Abstract
Background: Hydroxyethyl starch (HES) is one of the most used colloids for intravascular volume replacement during anesthesia. Aim: To investigate the existence of a chemical interaction between HES and the anesthetic propofol by in vitro propofol dosing, computational docking, and examination of a complex between propofol and HES by infrared (IR), ultraviolet (UV), and H-1 and C-13 nuclear magnetic resonance (NMR) spectroscopy. Methods: Ten samples with human plasma mixed with HES or lactated Ringers (n = 5 for each fluid) were prepared, and the propofol free fraction was quantified until 50 min, using gas chromatography-mass spectrometry. The docking study was performed between HES and propofol and compared with controls. The binding affinities between HES and the small molecules were evaluated by binding free energy approximation (Delta Gb, kJ mol(-1)). The IR, UV, and NMR spectra were measured for propofol, HES, and a mixture of both obtained by the kneading method. Results: Propofol concentrations were significantly lower in the HES samples than in the LR samples (p = .021). The spectroscopic characterization of propofol combined with HES revealed differences in spectra and docking studies reinforced a potential interaction between propofol and HES. Conclusions: Propofol and HES form a complex with different physical-bio-chemical behavior than the single drugs, which may be an important drug interaction. Further studies should evaluate its clinical effects.

Abstract
This paper shows how bibliometric models can be used to assist peers in selecting candidates for academic openings. Several studies have demonstrated that a relationship exists between results from peer-review evaluations and results obtained with certain bibliometric indicators. However, very little has been done to analyse the predictive power of models based on bibliometric indicators. Indicators with high predictive power will be seen as good instruments to support peer evaluations. The goal of this study is to assess the predictive power of a model based on bibliometric indicators for the results of academic openings at the level of Associado and Catedratico at Portuguese universities. Our results suggest that the model can predict the results of peer-review at this level with a reasonable degree of accuracy. This predictive power is better when only the scientific performance is assessed by peers.

Abstract
A model based on a set of bibliometric indicators is proposed for the prediction of the ranking of applicants to an academic position as produced by a committee of peers. The results show that a very small number of indicators may lead to a robust prediction of about 75% of the cases. We start with 12 indicators to build a few composite indicators by factor analysis. Following a discrete choice model, we arrive at 3 comparatively good predicative models. We conclude that these models have a surprisingly good predictive power and may help peers in their selection process.

Abstract
The design of injection mold tools is a complex process for which market pressures demand ever-shorter development time and higher quality level. Thus, it is considered imperative to adopt new methods and tools to support the design process, in order to achieve a more effective mold design solution. Based on this assumption, a multidisciplinary framework was developed, centered on Design for Six Sigma methodology and reinforced with a set of highly valued techniques, namely: the European Customer Satisfaction, the Axiomatic Design and the Multidisciplinary Design Optimization. As a result, a platform was built to support the design of any mold tool without undercuts, which tackles the design of an injection mold as a fully integrated multidisciplinary optimization problem, oriented by customer preferences and their impositions. A set of specific analysis sub-modules is integrated through an overseeing optimization code system, responsible for the numerical simulation of the injection process. This platform was validated through the comparison with an existing mold, whose results attained highlight the great potential of the proposed framework to achieve mold design improvement. In particular, the value of mold solutions generated led to a global improvement on mold performance by 5 %.

Abstract
Mobile devices are increasingly pervasive and are transforming information distribution paradigms. A rapidly growing segment of urban public transport passengers carry mobile computing devices, permanently and on the move. In a context of thinning financial resources, getting customers involved in the actual delivery of a public transport service as real-time information consumers and providers, may be a powerful method to enhance travel experience while reducing operational costs for the service operator. Each and every customer travelling on a public transport network has unique knowledge about the service operation as it unfolds. This paper proposes a framework that aims to unify public transport passengers' collective intelligence through crowdsourcing, using their mobile computing devices and dedicated web services. It strives to intensify win-win relationships between public transport passengers and operators. The structured exchange of information is sustained by a validation mechanism for data reliability, and an incentive mechanism to encourage passenger participation. Passengers benefit from rich real-time data tailored to their profiles, to ease their journeys and improve travel experience, in exchange for their own participation providing and validating information. Operators gain access to rich customer generated data, which in an aggregated format may provide a real-time assessment of customer experience and of local performance across the entire network operation. Operators may be required to reward travellers who become prolific co-creators of the public transport service, but higher customer experience levels, lesser needs for investment in controlling mechanisms, and continuous free monitoring of customer opinions can jointly lead to financial returns. (C) 2013 The Authors. Published by Elsevier Ltd.

Abstract
This paper summarizes existing systems and research on information transport services, and proposes a hypothetic scenario for future travellers using public transport. Increased distributed intelligence in pervasive mobile smart devices and in sensor networks in public transport vehicles is enabling a new approach for enhancing the experience of public transport customers. Such environment could be modelled through a distributed multi-agent service system. This paper presents advanced information services already available on such environments, in particular the MOVE-ME smartphone application, and indicates a possible service environment where people's feedback may benefit all transport service stakeholders. Mobile computing and crowdsourcing are key enablers for enhancing user experience in the transport services, and also for enhancing overall public transport services. Better experience leads to increased usage of shared mobility modes, and therefore to more sustainable cities in the future. Concerns about data security, and anonymity of travellers will need to be adequately addressed in the future scenarios presented.