Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

User modeling In adaptive hypermedia educational systems

This document is a survey in the research area of User Modeling (UM) for the specific field of Adaptive Learning. The aims of this document are: To define what it is a User Model; To present existing and well known User Models; To analyze the existent standards related with UM; To compare existing systems. In the scientific area of User Modeling (UM), numerous research and developed systems already seem to promise good results, but some experimentation and implementation are still necessary to conclude about the utility of the UM. That is, the experimentation and implementation of these systems are still very scarce to determine the utility of some of the referred applications. At present, the Student Modeling research goes in the direction to make possible reuse a student model in different systems. The standards are more and more relevant for this effect, allowing systems communicate and to share data, components and structures, at syntax and semantic level, even if most of them still only allow syntax integration.

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

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Service offloading in adaptive real-time systems

Smartphones and other internet enabled devices are now common on our everyday life, thus unsurprisingly a current trend is to adapt desktop PC applications to execute on them. However, since most of these applications have quality of service (QoS) requirements, their execution on resource-constrained mobile devices presents several challenges. One solution to support more stringent applications is to offload some of the applications’ services to surrogate devices nearby. Therefore, in this paper, we propose an adaptable offloading mechanism which takes into account the QoS requirements of the application being executed (particularly its real-time requirements), whilst allowing offloading services to several surrogate nodes. We also present how the proposed computing model can be implemented in an Android environment

Hybrid adaptive control of a dragonfly model

Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive (HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence.

Self-adaptive combination of global tabu search and local search for nonlinear equations

Solving systems of nonlinear equations is a very important task since the problems emerge mostly through the mathematical modelling of real problems that arise naturally in many branches of engineering and in the physical sciences. The problem can be naturally reformulated as a global optimization problem. In this paper, we show that a self-adaptive combination of a metaheuristic with a classical local search method is able to converge to some difficult problems that are not solved by Newton-type methods.

Adaptive tackling of the swinging problem for a 2 DOF crane – payload system

The control of a crane carrying its payload by an elastic string corresponds to a task in which precise, indirect control of a subsystem dynamically coupled to a directly controllable subsystem is needed. This task is interesting since the coupled degree of freedom has little damping and it is apt to keep swinging accordingly. The traditional approaches apply the input shaping technology to assist the human operator responsible for the manipulation task. In the present paper a novel adaptive approach applying fixed point transformations based iterations having local basin of attraction is proposed to simultaneously tackle the problems originating from the imprecise dynamic model available for the system to be controlled and the swinging problem, too. The most important phenomenological properties of this approach are also discussed. The control considers the 4th time-derivative of the trajectory of the payload. The operation of the proposed control is illustrated via simulation results.

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 Penalty and Barrier function based on Fuzzy Logic

Optimization methods have been used in many areas of knowledge, such as Engineering, Statistics, Chemistry, among others, to solve optimization problems. In many cases it is not possible to use derivative methods, due to the characteristics of the problem to be solved and/or its constraints, for example if the involved functions are non-smooth and/or their derivatives are not know. To solve this type of problems a Java based API has been implemented, which includes only derivative-free optimization methods, and that can be used to solve both constrained and unconstrained problems. For solving constrained problems, the classic Penalty and Barrier functions were included in the API. In this paper a new approach to Penalty and Barrier functions, based on Fuzzy Logic, is proposed. Two penalty functions, that impose a progressive penalization to solutions that violate the constraints, are discussed. The implemented functions impose a low penalization when the violation of the constraints is low and a heavy penalty when the violation is high. Numerical results, obtained using twenty-eight test problems, comparing the proposed Fuzzy Logic based functions to six of the classic Penalty and Barrier functions are presented. Considering the achieved results, it can be concluded that the proposed penalty functions besides being very robust also have a very good performance.

Adaptive modeling and high quality spectral estimation for speech enhancement

In this work an adaptive modeling and spectral estimation scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for speech enhancement. Both speech and noise signals are modeled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. The model parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The speech enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. This approach is particularly useful as a pre-processing module for parametric based speech recognition systems that rely on spectral time dependent models. The system performance has been evaluated by a set of human listeners and by spectral distances. In both cases the use of this pre-processing module has led to improved results.

Adaptive filtering for high quality HMM based speech synthesis

In this work an adaptive filtering scheme based on a dual Discrete Kalman Filtering (DKF) is proposed for Hidden Markov Model (HMM) based speech synthesis quality enhancement. The objective is to improve signal smoothness across HMMs and their related states and to reduce artifacts due to acoustic model's limitations. Both speech and artifacts are modelled by an autoregressive structure which provides an underlying time frame dependency and improves time-frequency resolution. Themodel parameters are arranged to obtain a combined state-space model and are also used to calculate instantaneous power spectral density estimates. The quality enhancement is performed by a dual discrete Kalman filter that simultaneously gives estimates for the models and the signals. The system's performance has been evaluated using mean opinion score tests and the proposed technique has led to improved results.

Insights into the molecular mechanisms of protein stabilization by osmolytes of hyperthermophiles

Dissertation presented to obtain the Ph.D degree in Biochemistry

The impact of job insecurity on adaptive performance via burnout

Difficult and unpredictable times, due to economic instability, lead employees to feel high job insecurity. Organizations’ only way to subsistence is to search innovative ways of solving problems and find creative solutions. This study focuses on the impact that job insecurity has on adaptive performance, a recent measure integrating the response of creativity, reactivity in the face of emergencies, interpersonal adaptability, training effort, and handling work stress, and, mediated by burnout. From the responses of two questionnaires (????????1=252; ????????2=145), we conclude that job insecurity leads to exhaustion, but not to disengagement. In turn, it is the latter that demonstrates to have negative relations with some measures of adaptive performance. Thus, it is crucial to understand how organizations can minimize the inherent process.

HARNESSING iNKT CELLS WITH RECOMBINANT CD1d FUSION PROTEINS TO REDIRECT INNATE AND ADAPTIVE IMMUNITY TO THE TUMOR

Les thérapies du cancer, comme la radiothérapie et la chimiothérapie, sont couramment utilisées mais ont de nombreux effets secondaires. Ces thérapies invasives pour le patient nécessitent d'être améliorées et de nombreuses avancées ont été faites afin d'adapter et de personnaliser le traitement du cancer. L'immunothérapie a pour but de renforcer le système immunitaire du patient et de le rediriger de manière spécifique contre la tumeur. Dans notre projet, nous activons les lymphocytes Invariant Natural Killer T (iNKT) afin de mettre en place une immunothérapie innovatrice contre le cancer. Les cellules iNKT sont une unique sous-population de lymphocytes T qui ont la particularité de réunir les propriétés de l'immunité innée ainsi qu'adaptative. En effet, les cellules iNKT expriment à leur surface des molécules présentes aussi sur les cellules tueuses NK, caractéristique de l'immunité innée, ainsi qu'un récepteur de cellules T (TCR) qui représente l'immunité adaptative. Les cellules iNKT reconnaissent avec leur TCR des antigènes présentés par la molécule CD1d. Les antigènes sont des protéines, des polysaccharides ou des lipides reconnus par les cellules du système immunitaire ou les anticorps pour engendrer une réponse immunitaire. Dans le cas des cellules iNKT, l'alpha-galactosylceramide (αGC) est un antigène lipidique fréquemment utilisé dans les études cliniques comme puissant activateur. Après l'activation des cellules iNKT avec l'αGC, celles-ci produisent abondamment et rapidement des cytokines. Ces cytokines sont des molécules agissant comme des signaux activateurs d'autres cellules du système immunitaire telles que les cellules NK et les lymphocytes T. Cependant, les cellules iNKT deviennent anergiques après un seul traitement avec l'αGC c'est à dire qu'elles ne peuvent plus être réactivées, ce qui limite leur utilisation dans l'immunothérapie du cancer. Dans notre groupe, Stirnemann et al ont publié une molécule recombinante innovante, composée de la molécule CD1d soluble et chargée avec le ligand αGC (αGC/sCD1d). Cette protéine est capable d'activer les cellules iNKT tout en évitant l'anergie. Dans le système immunitaire, les anticorps sont indispensables pour combattre une infection bactérienne ou virale. En effet, les anticorps ont la capacité de reconnaître et lier spécifiquement un antigène et permettent l'élimination de la cellule qui exprime cet antigène. Dans le domaine de l'immunothérapie, les anticorps sont utilisés afin de cibler des antigènes présentés seulement par la tumeur. Ce procédé permet de réduire efficacement les effets secondaires lors du traitement du cancer. Nous avons donc fusionné la protéine recombinante αGC/CD1d à un fragment d'anticorps qui reconnaît un antigène spécifique des cellules tumorales. Dans une étude préclinique, nous avons démontré que la protéine αGC/sCD1d avec un fragment d'anticorps dirigé contre la tumeur engendre une meilleure activation des cellules iNKT et entraîne un effet anti-tumeur prolongé. Cet effet anti-tumeur est augmenté comparé à une protéine αGC/CD1d qui ne cible pas la tumeur. Nous avons aussi montré que l'activation des cellules iNKT avec la protéine αGC/sCD1d-anti-tumeur améliore l'effet anti- tumoral d'un vaccin pour le cancer. Lors d'expériences in vitro, la protéine αGC/sCD1d-anti- tumeur permet aussi d'activer les cellules humaines iNKT et ainsi tuer spécifiquement les cellules tumorales humaines. La protéine αGC/sCD1d-anti-tumeur représente une alternative thérapeutique prometteuse dans l'immunothérapie du cancer. - Les cellules Invariant Natural Killer T (iNKT), dont les effets anti-tumoraux ont été démontrés, sont de puissants activateurs des cellules Natural Killer (NK), des cellules dendritiques (DC) et des lymphocytes T. Cependant, une seule injection du ligand de haute affinité alpha-galactosylceramide (αGC) n'induit une forte activation des cellules iNKT que durant une courte période. Celle-ci est alors suivie d'une longue phase d'anergie, limitant ainsi leur utilisation pour la thérapie. Comme alternative prometteuse, nous avons montré que des injections répétées d'αGC chargé sur une protéine recombinante de CD1d soluble (αGC/sCD1d) chez la souris entraînent une activation prolongée des cellules iNKT, associée à une production continue de cytokine. De plus, le maintien de la réactivité des cellules iNKT permet de prolonger l'activité anti-tumorale lorsque la protéine αGC/sCD1d est fusionnée à un fragment d'anticorps (scFv) dirigé contre la tumeur. L'inhibition de la croissance tumorale n'est optimale que lorsque les souris sont traitées avec la protéine αGC/sCD1d-scFv ciblant la tumeur, la protéine αGC/sCD1d-scFv non-appropriée étant moins efficace. Dans le système humain, les protéines recombinantes αGC/sCD1d-anti-HER2 et anti-CEA sont capables d'activer et de faire proliférer des cellules iNKT à partir de PBMCs issues de donneurs sains. De plus, la protéine αGC/sCD1d-scFv a la capacité d'activer directement des clones iNKT humains en l'absence de cellules présentatrices d'antigènes (CPA), contrairement au ligand αGC libre. Mais surtout, la lyse des cellules tumorales par les iNKT humaines n'est obtenue que lorsqu'elles sont incubées avec la protéine αGC/sCD1d-scFv anti- tumeur. En outre, la redirection de la cytotoxicité des cellules iNKT vers la tumeur est supérieure à celle obtenue avec une stimulation par des CPA chargées avec l'αGC. Afin d'augmenter les effets anti-tumoraux, nous avons exploité la capacité des cellules iNKT à activer l'immunité adaptive. Pour ce faire, nous avons combiné l'immunothérapie NKT/CD1d avec un vaccin anti-tumoral composé d'un peptide OVA. Des effets synergiques ont été obtenus lorsque les traitements avec la protéine αGC/sCD1d-anti-HER2 étaient associés avec le CpG ODN comme adjuvant pour la vaccination avec le peptide OVA. Ces effets ont été observés à travers l'activation de nombreux lymphocytes T CD8+ spécifique de la tumeur, ainsi que par la forte expansion des cellules NK. Les réponses, innée et adaptive, élevées après le traitement avec la protéine αGC/sCD1d-anti-HER2 combinée au vaccin OVA/CpG ODN étaient associées à un fort ralentissement de la croissance des tumeurs B16- OVA-HER2. Cet effet anti-tumoral corrèle avec l'enrichissement des lymphocytes T CD8+ spécifiques observé à la tumeur. Afin d'étendre l'application des protéines αGC/sCD1d et d'améliorer leur efficacité, nous avons développé des fusions CD1d alternatives. Premièrement, une protéine αGC/sCD1d dimérique, qui permet d'augmenter l'avidité de la molécule CD1d pour les cellules iNKT. Dans un deuxième temps, nous avons fusionné la protéine αGC/sCD1d avec un scFv dirigé contre le récepteur 3 du facteur de croissance pour l'endothélium vasculaire (VEGFR-3), afin de cibler l'environnement de la tumeur. Dans l'ensemble, ces résultats démontrent que la thérapie médiée par la protéine recombinante αGC/sCD1d-scFv est une approche prometteuse pour rediriger l'immunité innée et adaptive vers le site tumoral. - Invariant Natural Killer T cells (iNKT) are potent activators of Natural Killer (NK), dendritic cells (DC) and T lymphocytes, and their anti-tumor activities have been well demonstrated. However, a single injection of the high affinity CD1d ligand alpha-galactosylceramide (αGC) leads to a strong but short-lived iNKT cell activation followed by a phase of long-term anergy, limiting the therapeutic use of this ligand. As a promising alternative, we have demonstrated that when αGC is loaded on recombinant soluble CD1d molecules (αGC/sCD1d), repeated injections in mice led to the sustained iNKT cell activation associated with continued cytokine secretion. Importantly, the retained reactivity of iNKT cell led to prolonged antitumor activity when the αGC/sCD1d was fused to an anti-tumor scFv fragments. Optimal inhibition of tumor growth was obtained only when mice were treated with the tumor-targeted αGC/CD1d-scFv fusion, whereas the irrelevant αGC/CD1d-scFv fusion was less efficient. When tested in a human system, the recombinant αGC/sCD1d-anti-HER2 and -anti-CEA fusion proteins were able to expand iNKT cells from PBMCs of healthy donors. Furthermore, the αGC/sCD1d-scFv fusion had the capacity to directly activate human iNKT cells clones without the presence of antigen-presenting cells (APCs), in contrast to the free αGC ligand. Most importantly, tumor cell killing by human iNKT cells was obtained only when co- incubated with the tumor targeted sCD1d-antitumor scFv, and their direct tumor cytotoxicity was superior to the bystander killing obtained with αGC-loaded APCs stimulation. To further enhance the anti-tumor effects, we exploited the ability of iNKT cells to transactivate the adaptive immunity, by combining the NKT/CD1d immunotherapy with a peptide cancer vaccine. Interestingly, synergistic effects were obtained when the αGC/sCD1d- anti-HER2 fusion treatment was combined with CpG ODN as adjuvant for the OVA peptide vaccine, as seen by higher numbers of activated antigen-specific CD8 T cells and NK cells, as compared to each regimen alone. The increased innate and adaptive immune responses upon combined tumor targeted sCD1d-scFv treatment and OVA/CpG vaccine were associated with a strong delay in B16-OVA-HER2 melanoma tumor growth, which correlated with an enrichment of antigen-specific CD8 cells at the tumor site. In order to extend the application of the CD1d fusion, we designed alternative CD1d fusion proteins. First, a dimeric αGC/sCD1d-Fc fusion, which permits to augment the avidity of the CD1d for iNKT cells and second, an αGC/sCD1d fused to an anti vascular endothelial growth factor receptor-3 (VEGFR-3) scFv, in order to target tumor stroma environment. Altogether, these results demonstrate that the iNKT-mediated immunotherapy via recombinant αGC/sCD1d-scFv fusion is a promising approach to redirect the innate and adaptive antitumor immune response to the tumor site.

How the gut links innate and adaptive immunity.

Mucosal surfaces represent the main sites in which environmental microorganisms and antigens interact with the host. Sentinel cells, including epithelial cells, lumenal macrophages, and intraepithelial dendritic cells, continuously sense the environment and coordinate defenses for the protection of mucosal tissues. The mucosal epithelial cells are crucial actors in coordinating defenses. They sense the outside world and respond to environmental signals by releasing chemokines and cytokines that recruit inflammatory and immune cells to control potential infectious agents and to attract cells able to trigger immune responses. Among immune cells, dendritic cells (DC) play a key role in controlling adaptive immune responses, due to their capacity to internalize foreign materials and to present antigens to naive T and B lymphocytes, locally or in draining organized lymphoid tissues. Immune cells recruited in epithelial tissues can, in turn, act upon the epithelial cells and change their phenotype in a process referred to as epithelial metaplasia.