A fuzzy taxonomy for e-Health projects

Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects.Evaluating the impact of Information Technology (IT) projects represents a problematic task for policy and decision makers aiming to define roadmaps based on previous experiences. Especially in the healthcare sector IT can support a wide range of processes and it is difficult to analyze in a comparative way the benefits and results of e-Health practices in order to define strategies and to assign priorities to potential investments. A first step towards the definition of an evaluation framework to compare e-Health initiatives consists in the definition of clusters of homogeneous projects that can be further analyzed through multiple case studies. However imprecision and subjectivity affect the classification of e-Health projects that are focused on multiple aspects of the complex healthcare system scenario. In this paper we apply a method, based on advanced cluster techniques and fuzzy theories, for validating a project taxonomy in the e-Health sector. An empirical test of the method has been performed over a set of European good practices in order to define a taxonomy for classifying e-Health projects.Articles published in or submitted to a Journal without IF refereed / of international relevanc

A Bayesian network to analyse basketball players’ performances: a multivariate copula-based approach

Statistics in sports plays a key role in predicting winning strategies and providing objective performance indicators. Despite the growing interest in recent years in using statistical methodologies in this field, less emphasis has been given to the multivariate approach. This work aims at using the Bayesian networks to model the joint distribution of a set of indicators of players’ performances in basketball in order to discover the set of their probabilistic relationships as well as the main determinants affecting the player’s winning percentage. From a methodological point of view, the interest is to define a suitable model for non-Gaussian data, relaxing the strong assumption on normal distribution in favour of Gaussian copula. Through the estimated Bayesian network, we discovered many interesting dependence relationships, providing a scientific validation of some known results mainly based on experience. At last, some scenarios of interest have been simulated to understand the main determinants that contribute to rising in the number of won games by a player

Fuzzy clustering of spatial interval-valued data

In this paper, two fuzzy clustering methods for spatial intervalvalued data are proposed, i.e. the fuzzy C-Medoids clustering of spatial interval-valued data with and without entropy regularization. Both methods are based on the Partitioning Around Medoids (PAM) algorithm, inheriting the great advantage of obtaining non-fictitious representative units for each cluster. In both methods, the units are endowed with a relation of contiguity, represented by a symmetric binary matrix. This can be intended both as contiguity in a physical space and as a more abstract notion of contiguity. The performances of the methods are proved by simulation, testing the methods with different contiguity matrices associated to natural clusters of units. In order to show the effectiveness of the methods in empirical studies, three applications are presented: the clustering of municipalities based on interval-valued pollutants levels, the clustering of European fact-checkers based on interval-valued data on the average number of impressions received by their tweets and the clustering of the residential zones of the city of Rome based on the interval of price values

ANN Modelling to Optimize Manufacturing Process

Neural network (NN) model is an efficient and accurate tool for simulating manufacturing processes. Various authors adopted artificial neural networks (ANNs) to optimize multiresponse parameters in manufacturing processes. In most cases the adoption of ANN allows to predict the mechanical proprieties of processed products on the basis of given technological parameters. Therefore the implementation of ANN is hugely beneficial in industrial applications in order to save cost and material resources. In this chapter, following an introduction on the application of the ANN to the manufacturing process, it will be described an important study that has been published on international journals and that has investigated the use of the ANNs for the monitoring, controlling and optimization of the process. Experimental observations were collected in order to train the network and establish numerical relationships between process-related factors and mechanical features of the welded joints. Finally, an evaluation of time-costs parameters of the process, using the control of the ANN model, is conducted in order to identify the costs and the benefits of the prediction model adopted

Measuring Competitiveness at NUTS3 Level and Territorial Partitioning of the Italian Provinces

In this paper we propose a dashboard of indicators of territorial attractiveness at NUTS3 level in the framework of the EU Regional Competitiveness Index (RCI). Then, the Fuzzy C-Medoids Clustering model with multivariate data and contiguity constraints is applied for partitioning the Italian provinces (NUTS3). The novelty is the territorial level analized, and the identification of the elementary indicators at the basis of the construction of the eleven composite competitiveness pillars. The positioning of the Italian provinces is deeply analyzed. The clusters obtained with and without contraints are compared. The obtained partition may play an important role in the design of policies at the NUTS3 level, a route already considered by the Italian government. The analysis developed and the related set of indicators at NUTS3 level constitute an information base that could be effectively used for the implementation of the National Recovery and Resilience Plan (NRRP)

GARCH-based robust clustering of time series

3nopartially_openIn this paper we propose different robust fuzzy clustering models for classifying heteroskedastic (volatility) time series, following the so-called model-based approach to time series clustering and using a partitioning around medoids procedure. The proposed models are based on a GARCH parametric modelingof the time series, i.e. the unconditional volatility and the time-varying volatility GARCH representation of the time series. We first suggest a timid robustification of the fuzzy clustering. Then, we propose three robust fuzzy clustering models belonging to the so-called metric, noise and trimmed approaches, respectively. Each model neutralizes the negative effects of the outliers in the clustering process in a different manner. In particular, the first robust model, based on the metric approach, achieves its robustness with respect to outliers by taking into account a “robust” distance measure; the second, based on the noise approach, achieves its robustness by introducing a noise cluster represented by a noise prototype; the third, based on the trimmed approach, achieves its robustness by trimming away a certain fraction of outlying time series. The usefulness and effectiveness of the proposed clustering models is illustrated by means of a simulation study and two applications in finance and economics.embargoed_20180131De Giovanni, Livia; D'Urso, Pierpaolo; Massari, RiccardoDE GIOVANNI, Livia; D'Urso, Pierpaolo; Massari, Riccard

Low-Power Ultrasounds as a Tool to Culture Human Osteoblasts inside Cancellous Hydroxyapatite

Bone graft substitutes and cancellous biomaterials have been widely used to heal critical-size long bone defects due to trauma, tumor resection, and tissue degeneration. In particular, porous hydroxyapatite is widely used in reconstructive bone surgery owing to its biocompatibility. In addition, the in vitro modification of cancellous hydroxyapatite with osteogenic signals enhances the tissue regeneration in vivo, suggesting that the biomaterial modification could play an important role in tissue engineering. In this study, we have followed a tissue-engineering strategy where ultrasonically stimulated SAOS-2 human osteoblasts proliferated and built their extracellular matrix inside a porous hydroxyapatite scaffold. The ultrasonic stimulus had the following parameters: average power equal to 149 mW and frequency of 1.5 MHz. In comparison with control conditions, the ultrasonic stimulus increased the cell proliferation and the surface coating with bone proteins (decorin, osteocalcin, osteopontin, type-I collagen, and type-III collagen). The mechanical stimulus aimed at obtaining a better modification of the biomaterial internal surface in terms of cell colonization and coating with bone matrix. The modified biomaterial could be used, in clinical applications, as an implant for bone repair

T-wave axis deviation, metabolic syndrome and cardiovascular risk: results from the MOLI-SANI study

Early recognition of patients at increased cardiovascular risk is a major challenge. The surface electrocardiogram provides a useful platform and it has been used to propose several indexes. T wave axis abnormality is associated with an increased risk of cardiovascular mortality, independently of other risk factors and can be associated with the presence of the metabolic syndrome (MetS). We assessed the prevalence of T axis abnormalities and its relationship with MetS and its components in a large population of Italian adults. Data concerning 11,143 women (54±11years) and 9742 men (55±11years) randomly recruited from a general population (Moli-sani cohort) were analyzed. After excluding subjects with incomplete data and with history of cardiac disease or left ventricular hypertrophy, T-wave axis was normal in 74.5% of men and 80.9% of women, borderline in 23.6% and 17.3% and abnormal in 1.9% and 1.8%. In subjects with MetS, the prevalence of borderline or abnormal T-wave axis deviation was higher than in subjects without MetS (in men: 26.6% vs. 22.1% and 2.5% vs. 1.7%; in women: 25% vs. 15% and 2.4% vs. 1.6%, respectively for borderline and abnormal levels, pb0.0001). Each component of MetS increased the odds of having borderline or abnormal T-wave axis deviation by 1.21 in men and 1.31 in women. T wave axis deviation is associated with MetS and its individual components. These findings confirm previous reported results, expanding them to a large and representative sample of European population of Caucasian ethnicity

NF1 truncating mutations associated to aggressive clinical phenotype with elephantiasis neuromatosa and solid malignancies

Background/aim: Von Recklinghausen disease is a syndrome characterized by a wide phenotypic variability giving rise to both, cutaneous and visceral benign and malignant neoplasms. The first include cutaneous neurofibromas, subcutaneous and plexiform neurofibromas. The latter can undergo malignant transformation and/or determine elephantiasis neuromatosa. Visceral tumors may include malignant peripheral nerve sheet tumors, gastrointestinal stromal tumors, cerebral gliomas and abdominal neurofibromas. In the present study, the authors discuss the clinical and biomolecular characterization of a cohort of 20 families with a diagnosis of type 1 neurofibromatosis. Patients and methods: Clinically, the cohort includes three probands with elephantiasis neuromatosa and a peculiarly high incidence of breast and gastrointestinal cancer. Results: Among the 14 NF1 mutations documented, 10 encoding for a truncated protein have been associated to particularly aggressive clinical phenotypes including elephantiasis neuromatosa, malignant peripheral nerve sheet tumors, breast cancer, gastrointestinal stromal tumors. Conclusion: This effect on protein synthesis, rather than the type of NF1 mutation, is the key to the explanation of the genotype-phenotype correlations in the context of neurofibromatosis type 1