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This paper investigates the effects of obesity, socio-economic variables, and individual-specific factors on work productivity across Italian regions. A dynamic panel data with correlated random effects is used to jointly deal with incidental parameters, endogeneity issues, and functional forms of misspecification. Methodologically, a hierarchical semiparametric Bayesian approach is involved in shrinking high dimensional model classes, and then obtaining a subset of potential predictors affecting outcomes. Monte Carlo designs are addressed to construct exact posterior distributions and then perform accurate forecasts. Cross-sectional Heterogeneity is modelled nonparametrically allowing for correlation between heterogeneous parameters and initial conditions as well as individual-specific regressors. Prevention policies and strategies to handle health and labour market prospects are also discussed., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Around the world, the occurrence of landslides has become one of the greatest threats to human life, property, infrastructure, and natural environments. Despite extensive research and discussions on the spatiotemporal dependence of landslide displacements, there is still a lack of understanding concerning the factors that appear to control displacement distribution in landslides because of their significant variations. This paper implements a Graph Convolutional Network (GCN) to predict displacement following the Moio della Civitella landslide in southern Italy and identify factors that may affect the distribution of movement following the landslide. An interferometric technique, known as permanent scatter interferometry (PSI), has been developed based on Synthetic Aperture Radar (SAR) satellite imagery to derive permanent scatter points that can be used to represent the deformation of landslides. This study utilized the GCN regression model applied to PSs points and data reflecting geological and geomorphological factors to extract the interdependency between paired data points, resulting in an adjacency matrix of the interval [0, 0,8). The proposed model outperforms conventional machine learning and deep learning algorithms such as linear regression (LR), K-nearest neighbors (KNN), Support vector regression (SVR), Decision tree, lasso, and artificial neural network (ANN). The absolute error between the actual and predicted deformation is used to evaluate the proposed model, which is less than 2 millimeters for most test set points. [ABSTRACT FROM AUTHOR]

The article discusses the intercomparison of ground-based ultraviolet (UV) radiometers conducted by the Environmental Protection Agency of Aosta Valley (ARPA Valle d'Aosta) in Saint-Christophe, Italy from June 8-23, 2010. It states that the downward global solar UV irradiance at the ground have been measured in a synchronized manner and the raw series has been processed by the respective operators. It also explores the development of an algorithm for comparing broadband spectra and data.

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]

The Italian Government planned to invest €15 billion of European funds on National Health Service digitalization and primary care enhancement. The critical burden brought by the pandemic upon hospital care mean these investments could no longer be delayed, considering the extraordinary backlogs of many treatments and the ordinary gaps of fragmented long-term care, in Italy and abroad. National guidelines have been published to standardize interventions across the Italian regions, and telemedicine is frequently mentioned as a key innovation to achieve both goals. The professional resources needed to run the facilities introduced in primary care are defined with great precision, but no details are given on how digitalization and remote care technologies must be implemented in this context. Building on this policy case, this paper focuses on what contribution digitalization and telemedicine can offer to specific primary care innovations, drawing from implemented technology-driven policies which may support the effective stratification, prevention and management of chronic patient needs, including anticipatory healthcare, population health management, adjusted clinical groups, chronic care management, quality and outcomes frameworks, patient-reported outcomes and patient-reported experience. All these policies can benefit significantly from digitalization and remote care technology, provided that some risks and limitations are considered by design. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the impact of clouds on the MAX-DOAS observations during the measurement campaign in Milano, Italy in September 2003. The study applies a MAX-DOAS inversion algorithms for aerosols and trace gases with assumptions on the relative profile shapes, and compares the inversion results to independent measurements. The results show a reasonable agreement between the formaldehyde (HCHO) and aerosol profiles and the MAX-DOAS layer heights.

Background: This paper aims to present open-source computer simulation programs developed to simulate, track, and estimate the COVID-19 outbreak. Methods: The programs included two separate parts, one set of programs built in Simulink with a block diagram display, and another one coded as a script in MATLAB R2020b. The mathematical model used in this package was the suspectable-infected-removed (SIR), suspectable-exposed-infected-removed (SEIR), and susceptible-exposed-infected-recovereddeceased (SEIRD) models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) were computed using the parameter estimation tool in MATLAB. Several statistical methods were applied for the rate function, including linear, Mean±SD and root mean square (RMS). In addition, an adaptive neuro-fuzzy inference system (ANFIS) was employed and proposed to train the model and predict its output. Conclusion: This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including suspectable-infected-removed (SIR), which is sensitive to pollution, etc. This program can be used as an educational tool or for research studies and this article promises some lasting contributions in the field of COVID-19. [ABSTRACT FROM AUTHOR]

Technologically advanced strategies in infrastructural maintenance are increasingly required in countries such as Italy, where recovery and rehabilitation interventions are preferred to new works. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) techniques have been employed in recent years, achieving reliable outcomes in the identification of infrastructural instabilities. Nevertheless, using the InSAR survey exclusively, it is not feasible to recognize the reasons for such vulnerabilities, and further in-depth investigations are essential.The primary purpose of this paper is to predict infrastructural displacements connected to surface motion and the related causes by combining InSAR techniques and Machine Learning algorithms. The development and application of a Regression Tree-based algorithm have been carried out for estimating the displacement of road pavement structures detected by the Persistent Scatterer InSAR technique.The study area is located in the province of Pistoia, Tuscany, Italy. Sentinel-1 images from 2014 to 2019 were used for the interferometric process, and a set of 29 environmental parameters was collected in a GIS platform. The database is randomly split into a Training (70%) and Test sets (30%). With the Training set, through a 10-Fold Cross-Validation, the model is trained, validated, and the Goodness-of-Fit is evaluated. Also, with the Test set, the Predictive Performance of the model is assessed. Lastly, we applied the model onto a stretch of a two-lane rural road that crosses the area. Results show that the suggested procedure can be used for supporting decision-making processes on planning road maintenance by National Road Authorities. [ABSTRACT FROM AUTHOR]

Classification of outdoor point clouds is an intensely studied topic, particularly with respect to the separation of vegetation from the terrain and manmade structures. In the presence of many overhanging and vertical structures, the (relative) height is no longer a reliable criterion for such a separation. An alternative would be to apply supervised classification; however, thousands of examples are typically required for appropriate training. In this paper, an unsupervised and rotation-invariant method is presented and evaluated for three datasets with very different characteristics. The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. The performance of our method is compared to that produced by supervised classifiers common for remote sensing settings: random forest as learner and feature sets for point cloud processing, like covariance-based features or point descriptors. It is shown that for point clouds resulting from airborne laser scans, the detection accuracy of the proposed method is over 96% and, as such, higher than that of standard supervised classification approaches. Because of artifacts caused by interpolation during 3D stereo matching, the overall accuracy was lower for photogrammetric point clouds (74-77%). However, using additional salient features, such as the normalized green-red difference index, the results became more accurate and less dependent on the data source.

The nutritional and commercial value of the sweet cherry provides it a great economic importance in Italy. The aim of this study was to characterize 35 sweet cherry cultivars and one of sour cherry, by analyzing values of different pomological and nutraceutical traits, identifying cultivars with antioxidant activity and total anthocyanins content closest to those present in literature for Ferrovia (largely diffused in Italy). To this goal, a multivariate metric index through the Soft Independent Modeling of Class Analogy analyzing an artificial dataset and testing a real one, two hierarchical clustering and a principal component analysis, were performed. The multivariate analyses result simultaneously investigated all the variables highlighting cvs. Sylvia, Graffione nero Col di Mosso, Ferrovia, Mora della Punta, Bianchetta Nuchis and Sandra to be more similar to literature data of Ferrovia. This matrix index was a useful tool, to select the most commercial promising varieties., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

A three-dimensional survey of natural caves is often a difficult task due to the roughness of the investigated area and the problems of accessibility. Traditional adopted techniques allow a simplified acquisition of the topography of caves characterized by an oversimplification of the geometry. Nowadays, the advent of LiDAR and Structure from Motion applications eased three-dimensional surveys in different environments. In this paper, we present a comparison between other three-dimensional survey systems, namely a Terrestrial Laser Scanner, a SLAM-based portable instrument, and a commercial photo camera, to test their possible deployment in natural caves survey. We presented a comparative test carried out in a tunnel stretch to calibrate the instrumentation on a benchmark site. The choice of the site is motivated by its regular geometry and easy accessibility. According to the result obtained in the calibration site, we presented a methodology, based on the Structure from Motion approach that resulted in the best compromise among accuracy, feasibility, and cost-effectiveness, that could be adopted for the three-dimensional survey of complex natural caves using a sequence of images and the structure from motion algorithm. The methods consider two different approaches to obtain a low resolution complete three-dimensional model of the cave and ultra-detailed models of most peculiar cave morphological elements. The proposed system was tested in the Gazzano Cave (Piemonte region, Northwestern Italy). The obtained result is a three-dimensional model of the cave at low resolution due to the site's extension and the remarkable amount of data. Additionally, a peculiar speleothem, i.e., a stalagmite, in the cave was surveyed at high resolution to test the proposed high-resolution approach on a single object. The benchmark and the cave trials allowed a better definition of the instrumentation choice for underground surveys regarding accuracy and feasibility. [ABSTRACT FROM AUTHOR]

The global COVID-19 outbreak is worrisome both for its high rate of spread, and the high case fatality rate reported by early studies and now in Italy. We report a new methodology, the Patient Information Based Algorithm (PIBA), for estimating the death rate of a disease in real-time using publicly available data collected during an outbreak. PIBA estimated the death rate based on data of the patients in Wuhan and then in other cities throughout China. The estimated days from hospital admission to death was 13 (standard deviation (SD), 6 days). The death rates based on PIBA were used to predict the daily numbers of deaths since the week of February 25, 2020, in China overall, Hubei province, Wuhan city, and the rest of the country except Hubei province. The death rate of COVID-19 ranges from 0.75% to 3% and may decrease in the future. The results showed that the real death numbers had fallen into the predicted ranges. In addition, using the preliminary data from China, the PIBA method was successfully used to estimate the death rate and predict the death numbers of the Korean population. In conclusion, PIBA can be used to efficiently estimate the death rate of a new infectious disease in real-time and to predict future deaths. The spread of 2019-nCoV and its case fatality rate may vary in regions with different climates and temperatures from Hubei and Wuhan. PIBA model can be built based on known information of early patients in different countries., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: A rationalized model of clinical and therapeutic management of hidradenitis suppurativa (HS) should place the patients at the heart of the process, facilitating their access to diagnostic tests and treatments, providing the appropriate care for each grade of disease severity and optimizing the use of healthcare resources, both in economic and human terms., Material and Methods: This paper reports the results of a Consensus of the Tuscany HS working group for a rationalized model of diagnosis and management of HS., Results: The diagnostic and therapeutic protocols, the available technological equipments and the management models, are presented in the light of today's scientific evidence., Conclusion: The goal of the Consensus is to bring the issue of HS management to the attention of the Tuscan regional government, in order to create unanimously accepted diagnostic and therapeutic protocols., (© 2019 European Academy of Dermatology and Venereology.)

Background: Parkinson's Disease (PD), Multiple Sclerosis (MS), and Epilepsy are three highly impactful health conditions affecting the nervous system. PD, MS, and epilepsy cases can be identified by means of Healthcare Administrative Databases (HADs) to estimate the occurrence of these diseases, to better monitor the adherence to treatments, and to evaluate patients' outcomes. Nevertheless, the absence of a validated and standardized approach makes it hard to quantify case misclassification., Objectives: to identify and describe all PD, MS, and epilepsy case-identification algorithms by means of Italian HADs, through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded., Results: the search strategy led to the identification of 70 papers for PD, 154 for MS, and 100 for epilepsy, of which 3 papers for PD, 6 for MS, and 5 for epilepsy were considered pertinent. Most articles were published in the last three years (2014-2017) and focused on a region-wide setting. Out of all pertinent articles, 3 original algorithms for PD, 4 for MS, and 4 for epilepsy were identified. The Drug Prescription Database (DPD) and Hospital Discharge record Database (HDD) were used by almost all PD, MS, and epilepsy case-identification algorithms. The Exemption from healthcare Co-payment Database (ECD) was used by all PD and MS case-identification algorithms, while only 1 epilepsy case-identification algorithm used this source. All epilepsy case-identification algorithms were based on at least a combination of electroencephalogram (EEG) and drug prescriptions. An external validation had been performed by 2 papers for MS, 2 for epilepsy, and only 1 for PD., Conclusion: the results of our review highlighted the scarce use of HADs for the identification of cases affected by neurological diseases in Italy. While PD and MS algorithms are not so heterogeneous, epilepsy case-identification algorithms have increased in complexity over time. Further validations are needed to better understand the specific characteristics of these algorithms.

Objectives: to identify and describe all Inflammatory Bowel Disease (IBD), Celiac Disease (CD), and Chronic Kidney Disease (CKD) case-identification algorithms by means of Italian Healthcare Administrative Databases (HADs), through a review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers; exclusion criteria were the following: no details of algorithms reported, algorithm not developed in the Italian context, exclusive use of data from the death certificate register, or from general practitioner or pediatrician databases. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence, II population/cohort selection, III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, followback periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded., Results: the search string led to the identification of 98 articles for IBD, 42 articles for CD, and 390 for CKD. By screening the references, one paper for IBD was added. Finally, this led to 5, 9, and 8 pertinent papers respectively for IBD, CD, and CKD. Considering the papers on IBD and CD, specific age selections were applied to focus on children and young adult populations. When a selection on age was applied for CKD, instead, it mostly considered individuals aged more than 18 years. Three algorithms for IBD, 4 for CD, and 5 for CKD were extracted from papers and characterized. Drug prescription databases were used for both IBD and CKD algorithms, whereas the hospital discharge database and co-payment exemption database were used for IBD and CD. Pathology records and specialist visit databases were also used for CD and CKD, respectively. For each disease only one algorithm applied criteria for the exclusion of prevalent cases. External validation was performed only for Crohn's disease among IBDs, in one algorithm., Conclusions: the results of this review indicate that case identification for IBD and CD from routinely collected data can be considered feasible and can be used to perform different kinds of epidemiological studies. The same is not true for CKD, which requires further efforts, mainly to improve the detection of early stage patients.

Objectives: to identify and describe all asthma and Chronic Obstructive Pulmonary Disease (COPD) case-identification algorithms by means of Italian Healthcare Administrative Databases (HADs), through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers; exclusion criteria were the following: no description of reported algorithms, algorithm developed outside of the Italian context, exclusive use of death certificates, pathology register, general practitioner or pediatrician data. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded., Results: the search string led to the identification of 98 and 147 papers, respectively for asthma and COPD. By screening the references, 2 papers for asthma and 7 for COPD were added. At the end of the screening process, 14 pertinent papers were identified for asthma and 31 for COPD. Half of these used healthcare data covering a time period between 2008 and 2014. More than 75% considered the age range 6-17 for asthma and >=45 for COPD. About one-third of the articles used algorithms to estimate the occurrence of these diseases. Fourteen algorithms for asthma and 16 for COPD were extracted from the papers and characterized. The Drug Prescription Database (DPD) was used by almost all asthma case-identification algorithms, while only 7 COPD algorithms used this data source. The spectrum of active ingredients was strongly overlapping between the two diseases, with different combinations of drugs and administration routes, as well as specific number of prescriptions, follow-back years, and age ranges. Age class and chronic treatment were the main disease-specific traits that emerged from the algorithms. Three external validation processes have been performed for asthma and three for COPD. High accuracy levels have been found for asthma. COPD sensitivity analyses were unsatisfactory, while a high specificity was found for algorithms based on hospital discharge records., Conclusion: elements from the review on the use of healthcare administrative databases represent a useful tool to decide which algorithm to adopt, based on the algorithm's individual requirements, limits, and accuracy, taking into account the specific research objective.

Background: there has been a long-standing, consistent use worldwide of Healthcare Administrative Databases (HADs) for epidemiological purposes, especially to identify acute and chronic health conditions. These databases are able to reflect health-related conditions at a population level through disease-specific case-identification algorithms that combine information coded in multiple HADs. In Italy, in the past 10 years, HAD-based case-identification algorithms have experienced a constant increase, with a significant extension of the spectrum of identifiable diseases. Besides estimating incidence and/or prevalence of diseases, these algorithms have been used to enroll cohorts, monitor quality of care, assess the effect of environmental exposure, and identify health outcomes in analytic studies. Despite the rapid increase in the use of case-identification algorithms, information on their accuracy and misclassification rate is currently unavailable for most conditions., Objectives: to define a protocol to systematically review algorithms used in Italy in the past 10 years for the identification of several chronic and acute diseases, providing an accessible overview to future users in the Italian and international context., Methods: PubMed will be searched for original research articles, published between 2007 and 2017, in Italian or English. The search string consists of a combination of free text and MeSH terms with a common part on HADs and a disease-specific part. All identified papers will be screened for eligibility by two independent reviewers. All articles that used/defined an algorithm for the identification of each disease of interest using Italian HADs will be included. Algorithms with exclusive use of death certificates, pathology register, general practitioner or pediatrician data will be excluded. Pertinent papers will be classified according to the objective for which the algorithm was used, and only articles that used algorithms with "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) will be considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms will be collected. Further information on specific accuracy measures from external validations, sensitivity analyses, and the contribution of each source will be recorded. This protocol will be applied for 16 different systematic reviews concerning eighteen diseases (Hypothyroidism, Hyperthyroidism, Diabetes mellitus, Type 1 diabetes mellitus, Acute myocardial infarction, Ischemic heart disease, Stroke, Hypertension, Heart failure, Congenital heart anomalies, Parkinson's disease, Multiple sclerosis, Epilepsy, Chronic obstructive pulmonary disease, Asthma, Inflammatory bowel disease, Celiac disease, Chronic kidney failure)., Conclusion: this protocol defines a standardized approach to extensively examine and compare all experiences of case identification algorithms in Italy, on the 18 abovementioned diseases. The methodology proposed may be applied to other systematic reviews concerning diseases not included in this project, as well as other settings, including international ones. Considering the increasing availability of healthcare data, developing standard criteria to describe and update characteristics of published algorithms would be of great use to enhance awareness in the choice of algorithms and provide a greater comparability of results.

Background: acute myocardial infarction (AMI), ischemic heart diseases (IHDs) and stroke are serious cardiovascular diseases (CVDs) which may lead to hospitalizations, require periodical medical monitoring and life-long drugs use, thus having a high impact on public health and Healthcare Service expenditure. In this contest, Italian Healthcare Administrative Databases (HADs), which routinely collect patientlevel information on healthcare services reimbursed by the National Healthcare service, are increasingly used for identification of these CVDs., Objectives: to identify and describe all AMI, IHDs and stroke case-identification algorithms by means of Italian HADs, through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded., Results: the search strategy has led to the identification of 611 papers for AMI,801 for IHDs and 791 for stroke. Among these,45,12 and 31 papers for AMI, IHDs and stroke respectively, were considered pertinent for inclusion in the systematic review. The majority of the works was published during 2014-2017. The setting of the studies was mainly regional for AMI and stroke, while the majority of IHD's papers was based on a national multicenter context. By screening full texts, a total of 17,5 and 28 original algorithms for AMI, IHDs and stroke respectively, intended for the above-mentioned objectives, were found. Moreover, 3 original algorithms for STEMI, 3 for NSTEMI, 8 for ischemic stroke and 3 for hemorrhagic stroke were identified. The hospital discharge diagnosis database (HDD) was used in all algorithms. In only a few cases the co-payment exemption registry, drug prescription database, and mortality registry database were used as additional algorithm components. For the same event, there was always a difference of >=1 code. External validation was performed in only one case for AMI and stroke identification., Conclusion: a remarkable heterogeneity, in terms of both data sources and codes used, was observed for algorithms aimed to identify AMI, IHDs and stroke in HADs. This was likely due to the paucity of validation studies. Administrative data sources other than HDD remain underutilized.

Objectives: to identify and describe all hypertension, heart failure (HF), and congenital heart disease case-identification algorithms by means of Italian Healthcare Administrative Databases (HADs), through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for primary objectives (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses, and the contribution of each HAD have also been recorded., Results: the search strategy identified 429 papers for hypertension, 479 for HF, and 138 for congenital heart diseases. After title/abstract and full-text screening, the review led to the inclusion of 21 articles for hypertension, 24 for HF, and only 1 for congenital heart diseases. Eighteen algorithms had a primary objective (5 hypertension, 12 HF, 1 congenital heart diseases). All the hypertension algorithms were based on the drug prescription database, except for one algorithm that also used the hospital discharge records and the exemption from co-payment database. As for HF, all the algorithms employed the hospital discharge record database and only two algorithms used another information source. The only algorithm identified for congenital heart diseases was based on the hospital discharge database. The algorithm identified for congenital heart diseases was validated, showing excellent performance. Conversely, only one hypertension algorithm was validated, and none of the HF algorithms were validated - even though 5 out of 12 algorithms were based on previous algorithms used at both national and international level., Conclusion: the findings of the present study showed wide use of Italian administrative databases for the detection of hypertension and heart failure cases. However, the validity of the algorithms in most cases has not been tested, highlighting the need for introducing stricter requirements to enforce the assessment of the validity of the algorithms used.

Background: diabetes mellitus (DM) and thyroid disorders (TDs) are two of the most prevalent and relevant endocrine disorders worldwide, and determining their occurrence and their follow-up pathways is essential. In Italy, due to the presence of a universal health care system, administrative data can be effectively used to determine these measurements. DM is an ideal model for surveillance with administrative data, due to its specific pharmacologic treatment, high rate of hospitalization, and specific care units. The identification of TDs, conversely, is more challenging: they are less frequently managed in a hospital setting, and even if the treatment is highly specific, subclinical forms often do not need any pharmacological treatment., Objectives: to identify and to describe all DM and TD caseidentification algorithms by means of Italian Healthcare Administrative Databases (HADs), through the review of papers published in the past 10 years., Methods: this study is part of a project that systematically reviewed case-identification algorithms for 18 acute and chronic conditions by means of HADs in Italy. PubMed was searched for original articles, published between 2007 and 2017, in Italian or English. The search string consisted of a combination of free text and MeSH terms with a common part that focused on HADs and a disease-specific part. All identified papers were screened by two independent reviewers. Pertinent papers were classified according to the objective for which the algorithm had been used, and only articles that used algorithms for "primary objectives" (I disease occurrence; II population/cohort selection; III outcome identification) were considered for algorithm extraction. The HADs used (hospital discharge records, drug prescriptions, etc.), ICD-9 and ICD-10 codes, ATC classification of drugs, follow-back periods, and age ranges applied by the algorithms have been reported. Further information on specific objective(s), accuracy measures, sensitivity analyses and the contribution of each HAD, have also been recorded. Algorithms were divided between those identifying type 2/not specified DM and type 1 DM, and those created to identify hypo- and hyperthyroidism., Results: of the 780 articles identified for DM, 77 were included and a further 14 papers were added by screening the references. For TD, 65 articles were identified through the search string and 5 of them were included. Of the selected articles, 64% and 80% were published after 2014 for DM and TD, respectively, and 33% (for DM) and 20% (for TD) used multicentric national or international data. Forty original algorithms for DM (29 for type 2 DM/not-specified DM, and 11 for type 1 DM) and 9 for TD (6 for hypo- and 3 for hyperthyroidism) were extracted. In 6 algorithms, specific selections were made so as not to include gestational diabetes. With regard to type 2 DM, the most commonly used sources were the drug prescription database (DPD, 27 cases), hospital discharge record database (HDD, 23 cases), and exemption from healthcare co-payment database (ECD, 19 cases). Other sources were the ambulatory care services database (ACD), birth register, and mortality record database (MRD). Among the 11 algorithms to identify type 1 DM, 9 used DPD, 7 ECD, and 6 HDD; in one case ACD codes were added, and all 11 algorithms but one was applied to a population of young people (always <35 years old). With regard to TDs, 2 algorithms from one paper for hypo and hyperthyroidism relied on DPD as the only source, the other 7 original algorithms combined DPD with HDD (5 cases), ECD (3 cases), and ACD (1 case). One paper identified autoimmune/iodine deficiency hypothyroidism by subtracting iatrogenic hypothyroidism cases (identified through records of previous procedures from HDD and ACD) from the whole hypothyroid population (identified through DPD). External validation was performed for two algorithms for DM and none for TD. The first algorithm for DM was obtained through HDD only and its sensitivity ranged from 61% to 70%, the second had a sensitivity of 71%., Conclusion: Italian literature on the use of administrative healthcare data for case identification of diabetes is vast; the proposed algorithms are quite similar to one another, and the differences between them are rarely accompanied by clinical justification. On the contrary, the literature concerning thyroid disorders is relatively poor. Further validations of the proposed algorithms, as well as their further implementation, are needed.

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

Copyright of Regional Science Policy & Practice is the property of Wiley-Blackwell 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.)

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]

Copyright of Revista Técnica Tributaria is the property of Asociacion Espanola de Asesores Fiscales 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.)

Background: Head and neck space infections present with a potential mortality rate of 40-50 per cent. This paper proposes an algorithm-based management of head and neck space infection to prevent life-threatening events., Methods: A total of 225 patients with head and neck space infection were prospectively analysed at our institution. An experimental scoring system determined the level of clinical risk for the development of major complications. Accordingly, patients were classified into three risk groups: low-, intermediate- and high-risk., Results: Only intermediate- and high-risk patients were hospitalised. Intermediate-risk patients received intravenous medical therapy with daily re-evaluation; 18 of them required delayed surgery. Of the high-risk patients, three required immediate surgical treatment and five received delayed surgery, while in five cases medical therapy was the only treatment received. Low-risk patients were treated in an out-patient setting., Conclusion: The algorithm-based management of head and neck space infection was successful in enabling the avoidance of lethal complications onset.

Background: Most patients with advanced non-small cell lung cancer (NSCLC) have a poor prognosis and receive limited benefit from conventional treatments, especially in later lines of therapy. In recent years, several novel therapies have been approved for second- and third-line treatment of advanced NSCLC beyond old chemotherapy agents (docetaxel and pemetrexed) and the epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI, erlotinib). In particular, the new antiangiogenetics (nindetanib and ramucirumab) in combination with docetaxel and immunotherapy (nivolumab, pembrolizumab and atezolizumab) have been recently approved and represent new treatment options., Methods: The Italian Association of Thoracic Oncology (AIOT) organized five meetings in different Italian regions representing North, Middle and South of the country in order to discuss the issue., Results: In light of these new approvals, it is valuable to understand the uptake of these new therapies in routine clinical practice and their impact on patient care. With these treatment options to define an appropriate algorythm is object of debate., Conclusion: The present paper discusses the old and new treatment opportunities, proposing a shared algorithm for second-line setting in advanced NSCLC., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Although material requirement planning (MRP) systems are widely used in manufacturing companies, they exhibit significant drawbacks. Rev MRP algorithm has been developed in order to reduce the system nervousness, generate a more regular pattern of order released and lower inventory levels. The aim of this paper is to test the Rev MRP algorithm on the case of one of the largest Italian manufacturing companies of home appliances. The company provided three sets of real data, referring to three different runs of their MRP. Results showed that the Rev MRP outperformed the company’s MRP smoothing the production order releases, eliminating overshoots and drastically reducing the inventory levels along the supply chain, even in case of frequent forecasts updates. [ABSTRACT FROM AUTHOR]

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

The field of additive manufacturing, particularly 3D printing, has ushered in a significant transformation in the realm of joint arthritis treatment through prosthetic surgery. This innovative technology allows for the creation of bespoke prosthetic devices that are tailored to meet the specific needs of individual patients. These devices are constructed using high-performance materials, including titanium and cobalt-chrome alloys. Nevertheless, the routine physical activities of patients, such as walking, sitting, and running, can induce wear and tear on the materials comprising these prosthetic devices, subsequently diminishing their functionality and durability. In response to this challenge, this research has endeavored to leverage novel techniques. The primary focus of this study lies in the development of an algorithm designed to optimize hip replacement procedures via the mechanical design of the prosthesis. This optimization process exploits the capabilities of machine learning algorithms, multi-body dynamics, and finite element method (FEM) simulations. The paramount innovation in this methodology is the capacity to design a prosthetic system that intricately adapts to the distinctive characteristics of each patient (weight, height, gait cycle). The primary objective of this research is to enhance the performance and longevity of prosthetic devices by improving their fatigue strength. The evaluation of load distribution on the prosthetic device, facilitated by FEM simulations, anticipates a substantial augmentation in the useful life of the prosthetic system. This research holds promise as a notable advancement in prosthetic technology, offering a more efficacious treatment option for patients suffering from joint arthritis. The aim of this research is to make meaningful contributions to the enhancement of patient quality of life and the long-term performance of prosthetic devices. [ABSTRACT FROM AUTHOR]

This paper explores the relationship between speaker normalization and dialectal identity in sociolinguistic data, examining a database of vowel formants collected from 88 monolingual American English speakers in Michigan's Upper Peninsula. Audio recordings of Finnish- and Italian-heritage American English speakers reading a passage and a word list were normalized using two normalization procedures. These algorithms are based on different concepts of normalization: Lobanov, which models normalization as based on experience with individual talkers, and Labov ANAE, which models normalization as based on experience with scale-factors inherent in acoustic resonators of all kinds. The two procedures yielded different results; while the Labov ANAE method reveals a cluster shifting of low and back vowels that correlated with heritage, the Lobanov procedure seems to eliminate this sociolinguistic variation. The difference between the two procedures lies in how they treat relations between formant changes, suggesting that dimensions of variation in the vowel space may be treated differently by different normalization procedures, raising the question of how anatomical variation and dialectal variation interact in the real world. The structure of the sociolinguistic effects found with the Labov ANAE normalized data, but not in the Lobanov normalized data, suggest that the Lobanov normalization does over-normalize formant measures and remove sociolinguistically relevant information. [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]

Background: Resilience is defined as the ability to modify thoughts to cope with stressful events. Patients with schizophrenia (SCZ) having higher resilience (HR) levels show less severe symptoms and better real-life functioning. However, the clinical factors contributing to determine resilience levels in patients remain unclear. Thus, based on psychological, historical, clinical and environmental variables, we built a supervised machine learning algorithm to classify patients with HR or lower resilience (LR). Methods: SCZ from the Italian Network for Research on Psychoses (N = 598 in the Discovery sample, N = 298 in the Validation sample) underwent historical, clinical, psychological, environmental and resilience assessments. A Support Vector Machine algorithm (based on 85 variables extracted from the above-mentioned assessments) was built in the Discovery sample, and replicated in the Validation sample, to classify between HR and LR patients, within a nested, Leave-Site-Out Cross-Validation framework. We then investigated whether algorithm decision scores were associated with the cognitive and clinical characteristics of patients. Results: The algorithm classified patients as HR or LR with a Balanced Accuracy of 74.5% (p < 0.0001) in the Discovery sample, and 80.2% in the Validation sample. Higher self-esteem, larger social network and use of adaptive coping strategies were the variables most frequently chosen by the algorithm to generate decisions. Correlations between algorithm decision scores, socio-cognitive abilities, and symptom severity were significant (p FDR < 0.05). Conclusions: We identified an accurate, meaningful and generalizable clinical-psychological signature associated with resilience in SCZ. This study delivers relevant information regarding psychological and clinical factors that non-pharmacological interventions could target in schizophrenia. [ABSTRACT FROM AUTHOR]

Coastal tide gauges play a very important role in a tsunami warning system, since sea-level data are needed for a correct evaluation of the tsunami threat, and the tsunami arrival has to be recognized as early as possible. Real-time tsunami detection algorithms serve this purpose. For an efficient detection, they have to be calibrated and adapted to the specific local characteristics of the site where they are installed, which is easily done when the station has recorded a sufficiently large number of tsunamis. In this case the recorded database can be used to select the best set of parameters enhancing the discrimination power of the algorithm and minimizing the detection time. This chance is however rare, since most of the coastal tide-gauge stations, either historical or of new installation, have recorded only a few tsunamis in their lifetimes, if any. In this case calibration must be carried out by using synthetic tsunami signals, which poses the problem of how to generate them and how to use them. This paper investigates this issue and proposes a calibration approach by using as an example a specific case, which is the calibration of a real-time detection algorithm called TEDA (Tsunami Early Detection Algorithm) for two stations (namely Tremestieri and Catania) in eastern Sicily, Italy, which were recently installed in the frame of the Italian project TSUNET, aiming at improving the tsunami monitoring capacity in a region that is one of the most hazardous tsunami areas of Italy and of the Mediterranean. [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]

This paper describes a software tool that reconstructs entire genealogies from data collected from different and heterogeneous sources, including municipal and parish records archived over centuries. The tool exploits a record linkage algorithm relying on a rule-based data matching approach. It applies a general strategy for managing the ambiguities due to missing, imprecise or erroneous input data. The process follows an iterative approach that combines automatic pedigree reconstruction with software-empowered human data revision to improve the quality and the accuracy of the results and to optimize the matching rules. The paper discusses the results obtained by reconstructing the entire genealogy of the population of the Val Borbera, a geographically isolated valley in Northern Italy. The genealogy could be reconstructed from data going back as far as the XVI century. The resulting pedigree includes 75,994 trios, 58.9% of which belonging to a unique big family, reconstructed over 13 generations., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Purpose: In this paper a new, easy-to-manufacture and easy-to-use ultrasound (US) probe calibration phantom for 3D freehand scanning is presented and evaluated, together with a new method for achieving an accurate and user-robust calibration using virtual plane., Methods: The phantom allows the optically tracked US probe to perform two rotations and two translations while keeping the image of a tensioned wire in the image plane. This approach allows obtaining a sharp image of the wire independently from the probe pose. The virtual plane allows the calibration algorithm to converge minimizing the required number of US probe tracked poses. The US image and position data are synchronized via a CORBA interface, created within the Image Guided Surgery Toolkit (IGSTK) framework. The calibration algorithm and the calibration protocol were evaluated in a set of experiments carried out by different test-users., Results: The calibration method proved to be accurate and precise: 3D point reconstruction accuracy resulted 0.2 mm as mean value, while the precision was 0.4 mm as standard deviation., Conclusions: The technique showed to be suitable for medical applications from morphological diagnosis to intraoperative surgical planning adaption.

Background: To propose a new diagnostic algorithm for candidates for Fontan and identify those who can skip cardiac catheterization (CC)., Methods: Forty-four candidates for Fontan (median age 4.8 years, range: 2-29 years) were prospectively evaluated by trans-thoracic echocardiography (TTE), Cardiovascular magnetic resonance (CMR) and CC. Before CC, according to clinical, echo and CMR findings, patients were divided in two groups: Group I comprised 18 patients deemed suitable for Fontan without requiring CC; group II comprised 26 patients indicated for CC either in order to detect more details, or for interventional procedures., Results: In Group I ("CC not required") no unexpected new information affecting surgical planning was provided by CC. Conversely, in Group II new information was provided by CC in three patients (0 vs 11.5%, p = 0.35) and in six an interventional procedure was performed. During CC, minor complications occurred in one patient from Group I and in three from Group II (6 vs 14%, p = 0.7). Radiation Dose-Area product was similar in the two groups (Median 20 Gycm(2), range: 5-40 vs 26.5 Gycm(2), range: 9-270 p = 0.37). All 18 Group I patients and 19 Group II patients underwent a total cavo-pulmonary anastomosis; in the remaining seven group II patients, four were excluded from Fontan; two are awaiting Fontan; one refused the intervention., Conclusion: In this paper we propose a new diagnostic algorithm in a pre-Fontan setting. An accurate non-invasive evaluation comprising TTE and CMR could select patients who can skip CC.

Introduction: The purpose of this paper is to present the most recent revision of diagnostic therapeutic protocols regarding polytrauma that are operational in the Trauma Center of Cesena, and to check what impact the progressive implementation and review of these algorithms has had on predefined indicators of results and utilization of diagnostic and therapeutic resources. Finally for the purpose of comparing the results obtained in a subgroup of patients treated in the Trauma Center of Cesena, with those obtained in a group homogeneous for ISS and year of hospitalization stored in the RRGT (Registro Regionale Grandi Traumi - Regional Major Trauma Registry)., Materials and Methods: Through a retrospective study we analyzed a population of 21,704 patients hospitalized for trauma in our Trauma Center from 2001 to November 2009, 40.1% females and 59.9% males, aged between 0 and 105 years, who were treated with the protocols developed in the Trauma Service. Indicators of results and of diagnostic and therapeutic resource utilization were analyzed. All patients enrolled in the study were divided by year of admission to assess the performance of these indicators over the years. An ISS homogeneous subgroup including only patients hospitalized in the year 2007 was also created for comparison with the report of RRGT (Regional Major Trauma Registry). Emergency Department code yellow or red patients were divided into 3 groups based on the hemodynamic response after primary assessment. Group A included patients that were hemodynamically stable (ATLS criteria); Group B included patients that were hemodynamically stabilized; Group C included patients that were hemodynamically unstable. Each group of patients was treated according to precise diagnostic and therapeutic protocols., Results: The overall hospital mortality was 2.4%. Mortality at discharge from intensive care was 11.6% while at discharge from the Emergency Surgery was 0.2%. The total average hospital stay was 10.1 days. ICU stay was 7.8 days, while in Emergency Surgery was 12.4 days. 79.4% of the patients were discharged home. Overall, the percentage of patients undergoing surgery was 64.3%. Patients undergoing diagnostic level II with multislice CT were 19.3%; those undergoing CT of the chest and / or abdomen were 5%. A total 0.8% of patients underwent angiography, and 0.2% underwent embolization. The overall percentage of patients transfused with packed red blood cell was 3.9%., Conclusions: The hemodynamic response of patients after primary assessment determines the subsequent diagnostic and therapeutic procedures. The protocols that we utilized had a positive impact on the mortality of patients hospitalized in the intensive care and on the average intensive care stay, and were also associated with an increase in the number of angiography and arterial embolizations performed in sicker patients and those with relevant surgical lesions. The decrease in mortality is also observed in comparison with the RRGT data. We believe therefore that these protocols can provide a valuable and effective aid for those involved in the care of trauma victims, allowing them to always be able to quickly decide what to do, when to do, how to do, and where to do what needs to be done.

HIV/AIDS surveillance data indicate that, in 2008, approximately one-fourth of all HIV infections in adults remain undiagnosed in Italy and that close to 60% of AIDS diagnosed individuals discovered their seropositivity at the diagnosis of AIDS. Late diagnosis of HIV infection is associated with increased mortality and morbidity and increased cost to healthcare services. From a public health perspective, knowledge of HIV status is associated with a reduction in risk behaviour. Thus, a routine screening for HIV infection is important for both a better prognostic outcome, and control of HIV spreading in the population. In Italy there are not shared guidelines for the laboratory diagnosis. In this paper, we suggest two algorithms that can be adopted for the diagnosis of HIV infection in individuals undergoing HIV testing.

The paper analyses some critical situations in the assessment of the compliance with the human exposure limits to the low-frequency magnetic fields. The study is focused on non-sinusoidal and transient waveforms in the frequency range from 8 Hz to 65 kHz. It is carried out by combining measurements and numerical evaluations, starting from actual magnetic flux density behaviours. The results show that inconsistent indications of compliance with the exposure limits may arise not only because of the evaluation method adopted, but also as a result of the used time or frequency windows in relation to the investigated magnetic field waveform.

Objective: To measure and compare the reliability and predictive validity of a four-level triage system (I-4L) and the new four-level model triage emergency method (TEM)., Methods: This observational study was conducted in an urban hospital. Ten nurses were randomly selected to assign a triage level to 189 paper scenarios, using either the I-4L model (5 nurses) or the TEM model (5 nurses). We used weighted kappa statistics to measure the interrater and intrarater reliability of each triage tool and assessed the validity of each models based on the accuracy in predicting admission., Results: Interrater reliability was kappa=0.73 [95% CI (confidence interval): 0.59-0.87] and kappa=0.79 (95% CI: 0.65-0.93) with I-4L and TEM, respectively. Intrarater reliability was kappa=0.82 (95% CI: 0.67-0.96) and kappa=0.78 (95% CI: 0.62-0.93), respectively. The accuracy of triage rating for admission prediction was similarly good with I-4L and TEM, namely, 79% (95% CI: 74-85) and 77% (95% CI: 74-85). The proportion of patients admitted per triage level was similar with the two models., Conclusion: The interrater and intrarater reliability for rating triage acuity and for accuracy in patient admission prediction was good with both models. Performance with the new model was similar to that of I-4L despite the nurses' short experience. The new TEM model has the advantage of predicting utilization of emergency department resources.

This paper aims to quantify the soil sampling uncertainty arising from the short-range spatial variability of elemental concentrations in the topsoils of agricultural, semi-natural, and contaminated environments. For the agricultural site, the relative standard sampling uncertainty ranges between 1% and 5.5%. For the semi-natural area, the sampling uncertainties are 2-4 times larger than in the agricultural area. The contaminated site exhibited significant short-range spatial variability in elemental composition, which resulted in sampling uncertainties of 20-30%.

The radiation doses received by individuals from radionuclides which enter the human body cannot be measured directly but must be inferred. In these calculations, several measurable quantities (such as the internal whole body burden or urine daily excretion) and quantities derived from models are employed. The Radiation Protection Quantities for internal dosimetry are, in principle, the same as for external dosimetry with the addition of quantities taking into account that the doses in the body are protracted. Other parameters are also necessary for the dose assessment, such as the SAFs (Specific Absorbed Fractions). All these quantities are calculated using Monte Carlo codes and complex anthropomorphic phantoms. Monte Carlo codes are also widely employed as useful tools during the calibration procedure for in vivo measurements. This paper summarises the role played by Monte Carlo modelling in these fields.

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]

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

The next two decades will see dramatic changes in the health needs of the world's populations with chronic diseases as the leading causes of disability, according to recent World Health Organization reports. Increases in the senior population living "confined" in domestic area are also expected producing a steep increase in the need for long-term monitoring and home care services. Independently of the particular features and specific architectures, long-term monitoring systems usually produce a large amount of data to be analyzed and inspected by the practitioners and in particular by the cardiologists dealing with ECG recordings analysis. This problem is well known and regards also the traditional holter-based practice. In this paper we present a program for discovering patterns in ECG recordings, to be considered as a medical decision-making support. Computational methods are based on a QRS detector especially designed for noisy applications followed by a parameters space reduction operated by the KL transform modified on a "user-fit" basis. Events characterization is based on a recently introduced clustering method, called KHM (K-harmonic means). The most representative beat families and the corresponding prototypes (physiological and pathological) are then presented to the user through appropriate graphics to facilitate an easy and fast interpretation. We tested the QRS detection algorithm using the MIT-BIH arrhythmia database. Our method produced 565 false positive beats and 379 false negative beats and a total detection failure of 0.85% considering all the 109.809 annotated beats in the database. While a clinical experimentation of our program is on the way, we used the VALE Database to perform a preliminary evaluation of the methods used for data exploration (PCA, KHM). Considering the entire database, we succeeded in identifying pathological clusters in 97% of the cases.