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In this paper, we continue describing the project and the experimentation of "Ricerca Operativa Applicazioni Reali" (ROAR; in English, Real Applications of Operations Research), a three-year project for higher secondary schools, introduced. ROAR is composed of three teaching units, addressed to Grades 10, 11, and 12, respectively, having the main aim to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. In a previous paper, we reported on the design and implementation of the first unit, started in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy), in a Grade-10 class. Here, we focus on the second unit, carried out in Winter/Spring 2022 with the same students, now in a Grade-11 class. In particular, we describe objectives, prerequisites, topics and methods, the organization of the lectures, digital technologies used, and a challenging final project. Moreover, we analyze the feedback from students and teachers involved in the experimentation.

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.)

Within scientific debate on post-digital and education, we present a position paper to describe a research project aimed at the design of a predictive model for students' low achievements in mathematics in Italy. The model is based on the INVALSI data set, an Italian large-scale assessment test, and we use decision trees as the classification algorithm. In designing this tool, we aim to overcome the use of economic, social, and cultural context indices as main factors for the prediction of a learning gap occurrence. Indeed, we want to include a suitable representation of students' learning in the model, by exploiting the data collected through the INVALSI tests. We resort to a knowledge-based approach to address this issue and specifically, we try to understand what knowledge is introduced into the model through the representation of learning. In this sense, our proposal allows a students' learning encoding, which is transferable to different students' cohort. Furthermore, the encoding methods may be applied to other large-scale assessments test. Hence, we aim to contribute to a debate on knowledge representation in AI tool for education.

We introduce "Ricerca Operativa Applicazioni Reali" (ROAR; in English, "Real Applications of Operations Research"), a three-year project for higher secondary schools. Its main aim is to improve students' interest, motivation, and skills related to Science, Technology, Engineering, and Mathematics disciplines by integrating mathematics and computer science through operations research. ROAR offers examples and problems closely connected with students' everyday life or with the industrial reality, balancing mathematical modeling and algorithmics. The project is composed of three teaching units, addressed to grades 10, 11, and 12. The implementation of the first teaching unit took place in Spring 2021 at the scientific high school IIS Antonietti in Iseo (Brescia, Italy). In particular, in this paper, we provide a full description of this first teaching unit in terms of objectives, prerequisites, topics and methods, organization of the lectures, and digital technologies used. Moreover, we analyze the feedback received from students and teachers involved in the experimentation, and we discuss advantages and disadvantages related to distance learning that we had to adopt because of the COVID-19 pandemic.

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]

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]

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.)

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]

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]

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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.

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: 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.

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 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.

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.

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.

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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.)

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.

This paper aims to analyze, from a comparative perspective, the main elements of the discipline on the use of algorithms within administrative procedures developed by the national lawmakers of France, Spain and Italy. Furthermore, the article intends to verify, on the basis of the principle of good administration, the existence of a minimum core of guarantees addressed to the citizen who is the recipient of an automated decision. [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]

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]

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]

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ET a , the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm. • GPR algorithm is suitable to fill gaps in daily ET a time series. • The best m(x) and k(x,x') functions required by the GPR algorithm were identified. • The best results were obtained when the dataset included climate data, SWC and VIs. • The use of ERA5-L data and VIs retrieved by Sentinel 2 or MODIS is a good alternative. • GPR algorithm was tested for different crops in continental and temperate zones. [ABSTRACT FROM AUTHOR]

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]

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

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]

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]

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to examine the processes involved in a guided self‐help (GSH) pre‐treatment intervention (RecoveryMANTRA) for patients with anorexia nervosa (AN), by measuring the levels of patient/mentor Language Style Matching (LSM). RecoveryMANTRA was supported by student mentors or peer mentors (recovered individuals) over six weekly chat‐based sessions. We examined whether LSM during RecoveryMANTRA predicted patients'working alliance with the clinic therapist, motivation, eating disorder (ED) and general psychopathology. A further aim was to examine differences in LSM between student mentors and peer mentors. Method: 87 AN adults received RecoveryMANTRA plus treatment as usual. The LSM algorithm was used to calculate verbal attunement between patient and mentor. Participants were assessed at baseline and at the end of the intervention. Results: Both early (1st session) and late (6th session) LSM predicted higher working alliance with the clinic therapist. Moreover, late LSM predicted lower EDs symptoms at the end of the RecoveryMANTRA intervention. Patient/peer mentor dyads showed higher late verbal attunement than patient/student mentor dyads. Conclusions: These findings suggests that in the early phase of treatment relational aspects can impact on engagement with treatment. Verbal attunement in a GSH for AN is associated with working alliance and better clinical outcome. Highlights: Language Style Matching (LSM) between patients and mentors in both the early and late phase of the RecoveryMANTRA intervention predicted higher working alliance with the clinic therapist.LSM in the last phase (6th session) was associated with lower eating disorders (EDs) symptoms at the end of the RecoveryMANTRA intervention.Patients/peer mentors dyads showed higher late LSM than patients/student mentors dyads. [ABSTRACT FROM AUTHOR]

Fibromyalgia (FM) is a chronic pain syndrome with an unclear etiology. In addition to pain, FM patients suffer from a diverse array of symptoms and comorbidities, encompassing fatigue, cognitive dysfunction, mood disorders, sleep deprivation, and dizziness. Due to the complexity of FM, the diagnosis and treatment of it are highly challenging. The aim of the present work was to investigate some clinical and psychological characteristics of FM patients, and to uncover possible correlations with pharmacological and non-pharmacological therapies. We conducted a cross-sectional, questionnaire-based study aimed at evaluating pain, psychological traits, and the self-perceived effectiveness of pharmacological and non-pharmacological treatments in an Italian population of FM patients. Descriptive statistics, correlation, and inference analyses were performed. We found a prevalence of a neuropathic/nociplastic type of pain, which correlated with psychological traits such as anxiety, low mood, psychophysical discomfort, and the inability to relax. The pain type and psychological traits proved to play a role in determining the self-perceived effectiveness of therapeutic interventions. Patients revealed a better response to non-pharmacological therapies, particularly dietary interventions, relaxation techniques, and psychotherapy rather than pharmacological interventions. The sum of our data indicates that for better outcomes, the type of pain and psychological traits should be considered for tailor-made treatments considering non-pharmacological protocols as a complement to the use of drugs. [ABSTRACT FROM AUTHOR]

With seismic catalogues becoming progressively larger, extracting information becomes challenging and calls upon using sophisticated statistical analysis. Data are typically clustered by machine learning algorithms to find patterns or identify regions of interest that require further exploration. Here, we investigate two density-based clustering algorithms, DBSCAN and OPTICS, for their capability to analyse the spatial distribution of seismicity and their effectiveness in discovering highly active seismic volumes of arbitrary shapes in large data sets. In particular, we study the influence of varying input parameters on the cluster solutions. By exploring the parameter space, we identify a crossover region with optimal solutions in between two phases with opposite behaviours (i.e. only clustered and only unclustered data points). Using a synthetic case with various geometric structures, we find that solutions in the crossover region consistently have the largest clusters and best represent the individual structures. For identifying strong anisotropic structures, we illustrate the usefulness of data rescaling. Applying the clustering algorithms to seismic catalogues of recent earthquake sequences (2016 Central Italy and 2016 Kumamoto) confirms that cluster solutions in the crossover region are the best candidates to identify 3-D features of tectonic structures that were activated in a seismic sequence. Finally, we propose a list of recipes that generalizes our analyses to obtain such solutions for other seismic sequences. [ABSTRACT FROM AUTHOR]

Introduction: The Respiratory Disturbance Index (RDI) computed by an implantable cardioverter defibrillator (ICD) algorithm accurately identifies severe sleep apnea (SA). In the present analysis, we tested the hypothesis that RDI could also predict atrial fibrillation (AF) burden. Methods: Patients with ejection fraction ≤35% implanted with an ICD were enrolled and followed up for 24 months. One month after implantation, patients underwent a polysomnographic study. The weekly mean RDI value was considered, as calculated during the entire follow‐up period and over a 1‐week period preceding the sleep study. The endpoints were as follows: daily AF burden of ≥5 min, ≥6 h, ≥23 h. Results: Here, 164 patients had usable RDI values during the entire follow‐up period. Severe SA (RDI ≥ 30 episodes/h) was diagnosed in 92 (56%) patients at the time of the sleep study. During follow‐up, AF burden ≥ 5 min/day was documented in 70 (43%), ≥6 h/day in 48 (29%), and ≥23 h/day in 33 (20%) patients. Device‐detected RDI ≥ 30 episodes/h at the time of the polygraphy, as well as the polygraphy‐measured apnea hypopnea index ≥ 30 episodes/h, were not associated with the occurrence of the endpoints, using a Cox regression model. However, using a time‐dependent model, continuously measured weekly mean RDI ≥ 30 episodes/h was independently associated with AF burden ≥ 5 min/day (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: 1.24–3.65, p =.006), ≥6 h/day (HR: 2.75, 95% CI: 1.37–5.49, p =.004), and ≥23 h/day (HR: 2.26, 95% CI: 1.05–4.86, p =.037). Conclusions: In heart failure patients, ICD‐diagnosed severe SA on follow‐up data review identifies patients who are from two‐ to three‐fold more likely to experience an AF episode, according to various thresholds of daily AF burden. [ABSTRACT FROM AUTHOR]

Background The proportion of reported causes of death (CoDs) that are not underlying causes can be relevant even in high-income countries and seriously affect health planning. The Global Burden of Disease (GBD) study identifies these 'garbage codes' (GCs) and redistributes them to underlying causes using evidence-based algorithms. Planners relying on vital registration data will find discrepancies with GBD estimates. We analyse these discrepancies, through the analysis of GCs and their redistribution. Methods We explored the case of Italy, at national and regional level, and compared it to nine other Western European countries with similar population sizes. We analysed differences between official data and GBD 2019 estimates, for the period 1990–2017 for which we had vital registration data for most select countries. Results In Italy, in 2017, 33 000 deaths were attributed to unspecified type of stroke and 15 000 to unspecified type of diabetes, these making a fourth of the overall garbage. Significant heterogeneity exists on the overall proportion of GCs, type (unspecified or impossible underlying causes), and size of specific GCs among regions in Italy, and among the select countries. We found no pattern between level of garbage and relevance of specific GCs. Even locations performing below average show interesting lower levels for certain GCs if compared to better performing countries. Conclusions This systematic analysis suggests the heterogeneity in GC levels and causes, paired with a more detailed analysis of local practices, strengths and weaknesses, could be a positive element in a strategy for the reduction of GCs in Italy. [ABSTRACT FROM AUTHOR]

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

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

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

The paper presents a machine-learning based framework, named VULMA (VULnerability analysis using MAchine-learning), for vulnerability analysis of existing buildings. The underlying idea is to provide an indication of the seismic vulnerability by exploiting available photographs, which can be properly processed to provide some input data for empirical vulnerability algorithms. To this scope, a complete processing pipeline has been defined, which consists in four consecutive modules offering different and specific services. The first module, Street VULMA , performs the image gathering starting from the raw data; the second module, Data VULMA , provides a mean for the data labelling and storage; the third module, Bi VULMA , uses the collected data to train several machine-learning models for image classification; the fourth module, In VULMA , performs a ranking of the images, their analysis and consequently assigns the vulnerability index. The proposed procedure has been employed on the existing building portfolio in an extended area of the municipality of Bisceglie, Puglia, Southern Italy, for which all the modules have been tested and, above all, the machine-learning models of Bi VULMA have been trained. After, in order to test the efficiency and the reliability of the proposed tools, the entire procedure has been applied on five case study buildings. The results in terms of vulnerability index have been compared with the manual computations performed by the authors applying the same algorithm. Despite the proposed tool could be improved or modified in some of its modules, the obtained results show a good effectiveness of VULMA , which opens new scenarios in the field of vulnerability assessment procedures and risk mitigation strategies. • Proposal of a framework for the vulnerability analysis of existing building starting from a photo: VULMA ; • Definition of the four modules characterizing VULMA: Street VULMA , Data VULMA , Bi VULMA and In VULMA ; • Application and training of the proposed procedure to a dataset extracted from a municipality of Southern Italy and testing and validation of the tool; • Assessment and proposal of VULMA as new instrument for the definition of the vulnerability response of individual buildings and for the seismic risk estimate at large-scale. [ABSTRACT FROM AUTHOR]

This paper considers the modernization of an existing non-commercial Pulse Duplicator in use at the Healing Research Laboratory at the University of Padova, Italy. The system reproduces human systemic circulation and it is used to test heart medical devices. The focus of this study is the full automation of a crucial system component that is the peripheral resistance manual valve that is replaced by a motorized one. First, under certain technological constraints, the problem of the automatic setting adjustment of the valve is tackled by using a Sliding Mode Extremum Seeking Control (ESC) method. This approach guarantees the system fundamental pressure drop to simulate the peripheral resistance to flow in the human systemic circulation in various system configurations and operating conditions. Then, the Sliding Mode ESC algorithm is embedded in an Arduino board driving the motorized valve. Finally, experimental tests are performed to assess the effectiveness of the motorized valve. [ABSTRACT FROM AUTHOR]