Humble leadership and team voice

This study examines the direct and indirect effects of humble leadership on team voice. Although the relationship between leadership styles and voice is widely investigated, humble leadership and team voice, both relatively new constructs, remained out of sight. Drawing upon social interdependence theory, information exchange, team psychological safety, and team-efficacy are proposed to mediate the relationship between humble leadership and team voice. Research is conducted at the team-level analysis and involved 209 team members from 52 teams in 21 companies collected through a snowball sample. Results were provided by the SPSS macro PROCESS using the regression-based approach and bootstrapping techniques. Findings showed that humble leadership is positively related to team voice. Furthermore, findings supported the mediating effect of information exchange. However, no support was given for the mediating effects of team psychological safety and team-efficacy. Theoretical and practical implications of the findings are addressed.

Petri net model decomposition - a model based approach supporting distributed execution

Dissertação apresentada para obtenção do Grau de Doutor em Engenharia Electrotécnica, Especialidade de Sistemas Digitais, pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Dual-phase inkjet printed electrochromic layers based on PTA and WOX/TiO2 nanoparticles for electrochromic applications

Thesis submitted in Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa for the degree of Master in Materials Engineering

Landslide susceptibility assessment in Karanganyar regency - Indonesia - Comparison of knowledge-based and Data-driven Models

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

On-line system for faults detection in induction motors based on PCA

Dissertation to obtain the degree of Master in Electrical and Computer Engineering

A system’s approach to cache hierarchy-aware decomposition of data-parallel computations

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Development of a satellite based PM10 concentration product for urban areas

Dissertação para obtenção do Grau de Doutor em Engenharia do Ambiente

Petri net based development of globally-asynchronous locally-synchronous distributed embedded systems

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de Computadores

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.

Engineered MRI nanoprobes based on superparamagnetic iron oxide nanoparticles

This project aimed to engineer new T2 MRI contrast agents for cell labeling based on formulations containing monodisperse iron oxide magnetic nanoparticles (MNP) coated with natural and synthetic polymers. Monodisperse MNP capped with hydrophobic ligands were synthesized by a thermal decomposition method, and further stabilized in aqueous media with citric acid or meso-2,3-dimercaptosuccinic acid (DMSA) through a ligand exchange reaction. Hydrophilic MNP-DMSA, with optimal hydrodynamic size distribution, colloidal stability and magnetic properties, were used for further functionalization with different coating materials. A covalent coupling strategy was devised to bind the biopolymer gum Arabic (GA) onto MNPDMSA and produce an efficient contrast agent, which enhanced cellular uptake in human colorectal carcinoma cells (HCT116 cell line) compared to uncoated MNP-DMSA. A similar protocol was employed to coat MNP-DMSA with a novel biopolymer produced by a biotechnological process, the exopolysaccharide (EPS) Fucopol. Similar to MNP-DMSA-GA, MNP-DMSA-EPS improved cellular uptake in HCT116 cells compared to MNP-DMSA. However, MNP-DMSA-EPS were particularly efficient towards the neural stem/progenitor cell line ReNcell VM, for which a better iron dose-dependent MRI contrast enhancement was obtained at low iron concentrations and short incubation times. A combination of synthetic and biological coating materials was also explored in this project, to design a dynamic tumortargeting nanoprobe activated by the acidic pH of tumors. The pH-dependent affinity pair neutravidin/iminobiotin, was combined in a multilayer architecture with the synthetic polymers poy-L-lysine and poly(ethylene glycol) and yielded an efficient MRI nanoprobe with ability to distinguish cells cultured in acidic pH conditions form cells cultured in physiological pH conditions.