Abstract
This paper aims to explore the performance alignment in collaborative network environments. It has been stated that performance management based on data collection and the evaluation of key performance indicators (KPIs) may not be effective due to the different indicators and measurement systems in place for the various participants in a collaborative network (CN). Therefore, measuring the strategic and inter-organizational alignment based on key alignment indicators (KAIs) can be an excellent alternative to improve performance evaluation systems. This approach has led to the exploration of the performance prediction paradigm and develops tools to estimate a performance and evaluate the degree of alignment by creating instances of a future performance in collaborative networks.

Abstract
In order to develop the new industrial paradigm, it is necessary to explore a framework supported by planning and operational tools. Indeed, it becomes clear that more and more the next factories need to be increasingly more flexible, agile and knowledge-based in order to be able to adapt in real time to the continuously changing market demands, technology options and regulations, as well as accelerate the design process and optimise production. Therefore, in this paper a framework capable of supporting the factory planning will be presented based on predefined templates and product requirements. Furthermore, the Factory Templates will allow for process deployment, control and evaluation in real-time. © 2011 AISTI.

Abstract
This paper describes and discusses the results of an empirical study about social-networks/microblogging adoption in a financial institution. Social networking at the workplace literature is overviewed and then presented the methodology and hypothesis of the study. Interviews, a survey and a experimental pilot were used as data collection methods. An extract of the results related with the experimental pilot are discussed and some conclusions derived from them.

Abstract
Performance management in collaborative networks of organisations is a complex process due to the multiplicity of competing perspectives upon it. One of the more sensitive phases of this process is the agreement of the actors in the network regarding the performance evaluation model whose design is considered of great importance in the research literature. This paper proposes a method for the design of performance evaluation systems in collaborative networks through an innovative combination of performance information classification and multi-criteria decision model. The method is implemented in a web-based collaborative platform that enables the members of a collaborative network to efficiently achieve specific performance models that result from a collective and negotiated construction.

Abstract
With advancement in computer science and information technology, computing systems are becoming increasingly more complex with an increasing number of heterogeneous components. They are thus becoming more difficult to monitor, manage, and maintain. This process has been well known as labor intensive and error prone. In addition, traditional approaches for system management are difficult to keep up with the rapidly changing environments. There is a need for automatic and efficient approaches to monitor and manage complex computing systems. In this paper, we propose an innovative framework for scheduling system management by combining Autonomic Computing (AC) paradigm, Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.

Abstract
Ocean sewage outfalls are major sources of contaminants to coastal ocean ecosystems. This method of disposal has advantages in terms of economy and relative societal impact, but it also raises important concerns about public health and ecosystem preservation. Autonomous Underwater Vehicles have already been shown to be very useful for monitoring routine of ocean outfalls. The major advantage of this technology over traditional methods is the ability to collect high-resolution data which can be very valuable for environmental impact assessment and comparison with plume prediction models. Once the data has been collected in the field it is necessary to extrapolate from monitoring samples to unsampled locations. Geostatistics has been successfully used to obtain information, for example, regarding the spatial distribution of soil properties. In this work geostatistics is used to model and map the spatial distribution of temperature and salinity measurements gathered by MARES AUV in a monitoring campaign to Foz do Arelho outfall, with the aim of distinguishing the effluent plume from the receiving waters and characterizing its spatial variability in the vicinity of the discharge. The results demonstrate that this methodology provides good estimates of the dispersion of effluent and it is therefore very valuable in assessing the environmental impact and managing sea outfalls.