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

Development of new membranes based on ionic liquid materials for gas separation

In the field of energy, natural gas is an essential bridge to a clean, low carbon, renewable energy era. However, natural gas processing and transportation regulation require the removal of contaminant compounds such as carbon dioxide (CO2). Regarding clean air, the increasing atmospheric concentrations of greenhouse gases, specifically CO2, is of particular concern. Therefore, new costeffective, high performance technologies for carbon capture have been researched and the design of materials with the ability to efficiently separate CO2 from other gases is of vital importance.(...)

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Post-merger innovative performance in the renewable energy sector

Due to global warming and shrinking fossil fuel resources, politics as well as society urge for a reduction of green house gas (GHG) emissions. This leads to a re-orientation towards a renewable energy sector. In this context, innovation and new technologies are key success factors. Moreover, the renewable energy sector has entered a consolidation stage, where corporate investors and mergers and acquisitions (M&A) gain in importance. Although both M&A and innovation in the renewable energy sector are important corporate strategies, the link between those two aspects has not been examined before. The present thesis examines the research question how M&A influence the acquirer’s post-merger innovative performance in the renewable energy sector. Based on a framework of relevant literature, three hypotheses are defined. First, the relation between non-technology oriented M&A and post-merger innovative performance is discussed. Second, the impact of absolute acquired knowledge on postmerger innovativeness is examined. Third, the target-acquirer relatedness is discussed. A panel data set of 117 firms collected over a period of six years has been analyzed via a random effects negative binomial regression model and a time lag of one year. The results support a non-significant, negative impact of non-technology M&A on postmerger innovative performance. The applied model did not support a positive and significant impact of absolute acquired knowledge on post-merger innovative performance. Lastly, the results suggest a reverse relation than postulated by Hypothesis 3. Targets from the same industry significantly and negatively influence the acquirers’ innovativeness.

Bioproduction of succinic acid from renewable feedstocks by Actinobacillus succinogenes 130Z

Succinic acid (SA) is a highly versatile building block that is used in a wide range of industrial applications. The biological production of succinic acid has emerged in the last years as an efficient alternative to the chemical production based on fossil fuels. However, in order to fully replace the competing petro-based chemical process from which it has been produced so far, some challenges remain to be surpassed. In particular, one main obstacle would be to reduce its production costs, mostly associated to the use of refined sugars. The present work is focused on the development of a sustainable and cost-e↵ective microbial production process based on cheap and renewable resources, such as agroindustrial wastes. Hence, glycerol and carob pods were identified as promising feedstocks and used as inexpensive carbon sources for the bioproduction of succinic acid by Actinobacillus succinogenes 130Z, one of the best naturally producing strains. Even though glycerol is a highly available carbon source, as by-product of biodiesel production, its consumption by A. succinogenes is impaired due to a redox imbalance during cell growth. However, the use of an external electron acceptor such as dimethylsulfoxide (DMSO) may improve glycerol metabolism and succinic acid production by this strain. As such, DMSO was tested as a co-substrate for glycerol consumption and concentrations of DMSO between 1 and 4% (v/v) greatly promoted glycerol consumption and SA production by this biocatalyst. Aiming at obtaining higher succinic acid yield and production rate, batch and fed-batch experiments were performed under controlled cultivation conditions. Batch experiments resulted in a succinic acid yield on glycerol of 0.95 g SA/g GLY and a production rate of 2.13 g/L.h, with residual production of acetic and formic acids. In fed-batch experiment, the SA production rate reached 2.31 g/L.h, the highest value reported in the literature for A. succinogenes using glycerol as carbon source. DMSO dramatically improved the conversion of glycerol by A. succinogenes and may be used as a co-substrate, opening new perspectives for the use of glycerol by this biocatalyst. Carob pods, highly available in Portugal as a residue from the locust bean gum industry, contain a significant amount of fermentable sugars such as sucrose, glucose and fructose and were also used as substrate for succinic acid production. Sugar extraction from raw and roasted carobs was optimized varying solid/water ratio and extraction time, maximizing sugar recovery while minimizing the extraction of polyphenols. Kinetic studies of glucose, fructose and sucrose consumption by A. succinogenes as individual carbon sources till 30 g/L were first determined to assess possible metabolic diferences. Results showed no significant diferences related to sugar consumption and SA production between the diferent sugars. Carob pods water extracts were then used as carbon source during controlled batch cultivations. (...)

Optimization of production of anti-cancer vaccines based on dendritic cells

Cancer remains as one of the top killing diseases in first world countries. It’s not a single, but a set of various diseases for which different treatment approaches have been taken over the years. Cancer immunotherapy comes as a “new” breath on cancer treatment, taking use of the patients’ immune system to induce anti-cancer responses. Dendritic Cell (DC) vaccines use the extraordinary capacity of DCs’ antigen presentation so that specific T cell responses may be generated against cancer. In this work, we report the ex vivo generation of DCs from precursors isolated from clinical-grade cryopreserved umbilical cord blood (UCB) samples. After the thawing protocol for cryopreserved samples was optimized, the generation of DCs from CD14+ monocytes, i.e., moDCs, or CD34+ hematopoietic stem cells (HSCs), i.e, CD34-derived DCs, was followed and their phenotype and function evaluated. Functional testing included the ability to respond to maturation stimuli (including enzymatic removal of surface sialic acids), Ovalbumin-FITC endocytic capacity, cytokine secretion and T cell priming ability. In order to evaluate the feasibility of using DCs derived from UCB precursors to induce immune responses, they were compared to peripheral blood (PB) moDCs. We observed an increased endocytosis capacity after moDCs were differentiated from monocyte precursors, but almost 10-fold lower than that of PB moDCs. Maturation markers were absent, low levels of inflammatory cytokines were seen and T cell stimulatory capacity was reduced. Sialidase enzymatic treatment was able to mature these cells, diminishing endocytosis and promoting higher T cell stimulation. CD34-derived DCs showed higher capacity for both maturation and endocytic capacity than moDCs. Although much more information was acquired from moDCs than from CD34-derived DCs, we conclude the last as probably the best suited for generating an immune response against cancer, but of course much more research has to be performed.

Control in distribution networks with demand side management

The way in which electricity networks operate is going through a period of significant change. Renewable generation technologies are having a growing presence and increasing penetrations of generation that are being connected at distribution level. Unfortunately, a renewable energy source is most of the time intermittent and needs to be forecasted. Current trends in Smart grids foresee the accommodation of a variety of distributed generation sources including intermittent renewable sources. It is also expected that smart grids will include demand management resources, widespread communications and control technologies required to use demand response are needed to help the maintenance in supply-demand balance in electricity systems. Consequently, smart household appliances with controllable loads will be likely a common presence in our homes. Thus, new control techniques are requested to manage the loads and achieve all the potential energy present in intermittent energy sources. This thesis is focused on the development of a demand side management control method in a distributed network, aiming the creation of greater flexibility in demand and better ease the integration of renewable technologies. In particular, this work presents a novel multi-agent model-based predictive control method to manage distributed energy systems from the demand side, in presence of limited energy sources with fluctuating output and with energy storage in house-hold or car batteries. Specifically, here is presented a solution for thermal comfort which manages a limited shared energy resource via a demand side management perspective, using an integrated approach which also involves a power price auction and an appliance loads allocation scheme. The control is applied individually to a set of Thermal Control Areas, demand units, where the objective is to minimize the energy usage and not exceed the limited and shared energy resource, while simultaneously indoor temperatures are maintained within a comfort frame. Thermal Control Areas are overall thermodynamically connected in the distributed environment and also coupled by energy related constraints. The energy split is performed based on a fixed sequential order established from a previous completed auction wherein the bids are made by each Thermal Control Area, acting as demand side management agents, based on the daily energy price. The developed solutions are explained with algorithms and are applied to different scenarios, being the results explanatory of the benefits of the proposed approaches.