Abstract
This paper underlines how the use of Soft Systems Methodology (SSM) for an efficient planning, implementation and monitoring of a dynamic performance management system supported by a conceptual scheme that enables a conscious and prepared implementation, can provide instances of performance of a collaborative network, and also promote alignment among the partners. A systematic way to implement it and a review on two practical applications in Brazilian collaborative networks of SMEs are also presented.

Abstract
During the 2004, 2005 and 2006 growing seasons, physiological and anatomical leaf characteristics and productivity were studied in field-grown grapevines (Vitis vinifera L.) cv. 'Touriga Franca' under ambient (C, 365 +/- 10 ppm) or elevated carbon dioxide vertical bar CO2 vertical bar, (E, 500 +/- 16 ppm) under Open-top chambers (OTC-C and OTC-E, respectively). The elevated vertical bar CO2 vertical bar concentration increased net photosynthetic rate (A), intrinsic water use efficiency (A/g(s)), leaf thickness, Mg concentration, C/N, K/N and Mg/N ratios and decreased stomatal density and N concentration. Nevertheless, stomatal conductance (g(s)), transpiration rate (E), photochemical efficiency (F-v/F-m), leaf water potential, SPAD-values and Red/Far-red ratio transmitted by leaves were not significantly affected by vertical bar CO2 vertical bar. Meanwhile, there is no evidence for downward acclimation of photosynthesis and stomatal conductance. Yield, cluster weight and vigour showed an increase in elevated vertical bar CO2 vertical bar treatment but yield to pruning mass ratio was unaffected. Despite elevated vertical bar CO2 vertical bar stimulates grapevine photosynthesis and yield, more long-term studies, particularly at sub-optimal nutrient and water availability, are needed in order to reveal the grapevine responses to climate change in the Mediterranean area.

Abstract
Affective computing has increased its significance both in terms of academic and industry attention and investment. Alongside, immersive digital environments have settled as a reliable domain, with progressively inexpensive hardware solutions. Having this in mind, the authors envisioned the automatic real-time user emotion extraction through biometric readings in an immersive digital environment. In the running example, the environment consisted in an aeronautical simulation, and biometric readings were based mainly on galvanic skin response, respiration rate and amplitude, and phalanx temperature. The assessed emotional states were also used to modify some simulation context variables, such as flight path, weather conditions and maneuver smoothness level. The results were consistent with the emotional states as stated by the users, achieving a success rate of 77%, considering single emotions and 86% considering a quadrant-based analysis. © Springer Science & Business Media BV 2009.

Abstract

Abstract
We propose an unsupervised method for propagating automatically extracted fine-grained topic labels among news items to improve their topic description for subsequent text classification procedure. This method compares vector representations of news items and assigns to each news item the label of its closest neighbour with a different topic label. Results obtained show that high precision can be achieved in propagating the top ranked topic label, and that 2-gram and 3-gram feature representations optimize the precision.

Abstract
Grid environments are dynamic and heterogeneous by nature, therefore requiring adaptive scheduling strategies. Reinforcement learning is an interesting and simple adaptive approach that may work well in actual grid environments. In this work, we employ reinforcement learning to classify available resources in a grid environment, giving support to two scheduling algorithms, AG and MQD. We study the makespan optimisation and load balancing. An algorithm known as RR is used for normalising purposes. Copyright © 2009 Inderscience Enterprises Ltd.