211818569 as measured at the Osservatorio Polifunzionale del Chianti, a new astro-nomical site in Italy. Commissioning observing runs have been done in order to test capabilities, systematics and limits of the system and to improve its accuracy. For this purpose, a software algorithm has been developed to estimate the differential photometric error of any transit observation, so that the integration time can be chosen to reach optimal signal-to-noise ratios, and to obtain a picture of what kind of transits this setup can reveal. Currently, the system is able to reach an accuracy of about 1 mmag and so it is ready for the much needed exoplanetary transit follow-up. [ABSTRACT FROM AUTHOR]

A Brain Computer Interface (BCI) allows communication for impaired people unable to express their intention with common channels. Electroencephalography (EEG) represents an effective tool to allow the implementation of a BCI. The present paper describes a modular framework for the implementation of the graphic interface for binary BCIs based on the selection of symbols in a table. The proposed system is also designed to reduce the time required for writing text. This is made by including a motivational tool, necessary to improve the quality of the collected signals, and by containing a predictive module based on the frequency of occurrence of letters in a language, and of words in a dictionary. The proposed framework is described in a top-down approach through its modules: signal acquisition, analysis, classification, communication, visualization, and predictive engine. The framework, being modular, can be easily modified to personalize the graphic interface to the needs of the subject who has to use the BCI and it can be integrated with different classification strategies, communication paradigms, and dictionaries/languages. The implementation of a scenario and some experimental results on healthy subjects are also reported and discussed: the modules of the proposed scenario can be used as a starting point for further developments, and application on severely disabled people under the guide of specialized personnel. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs. [ABSTRACT FROM AUTHOR]

Pattern informatics (PI) algorithm, which was introduced at the beginning of past decade, uses instrumental earthquake catalogs to investigate the time-dependent rate of seismicity in the study area and, based on the information from past events, calculates the probabilities for the occurrence of future large earthquakes. The main measure in this method is the number of events above a specified magnitude threshold M that is counted over a gridded area. PI has been applied in several regions of the world and different variants of the method have been developed over the past decade. Hence, the problem of formally evaluating and comparing the performances of the different PI variants needs to be addressed from an operational perspective, in order to identify the preferred application scheme and as well as to improve the performances of the method. In this study, PI is applied for the first time to the retrospective analysis of the earthquake catalogs of Iran and Italy, so as to check whether this method could forecast the past large events in these two regions with different level of data completeness and complex seismotectonic setting. The original PI algorithm and one of its modified variants, as well as the relative intensity (RI) model, are used to check the stability and statistical significance of the obtained results. In order to assess and compare the obtained results, the performances of the different PI variants are analyzed considering different evaluation strategies, which turn out to provide significantly different scores even for the same algorithm variant. We show that a critical point in the assessment of the obtained results is related with the definition and quantification of the space uncertainty of the issued forecasts, that is, with the extent of the territory where large earthquakes are to be expected. Accordingly, we emphasize the need for an appropriate definition of the evaluation strategies, clearly and unambiguously indicating the area where a large earthquake has to be expected. The study shows that, with respect to application in Iran and Italy, the performances of PI algorithm (both original and modified variants) are highly dependent on the selected evaluation strategy and do not provide better information than the simple RI model, which does not account for temporal properties of seismicity evolution. The overall performances can be improved by introducing specific thresholds that discard the less active cells; however, being based on some posterior optimization, a rigorous prospective testing is required to assess the forecasting capability of the method. In this paper, we aim to set up the rules for such testing, including advance definition of the evaluation strategy. [ABSTRACT FROM AUTHOR]

A reliable prediction of the spatio-temporal drought variation can lead to a reduction in vulnerability and an improvement in the management of drought-dependent businesses. In this study, three clustering algorithms, K-mean, Hierarchical and Expectation–Maximization, were first used to divide Southern Italy into homogeneous drought regions, based on gridded data of the Standardized Precipitation Evapotranspiration Index forecasting with a 6 months' time scale (SPEI6). The Hierarchical algorithm identified five well-distinct clusters characterized by drought events of different duration and severity, considering the different morphoclimatic characteristics of the study area. Then, the mean SPEI6 time series were evaluated for each cluster and used to assess the evolutionary drought trends. In addition, two Machine Learning (ML) algorithms, M5P and Support Vector Regression (SVR), were also used to develop forecasting models for the SPEI6, without the need for additional exogenous inputs. Moreover, the Stacking ML technique was used to develop a hybrid model based on both individual M5P and SVR algorithms. The clustering-forecasting combination makes it possible to identify the evolutionary trends taking place in the various homogeneous areas in a concise but effective manner. The hybrid M5P-SVR model (R2 up to 0.91, minimum Root Mean Square Error RMSE = 0.38) outperformed both M5P (R2 up to 0.87, minimum RMSE = 0.42) and SVR (R2 up to 0.89, minimum RMSE = 0.39) models, showing to be particularly suitable for drought forecasting in areas with long and severe drought events. [ABSTRACT FROM AUTHOR]

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

The article discusses a study which compared the performance of Information Extraction (IE) algorithms. Topics covered include the advantages and disadvantages of Injury Surveillance Systems based on traditional hospital records or clinical data, the common sources of injury surveillance data and the integration of clinical based registries with unconventional data sources like newspaper stories.

The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of misinformation campaigns, but, differently from what already witnessed in other debates, the COVID-19 -intentional or not- flow of false information put at severe risk the public health, possibly reducing the efficacy of government countermeasures. In this manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract such communities, we start by focusing on verified users, i.e., accounts whose identity is officially certified by Twitter. We start by considering each couple of verified users and count how many unverified ones interacted with both of them via tweets or retweets: if this number is statically significant, i.e. so great that it cannot be explained only by their activity on the online social network, we can consider the two verified accounts as similar and put a link connecting them in a monopartite network of verified users. The discursive communities can then be found by running a community detection algorithm on this network. We observe that, despite being a mostly scientific subject, the COVID-19 discussion shows a clear division in what results to be different political groups. We filter the network of retweets from random noise and check the presence of messages displaying URLs. By using the well known browser extension NewsGuard, we assess the trustworthiness of the most recurrent news sites, among those tweeted by the political groups. The impact of low reputable posts reaches the 22.1% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96% of all non reputable URLs shared by political groups come from this community. [ABSTRACT FROM AUTHOR]

Bed sills are a kind of grade control structure built at the same level as the river bed to protect it against erosion and to reduce the longitudinal slope of the channel. The aim of the present research was to determine the optimal dimensions of the structure by devising a new methodology to minimize cost and maximize stability, based on the optimization model of the multi-objective non-dominated sorting genetic algorithm (NSGA-II). This is achieved by calculating the width, height and distance between bed sills with these stability and cost objectives in mind. The NSGA-II algorithm is applied to solve the problem, and a set of Pareto points is obtained. The results are verified by considering the bed sills of the River Madarsoo in Iran and the River Plima in Italy, and comparing the results with those obtained using the Branch-and-Bound (B-and-B) algorithm. The results indicate that the cost and stability could respectively be improved by up to 18 and 20% in the River Madarsoo, and up to 18 and 26% in the River Plima, if the NSGA-II algorithm is used. [ABSTRACT FROM AUTHOR]

The measure of the equitable and sustainable well-being (Bes) is of growing interest in the last years. The National Institute of Statistics (Istat) provides, for Italy, a wide set of indicators describing each domain of well-being that is, by definition, a multidimensional concept. In this study, we propose the use of Bayesian networks to deal with basic and composite Bes indicators. Its capability to model very complex multivariate dependence structures is useful to describe the relationships between indicators belonging to different domains and, being a probabilistic expert system, the estimated network could be also useful for probabilistic inference and what-if analysis. In this study, all the Bayesian networks structures have been estimated by means of the hill climbing algorithm based on bootstrap resampling and model averaging in order to prevent bias due to deviations from the normality assumption. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a chronic disease with unknown etiology and poor prognosis. Little is known about the epidemiology of this disease; most of the studies are limited by small and restricted cohort studies. We aim to investigate the epidemiology of IPF in the Italian primary care setting using the Health Search Database (HSD) between January 2002 and June 2017. In an attempt to define cases of IPF we adopted iterative combinations of International Classification of Diseases Ninth Revision (ICD-9-CM) and other clinical investigations according to three different operational Algorithms. Incidence and prevalence rate, according to the three Algorithms defining IPF, were calculated and the association with candidate determinants [sex, age, gastro-esophageal reflux (GERD) and smoking status] was evaluated. We identified 1,104,307 eligible patients. The prevalence rate of IPF varies between 2.6 to 24.3 per 100.000 person-year, using algorithm 1 and from 0.8 to 7 using algorithm 3. The incidence rate of IPF varies between 1.25 and 3.77 per 100.000 person-years, using algorithm 1 and from 0.10 to 1.61 using algorithm 3. The mean adjusted incidence rate ratio of IPF, using algorithm 1, is 2.33 (95% CI 2.11-2.57) per 100.000 person-years. Over the study years, the trend of prevalence was statistically significantly increasing while the incidence rate started to increase in the last 3 years. The analyses on candidate determinants showed that patients aged 61 years or older, those suffering from GERD, and former smokers were statistically significantly at greater risk of incurring IPF. To our knowledge, this is one of the first European IPF epidemiological studies conducted in primary care. The increase of the incidence rates is likely due to a growing awareness for IPF among General Practitioners, while the increase of prevalence rates may be due to an increase of survival, a result of recent advances in the diagnosis, management and therapies for the disease. [ABSTRACT FROM AUTHOR]

Background: Dependency in older ages is increasing. Many older persons receive care while living in the community. We aimed to identify the predictive value of four clinical measurements to predict home care intensity in older patients following discharge from hospital to home care over 90 days. Methods: We included 425 inpatients from the "Frailty Department–Local Palliative Care Network" of the local social health authority (ASST) Lecco, Italy (mean age 75.4 years, SD 14.5; female 75.5%). Changes in Health, End-stage disease, and Signs and Symptoms, light version (CHESS-Lite), activities of daily living (ADL), frailty, and the Service Urgency Algorithm. Receiver operative curves were used to calculate the area under the curve (AUC) for predicting Home Care Intensity coefficient (ratio of the number of days when any home care was provided 90 days post-discharge). The interRAI Contact Assessment Instrument was used to calculate these measures. Results: Analysis was stratified using six different home care intensity score cut-offs. CHESS-Lite had a higher AUC for predicting home care intensity at all cut-off levels but was best for predicting the highest level of home care intensity (≥ 0.8) where the AUC was 0.71 (0.64–0.79). The frailty index also had an acceptable AUC. ADL had the lowest AUC. Conclusions: Health instability measured with CHESS-Lite has a high predictive value for identifying home care intensity in geriatric patients after discharge from hospital to home, especially in persons with higher home care intensity scores. Geriatric patients with high health instability should be focused on at discharge to prioritize assessment and initiate timely services for home care support. [ABSTRACT FROM AUTHOR]

We apply a computer-aided methodology to assess macroseismic intensity from the descriptions reported by documentary material available for eight Italian earthquakes occurred around the beginning of the instrumental era. The procedure consists of three phases: (i) the identification of significant macroseismic effects on the sources and their archiving in a georeferenced database, (ii) the association between the effects and the degrees of the intensity scale by the comparison with traditional estimates made by macroseismic experts, (iii) the assessment of intensities using a multi-attribute decision-making algorithm based on the Fuzzy Sets logic. This work represents a substantial improvement of our previous efforts as we completely redesigned the three phases of the procedure in the light of the experience of the last 10 years and analyzed six further Italian earthquakes so that our database now includes more than 19,000 encoded effects. Our formalized procedure allows to tracing all of the steps of intensity assessment process so that to identify discrepancies with respect to the expert evaluations that might be possibly due to mistakes or to the incomplete account of the available information. Hence, this approach may be useful for providing a systematic and reproducible intensity assessment as well as for supporting standard man-made assessments. The database of effects we have built could also be employed for testing the internal consistency of the macroseismic scale as well as for designing an improved macroseismic scale, based on consistent statistical criteria. [ABSTRACT FROM AUTHOR]

In Italy, rainfall-induced landslides are recurrent phenomena that cause societal and economic damage. Thus, assessing the rainfall conditions responsible for landslides is important and may contribute to reducing risk. The prediction of rainfall-induced landslides relies primarily on empirical rainfall thresholds. However, the thresholds are affected by uncertainties that limit their use in operational warning systems. A source of uncertainty lies in the characterization of the rainfall events responsible for landslides. Objective criteria for the definition of rainfall events are lacking. To overcome the problem, we propose an algorithm that reconstructs the rainfall events, identifies the rainfall conditions that have resulted in landslides, and measures the duration and the cumulated rainfall for the events. The algorithm is independent from the local settings and uses a reduced set of parameters to account for different physical settings and operational conditions. We tested the algorithm in Sicily, Italy, with rainfall and landslide information between January 2002 and December 2012. The rainfall conditions responsible for landslides identified by the algorithm were compared against results obtained manually. The algorithm was proven capable of accurately reconstructing most (87.7 %) of the rainfall events. For each landslide, the algorithm identified a variable number of rainfall conditions responsible for the failures, which are equally likely triggers of the landslide. This opens the possibility of evaluating the uncertainty introduced by different criteria to determine the rainfall events responsible for landslides. Use of the algorithm shall contribute to reducing the uncertainty in the definition of landslide-triggering rainfall events, to compiling large catalogues of rainfall events with landslides and to determining reliable rainfall thresholds for possible landslide occurrence. [ABSTRACT FROM AUTHOR]

The article discusses the application of Inter-Greedy (IG) approach in the measurement of intima-media thickness (IMT) of carotid arteries in ultrasound images. Topics covered include the deployment of a fusion technique to remove bias in segmentation algorithms, the estimation of IMT through fused boundaries from multiple sources, and the mean error of Inter-Greedy technique. Also mentioned is the ability of the Inter-Greedy technique to enhance segmentation and measurement performance.

Most patients with advanced or metastatic cancer experience pain and despite several guidelines, undertreatment is well documented. A multicenter, open-label, prospective, non-randomised study was launched in Italy in 2006 to evaluate the epidemiology, patterns and quality of pain care of cancer patients. To assess the adequacy of analgesic care, we used a standardised measure, the pain management index (PMI), that compares the most potent analgesic prescribed for a patient with the reported level of the worst pain of that patient together with a selected list of clinical indicators. A total of 110 centres recruited 1801 valid cases. 61% of cases were received a WHO-level III opioid; 25.3% were classified as potentially undertreated, with wide variation (9.8-55.3%) according to the variables describing patients, centres and pattern of care. After adjustment with a multivariable logistic regression model, type of recruiting centre, receiving adjuvant therapy or not and type of patient recruited (new or already on follow-up) had a significant association with undertreatment. Non-compliance with the predefined set of clinical indicators was generally high, ranging from 41 to 76%. Despite intrinsic limitations of the PMI that may be considered as an indicator of the poor quality of cancer pain care, results suggest that the recourse to WHO third-level drugs still seems delayed in a substantial percentage of patients. This delay is probably related to several factors affecting practice in participating centres and suggests that the quality of cancer pain management in Italy deserves specific attention and interventions aimed at improving patients' outcomes. [ABSTRACT FROM AUTHOR]

To determine the diagnostic accuracy of an algorithm structured in four clinical scenarios to discriminate benign primary headaches from serious secondary non-traumatic headaches (NTH) in the emergency department (ED). NTH is usually a benign symptom but can occasionally result in serious outcome making the disposition of patients with NTH difficult in the ED. Consecutive adults patients referring to 8 EDs of the Emilia-Romagna region in Italy for NTH as the chief complaint were recruited in the study for a 30-day period. ED physicians attributed to each patient one of the four clinical scenarios (1, 2 and 3 identifying serious secondary headaches and scenario 4 identifying benign primary headaches) or an undetermined scenario when none of the four scenarios applied. Reference standards of the study were the head CT scan and a follow-up telephone interview after three months by the ED admission. The test was administered to 256 out of 302 (85 %) eligible patients. The analysis (scenario 1,2,3 vs scenario 4) was based on 180 patients who completed the follow-up showing a sensitivity of 100 % (95 % confidence interval, 81 % to 100 %) and a specificity of 64 % (56 % to 71 %). The likelihood ratio for a positive test was 2.67 (2.15 to 3.31) and the likelihood ratio for a negative test was 0.04 (0.003 to 0.64). An algorithm based on four clinical scenarios can be administered to the majority of patients presenting to the ED with the chief complaint of NTH. The algorithm showed a good accuracy in identifying patients with non-life threatening causes of headache and could be used as a risk stratification tool to improve clinical decision- making. Further studies are required to validate this diagnostic algorithm. [ABSTRACT FROM AUTHOR]

The possibility of intermediate-term earthquake prediction at Mt. Vesuvius by means of the CN algorithm is explored. CN was originally designed to identify the Times of Increased Probability (TIPs) for the occurrence of strong tectonic earthquakes, with magnitude M ≥ M 0, within a region a priori delimited. Here the CN algorithm is applied, for the first time, to the analysis of volcanic seismicity. The earthquakes recorded at Mt. Vesuvius during the period from February 1972 to June 2004 are considered, and the magnitude threshold M 0 selecting the events to be predicted is varied within the range: 3.0–3.3. Satisfactory prediction results are obtained, by retrospective analysis, when a time scaling is introduced. In particular, when the length of the time windows is reduced by a factor 2.5–3, with respect to the standard version of CN algorithm, more than 90% of the events with M ≥ M 0 occur within the TIP intervals, with TIPs occupying about 30% of the total time considered. The control experiment ``Seismic History'' demonstrates the stability of the obtained results and indicates that the CN algorithm can be applied to monitor the preparation of impending earthquakes with M ≥ 3.0 at Mt. Vesuvius. [ABSTRACT FROM AUTHOR]

Expanding the remark 5.2.7 of Segre (Segre, G. (2002).Algorithmic Information Theoretic Issues in Quantum Mechanics, PhD Thesis, Dipartimento di Fisica Nucleare e Teorica, Pavia, Italy. quant-ph/0110018.) the noncommutative bayesian statistical inference from one wedge of a bifurcate Killing horizon is analyzed looking at its interrelation with the Unruh effect. [ABSTRACT FROM AUTHOR]