Management Structure and Work Team Processes; Responsibilities and Responsiveness

This paper explores the management structure of the team-based organization. First it provides a theoretical model of structures and processes of work teams. The structure determines the team’s responsibilities in terms of authority and expertise about specific regulation tasks. The responsiveness of teams to these responsibilities are the processes of teamwork, in terms of three dimensions, indicating to what extent teams indeed use the space provided to them. The research question that this paper addresses is to what extent the position of responsibilities in the team-based organization affect team responsiveness. This is done by two hypotheses. First, the effect of the so-called proximity of regulation tasks is tested. It is expected that the responsibility for tasks positioned higher in the organization (i.e. further from the team) generally has a negative effect on team responsiveness, whereas tasks positioned lower in the organization (i.e. closer to the team) will have a positive effect on the way in which teams respond. Second, the relationship between the number of tasks for which the team is responsible with team responsiveness is tested. Theory suggests that teams being responsible for a larger number of tasks perform better, i.e. show higher responsiveness. These hypotheses are tested by a study of 109 production teams in the automotive industry. The results show that, as the theory predicts, increasing numbers of responsibilities have positive effects on team responsiveness. However, the delegation of expertise to teams seems to be the most important predictor of responsiveness. Also, not all regulation tasks show to have effects on team responsiveness. Most tasks do not show to have any significant effect at all. A number of tasks affects team responsiveness positively, when their responsibility is positioned lower in the organization, but also a number of tasks affects team responsiveness positively when located higher in the organization, i.e. further from the teams in the production. The results indicate that more attention can be paid to the distribution of responsibilities, in particular expertise, to teams. Indeed delegating more expertise improve team responsiveness, however some tasks might be located better at higher organizational levels, indicating that there are limitations to what responsibilities teams can handle.

The influence of geometric factors on the wall shear stress distribution in realistic human coronary arteries

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Biomédica. A presente dissertação foi desenvolvida no Erasmus Medical Center em Roterdão, Holanda

Numerical Method to Predict Void Formation during The Liquid Composite Molding Process

Void formation during the injection phase of the liquid composite molding process can be explained as a consequence of the non-uniformity of the flow front progression. This is due to the dual porosity within the fiber perform (spacing between the fiber tows is much larger than between the fibers within in a tow) and therefore the best explanation can be provided by a mesolevel analysis, where the characteristic dimension is given by the fiber tow diameter of the order of millimeters. In mesolevel analysis, liquid impregnation along two different scales; inside fiber tows and within the open spaces between the fiber tows must be considered and the coupling between the flow regimes must be addressed. In such cases, it is extremely important to account correctly for the surface tension effects, which can be modeled as capillary pressure applied at the flow front. Numerical implementation of such boundary conditions leads to illposing of the problem, in terms of the weak classical as well as stabilized formulation. As a consequence, there is an error in mass conservation accumulated especially along the free flow front. A numerical procedure was formulated and is implemented in an existing Free Boundary Program to reduce this error significantly.

Development of molecularly imprinted polymers using supercritical fluid technology

Dissertação para obtenção do Grau de Doutor em Química Sustentável

Target-specific multiphysics modeling for thermal medicine applications

Dissertation to obtain the degree of Doctor of Philosophy in Biomedical Engineering

Can the Yield Spread predict economic growth? An assessment for European countries

Double Degree. A Work Project presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA- School of Business and Economics and a Masters Degree in Management from Louvain School of Management

Experimental and modeling studies on reverse electrodialysis for sustainable power generation

A potentially renewable and sustainable source of energy is the chemical energy associated with solvation of salts. Mixing of two aqueous streams with different saline concentrations is spontaneous and releases energy. The global theoretically obtainable power from salinity gradient energy due to World’s rivers discharge into the oceans has been estimated to be within the range of 1.4-2.6 TW. Reverse electrodialysis (RED) is one of the emerging, membrane-based, technologies for harvesting the salinity gradient energy. A common RED stack is composed by alternately-arranged cation- and anion-exchange membranes, stacked between two electrodes. The compartments between the membranes are alternately fed with concentrated (e.g., sea water) and dilute (e.g., river water) saline solutions. Migration of the respective counter-ions through the membranes leads to ionic current between the electrodes, where an appropriate redox pair converts the chemical salinity gradient energy into electrical energy. Given the importance of the need for new sources of energy for power generation, the present study aims at better understanding and solving current challenges, associated with the RED stack design, fluid dynamics, ionic mass transfer and long-term RED stack performance with natural saline solutions as feedwaters. Chronopotentiometry was used to determinate diffusion boundary layer (DBL) thickness from diffusion relaxation data and the flow entrance effects on mass transfer were found to avail a power generation increase in RED stacks. Increasing the linear flow velocity also leads to a decrease of DBL thickness but on the cost of a higher pressure drop. Pressure drop inside RED stacks was successfully simulated by the developed mathematical model, in which contribution of several pressure drops, that until now have not been considered, was included. The effect of each pressure drop on the RED stack performance was identified and rationalized and guidelines for planning and/or optimization of RED stacks were derived. The design of new profiled membranes, with a chevron corrugation structure, was proposed using computational fluid dynamics (CFD) modeling. The performance of the suggested corrugation geometry was compared with the already existing ones, as well as with the use of conductive and non-conductive spacers. According to the estimations, use of chevron structures grants the highest net power density values, at the best compromise between the mass transfer coefficient and the pressure drop values. Finally, long-term experiments with natural waters were performed, during which fouling was experienced. For the first time, 2D fluorescence spectroscopy was used to monitor RED stack performance, with a dedicated focus on following fouling on ion-exchange membrane surfaces. To extract relevant information from fluorescence spectra, parallel factor analysis (PARAFAC) was performed. Moreover, the information obtained was then used to predict net power density, stack electric resistance and pressure drop by multivariate statistical models based on projection to latent structures (PLS) modeling. The use in such models of 2D fluorescence data, containing hidden, but extractable by PARAFAC, information about fouling on membrane surfaces, considerably improved the models fitting to the experimental data.

Valorization of olive pomace through combination of biocatalysis with supercritical fluid technology

A supercritical carbon dioxide (scCO2) based oil extraction method was implemented on olive pomace (alperujo), and an oil yield of 25,5 +/- 0,8% (goil/gdry residue) was obtained. By Soxhlet extraction with hexane, an oil extraction yield of 28,9 +/- 0,8 % was obtained, which corresponds to an efficiency of 88,4 +/- 4,8 % for the supercritical method. The scCO2 extraction process was optimized for operating conditions of 50 MPa and 348,15 K, for which an oil loading of 32,60 g oil/kg CO2 was calculated. As a proof of concept, olive pomace was used as feedstock for biodiesel production, in a process combining the use of lipase as a catalyst with the use of scCO2 as a solvent, and integrating the steps of oil extraction, oil to biodiesel transesterification and subsequent separation of the latter. In the conducted experiments, FAME (fatty acid methyl ester) purities of 90% were obtained, with the following operating parameters: an oil:methanol molar ratio of 1:24; a residence time of 7,33 and 11,6 mins; a pressure of 40 MPa; a temperature of 313,15 K; and Lipozyme (Mucor miehei; Sigma-Aldritch) as an enzyme. However, oscillations of FAME purity were registered throughout the experiments, which could possibly be due to methanol accumulation in the enzymatic reactor. Finally, the phenolic content of olive pomace, and the effect of the drying process – oven or freeze-drying – and the extraction methods – hydro-alcoholic method and supercritical method – on the phenolic content were analysed. It was verified that the oven-drying process on the olive pomace preserved 90,1 +/- 3,6 % of the total phenolic content. About 62,3 +/- 5,53% of the oven-dried pomace phenolic content was extracted using scCO2 at 60 MPa and 323,15 K. Seven individual phenols – hydroxytyrosol, tyrosol, oleuropein, quercetin, caffeic acid, ferulic acid and p-coumaric acid – were identified and quantified by HPLC.