Temporal disorder in up-down symmetric systems

The effect of temporal disorder on systems with up-down Z2 symmetry is studied. In particular, we analyze two well-known families of phase transitions: the Ising and the generalized voter universality classes, and scrutinize the consequences of placing them under fluctuating global conditions. We observe that variability of the control parameter induces in both classes "Temporal Griffiths Phases" (TGP). These recently-uncovered phases are analogous to standard Griffiths Phases appearing in systems with quenched spatial disorder, but where the roles of space and time are exchanged. TGPs are characterized by broad regions in parameter space in which (i) mean first-passage times scale algebraically with system size, and (ii) the system response (e.g. susceptibility) diverges. Our results confirm that TGPs are quite robust and ubiquitous in the presence of temporal disorder. Possible applications of our results to examples in ecology are discussed

Chronic bronchial infection in stable COPD: To treat or not to treat

Chronic bronchial infection; COPDInfección bronquial crónica; EPOCInfecció bronquial crònica; MPO

Modelling lane keeping by a hybrid open-closed-loop pulse control scheme

This paper presents a novel methodology for mod- elling human lane keeping control by characterizing a unique concept of elementary steering pulses, which are motor primi- tives in man-vehicle systems. The novelty of the paper is the introduction of elementary steering pulses that have been evidently extracted from naturalistic driving data through machine learning techniques (data-driven modelling), and are incorporated into an alternative steering control scheme. This newly proposed hybrid- open-closed- loop (HOCL) control scheme, where an elementary steering pulse starts with an open loop, representing real human’s reflex responses triggered by human lane keeping errors, and is adjusted back with the traditional close-loop control, has shown a significant improvement on both the stability and matching performance to real driving events. Online measurement of the key metrics in the steering process provides a new tool for monitoring driver states, and the biofidelic steering model may provide human-like qualities for future automated lane-keeping systems. Both will add to the array of tools available for achieving autonomous and semi-autonomous driving systems, which greatly benefits the current vehicle industry

Detection of emerging faults on industrial gas turbines using extended Gaussian mixture models

The paper extends traditional Gaussian Mixture Model (GMM) techniques to provide recognition of operational states and detection of emerging faults for industrial or complex systems. A Variational Bayesian (VB) method allows a GMM to cluster with its Mixture Components (MCs) to facilitate the extraction of steady-state operational behaviour — this is recognised as being a primary factor in reducing the susceptibility of alternative prognostic/diagnostic techniques which can initiate false-alarms resulting from control set-point and load changes. Furthermore, a GMM with an Outlier Component (GMMOC) is discussed and applied for direct fault detection. To demonstrate the efficacy of the proposed techniques, real-time measurements from operational Industrial Gas Turbines (IGTs) show that the resulting VBGMM facilitates the selection of the number of required MCs to cluster the data, and thereby provide essential input for operational signature recognition. Moreover, GMMOC is shown to facilitate the early detection of emerging faults. An advantage of the VBGMM over traditional pre-defined thresholds is the extraction of steady-state data during both full- and part-load cases, and a primary advantage of the GMMOC method is its applicability for novelty detection when there is a lack of prior knowledge of fault patterns. Results based on measurements taken from IGTs operating in the field are therefore also included which show that the techniques provide an integrated pre-processing, benchmarking and novelty/fault detection methodology

Prevención cardiovascular y de la osteoporosis con terapia hormonal sustitutiva

Se recomienda actualmente que todo médico -sea cual sea su especialidad- aproveche la oportunidad que supone la atención clínica a cualquier mujer postmenopáusica o que se halle en torno a la menopausia para aconsejarle acerca de los beneficios y riesgos que supone la terapia hormonal sustitutiva (THS) con estrógenos y progesterona. Una vez que sea adecuadamente informada de estos beneficios y riesgos, debe ser la propia mujer quien decida si va a iniciar o no esta terapia, teniendo en cuenta no sólo su propio perfil de riesgo sino también sus preferencias. Los beneficios más claros se refieren a la reducción del riesgo de osteoporosis y de fracturas, así como al control y prevención de algunos síntomas frecuentes en la menopausia. Para prevenir la osteoporosis se requiere un uso continuado de IaTHS. También existen cada vez más argumentos que sugieren una reducción del riesgo de un primer episodio coronario. No obstante, no hay pruebas de que laTHS sea útil en la prevención secundaria de la cardiopatía isquémica o aporte alguna protección frente a los accidentes cerebrovasculares. Las desventajas más importantes de la THS se refieren a un mayor riesgo de hiperplasia de endometrio y de cáncer endometrial cuando se usan estrógenos solos, sin combinarlos con progestágenos. También existe un pequeño incremento del riesgo de cáncer de mama cuando se prolonga la THS, también cuando se asocian progestágenos

Memory pattern identification for feedback tracking control in human-machine systems

Objective: The aim of this paper was to identify the characteristics of memory patterns with respect to a visual input, perceived by the human operator during a manual control task, which consisted in following a moving target on a display with a cursor.Background: Manual control tasks involve nondeclarative memory. The memory encodings of different motor skills have been referred to as procedural memories. The procedural memories have a pattern, which this paper sought to identify for the particular case of a onedimensional tracking task. Specifically, data recorded from human subjects controlling dynamical systems with different fractional order were investigated.Method: A Finite Impulse Response (FIR) controller was fitted to the data, and pattern analysis was performed to the fitted parameters. Then, the FIR model was further reduced to a lower order controller; from the simplified model, the stability analysis of the human-machine system in closedloop was conducted.Results: It is shown that the FIR model can be employed to identify and represent patterns in human procedural memories during manual control tasks. The obtained procedural memory pattern presents a time scale of about 650 ms before decay. Furthermore, the fitted controller is stable for systems with fractional order less or equal to 1.Conclusion: For systems of different fractional order, the proposed control scheme – based on a FIR model – can effectively characterize the linear properties of manual control in humans.Application: This research supports a biofidelic approach to human manual control modeling over feedback visual perceptions. Relevant applications of this research are: the development of shared-control systems, where a virtual human model assists the human during a control task, and human operator state monitoring.</div

Evolución de la competitividad y rentabilidad del cultivo del tomate rojo (lycopersicon esculentum l.) en Sinaloa, México

El tomate rojo mexicano es una de las hortalizas que generan más divisas para el país, ya que cerca de 30% de la producción nacional se exporta, principalmente a los Estados Unidos de Norteamérica (EE.UU.), por lo que su cultivo depende significativamente del comportamiento del mercado internacional. En este estudio se planeó el siguiente objetivo: analizar la rentabilidad, la competitividad y la ventaja comparativa del cultivo del tomate rojo en Sinaloa en el ciclo agrícola 1999/2000, para lo cual se utilizó la metodología de la Matriz de Análisis de Política (MAP) desarrollada por Monke y Pearson (1989)

A multiplicative human steering control model

A non-linear, yet simple, multiplicative human-control model is developed by studying the statistical properties of human subjects’ motor response to a visual input; statistical analysis of the magnitude of the steering angle, from subjects performing a tracking task with a steering wheel, shows that the data is consistent with a log-normal distribution. Thus the possibility of modelling human-control as a multiplicative process, that replicates the statistical properties found in the human-operator is considered. The proposed multiplicative controller is contrasted with real data and with the Crossover Model. This research has potential applications in a wide range of fields, from human performance modelling to the development of human-machine interfaces, particularly in the application of ground vehicle automation