In this chapter we examine the technical background behind the general problem of multimedia content deployment, and the architectural and technical choices and legal implications to be considered in order to build an effective client/server multimedia content deployment platform. This platform is suited for the implementation and spreading of a series of services, integrated with the Health Information System and the related educational and recreational facilities and support activities. Such infrastructure requires a strong convergence of expertise and innovative technologies to integrate system components and guarantee security, usability and interoperability as recommended by IHE.

The application of web marketing and definition of corporate strategies has become common practice in all companies, together with the use of mathematical models as a tool for planning and studying the dynamics of communication within the market. In our paper we apply an unsupervised artificial neural network for the classification of a series of olive farms to try to determine which features are most rewarding from the point of view of the communication strategies and market (including the identification of new situations and decision making). The objective is to identify and group companies that have similar characteristics through a set of common indicators and create a rating for defining which companies are the best performing and how companies in the sector are related. This work is made possible by the use of a computer software designed specifically for the olive oil sector, which examines many aspects of business life and also implements the platform through which businesses can talk to each other and the market.

The photovoltaic (PV) industry in Italy has already crossed the threshold of 1 GW of installed capacity. Currently there are approximately 70,000 certified facilities in operation for a power generation of 1,300 GWh/year. With these figures, Italy has become the second country in Europe for PV installed power after Germany. The energy produced would be sufficient to meet the power needs of approximately 1,200,000 people. This leads to some questions: Will this technology continue to grow exponentially even after the recent reduction in rates by the Energy Bill? Will the number of installed PV facilities still grow even with less public support and (probably) a reduction in the technology purchase price? The purpose of this paper is therefore to develop a conceptual model to make a prediction of the PV installed power in Italy through the use of “supervised” artificial neural networks. This model is also applied to the analysis of the spread of this technology in some other European countries.

The semiconductor manufacturing process involves long and complex activities, with intensive use of resources. Producers compete through the introduction of new technologies for increasing yield and reducing costs. So, yield improvement is becoming increasingly important as advanced production technologies are complex and interrelated. In particular, Wafer Bin Maps (WBMs) presenting specific fault models provide crucial information to keep track of process problems in semiconductor manufacturing: production control is often based on the “judgment” of expert engineers who, however, carry out the analysis of map templates through simple visual exploration. In this way, existing studies are subjective, time consuming, and are also limited by the capacity of human recognition. This study proposes a data mining approach derived from the analysis of social networks, which integrates correlation graphs with clustering analysis to quickly extract patterns from WBMs and then bind them to manufacturing defects. An empirical study of wafer clustering was conducted on real production data for validating the proposed clustering algorithm.

The purpose of this chapter is to introduce a powerful class of mathematical models: the artificial neural networks. This is a very general term that includes many different mathematical models and various types of approaches, both from statistics and computer science. Our aim is not to examine them all (it would be a very long discussion), but to understand the basic functionality and the possible implementations of this powerful tool. We initially introduce networks, by analogy with the human brain. The analogy is not very detailed, but it serves to introduce the concept of parallel and distributed computing. Then we analyze in detail a widely applied type of artificial neural network: the feed-forward network with error back- propagation algorithm. We illustrate the architecture of the models, the main learning methods and data representation. The final section deals with a series of applications and extensions to the basic model.

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Molecular biologists need robust computational tools to determine models that can learn to recognize DNA and amino acid sequences and assign protein structures to certain sequences. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this Chapter we describe the main clustering algorithms developed for analyzing gene expression data, comparing their results with the classification deriving by the application of unsupervised neural networks. In the analysis of gene expression data of particular interest is the search for correlated patterns, which is typically done by clustering analysis. DNA microarray technologies (Lockhart et al., 1996) allow the monitoring of thousand genes quickly and efficiently. These technologies have introduced new rules for the exploration of an organism with a genome wide-ranging vision. In particular, the study of gene expression of a complete genome (such as that of Saccharomyces cerevisiae) is now possible. Studies have also been developed (Perou et al., 1999) through the use of DNA microarrays until the complete mapping of the human genome. The production of targeted drugs and identification of drugs are other areas that can significantly benefit from these techniques. One problem inherent the use of DNA microarray technology is the huge amount of data available, the analysis of which is a significant problem per se. Several approaches are used in the analysis of gene expression data, grouped in two areas: clustering and classification. Clustering is a purely data-driven activity that uses only data from the study or experiment to group together measurements. Classification, in contrast, uses additional data, including heuristics, to assign measurements to groups. Among these, commonly statistical methods applied to microarray data are Hierarchical Clustering (Sneath & Sokal, 1973) and (Unsupervised) Neural Networks (Herrero et al., 2001): The identification of the optimal method for the analysis of these data is still a topic of discussion. In this Chapter we examine some methods for gene co-expression analysis, such as "correlation graphs" and supervised-unsupervised clustering methods. The next section is a brief exposition of the underlying background of clustering techniques. Then we detail the clustering algorithm based on correlation graphs. Next we examine the application of supervised and unsupervised techniques. The Chapter ends with some final considerations and further research directions.

Because the CPU is a very expensive resource in mobile ad hoc networks (MANETs), it is very important to consider the overhead introduced in a routing protocol. Many theories have been hypothesized with the aim of minimizing it. But how much is the energy consumption from a network node’s battery induced by the routing protocol overhead? In a previous work, we dealt with a routing protocol based on link stability (link duration observed in a time interval). In this work, we attempt to hypothesize a model for conserving the battery energy consumed by nodes in a MANET adopting the link stability routing protocol.

A very significant issue today concerns the problem of air pollution caused mainly by human activity. The statistics show that most of the pollutants in the atmosphere is due to emissions caused by anthropogenic factors (e.g. power and industrial plants, traffic and combustion phenomena in general). In this paper we evaluate the implementation of a model using artificial neural networks to forecast short-term rate of air pollution for supporting environmental policy decisions.

Applicazione sviluppata in ambiente Android ed Apple iOS che interfaccia i web-services del back-end del sistema informativo StudentCard per erogare servizi di accesso, consultazione e gestione appelli d'esame agli studenti dell'Università di Foggia.

--Objectives-- A series of patients affected by desquamative gingivitis (DG) was investigated in order to evaluate relation patterns among clinical parameters relevant to plaque-induced periodontitis, periodontal microbiological data and the presence of DG lesions. --Patients and methods-- Eight oral lichen planus (OLP) and four mucous membrane pemphigoid (MMP) patients were examined. Periodontal measurements (performed at six sites per tooth on all teeth) included probing depth (PD), gingival recession (REC), clinical attachment loss (CAL) and full-mouth plaque (FMPS) and bleeding (FMBS) scores; the presence and the exact location (site by site) of DG lesions were carefully recorded. Sub-gingival plaque samples were collected and examined by means of real-time PCR for the quantitative determination of the six most important marker organisms of periodontitis. Statistically significant differences and correlation of studied variables between DG-positive and DG- negative sites were investigated in MMP and OLP cases using Mann–Whitney test (p<0.05) and the Spearman rank correlation coefficient, respectively. --Results-- OLP gingival lesions do not significantly affect CAL, although the presence of such lesions may reduce REC and increase PD and FMPS. MMP gingival lesions significantly worsened CAL and increased REC and FMPS. In both OLP and MMP cases, no significant difference was found between DG- positive and DG-negative sites as regards the relative percentage of the investigated species on the total bacterial load. Correlations between the presence of DG lesions and clinical parameters (CAL, PD, REC) were not significant (p<0.05). Significant correlations were found for the presence of gingival OLP lesions and Aggregatibacter actinomycetemcomitans (AA) and for the absence of gingival MMP lesions and AA. --Conclusions-- These findings are not definitive, but highlight the need for further investigations of periodontal clinical and microbiological aspects of disorders causing DG in order to clarify their potential interference with plaque-related periodontitis.

Prediction of various market indicators is an important issue in finance. This can be accomplished through computer models and related applications. It turned out that artificial models have both great advantages and some limitations for learning the data patterns and predicting future values of the financial phenomenon under analysis. In this paper we analyze the particular financial market called Forex and the way computing models are used to automate trading strategies by making affordable predictions on the evolution of exchange rates between currencies.

Within technologies for the safe use of interconnected computer systems, Virtual Private Networks (VPNs) represent a segment with a remarkable development both from the commercial (both in the private sector and the public administration) and the technological side, where we see significant investments by vendors and system integrators. All these involve rapid and interesting innovations in the proliferation of advanced services by specialized operators and, in general, in the growth of this sector. A VPN (Connolly, 2002; Golen, 2002; Tyson, 2008) enables you to separate different types of traffic and implement secure private connections across public networks through labeling techniques, tunneling and traffic encryption (Browne, 2001; Cisco Systems 1999). VPNs are an effective and safe way to extend services, applications and enterprise networks, beyond the physical boundaries of individual organizations by transparently supporting the innovative services of today’s network infrastructures. Development supported by the investments from the main protagonists of networking (satisfying functionality, manageability, scalability, and security) has led to a gradual improvement in the functionality of encryption techniques, authentication sessions, tunneling and traffic engineering. To these basic functionalities, we can add other features such as the support for Voice and Video applications over IPSec VPNs (Cisco Systems 2002), or the possibility of configuring multi-point VPNs by dynamically adding and/or removing nodes. Today it is possible to administer, from a single management point, the deployment and configuration of tens of thousands of VPNs, centrally administer the security policies for each user, and remotely set the configurations of the various hardware and software devices, making it also extremely simple and transparent to network users (Awad et al., 2013). All these are elements that describe the two main strands on which the further development of VPN technologies is also based: support for advanced converged networks (data, voice, video, storage on a single IP network infrastructure), and simplifying the implementation of such systems.

The best tools to manage the exchange of information and services between heterogeneous subjects through new technological tools with particular reference to information systems are certainly the Web-based information systems. Leveraging the infrastructure of the Web, these systems may be able to handle multimedia data, to perform distributed and cooperative applications based on service, in addition to customizing applications and related data. This paper provides an overview on Web Information Systems with particular reference to GIS, presenting a description of the usage scenarios and a comparison between two significant platform for publishing spatial data.