Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Abstract

Abstract
In a climate change context, changes in extreme sea-levels rather than changes in the mean are of particular interest from the coastal protection point of view. In this work, extreme sea-levels in the Baltic Sea are investigated based on daily tide gauge records for the period 1916-2005 using the annual block maxima approach. Extreme events are analysed based on the generalised extreme value distribution considering both stationary and time-varying models. The likelihood ratio test is applied to select between stationary and non-stationary models for the maxima and return values are estimated from the final model. As an independent and complementary approach, quantile regression is applied for comparison with the results from the extreme value approach. The rates of change in the uppermost quantiles are in general consistent and most pronounced for the northernmost stations.

Abstract
Service experience is the core of service offering. However, research on the role of the service environment when creating experiences is still underdeveloped, and even less empirical evidence is available that relates servicescape with customers' perceptions of value. Our purpose is to focus on the role of servicescape as a driver of customer experience by analyzing its impact on customer value creation and behavior. We have chosen an experience-centric service organization, a football club, to develop our analysis. A regression analysis was performed to determine the main drivers of value among ‘sportscape’ components and how it influenced customers' attitude and behavior. A total of 349 questionnaires were collected during a match of the Portuguese Football League at Dragon Stadium. Our analysis showed that servicescape influences consumer perceived value, attitudes, and behavior when creating service experiences: value-in-context generates customers' satisfaction, which in turn has a positive effect on their desire to repeat the experience. © 2014, Taylor & Francis.

Abstract
Highly perishable food products can lose an important part of their value in the distribution process. We propose a novel multi-objective model that decouples the minimization of the distribution costs from the maximization of the freshness state of the delivered products. The main objective of the work is to examine the relation between distribution scenarios and the cost-freshness trade-off. Small size instances adapted from the vehicle routing problem with time windows are solved with an epsilon-constraint method and for large size instances a multi-objective evolutionary algorithm is implemented. The computational experiments show the conflicting nature of the two objectives.

Abstract
UNIX-based operative systems were always empowered by scriptable shell interfaces, with a core set of powerful tools to perform manipulation over files and data streams. However those tools can be difficult to manage at the hands of a non-expert programmer. This paper proposes the creation of a Collaborative Web Platform to easily create workflows using common UNIX command line tools for processing Big Data through a collaborative web GUI.