Neural reflex regulation of arterial pressure in pathophysiological conditions : interplay among the baroreflex, the cardiopulmonary reflexes and the chemoreflex

The maintenance of arterial pressure at levels adequate to perfuse the tissues is a basic requirement for the constancy of the internal environment and survival. The objective of the present review was to provide information about the basic reflex mechanisms that are responsible for the moment-to-moment regulation of the cardiovascular system. We demonstrate that this control is largely provided by the action of arterial and non-arterial reflexes that detect and correct changes in arterial pressure (baroreflex), blood volume or chemical composition (mechano- and chemosensitive cardiopulmonary reflexes), and changes in blood-gas composition (chemoreceptor reflex). The importance of the integration of these cardiovascular reflexes is well understood and it is clear that processing mainly occurs in the nucleus tractus solitarii, although the mechanism is poorly understood. There are several indications that the interactions of baroreflex, chemoreflex and Bezold-Jarisch reflex inputs, and the central nervous system control the activity of autonomic preganglionic neurons through parallel afferent and efferent pathways to achieve cardiovascular homeostasis. It is surprising that so little appears in the literature about the integration of these neural reflexes in cardiovascular function. Thus, our purpose was to review the interplay between peripheral neural reflex mechanisms of arterial blood pressure and blood volume regulation in physiological and pathophysiological states. Special emphasis is placed on the experimental model of arterial hypertension induced by N-nitro-L-arginine methyl ester (L-NAME) in which the interplay of these three reflexes is demonstrable.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Intelligent Bio-Inspired system for manufacturing scheduling under uncertainties

A novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS), Case-based Reasoning (CBR), and Bio-Inspired Optimization Techniques (BIT) will be described. AC has emerged as a paradigm aiming at incorporating applications with a management structure similar to the central nervous system. The main intentions are to improve resource utilization and service quality. In this paper we envisage the use of MAS paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with AC properties, in order to reduce the complexity of managing manufacturing systems and human interference. The proposed CBR based Intelligent Scheduling System was evaluated under different dynamic manufacturing scenarios.

Meta-heuristics self-configuration for scheduling

Scheduling resolution requires the intervention of highly skilled human problemsolvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. This paper addresses the resolution of complex scheduling problems using cooperative negotiation. A Multi-Agent Autonomic and Meta-heuristics based framework with self-configuring capabilities is proposed.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

A marcha humana: circuitos neuronais entre estruturas corticais vs sub-corticais

As conexões entre os níveis corticais e sub-corticais na activação da marcha Humana carecem de discussão. As alterações da marcha por lesão no território da Artéria Cerebral Média (ACM) podem ser explicadas pela disfunção de circuitos neuronais dos Núcleos da Base ao córtex e Núcleos Pedúnculo-Pontino. Este trabalho tem como objectivo identificar os percursos anatómicos das principais conexões entre as estruturas encefálicas referidas no território da ACM. Com base na topografia das conexões neuronais, é aceitável que as alterações da marcha sejam explicadas por alterações na função dos Núcleos da Base, através das suas conexões, enquanto moduladores da actividade motora.

Self-organization for scheduling in agile manufacturing

Agility refers to the manufacturing system ability to rapidly adapt to market and environmental changes in efficient and cost-effective ways. This paper addresses the development of self-organization methods to enhance the operations of a scheduling system, by integrating scheduling system, configuration and optimization into a single autonomic process requiring minimal manual intervention to increase productivity and effectiveness while minimizing complexity for users. We intend to conceptualize real manufacturing systems as interacting autonomous entities in order to build future Decision Support Systems (DSS) for Scheduling in agile manufacturing environments.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Self-managing agents for dynamic scheduling in manufacturing

The main purpose of this paper is to propose a Multi-Agent Autonomic and Bio-Inspired based framework with selfmanaging capabilities to solve complex scheduling problems using cooperative negotiation. Scheduling resolution requires the intervention of highly skilled human problem-solvers. This is a very hard and challenging domain because current systems are becoming more and more complex, distributed, interconnected and subject to rapidly changing. A natural Autonomic Computing (AC) evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference.

A diplomacia Norte-Americana e as movimentações independentistas nos Açores em 1975: uma neutralidade atenta

Dissertação de Mestrado, Relações Internacionais, 3 de Dezembro de 2013, Universidade dos Açores.

Circuitos neuronais entre estruturas corticais vs sub-corticais marcha humana

As conexões entre os níveis corticais e os níveis sub-corticais na activação da marcha Humana carecem de discussão. As alterações da marcha por lesão no território da Artéria Cerebral Média podem ser explicadas pela disfunção de circuitos neuronais dos Núcleos da Base ao córtex e Núcleos Pedúnculo-Pontinos, sendo para isso necessário identificar os percursos anatómicos das principais conexões entre as estruturas encefálicas referidas. Com base na topografia das conexões neuronais, é aceitável que as alterações da marcha possam existir também devido a alterações na função dos Núcleos da Base, através das suas conexões, enquanto moduladores da actividade motora.

Intervenção baseada no conceito de Bobath em indivíduos com doença de Parkinson: quatro estudos de caso

Introdução: O padrão de recrutamento temporal inerente a uma sequência de ativação muscular (SAM), permite a organização multi-segmentar para a realização de uma tarefa motora. Este depende da conexão neural entre estruturas corticais e sub-corticais, incluindo os núcleos da base (NB), podendo, assim, estar comprometido em indivíduos com Doença de Parkinson (DP). As SAM poderão ser melhoradas através de uma intervenção baseada no conceito de Bobath (CB). Objetivo: Estudar o potencial da intervenção, baseada no CB, a longo prazo, nas SAM ao nível da tibio-társica (TT), durante as tarefas motoras sit-to-stand (SitTS) e o stand-to-sit (StandTS), em quatro indivíduos com DP. Metodologia: O estudo apresenta quatro casos de indivíduos com DP, que realizaram intervenção em fisioterapia baseada no CB, durante 12 semanas. Antes e após a intervenção, foram avaliadas as sequências de ativação do gastrocnémio medial (GM), do solear (SOL) e do tibial anterior (TA), durante as tarefas SitTS e StandTS, recorrendo à eletromiografia de superfície e à plataforma de forças, para a divisão cinética das diferentes fases das tarefas. Avaliou-se ainda o equilíbrio funcional, através da Escala de Berg, e a percepção subjetiva dos indivíduos acerca da sua capacidade para realizar atividades sem cair, através da Modified Falls Efficacy Scale. Resultados: Após a intervenção, os indivíduos em estudo apresentaram, na sua maioria, uma diminuição da co-ativação muscular, bem como um aumento do equilíbrio funcional e diminuição da probabilidade de risco de queda, refletindo uma melhoria do controlo postural (CP). As modificações na percepção subjetiva dos indivíduos acerca da sua capacidade para realizar atividades sem cair não foram homogéneas. Conclusão: A intervenção baseada no CB teve efeitos positivos do ponto de vista do CP nos quatro indivíduos com DP. Pensa-se que uma intervenção mais duradoura poderá intensificar as melhorias observadas.

Aspectos de aprendizagem em optimização

A optimização e a aprendizagem em Sistemas Multi-Agente são consideradas duas áreas promissoras mas relativamente pouco exploradas. A optimização nestes ambientes deve ser capaz de lidar com o dinamismo. Os agentes podem alterar o seu comportamento baseando-se em aprendizagem recente ou em objectivos de optimização. As estratégias de aprendizagem podem melhorar o desempenho do sistema, dotando os agentes da capacidade de aprender, por exemplo, qual a técnica de optimização é mais adequada para a resolução de uma classe particular de problemas, ou qual a parametrização é mais adequada em determinado cenário. Nesta dissertação são estudadas algumas técnicas de resolução de problemas de Optimização Combinatória, sobretudo as Meta-heurísticas, e é efectuada uma revisão do estado da arte de Aprendizagem em Sistemas Multi-Agente. É também proposto um módulo de aprendizagem para a resolução de novos problemas de escalonamento, com base em experiência anterior. O módulo de Auto-Optimização desenvolvido, inspirado na Computação Autónoma, permite ao sistema a selecção automática da Meta-heurística a usar no processo de optimização, assim como a respectiva parametrização. Para tal, recorreu-se à utilização de Raciocínio baseado em Casos de modo que o sistema resultante seja capaz de aprender com a experiência adquirida na resolução de problemas similares. Dos resultados obtidos é possível concluir da vantagem da sua utilização e respectiva capacidade de adaptação a novos e eventuais cenários.

Enabling Global Experiments With Interactive Reconfiguration And Steering By Multiple Users

In global scientific experiments with collaborative scenarios involving multinational teams there are big challenges related to data access, namely data movements are precluded to other regions or Clouds due to the constraints on latency costs, data privacy and data ownership. Furthermore, each site is processing local data sets using specialized algorithms and producing intermediate results that are helpful as inputs to applications running on remote sites. This paper shows how to model such collaborative scenarios as a scientific workflow implemented with AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic), a decentralized framework offering a feasible solution to run the workflow activities on distributed data centers in different regions without the need of large data movements. The AWARD workflow activities are independently monitored and dynamically reconfigured and steering by different users, namely by hot-swapping the algorithms to enhance the computation results or by changing the workflow structure to support feedback dependencies where an activity receives feedback output from a successor activity. A real implementation of one practical scenario and its execution on multiple data centers of the Amazon Cloud is presented including experimental results with steering by multiple users.