Parallel real-time support for distributed adaptive embedded applications

Real-time embedded applications require to process large amounts of data within small time windows. Parallelize and distribute workloads adaptively is suitable solution for computational demanding applications. The purpose of the Parallel Real-Time Framework for distributed adaptive embedded systems is to guarantee local and distributed processing of real-time applications. This work identifies some promising research directions for parallel/distributed real-time embedded applications.

Recomendações personalizadas de alunos em sistemas de hipermédia adaptativa educacional usando Data Mining

O aumento de tecnologias disponíveis na Web favoreceu o aparecimento de diversas formas de informação, recursos e serviços. Este aumento aliado à constante necessidade de formação e evolução das pessoas, quer a nível pessoal como profissional, incentivou o desenvolvimento área de sistemas de hipermédia adaptativa educacional - SHAE. Estes sistemas têm a capacidade de adaptar o ensino consoante o modelo do aluno, características pessoais, necessidades, entre outros aspetos. Os SHAE permitiram introduzir mudanças relativamente à forma de ensino, passando do ensino tradicional que se restringia apenas ao uso de livros escolares até à utilização de ferramentas informáticas que através do acesso à internet disponibilizam material didático, privilegiando o ensino individualizado. Os SHAE geram grande volume de dados, informação contida no modelo do aluno e todos os dados relativos ao processo de aprendizagem de cada aluno. Facilmente estes dados são ignorados e não se procede a uma análise cuidada que permita melhorar o conhecimento do comportamento dos alunos durante o processo de ensino, alterando a forma de aprendizagem de acordo com o aluno e favorecendo a melhoria dos resultados obtidos. O objetivo deste trabalho foi selecionar e aplicar algumas técnicas de Data Mining a um SHAE, PCMAT - Mathematics Collaborative Educational System. A aplicação destas técnicas deram origem a modelos de dados que transformaram os dados em informações úteis e compreensíveis, essenciais para a geração de novos perfis de alunos, padrões de comportamento de alunos, regras de adaptação e pedagógicas. Neste trabalho foram criados alguns modelos de dados recorrendo à técnica de Data Mining de classificação, abordando diferentes algoritmos. Os resultados obtidos permitirão definir novas regras de adaptação e padrões de comportamento dos alunos, poderá melhorar o processo de aprendizagem disponível num SHAE.

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.

Adaptive Learning in Games: Defining Profiles of Competitor Players

Artificial Intelligence has been applied to dynamic games for many years. The ultimate goal is creating responses in virtual entities that display human-like reasoning in the definition of their behaviors. However, virtual entities that can be mistaken for real persons are yet very far from being fully achieved. This paper presents an adaptive learning based methodology for the definition of players’ profiles, with the purpose of supporting decisions of virtual entities. The proposed methodology is based on reinforcement learning algorithms, which are responsible for choosing, along the time, with the gathering of experience, the most appropriate from a set of different learning approaches. These learning approaches have very distinct natures, from mathematical to artificial intelligence and data analysis methodologies, so that the methodology is prepared for very distinct situations. This way it is equipped with a variety of tools that individually can be useful for each encountered situation. The proposed methodology is tested firstly on two simpler computer versus human player games: the rock-paper-scissors game, and a penalty-shootout simulation. Finally, the methodology is applied to the definition of action profiles of electricity market players; players that compete in a dynamic game-wise environment, in which the main goal is the achievement of the highest possible profits in the market.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Sialyl Tn-Expressing Bladder Cancer Cells Induce a Tolerogenic Phenotype in Innate and Adaptive Immune Cells

Despite the wide acceptance that glycans are centrally implicated in immunity, exactly how they contribute to the tilt immune response remains poorly defined. In this study, we sought to evaluate the impact of the malignant phenotype-associated glycan, sialyl-Tn (STn) in the function of the key orchestrators of the immune response, the dendritic cells (DCs). In high grade bladder cancer tissue, the STn antigen is significantly overexpressed and correlated with the increased expression of ST6GALNAC1 sialyltransferase. Bladder cancer tissue presenting elevated expression of ST6GALNAC1 showed a correlation with increased expression of CD1a, a marker for bladder immature DCs and showed concomitant low levels of Th1-inducing cytokines IL-12 and TNF-α. In vitro, human DCs co-incubated with STn+ bladder cancer cells, had an immature phenotype (MHC-IIlow, CD80low and CD86low) and were unresponsive to further maturation stimuli. When contacting with STn+ cancer cells, DCs expressed significantly less IL-12 and TNF-α. Consistent with a tolerogenic DC profile, T cells that were primed by DCs pulsed with antigens derived from STn+ cancer cells were not activated and showed a FoxP3high IFN-γlow phenotype. Blockade of STn antigens and of STn+ glycoprotein, CD44 and MUC1, in STn+ cancer cells was able to lower the induction of tolerance and DCs become more mature. Overall, our data suggest that STn-expressing cancer cells impair DC maturation and endow DCs with a tolerogenic function, limiting their capacity to trigger protective anti-tumour T cell responses. STn antigens and, in particular, STn+ glycoproteins are potential targets for circumventing tumour-induced tolerogenic mechanisms.

Enhancing physical activity coaching through personalized motivational strategies and self-adaptive goal-setting: development of self-adaptive processes in a monitoring and coaching smartphone application

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Integrated management systems as complex adaptive systems

It is a difficult task to avoid the “smart systems” topic when discussing smart prevention and, similarly, it is a difficult task to address smart systems without focusing their ability to learn. Following the same line of thought, in the current reality, it seems a Herculean task (or an irreparable omission) to approach the topic of certified occupational health and safety management systems (OHSMS) without discussing the integrated management systems (IMSs). The available data suggest that seldom are the OHSMS operating as the single management system (MS) in a company so, any statement concerning OHSMS should mainly be interpreted from an integrated perspective. A major distinction between generic systems can be drawn between those that learn, i.e., those systems that have “memory” and those that have not. These former systems are often depicted as adaptive since they take into account past events to deal with novel, similar and future events modifying their structure to enable success in its environment. Often, these systems, present a nonlinear behavior and a huge uncertainty related to the forecasting of some events. This paper seeks to portray, for the first time as we were able to find out, the IMSs as complex adaptive systems (CASs) by listing their properties and dissecting the features that enable them to evolve and self-organize in order to, holistically, fulfil the requirements from different stakeholders and thus thrive by assuring the successful sustainability of a company. Based on the revision of literature carried out, this is the first time that IMSs are pointed out as CASs which may develop fruitful synergies both for the MSs and for CASs communities. By performing a thorough revision of literature and based on some concepts embedded in the “DNA” of the subsystems implementation standards it is intended, specifically, to identify, determine and discuss the properties of a generic IMS that should be considered to classify it as a CAS.

Interim analysis of the Reitan Catheter Pump (RCP) heart failure efficacy study: RCP improves cardiovascular and renal function in acute decompensated heart failure (ADHF)

Background: The RCP is a 14 French collapsable percutaneous cardiovascular support device positioned in the descending part of the thoracic aorta via the femoral artery. A 10 patient first in man study demonstrated device safety and significant improvement in renal function among high risk PCI patients. We now report haemodynamic and renal efficacy in patients with ADHF.Methods: Prospective non randomised study seeking to recruit 20 patients with ADHF with a need for inotropic or mechanical circulatory support with: i) EF < 30% ii)Cardiac index(CI) < 2.2 L / min / m2 Outcome measures included: 1) Cardiac index (CI) 2) Pulmonary Capillary Wedge Pressure (PCWP) 3) Urine output / serum creatinine 4) Vascular / device complications 5) 30 day mortalityResults: INTERIM ANALYSIS (n=12) The mean age of the study group was 64 years, with a mean baseline creatinine of 193 umol/L, eGFR 38 ml/min. The intended RCP treatment period was 24 hours. During RCP treatment there was a significant mean reduction of PCWP at 4 hours of 17% (25 to 21 mmHg p=0.04). Mean CI increased at 12 hours by 11%, though not reaching significance (1.78 to 1.96 L/min/m2 p=0.08). RCP insertion prompted substantial diuresis. Urine output tripled over the first 12 hours compared to baseline (55 ml/hr vs 213 ml/hr p=0.03). This was associated with significantly improved renal function, a 28% reduction in serum creatinine at 12 hours (193 to 151 umol/L p=0.003), and a increase in eGFR from 38 ml/min to 50 ml/min (p=0.0007). 2 patients previously refused cardiac transplantation were reassessed and successfully transplanted within 9 months of RCP treatment on the basis of demonstrable renal reversibility. There were no vascular or device complications. There were 2 deaths at 30 days, one from multi-organ failure and sepsis, and one from intractable heart failure - neither were device related.Conclusion: RCP support in ADHF patients was associated with improved haemodynamics, and an improvement in renal function. The Reitan Catheter Pump may have a role in providing percutaneous cardiovascular and renal support in the acutely decompensated cardiac patient, and may have a role in suggesting renal reversibility in potential cardiac transplant patients. Further data will be reported at recruitment completion.

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations.

Adaptive Privacy Management System Design For Context-Aware Mobile Devices

While mobile technologies can provide great personalized services for mobile users, they also threaten their privacy. Such personalization-privacy paradox are particularly salient for context aware technology based mobile applications where user's behaviors, movement and habits can be associated with a consumer's personal identity. In this thesis, I studied the privacy issues in the mobile context, particularly focus on an adaptive privacy management system design for context-aware mobile devices, and explore the role of personalization and control over user's personal data. This allowed me to make multiple contributions, both theoretical and practical. In the theoretical world, I propose and prototype an adaptive Single-Sign On solution that use user's context information to protect user's private information for smartphone. To validate this solution, I first proved that user's context is a unique user identifier and context awareness technology can increase user's perceived ease of use of the system and service provider's authentication security. I then followed a design science research paradigm and implemented this solution into a mobile application called "Privacy Manager". I evaluated the utility by several focus group interviews, and overall the proposed solution fulfilled the expected function and users expressed their intentions to use this application. To better understand the personalization-privacy paradox, I built on the theoretical foundations of privacy calculus and technology acceptance model to conceptualize the theory of users' mobile privacy management. I also examined the role of personalization and control ability on my model and how these two elements interact with privacy calculus and mobile technology model. In the practical realm, this thesis contributes to the understanding of the tradeoff between the benefit of personalized services and user's privacy concerns it may cause. By pointing out new opportunities to rethink how user's context information can protect private data, it also suggests new elements for privacy related business models.

Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

Purification and functional reconstitution of the tonoplast pyrophosphate-dependent proton pump fron Rubus hispidus cell cultures.

The hydrolytic subunit of the H+-translocating inorganic pyrophosphatase (V-PPase EC 3.6.1.1.) prepared from Rubus hispidus cell cultures has been purified from tonoplast-enriched membranes and analysed by SDS-polyacrylamide gel electrophoresis, Only one polypeptide of M(r) 70 000 was recovered with the V-PPase activity after solubilization in the presence of Triton X-100, purification by gel filtration (Superose) and anion exchange (Mono Q) chromatography. This polypeptide strongly cross-reacted with an antibody raised against the V-PPase from Vigna radiata. The tonoplast-enriched fraction was also used to solubilize and reconstitute the-V-PPase. The proteoliposomes showing a PPi-dependent proton transport activity were purified by gel filtration (Superose) and analysed by SDS-polyacrylamide gel electrophoresis. Only one polypeptide of M(r) 70 000 was recovered with the proton-pumping activity. All these data suggest that the native V-PPase from Rubus is composed of a single kind of polypeptide with an M(r) of 70 000 and representing the catalytic subunit.

Schistosoma mansoni triggers Dectin-2, which activates the Nlrp3 inflammasome and alters adaptive immune responses.

The propensity of helminths, such as schistosomes, to immunomodulate the host's immune system is an essential aspect of their survival. Previous research has demonstrated how soluble schistosomal egg antigens (SEA) dampen TLR-signaling during innate immune responses. We show here that the suppressive effect by SEA on TLR signaling is simultaneously coupled to the activation of the Nlrp3 (NLR family, pyrin domain containing 3) inflammasome and thus IL-1β production. Therefore, the responsible protein component of SEA contains the second signal that is required to trigger proteolytic pro-IL-1β processing. Moreover, the SEA component binds to the Dectin-2/FcRγ (Fc receptor γ chain) complex and activates the Syk kinase signaling pathway to induce reactive oxygen species and potassium efflux. As IL-1β has been shown to be an essential orchestrator against several pathogens we studied the in vivo consequences of Schistosoma mansoni infection in mice deficient in the central inflammasome adapter ASC and Nlrp3 molecule. These mice failed to induce local IL-1β levels in the liver and showed decreased immunopathology. Interestingly, antigen-specific Th1, Th2, and Th17 responses were down-regulated. Overall, these data imply that component(s) within SEA induce IL-1β production and unravel a crucial role of Nlrp3 during S. mansoni infection.

Endoscopic off-pump aortic valve replacement: does the pericardial cuff improve the sutureless closure of left ventricular access?

OBJECTIVE: Off-pump trans left ventricular approach provides more precise deployment of stented aortic valve of any size with respect to the endovascular replacement. One of the key steps of this procedure is the ventricle repair after catheter withdrawing. We designed an animal study to compare the consistency of a sutureless repair of the left ventricle access using nitinol occluder with and without pericardial cuff on the ventricular side. METHODS: Material description: The Amplatz-nitinol occluder consists of two square heads squeezing ventricle wall in between them, sealing the defect. To improve its sealing property, a pericardial patch was sutured to the ventricular head of the occluder. Animal study setup: In adult pigs, a 30F sheath was inserted into the epigastric area through the cardiac apex, up to the left ventricle, simulating the approach for off-pump aortic valve replacement. The sheath was then removed and the ventricle closed with standard occluder in half of the animals, and cuffed occluder in the other half. Animals were followed-up for 3h, collecting haemodynamics data and pericardial bleeding. RESULTS: Device was successfully deployed in 12 animals in less than 1min. In the group where the standard occluder was used, bleeding during the deployment was 80+/-20ml and after the deployment was 800+/-20ml over 3h. In the group where the cuffed occluder was used, bleeding during the deployment was 85+/-20ml and after the deployment was 100+/-5ml over 3h. In the cuffed group, bleeding was significantly lower than the standard group, p-value being <0.001. CONCLUSIONS: The occluder is easy to use and the pericardial cuff dramatically increases its efficacy as demonstrated by a significant reduction of blood loss. The cuffed occluder opens the way for endoscopic, off-pump, transventricular aortic valve replacement.