Abstract
Smart specialisation is a major driver of contemporary regional development policy in the European Union. Politicians, policymakers, and academics enthusiastically wave smart specialisation as the remedy that will fix the problems affecting previous policy rounds. The expectations towards the ‘remedial' effect of smart specialisation bear on the assumption that the policy approach will place the emphasis on what is unique in a given region by means of a so-called entrepreneurial process of discovery, basically a wide participatory process, underpinning a learning mechanism aimed at revealing the R&D and innovation domains in which that region can hope to excel. Universities are generally seen as central organisations in smart specialisation strategies, as made explicit in policy documents and academic papers. The chapter aims at knowing more about the power of smart specialisation policies to induce change in university-industry interactions and the promotion of sustainable growth.

Abstract
The injection mold is a high precision tool responsible for the production of most plastic parts used everywhere. Its design is considered critically important for the quality of the product and efficient processing, as well as determinant for the economics of the entire injection molding process. However, typically, no formal engineering analysis is carried out during the mold design stage. In fact, traditionally, designers rely on their skills and intuition, following a set of general guidelines. This does not ensure that the final mold design is acceptable or the best option. At the same time, mold makers are now highly pressured to shorten both leading times and cost, as well as to accomplish higher levels of mold performance. For these reasons, it is imperative to adopt new methods and tools that allow for faster and higher integrated mold design. To that end, a new global approach, based on the integration of well-known quantitative techniques, such as Design for Six Sigma (DFSS), Structural Equation Modeling (SEM), Axiomatic Design (AD) and Multidisciplinary Design Optimization (MDO) is presented. Although some of these methods have been largely explored, individually or in combination with other methodologies, a quantitative integration of all aspects of design, in such a way that the whole process becomes logical and comprehensible, has not yet been considered. To that end, the DFSS methodology, through its IDOV roadmap, was adopted. It is based on the ICOV Yang and El-Haik proposal, establishing four stages for the design process: Identify, which aims to define customers’ requirements/expectations; Design, where the creation of a product concept, and its system-level design, is performed; Optimization, in which all the detailed design, through product optimization, is handled; and finally, Validation, where all product design decisions are validated, in order to verify if the new designed entity indeed meets customer and other requirements. As a result, this approach tackles the design of an injection mold in a global and quantitative approach, starting with a full understanding of customer requirements and converting them into optimal mold solutions. In order to validate it, an integrated platform was developed, where all different analysis modules were inserted and optimized through an overseeing code system. The results attained highlight the great potential of the proposed framework to achieve mold design improvements, with consequent reduction of rework and time savings for the entire mold design process. © 2011 by Nova Science Publishers, Inc.

Abstract

Abstract
Our society generates massive amounts of digital data, significant portion of which is being archived and made accessible to the public for the current and future use. In addition, historical born-analog documents are being increasingly digitized and included in document archives which are available online. Professionals who use document archives tend to know what they wish to search for. Yet, if the results are to be useful and attractive for ordinary users they need to contain content which is interesting and familiar. However, the state-of-the-art retrieval methods for document archives basically apply same techniques as search engines for synchronic document collections. In this paper, we introduce a novel concept of estimating the relation of archival documents to the present times, called contemporary relevance. Contemporary relevance can be used for improving access to archival document collections so that users have higher probability of finding interesting or useful content. We then propose an effective method for computing contemporary relevance degrees of news articles using Learning to Rank with a range of diverse features, and we successfully test it on the New York Times Annotated document collection. Our proposal offers a novel paradigm of information access to archival document collections by incorporating the context of contemporary time.

Abstract
Wind power forecast evaluation matters greatly as wind power has an ever-increasing proportion in the power system. Generally speaking the forecasting result can be divided into lead-lag scenarios and common scenarios which depends on whether the wind process is predicted on time. During the lead-lag scenarios the errors usually change from large positive numbers to negative ones (or the opposite), especially in the both ends of the period. Compared with the common scenarios in the same value of root mean square error (RMSE), large changes in errors from positive to negative in a short time can cost nearly two times of spinning reserve but get the same assessment score. For power system the two scenarios should be evaluated differently, however, few metrics in the evaluation can indicate the lead-lag scenarios in that they dispose the errors ignoring the signs or time continuity of the errors, or analysis the errors in a macro-scale sight like 24 hours horizon scale. This paper proposes a new metric based on RMSE which detects the changes of signs of errors in a process of moving average. Except for normal advantages like objectivity, adaptability, unity, symmetry and stability, the new metric has the ability to reflect both the lead-lag scenarios and common scenarios. The new metric can be used in the evaluation of wind and solar power, load, price, demand response forecasting and the process of neural network parameter training.

Abstract
This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R-2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from -9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 degrees C (from 8.0 degrees C) and 16.1 degrees C (from 20.5 degrees C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.