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The objective of this article is to understand the term of organizational trust from the perspective of Adaptive Complex Systems Theory. The methodology used is a theoretical analysis about the trust as key element found within organizations as part of the internal mechanism that allows communication and cooperation between their members, therefore, the principal idea is that organizations are complex adaptive systems and the trust is a nonlinear regulatory component with feedbacks at different organizational hierarchical levels that allow it to selforganize and self-adapt. In this way, we expect that the results obtained help to the theoretical contribution of complex adaptive systems in organizations, focusing on trust as a complex adaptive product that is present in the organizational functions and dysfunctions. [ABSTRACT FROM AUTHOR]

The development of the Artificial Pancreas, which consists of the implementation of a continuous insulin infusion pump and sensors for blood glucose monitoring, is a priority for the treatment of subjects with type 1 diabetes. To achieve a complete automation of this process several control strategies have been proposed with the aim of maintaining blood glucose levels in the normoglycemic range. Among the developed strategies, those based on adaptive control stand out, to counteract the effect of physiological variations that are inherent to the patient. However, these strategies require full state measurement, which is not an easy task because of sensors cost and the inherent difficulty in direct measurement of physiological variables. Therefore, in this work, an adaptive control strategy with guaranteed stability is used for an insulin control model, and a nonlinear sliding mode observer is designed to estimate the state variables that the controller requires. With the complete control-observer system, satisfactory results are obtained by compensating for meal intake and errors between the model and subject parameters. [ABSTRACT FROM AUTHOR]

This paper presents the procedure of tuning and simulation of an adaptive predictive control strategy to control the longitudinal and latero-directional movements of a unmanned aerial vehicle (UAV), using the Matlab ® - Simulink® platform. Uses the mathematical model of a UAV which are initially performed simulations of the system in open loop, identification is carried on online, and predictive control and PID controllers algorithms are implemented with the parameters obtained, to control the angle of pitch, roll and yaw. Control strategies used presented a good performance and manage to stabilize the aircraft properly. Finally, they are presented and analyzed the results of the performance of the system with predictive control and PID control using metrics of the integral of the error and of the work of the manipulated variable. [ABSTRACT FROM AUTHOR]

The design and implementation of a predictive/adaptive control system is presented, using neural engineering techniques to control a non-linear MIMO system in order to control, at a later stage, the temperature and level in a non-linear conical plant. Preliminarily, conventional control structures were tested, which gave rise to the need to test intelligent control structures that allow the control objectives to be met more effectively. The process begins with the experimentation of different neuronal control structures, and then escalates to a predictive/adaptive neuronal control system. The results achieved at the simulation level, testing the proposed design on mathematical models of non-linear MIMO systems, were satisfactory and met the control objectives established, therefore, in the next stage of the project, the experimentation is estimated in the real plant under study. [ABSTRACT FROM AUTHOR]

Several decision scenarios can be modeled as dynamic optimization problems (DOPs), which have been tackled by metaheuristics techniques over the last years. However, so far, most related works assume that changes occur every equal time periods, which may be rather idealistic in real-world scenarios. In contrast, DOPs with variable change frequency (DOPVCFs) impose as main challenge to the algorithm: how to adapt to different environments during the run, as fast as possible. In that sense, self-adaptation is parameter control technique with remarkable success in complex scenarios. So, in the present work we aim to analyze the impact of self-adaptation in solving DOPVCFs. To achieve this, we have designed an experimental study by considering a recently proposed self-adaptive technique over several test scenarios. The results confirm that self-adaptation has not only a positive, but also significant impact in the algorithm performance. [ABSTRACT FROM AUTHOR]

Science is an ever-changing discipline. Modern medical knowledge is based on science. Current medical paradigm is both linear and reductionist. There is a new general theory validated by computer's calculations named chaos and complexity theory. This new paradigm will probably have an impact on medical practice. Cardiovascular physiology may display fractal and/ or chaotic behavior. Computerized heart rhythm analyses enhanced our understanding of complex diseases otherwise not explainable by current linear-reductionist paradigms. Cases in point are diverse dysautonomia including orthostatic intolerance, cardiac X syndrome and fibromyalgia. Derived from this, new knowledge is a different diagnostic and therapeutic stance: scientific holism. [ABSTRACT FROM AUTHOR]

This paper shows for an adaptive Self-Tuning Control, Its researh and development results, algoritms, program validation results using plantas with high variation dynamics and some important conclusions to implementate it. [ABSTRACT FROM AUTHOR]