A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

The river solofrana bicycle network: a project of green mobility

The article presents the “LungoSolofrana” project, carried out during the course “Urban and Mobility” in the academic year 2009/2010, held during the bachelor in Environmental Engineering at the University of Naples “Federico II”. The work has also been chosen as a finalist at the “UrbanPromo 2010” contest, the urban and territorial marketing event sponsored by the National Institute of Urban Planning and Urbit which was held in Venice in 2010. The project consists in a green mobility proposal, developed with an approach based on the integration of the environmental redevelopment of a portion of river Solofrana, located in the Salerno Province, and of the renewal of seven local stations of the railway line Mercato San Severino – Nocera Inferiore, including the realization of a cycle-path network for the natural environment fruition. Furthermore the work drew attention to the local and regional administration. The main intent of the project is to integrate sustainable mobility themes with the environment recovery in a territory affected by high environmental troubles. The area includes the municipalities of Nocera Inferiore, Nocera Superiore, Mercato San Severino, Castel San Giorgio and Roccapiemonte, situated in Salerno’s province, with a total population about 114.000 (font Demo ISTAT 2010). The area extension is about 84,30 sqkm and it is crossed by river Solofrana that is the central point of the project idea. The intervention strategy is defined in two kinds of actions: internal and external rail station interventions. The external rail station interventions regard the construction of pedestrian-cycle paths with the scope of increasing the spaces dedicated to cyclists and to pedestrians along the river Solofrana sides and to connect the urban areas with the railway station. In this way, it’s also possible to achieve an urban requalification of the interested area. On the other side, the interventions inside the station , according to Transit Oriented Development principles, aim at redeveloping common spaces with the insertion of new activities and at realizing new automatic cycle parks covered by photovoltaic panels. The project proposal consists of the urban regeneration of small railway stations along the route-Nocera-Codola Mercato San Severino in the province of Salerno, through interventions aimed at improving pedestrian accessibility. The project involves in particular the construction of pedestrian paths protected access to the station and connecting with neighboring towns and installation of innovative bike parking stations in elevation, covering surfaces coated with solar panels and spaces information. The project is aimed to propose a new model of sustainable transport for small and medium shifts as an alternative to private transportation

Dynamic Network Mechanisms of Relational Integration

A prominent hypothesis states that specialized neural modules within the human lateral frontopolar cortices (LFPCs) support “relational integration” (RI), the solving of complex problems using inter-related rules. However, it has been proposed that LFPC activity during RI could reflect the recruitment of additional “domain-general” resources when processing more difficult problems in general as opposed to RI specifi- cally. Moreover, theoretical research with computational models has demonstrated that RI may be supported by dynamic processes that occur throughout distributed networks of brain regions as opposed to within a discrete computational module. Here, we present fMRI findings from a novel deductive reasoning paradigm that controls for general difficulty while manipulating RI demands. In accordance with the domain- general perspective, we observe an increase in frontoparietal activation during challenging problems in general as opposed to RI specifically. Nonetheless, when examining frontoparietal activity using analyses of phase synchrony and psychophysiological interactions, we observe increased network connectivity during RI alone. Moreover, dynamic causal modeling with Bayesian model selection identifies the LFPC as the effective connectivity source. Based on these results, we propose that during RI an increase in network connectivity and a decrease in network metastability allows rules that are coded throughout working memory systems to be dynamically bound. This change in connectivity state is top-down propagated via a hierarchical system of domain-general networks with the LFPC at the apex. In this manner, the functional network perspective reconciles key propositions of the globalist, modular, and computational accounts of RI within a single unified framework.