Implementation of a quality system in joint master programmes

Joint master programmes are systems which by default demand a proper quality system in order to sustain and improve. Objective of this thesis is analysing and proposing solutions to difficulties associated with the implementation of a quality management system to joint master programmes, with the focus on international joint master programmes. The application of the analysis to the Erasmus Mundus joint master programme European Master in Quality in Analytical Laboratories (EMQAL) is discussed. QA systems implementation in HEIs in Europe is an ongoing process, and implementation of such systems in JPs is one step further to enhancing quality in higher education in Europe. The issue closely discussed in this thesis is: should QMS be developed independently from the institutions, or should the institutions, when developing their quality management systems, take into account the (future) development of joint courses and prepare their quality procedures accordingly? A quality management system is normally developed for one organization, and different aspects of cooperation are considered within. A joint master programme is a result of successful cooperation of two or more organizations; therefore a development of its quality management system must be approached in a different manner. This thesis proposes a QMS with emphasis both on the HEI and the consortium. Different processes in the QMS can be managed independently at the level of the HEI or at the level of the consortium. Most processes in joint master programmes should be designed in programmes’ and institutions’ QMSs. Quality of a joint master programme cannot be analyzed separately from the higher education institutions which are organizing it. Comparative analysis of organization of one Erasmus Mundus Master programme to the solutions proposed in discussion showed that from all of the aspects considered, processes in EMQAL are organized in harmony with the proposed delegation of processes of the QMS for a joint master programme. The solutions proposed in the discussion are based on theoretical application of the quality principles and concepts. Comparison to the quality processes and procedures in an existing EM programme showed that analysis is applicable in practice.

Production and characterisation of gilthead sea bream (Sparus auratus) recombinant parathyroid hormone related protein.

The production and puriWcation of gilthead sea bream recombinant parathyroid hormone related protein [sbPTHrP(1–125)] using an Escherichia coli system and one step puriWcation process with continuous elution gel electrophoresis is reported. The cDNA encoding sbPTHrP(1–125) was cloned into a prokaryotic expression vector pET-11a. The recombinant plasmid was used to transfect E. coli BL21(DE3) pLysS and sbPTHrP(1–125) synthesis was induced by addition of 1mM isopropyl- -D-thiogalactopyranoside. The rapid one step isolation method gave pure sbPTHrP(1–125) as judged by SDS–PAGE and yielded up to 40mg/L of culture medium (3.3mg protein/g of bacteria). The bioactivity of recombinant sbPTHrP(1–125) assessed using an in vitro scale bioassay was found to be equipotent to PTHrP(1–34) in stimulating cAMP accumulation. Assessment of the immunological reactivity of the isolated protein by Western blot revealed it cross-reacts with antisera speciWc for the N-terminal and C-terminal region of PTHrP. In a radioimmunoassay speciWc for piscine N-terminal (1–34 aa) PTHrP, the recombinant sbPTHrP(1–125) was equipotent with PTHrP(1–34) in displacing labelled 125I-PTHrP(1–36) PTHrP from the antisera. The availability of recombinant sbPTHrP will allow the development of region speciWc assays and studies aimed at deWning post-secretory processing of this protein and its biological activity in Wsh.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.