Abstract
A method for the separation and fractionation of the major whey proteins from a whey protein concentrate (WPC80) by anion-exchange chromatography coupled to a Fast Protein Liquid Chromatography (FPLC) system is proposed. The method is based on the use of an ionic column (Mono Q) and a salt gradient elution by increasing the ionic strength of the elution buffer (Tris-HCl 20 mM plus 0 to 1 M NaCl). The proposed method was found to be suitable to fractionate the major whey proteins from the WPC80 in different fractions, namely one fraction containing all the alpha-Lactalbumin and immunoglobulins; another fraction containing all the bovine serum albumin; and two distinct fractions each containing a different variant of p-Lactoglobulin. A 60.5% (w/w) recovery of the two main p-Lactoglobulin variants was obtained.

Abstract
Long term mean sea level in the Chagos Archipelago has been relatively stable over the similar to 20 year length of the available instrumental records. Tide-gauge data from Diego Garcia (1988-2000, and 2003-2011) show no statistically significant long-term rise, whilst the rates of rise obtained from the satellite altimeter record for 1993-2011 span the range of 0.16-4.56 mm yr(-1) in the surrounding sea areas (70-74 degrees E and 4-9 degrees S) and are also consistent with a zero rate except in the far south of the region. The dominant feature is one of considerable inter-annual variability in mean sea level of up to similar to 10 cm, such that the very weak seasonal pattern of highest and lowest sea level in February and May respectively, is absent or reversed in some years. The Indian Ocean Dipole appears to exert an important influence on mean sea level in the area, with positive and negative dipole mode indices preceding periods of elevated or lowered sea levels respectively. The Chagos also lie outside the Indian Ocean cyclone belt and experience relatively low wind speeds, and there is no evidence of changes in the wind or wave environment in the past 20 years. Although in an area of seismic activity, there is no record of island subsidence, indeed on Diego Garcia minor crustal uplift of 0.63 +/- 0.28 SE mm yr(-1) has occurred between 1996 and 2009. Collectively, these results suggest that this has been a relatively stable physical environment, and that these low-lying coral islands should continue to be able to support human habitation, as they have done for much of the last 200 years. Nonetheless, future sea-level rise and its effect on the Chagos remains an important issue for further studies such as those of the Intergovernmental Panel on Climate Change.

Abstract
Newly devices allow the analysis and collection of very long-term electrocardiogram (ECG). However, associated with this devices and long-term signal, are artefacts that conduce to misleading interpretations and diagnosis. So, new developments over automatic ECG classification are needed for a reliable interpretation. The feasibility of the cardiac systems is one of the main concerns, once they are currently used as diagnosis or help systems. In this project, an artefact detection algorithm is developed, dividing the time-series in intervals of signal and artefact. The algorithm is based on the assumption that, if the analysed frame is signal, there is not an abrupt alteration over consecutive short windows. So, the time-series is divided in consecutive nonoverlapped short windows. Over these windows, it is calculated the time-series standard deviation, the maximum and minimum slope. A threshold-based rule is applied, and the algorithm reveals that, in mean, it is verified a 99.29% of correctly classified signal and only 0.71% of signal erroneously classified. Over the results obtained, the algorithm seems to present good results, however it is needed its validation in a wider and representative sample with segments marked as artefact by multiple specialists.

Abstract
Cloud computing infrastructures are the most recent approach to the development and conception of computational systems. Cloud infrastructures are complex environments with various subsystems, each one with their own challenges. Cloud systems should be able to provide the following fundamental property: elasticity. Elasticity is the ability to automatically add and remove instances according to the needs of the system. This is a requirement for pay-per-use billing models. Various open source software solutions allow companies and institutions to build their own Cloud infrastructure. However, in most of these, the elasticity feature is quite immature. Monitoring and timely adapting the active resources of a Cloud computing infrastructure is key to provide the elasticity required by diverse, multi-tenant and pay-per-use business models. In this paper, we propose Elastack, an automated monitoring and adaptive system, generic enough to be applied to existing IaaS frameworks, and intended to enable the elasticity they currently lack. Our approach offers any Cloud infrastructure the mechanisms to implement automated monitoring and adaptation as well as the flexibility to go beyond these. We evaluate Elastack by integrating it with the OpenStack showing how easy it is to add these important features with a minimum, almost imperceptible, amount of modifications to the default installation. © 2012 ACM.

Abstract
Unexpectedly frequent subgraphs, known as motifs, can help in characterizing the structure of complex networks. Most of the existing methods for finding motifs are designed for unweighted networks, where only the existence of connection between nodes is considered, and not their strength or capacity. However, in many real world networks, edges contain more information than just simple node connectivity. In this paper, we propose a new method to incorporate edge weight information in motif mining. We think of a motif as a subgraph that contains unexpected information, and we define a new significance measurement to assess this subgraph exceptionality. The proposed metric embeds the weight distribution in subgraphs and it is based on weight entropy. We use the g-trie data structure to find instances of k-sized subgraphs and to calculate its significance score. Following a statistical approach, the random entropy of subgraphs is then calculated, avoiding the time consuming step of random network generation. The discrimination power of the derived motif profile by the proposed method is assessed against the results of the traditional unweighted motifs through a graph classification problem. We use a set of labeled ego networks of co-authorship in the biology and mathematics fields. The new proposed method is shown to be feasible, achieving even slightly better accuracy. Since it does not require the generation of random networks, it is also computationally faster, and because we are able to use the weight information in computing the motif importance, we can avoid converting weighted networks into unweighted ones.

Abstract
This paper presents the use of simulation to assist the decision-making process involved in implementing lean manufacturing principles at a carton box die factory. The paper describes the application of discrete event simulation to improve and optimise the performance of the carton box die assembly line. Simulation experiments measure each system's resource requirements and performance, quantifying benefits to be derived from applying the principles of lean manufacturing. In this study, Simio® is used to model and simulate different experimental scenarios in order to quantify the impact of selected input parameters on objective functions such as lead time. Results show that changes in the layout can reduce workers' movements and increase productivity.