Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.

Change escalation processes and complex adaptive systems: from incremental reconfigurations to discontinuous restructuring

This study examines when “incremental” change is likely to trigger “discontinuous” change, using the lens of complex adaptive systems theory. Going beyond the simulations and case studies through which complex adaptive systems have been approached so far, we study the relationship between incremental organizational reconfigurations and discontinuous organizational restructurings using a large-scale database of U.S. Fortune 50 industrial corporations. We develop two types of escalation process in organizations: accumulation and perturbation. Under ordinary conditions, it is perturbation rather than the accumulation that is more likely to trigger subsequent discontinuous change. Consistent with complex adaptive systems theory, organizations are more sensitive to both accumulation and perturbation in conditions of heightened disequilibrium. Contrary to expectations, highly interconnected organizations are not more liable to discontinuous change. We conclude with implications for further research, especially the need to attend to the potential role of managerial design and coping when transferring complex adaptive systems theory from natural systems to organizational systems.

Study of Singularities in Nonsmooth Dynamical Systems via Singular Perturbation

In this article we describe some qualitative and geometric aspects of nonsmooth dynamical systems theory around typical singularities. We also establish an interaction between nonsmooth systems and geometric singular perturbation theory. Such systems are represented by discontinuous vector fields on R(l), l >= 2, where their discontinuity set is a codimension one algebraic variety. By means of a regularization process proceeded by a blow-up technique we are able to bring about some results that bridge the space between discontinuous systems and singularly perturbed smooth systems. We also present an analysis of a subclass of discontinuous vector fields that present transient behavior in the 2-dimensional case, and we dedicate a section to providing sufficient conditions in order for our systems to have local asymptotic stability.

Local dimension and finite time prediction in spatiotemporal chaotic systems

Predictability is related to the uncertainty in the outcome of future events during the evolution of the state of a system. The cluster weighted modeling (CWM) is interpreted as a tool to detect such an uncertainty and used it in spatially distributed systems. As such, the simple prediction algorithm in conjunction with the CWM forms a powerful set of methods to relate predictability and dimension.

Management of vomplex systems: Modeling the biological pest control

The aim of this paper is to study the cropping system as complex one, applying methods from theory of dynamic systems and from the control theory to the mathematical modeling of the biological pest control. The complex system can be described by different mathematical models. Based on three models of the pest control, the various scenarios have been simulated in order to obtain the pest control strategy only through natural enemies' introduction. © 2008 World Scientific Publishing Company.

Generic bifurcation of refracted systems

In this article we discuss some qualitative and geometric aspects of non-smooth dynamical systems theory. Our goal is to study the diagram bifurcation of typical singularities that occur generically in one parameter families of certain piecewise smooth vector fields named Refracted Systems. Such systems has a codimension-one submanifold as its discontinuity set. © 2012 Elsevier Ltd.

Comments on the T-dual of the gravity dual of D5-branes on S-3

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Confluências entre mito, literatura e direito em Édipo Rei, de Sófocles

Pós-graduação em Letras - IBILCE

Traumatic memories: bridging the gap between functional neuroimaging and psychotherapy

Objective: Neuroimaging studies have highlighted important issues related to structural and functional brain changes found in sufferers of psychological trauma that may influence their ability to synthesize, categorize, and integrate traumatic memories. Methods: Literature review and critical analysis and synthesis. Results: Traumatic memories are diagnostic symptoms of post-traumatic stress disorder (PTSD), and the dual representation theory posits separate memory systems subserving vivid re-experiencing (non-hippocampally dependent) versus declarative autobiographical memories of trauma (hippocampally dependent). But the psychopathological signs of trauma are not static over time, nor is the expression of traumatic memories. Multiple memory systems are activated simultaneously and in parallel on various occasions. Neural circuitry interaction is a crucial aspect in the development of a psychotherapeutic approach that may favour an integrative translation of the sensory fragments of the traumatic memory into a declarative memory system. Conclusion: The relationship between neuroimaging findings and psychological approaches is discussed for greater efficacy in the treatment of psychologically traumatized patients.

Discrete Event Systems based Design Patterns for Diagnosability Analysis of Automated Manufacturing Systems

The main goal of this thesis is to facilitate the process of industrial automated systems development applying formal methods to ensure the reliability of systems. A new formulation of distributed diagnosability problem in terms of Discrete Event Systems theory and automata framework is presented, which is then used to enforce the desired property of the system, rather then just verifying it. This approach tackles the state explosion problem with modeling patterns and new algorithms, aimed for verification of diagnosability property in the context of the distributed diagnosability problem. The concepts are validated with a newly developed software tool.

Neural Networks as Cybernetic Systems - Part I

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Neural Networks as Cybernetic Systems - Part II

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology.

Neural Networks as Cybernetic Systems (3rd and revised edition) - Part I

Neural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology. This third edition essentially compares with the 2nd one, but has been improved by correction of errors and by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes hopefully allow for an easier comprehension of the essential aspects of this important domain that has received growing attention during the last years.

Neural Networks as Cybernetic Systems (3rd and revised edition) - Part 2

eural Networks as Cybernetic Systems is a textbox that combines classical systems theory with artificial neural network technology. This third edition essentially compares with the 2nd one, but has been improved by correction of errors and by a rearrangement and minor expansion of the sections referring to recurrent networks. These changes hopefully allow for an easier comprehension of the essential aspects of this important domain that has received growing attention during the last years.

Control and command systems concepts from early work on a Mars Rover

We recover and develop some robotic systems concepts (on the light of present systems tools) that were originated for an intended Mars Rover in the sixties of the last century at the Instrumentation Laboratory of MIT, where one of the authors was involved. The basic concepts came from the specifications for a type of generalized robot inspired in the structure of the vertebrate nervous systems, where the decision system was based in the structure and function of the Reticular Formation (RF). The vertebrate RF is supposed to commit the whole organism to one among various modes of behavior, so taking the decisions about the present overall task. That is, it is a kind of control and command system. In this concepts updating, the basic idea is that the RF comprises a set of computing units such that each computing module receives information only from a reduced part of the overall, little processed sensory inputs. Each computing unit is capable of both general diagnostics about overall input situations and of specialized diagnostics according to the values of a concrete subset of the input lines. Slave systems to this command and control computer, there are the sensors, the representations of external environment, structures for modeling and planning and finally, the effectors acting in the external world.