Cost, outcomes, treatment pathways and challenges for diabetes care in Italy

Background: In Italy both incidence and prevalence of diabetes are increasing and age at diagnosis is decreasing in type 2 diabetes. Diabetes is one of the major causes of morbidity in Italy, causing several disabilities and affecting the economically active population. The objective of this paper is to identify and discuss costs, outcomes and some of the challenges of diabetes care in Italy in the context of recent policy changes. Methods: The study collected data and evidence from both primary and secondary sources. A total of 10 experts, including clinicians (diabetologists/endocrinologists) and decision makers, both at national and regional levels, were interviewed through face-to-face semi-structured interviews. The secondary sources include peer review literature from Medline, grey literature, reports from national and international sources, including professional bodies and organizations. Results: The total direct cost of diabetes for the Italian NHS in 2012 is estimated to be above €9 billion, of which more than half for hospital admissions (57%), and the remaining half for drugs (30%) and outpatient care (13%). However, there is scant evidence on indirect and intangible costs of diabetes in Italy. The quality of care addressed via the AMD Annals found overall good performance with both process and intermediate outcome indicators showing positive and improving results from 2004 to 2011, except for few parameters, including renal function and foot monitoring, which are still inadequate. Major challenges are the rising diabetes prevalence, the difficulty in meeting the rising demand for care and the scant development of multidisciplinary delivery of care, especially in the predominantly ambulatory setting of the Southern diabetes centres. Conclusions: Prevention of diabetes, especially adopting a multi-sectorial approach, should be further empowered by policy makers through promoting healthy lifestyles and specifically targeting child obesity. Other key strategies include more information and education, better diabetes management through the adoption of a chronic model of care, more focus on appropriateness and efficiency of care and better communication between diabetes centres within every Region

Architecture and Methods for Innovative Heterogeneous Wireless Sensor Network Applications

Nowadays wireless sensor netwoks (WSN) technology, wireless communications and digital electronics have made it realistic to produce a large scale miniaturized devices integrating sensing, processing and communication capabilities. The focus of this paper is to present an innovative mobile platform for heterogeneous sensor networks, combined with adaptive methods to optimize the communication architecture for novel potential applications in multimedia and entertainment. In fact, in the near future, some of the applications foreseen for WSNs will employ multi-platform systems with a high number of different devices, which may be completely different in nature, size, computational and energy capabilities, etc. Nowadays, in addition, data collection could be performed by UAV platforms which can be a sink for ground sensors layer, acting essentially as a mobile gateway. In order to maximize the system performances and the network lifespan, the authors propose a recently developed hybrid technique based on evolutionary algorithms. The goal of this procedure is to optimize the communication energy consumption in WSN by selecting the optimal multi-hop routing schemes, with a suitable hybridization of different routing criteria. The proposed approach can be potentially extended and applied to ongoing research projects focused on UAV-based sensing with WSN augmentation and real-time processing for immersive media experiences

Computational assessment of k-means clustering on a Structural Equation Model based index

This paper proposes an alternative method for the choice of the number of centroids in a cluster analysis, when the groups’ order is relevant. Differently from commonly used approaches, aimed at finding the minimum number of clusters, the illustrated method aims at finding the maximum one. Given that the clusters are ordered, this allows to define a granular ranking among them. The k-means partitioning algorithm is applied to an index resulting from a Structural Equation Model. The procedure is implemented on a measure of air pollution in urban areas: a clustering of main Italian cities, according to the optimal number of air pollution levels, is the final result. The analysis’ interpretation provides useful information to design policies aimed at reducing air pollution

Assessing environmental quality by clustering a structural equation model based index

This paper proposes an innovative computational procedure to determine the optimal number of clusters. The aim is to identify the maximum number of significantly distinct clusters, when the centroids are orderable and order is relevant. The insight is that ranking according to this optimal number of clusters allows to better classify units in order to assess their quality with regard to a variable of interest. By means of bootstrap confidence intervals estimated on clusters’ centroids, the procedure allows to identify the optimal number of “well-separated” groups. The centroids are obtained applying a unidimensional k-means clustering and they allow to classify and rank the measure of an Index based on a Structural Equation Model. The procedure ranks European cities according to their level of air pollution

Hybrid Predictive Models for Accurate Forecasting in PV Systems

The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error

Optimization Models for islanded micro-grids: A comparative analysis between linear programming and mixed integer programming

This paper presents a comparison of optimization methods applied to islanded micro-grids including renewable energy sources, diesel generators and battery energy storage systems. In particular, a comparative analysis between an optimization model based on linear programming and a model based on mixed integer programming has been carried out. The general formulation of these models has been presented and applied to a real case study micro-grid installed in Somalia. The case study is an islanded micro-grid supplying the city of Garowe by means of a hybrid power plant, consisting of diesel generators, photovoltaic systems and batteries. In both models the optimization is based on load demand and renewable energy production forecast. The optimized control of the battery state of charge, of the spinning reserve and diesel generators allows harvesting as much renewable power as possible or to minimize the use of fossil fuels in energy production

ANN sizing procedure for the day-ahead output power forecast of a PV plant

Since the beginning of this century, the share of renewables in Europe's total power capacity has almost doubled, becoming the largest source of its electricity production. In 2015 alone, photovoltaic (PV) energy generation rose with a rate of more than 5%; nowadays, Germany, Italy, and Spain account together for almost 70% of total European PV generation. In this context, the so-called day-ahead electricity market represents a key trading platform, where prices and exchanged hourly quantities of energy are defined 24 h in advance. Thus, PV power forecasting in an open energy market can greatly benefit from machine learning techniques. In this study, the authors propose a general procedure to set up the main parameters of hybrid artificial neural networks (ANNs) in terms of the number of neurons, layout, and multiple trials. Numerical simulations on real PV plant data are performed, to assess the effectiveness of the proposed methodology on the basis of statistical indexes, and to optimize the forecasting network performance

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications

Optimization of a dual ring antenna by means of artifcial neural network

In literature, heuristic algorithms have been successfully applied to a number of electromagnetic problems. The associated cost functions are commonly linked to full-wave analysis, leading to complexity and high computational expense. Arti-cial Neural Network is one of the most e®ective biological inspired techniques. In this article, an e±cient surrogate model is trained to replace the full-wave analysis in optimizing the bandwidth of microstrip antenna. The numerical comparison between ANN substitution model and full-wave characterization shows signi-cant improvements in time convergence and computational cost. To verify the robustness of this approach, all these concepts are integrated into a case study represented by a rectangular ring antenna with proximity-coupled feed antenna