University of Madeira

About: University of Madeira is a education organization based out in Funchal, Portugal. It is known for research contribution in the topics: Population & Dendrimer. The organization has 1014 authors who have published 2759 publications receiving 59457 citations.

A discourse on complexity of process models

Abstract: Complexity has undesirable effects on, among others, the correctness, maintainability, and understandability of business process models. Yet, measuring complexity of business process models is a rather new area of research with only a small number of contributions. In this paper, we survey findings from neighboring disciplines on how complexity can be measured. In particular, we gather insight from software engineering, cognitive science, and graph theory, and discuss in how far analogous metrics can be defined on business process models.

TRANSATLANTIC DEVELOPMENTAL MIGRATIONS OF LOGGERHEAD SEA TURTLES DEMONSTRATED BY mtDNA SEQUENCE ANALYSIS

Abstract: Molecular markers based on mitochondrial (mt) DNA control region sequences were used to test the hypothesis that juvenile loggerhead sea turtles (Caretta caretta) in pelagic habitats of the eastern Atlantic are derived from nesting populations in the western Atlantic. We compared mtDNA haplotypes from 131 pelagic juvenile turtles (79 from the Azores and 52 from Madeira) to mtDNA haplotypes observed in major nesting colonies of the Atlantic Ocean and Mediterranean Sea. A subset of 121 pelagic samples (92%) contained haplotypes that match mtDNA sequences observed in nesting colonies. Maximum likelihood analyses (UCON, SHADRACQ) estimate that 100% of these pelagic juveniles are from the nesting populations in the southeastern United States and adjacent Yucatan Peninsula, Mexico. Estimated contributions from nesting populations in south Florida (0.71, 0.72), northern Florida to North Carolina (0.19, 0.17), and Quintana Roo, Mexico (0.11, 0.10) are consistent with the relative size of these nesting aggregates....

Fast-Predictive Optimal Control of NPC Multilevel Converters

Abstract: The development of high-processing-capability microprocessors allows the implementation of new digital control methods for neutral-point-clamped (NPC) multilevel converter in power-electronic applications. This paper presents a new predictive digital control method for multilevel converters, called “fast predictive.” This method computes the optimal vector using the NPC three-phase multilevel dynamic model equations just once in each control cycle, while current predictive methods need 27 calculations. The closest vector to the optimal vector is found by minimizing the distance between each one of the 27 available vectors to the optimal vector. Space vector modulation could be also used. The obtained performance is similar to the predictive optimal control that uses the converter model to find all the 27 responses of the multilevel and then searches for the vector that minimizes control errors. Relative to predictive optimal control, the fast predictive improves digital processing speed by at least 150% in multilevel converters with 27 vectors. This speed improvement would allow multilevel converters with five or higher number of levels (125 instead of 27 vectors) to be controlled using the same sampling frequency of the three-level inverter. The fast-predictive controller is used in a multilevel rectifier with near-unity power factor to enforce the ac currents. Fast predictive control is also used in the rectifier dc voltage to reduce sensitivity of the dc voltage to dc load disturbances. The simulation and experimental results show that the fast-predictive controller is able to control the ac currents of a three-phase multilevel rectifier, achieving nearly 1.5% total harmonic distortion while balancing the capacitors' dc voltages. The use of predictive control to regulate the dc voltage shows an improvement of approximately 7% compared to a proportional-integral controller.