A negociação no mercado a prazo do MIBEL

This master thesis has been developed during the internship in the Supervision Department of Supervision of the Intermediation and Market Structures of CMVM. My collaboration in such department was mainly focused on the derivatives market of the Iberian Electricity Market (MIBEL). MIBEL embodies two organized markets – the derivatives market in Portugal and the spot market in Spain The trading activity in the derivatives market of MIBEL is processed through the trading platform of the regulated market managed by OMIP, however, much of the negotiation is over-the-counter. The aim of this work is to describe the market from a legal and economic perspective and to analyse the evolution of the negotiation, namely the impact of OTC in the regulated market trading. To achieve this, I propose to analyse also MiFID and EMIR rules over derivative contracts and the role of central counterparties, as they both are important to the discussion. In parallel, we found that OTC transactions are considerably higher than those traded in the regulated market managed by OMIP, those findings can be justified by the contractual relationships based on trust already established between the partiesarties. Nevertheless, since 2011 this trend changed by an increase of the registered OTC. Thereafter, although the parties continued to trade bilaterally, these transactions were registered in a central counterparty in order to eliminate the inherent risks related to the OTC derivatives transactions. This change in the negotiation pattern may also be influenced by the mandatory reporting of transactions imposed by EMIR, that requires for some classes of derivatives the centralized clearing and for all other requires the implementation of risk mitigation techniques.

Proving a market for a startup befor scaling using the hypothesis-driven entrepreneurship process

The year is 2015 and the startup and tech business ecosphere has never seen more activity. In New York City alone, the tech startup industry is on track to amass $8 billion dollars in total funding – the highest in 7 years (CB Insights, 2015). According to the Kauffman Index of Entrepreneurship (2015), this figure represents just 20% of the total funding in the United States. Thanks to platforms that link entrepreneurs with investors, there are simply more funding opportunities than ever, and funding can be initiated in a variety of ways (angel investors, venture capital firms, crowdfunding). And yet, in spite of all this, according to Forbes Magazine (2015), nine of ten startups will fail. Because of the unpredictable nature of the modern tech industry, it is difficult to pinpoint exactly why 90% of startups fail – but the general consensus amongst top tech executives is that “startups make products that no one wants” (Fortune, 2014). In 2011, author Eric Ries wrote a book called The Lean Startup in attempts to solve this all-too-familiar problem. It was in this book where he developed the framework for The Hypothesis-Driven Entrepreneurship Process, an iterative process that aims at proving a market before actually launching a product. Ries discusses concepts such as the Minimum Variable Product, the smallest set of activities necessary to disprove a hypothesis (or business model characteristic). Ries encourages acting briefly and often: if you are to fail, then fail fast. In today’s fast-moving economy, an entrepreneur cannot afford to waste his own time, nor his customer’s time. The purpose of this thesis is to conduct an in-depth of analysis of Hypothesis-Driven Entrepreneurship Process, in order to test market viability of a reallife startup idea, ShowMeAround. This analysis will follow the scientific Lean Startup approach; for the purpose of developing a functional business model and business plan. The objective is to conclude with an investment-ready startup idea, backed by rigorous entrepreneurial study.

Self-monitoring of freeze–thaw damage using triphasic electric conductive concrete

The effect of freeze–thaw cycles on concrete is of great importance for durability evaluation of concrete structures in cold regions. In this paper, damage accumulation was studied by following the fractional change of impedance (FCI) with number of freeze–thaw cycles (N). The nano-carbon black (NCB), carbon fiber (CF) and steel fiber (SF) were added to plain concrete to produce the triphasic electrical conductive (TEC) and ductile concrete. The effects of NCB, CF and SF on the compressive strength, flexural properties, electrical impedance were investigated. The concrete beams with different dosages of conductive materials were studied for FCI, N and mass loss (ML), the relationship between FCI and N of conductive concrete can be well defined by a first order exponential decay curve. It is noted that this nondestructive and sensitive real-time testing method is meaningful for evaluating of freeze–thaw damage in concrete.

Analysis of output properties of the Peroxiredoxin/Thioredoxin/Thioredoxin reductase system

Dissertação de mestrado em Bioinformática

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

A sixth-order finite volume method for the 1D biharmonic operator: application to intramedullary nail simulation

A new very high-order finite volume method to solve problems with harmonic and biharmonic operators for one- dimensional geometries is proposed. The main ingredient is polynomial reconstruction based on local interpolations of mean values providing accurate approximations of the solution up to the sixth-order accuracy. First developed with the harmonic operator, an extension for the biharmonic operator is obtained, which allows designing a very high-order finite volume scheme where the solution is obtained by solving a matrix-free problem. An application in elasticity coupling the two operators is presented. We consider a beam subject to a combination of tensile and bending loads, where the main goal is the stress critical point determination for an intramedullary nail.

DOTA-based Ga(III) and Gd(III) chelates for medical imaging (PET, SPECT and MRI)

PhD Thesis in Sciences Specialization in Chemistry

Thermal–mechanical behaviour of chitosan–cellulose derivative thermoreversible hydrogel films

CH, Chitosan; HPMC, (Hydroxypropyl)methyl cellulose; FT, Freeze-thaw; SC, Solvent casting; CH:HPMC (X:Y), pH Z, FT/SC, Chitosan and (hydroxypropyl)methyl cellulose hydrogel, at X and Y proportion (0-100), at Z pH (3.0-4.0) and prepared by freeze-thaw or solvent casting techniques; DSC, Differential scanning calorimetry; MDSC, Temperature modulated Differential scanning calorimetry; Tg, glass transition temperature; ΔH, enthalpy change; TGA, Thermogravimetric Analysis; TG, Thermogravimetry; DTG, Derivative or Differential thermogravimetry; σ, Tensile strength; ε, elongation at break; DMA, Dynamic mechanical analysis; X-Ray, X-radiation, FTIR-ATR, Attenuated total reflectance Fourier transform infrared spectroscopy; SEM, Scanning electron microscopy.

Erratum to: Thermal–mechanical behaviour of chitosan–cellulose derivative thermoreversible hydrogel films

CH, Chitosan; HPMC, (Hydroxypropyl)methyl cellulose; FT, Freeze-thaw; SC, Solvent casting; CH:HPMC (X:Y), pH Z, FT/SC, Chitosan and (hydroxypropyl)methyl cellulose hydrogel, at X and Y proportion (0-100), at Z pH (3.0-4.0) and prepared by freeze-thaw or solvent casting techniques; DSC, Differential scanning calorimetry; MDSC, Temperature modulated Differential scanning calorimetry; Tg, glass transition temperature; ΔH, enthalpy change; TGA, Thermogravimetric Analysis; TG, Thermogravimetry; DTG, Derivative or Differential thermogravimetry; σ, Tensile strength; ε, elongation at break; DMA, Dynamic mechanical analysis; X-Ray, X-radiation, FTIR-ATR, Attenuated total reflectance Fourier transform infrared spectroscopy; SEM, Scanning electron microscopy.

A high order model for piezoelectric rods: an asymptotic approach

Preprint submitted to International Journal of Solids and Structures. ISSN 0020-7683

Learning joint representations for order and timing of perceptual-motor sequences: a dynamic neural field approach

Many of our everyday tasks require the control of the serial order and the timing of component actions. Using the dynamic neural field (DNF) framework, we address the learning of representations that support the performance of precisely time action sequences. In continuation of previous modeling work and robotics implementations, we ask specifically the question how feedback about executed actions might be used by the learning system to fine tune a joint memory representation of the ordinal and the temporal structure which has been initially acquired by observation. The perceptual memory is represented by a self-stabilized, multi-bump activity pattern of neurons encoding instances of a sensory event (e.g., color, position or pitch) which guides sequence learning. The strength of the population representation of each event is a function of elapsed time since sequence onset. We propose and test in simulations a simple learning rule that detects a mismatch between the expected and realized timing of events and adapts the activation strengths in order to compensate for the movement time needed to achieve the desired effect. The simulation results show that the effector-specific memory representation can be robustly recalled. We discuss the impact of the fast, activation-based learning that the DNF framework provides for robotics applications.

A new competitive implementation of the electromagnetism-like algorithm for global optimization

The Electromagnetism-like (EM) algorithm is a population- based stochastic global optimization algorithm that uses an attraction- repulsion mechanism to move sample points towards the optimal. In this paper, an implementation of the EM algorithm in the Matlab en- vironment as a useful function for practitioners and for those who want to experiment a new global optimization solver is proposed. A set of benchmark problems are solved in order to evaluate the performance of the implemented method when compared with other stochastic methods available in the Matlab environment. The results con rm that our imple- mentation is a competitive alternative both in term of numerical results and performance. Finally, a case study based on a parameter estimation problem of a biology system shows that the EM implementation could be applied with promising results in the control optimization area.

Combining non-dominance, objective-order and spread metric to extend firefly algorithm to multi-objective optimization

In this paper, we propose an extension of the firefly algorithm (FA) to multi-objective optimization. FA is a swarm intelligence optimization algorithm inspired by the flashing behavior of fireflies at night that is capable of computing global solutions to continuous optimization problems. Our proposal relies on a fitness assignment scheme that gives lower fitness values to the positions of fireflies that correspond to non-dominated points with smaller aggregation of objective function distances to the minimum values. Furthermore, FA randomness is based on the spread metric to reduce the gaps between consecutive non-dominated solutions. The obtained results from the preliminary computational experiments show that our proposal gives a dense and well distributed approximated Pareto front with a large number of points.

Cortisol, family coping and burden in informal caregivers of addicts

This study focuses on the prospective mediation role of family coping between burden and cortisol levels in informal caregivers of addicts as well as on the feasible use of two different ways to analyse the salivary cortisol levels. Participants were 120 Portuguese informal caregivers of addicts. The cortisol samples were collected at awakening, 45 minutes later and after a 30 minute presentation of images taken from the International Affective Picture System. Family coping and caregiver burden were measured using the Portuguese versions of the Caregiver Reaction Assessment, and the Family Crisis Oriented Personal Evaluation Scale. Cortisol samples were collected in salivettes and the results were computed in order to determine the Area Under the Curve scores (AUCg, AUCi). Results found family coping to be negatively correlated with burden and AUCg levels (i.e. overall intensity) and positively correlated with either AUCg and AUCi (i.e. change over time). The mediation model revealed that family coping was a partial mediator in the relationship between the burden and AUCg levels. Therefore, Family Coping appears to be an essential variable in understanding the stress response and should be considered in further studies and interventions. In addition, the use of two different formulas for calculating cortisol levels provided important new information concerning the relationship between cortisol, burden and family coping. It seems that burden has a more profound effect on the overall intensity of the neuroendocrine response to caregiver stress and not so much on the sensitivity of the system.

Assessing the application of polypropylene multifilament yarns in brain phantoms

The currently available clinical imaging methods do not provide highly detailed information about location and severity of axonal injury or the expected recovery time of patients with traumatic brain injury [1]. High-Definition Fiber Tractography (HDFT) is a novel imaging modality that allows visualizing and quantifying, directly, the degree of axons damage, predicting functional deficits due to traumatic axonal injury and loss of cortical projections. This imaging modality is based on diffusion technology [2]. The inexistence of a phantom able to mimic properly the human brain hinders the possibility of testing, calibrating and validating these medical imaging techniques. Most research done in this area fails in key points, such as the size limit reproduced of the brain fibers and the quick and easy reproducibility of phantoms [3]. For that reason, it is necessary to develop similar structures matching the micron scale of axon tubes. Flexible textiles can play an important role since they allow producing controlled packing densities and crossing structures that match closely the human crossing patterns of the brain. To build a brain phantom, several parameters must be taken into account in what concerns to the materials selection, like hydrophobicity, density and fiber diameter, since these factors influence directly the values of fractional anisotropy. Fiber cross-section shape is other important parameter. Earlier studies showed that synthetic fibrous materials are a good choice for building a brain phantom [4]. The present work is integrated in a broader project that aims to develop a brain phantom made by fibrous materials to validate and calibrate HDFT. Due to the similarity between thousands of hollow multifilaments in a fibrous arrangement, like a yarn, and the axons, low twist polypropylene multifilament yarns were selected for this development. In this sense, extruded hollow filaments were analysed in scanning electron microscope to characterize their main dimensions and shape. In order to approximate the dimensional scale to human axons, five types of polypropylene yarns with different linear density (denier) were used, aiming to understand the effect of linear density on the filament inner and outer areas. Moreover, in order to achieve the required dimensions, the polypropylene filaments cross-section was diminished in a drawing stage of a filament extrusion line. Subsequently, tensile tests were performed to characterize the mechanical behaviour of hollow filaments and to evaluate the differences between stretched and non-stretched filaments. In general, an increase of the linear density causes the increase in the size of the filament cross section. With the increase of structure orientation of filaments, induced by stretching, breaking tenacity increases and elongation at break decreases. The production of hollow fibers, with the required characteristics, is one of the key steps to create a brain phantom that properly mimics the human brain that may be used for the validation and calibration of HDFT, an imaging approach that is expected to contribute significantly to the areas of brain related research.