La sabiduría como competencia gerencial

Aunque el concepto de sabiduría ha sido ampliamente estudiado por expertos de áreas como la filosofía, la religión y la psicología, aún enfrenta limitaciones en cuanto a su definición y evaluación. Por esto, el presente trabajo tiene como objetivo, formular una definición del concepto de sabiduría que permita realizar una propuesta de evaluación del concepto como competencia en los gerentes. Para esto, se realizó un análisis documental de tipo cualitativo. De esta manera, se analizaron diversos textos sobre la historia, las definiciones y las metodologías para evaluar tanto la sabiduría como las competencias; diferenciando la sabiduría de otros constructos y analizando la diferencia entre las competencias generales y las gerenciales para posteriormente, definir la sabiduría como una competencia gerencial. Como resultado de este análisis se generó un prototipo de prueba denominado SAPIENS-O, a través del cuál se busca evaluar la sabiduría como competencia gerencial. Como alcances del instrumento se pueden identificar la posibilidad de medir la sabiduría como competencia en los gerentes, la posibilidad de dar un nuevo panorama a las dificultades teóricas y empíricas sobre la sabiduría y la posibilidad de facilitar el estudio de la sabiduría en ambientes reales, más específicamente en ambientes organizacionales.

Simplification, approximation and deformation of large models

The high level of realism and interaction in many computer graphic applications requires techniques for processing complex geometric models. First, we present a method that provides an accurate low-resolution approximation from a multi-chart textured model that guarantees geometric fidelity and correct preservation of the appearance attributes. Then, we introduce a mesh structure called Compact Model that approximates dense triangular meshes while preserving sharp features, allowing adaptive reconstructions and supporting textured models. Next, we design a new space deformation technique called *Cages based on a multi-level system of cages that preserves the smoothness of the mesh between neighbouring cages and is extremely versatile, allowing the use of heterogeneous sets of coordinates and different levels of deformation. Finally, we propose a hybrid method that allows to apply any deformation technique on large models obtaining high quality results with a reduced memory footprint and a high performance.

Predicting Argentine ant invasion across spatial scales via ecological niche models

La formiga argentina (Linepithema humile) es troba entre les espècies més invasores: originària d'Amèrica del Sud, actualment ha envaït nombroses àrees arreu del món. Aquesta tesi doctoral intenta fer una primera anàlisi integrada i multiescalar de la distribució de la formiga argentina mitjançant l'ús de models de nínxol ecològic. D'acord amb els resultats obtinguts, es preveu que la formiga argentina assoleixi una distribució més àmplia que l'actual. Les prediccions obtingudes a partir dels models concorden amb la distribució actualment coneguda i, a més, indiquen àrees a prop de la costa i dels rius principals com a altament favorables per a l'espècie. Aquests resultats corroboren la idea que la formiga argentina no es troba actualment en equilibri amb el medi. D'altra banda, amb el canvi climàtic, s'espera que la distribució de la formiga argentina s'estengui cap a latituds més elevades en ambdós hemisferis, i sofreixi una retracció en els tròpics a escales globals.

Methodology to obtain the user's Human Values Scale from Smart User Models

En años recientes,la Inteligencia Artificial ha contribuido a resolver problemas encontrados en el desempeño de las tareas de unidades informáticas, tanto si las computadoras están distribuidas para interactuar entre ellas o en cualquier entorno (Inteligencia Artificial Distribuida). Las Tecnologías de la Información permiten la creación de soluciones novedosas para problemas específicos mediante la aplicación de los hallazgos en diversas áreas de investigación. Nuestro trabajo está dirigido a la creación de modelos de usuario mediante un enfoque multidisciplinario en los cuales se emplean los principios de la psicología, inteligencia artificial distribuida, y el aprendizaje automático para crear modelos de usuario en entornos abiertos; uno de estos es la Inteligencia Ambiental basada en Modelos de Usuario con funciones de aprendizaje incremental y distribuido (conocidos como Smart User Model). Basándonos en estos modelos de usuario, dirigimos esta investigación a la adquisición de características del usuario importantes y que determinan la escala de valores dominantes de este en aquellos temas en los cuales está más interesado, desarrollando una metodología para obtener la Escala de Valores Humanos del usuario con respecto a sus características objetivas, subjetivas y emocionales (particularmente en Sistemas de Recomendación).Una de las áreas que ha sido poco investigada es la inclusión de la escala de valores humanos en los sistemas de información. Un Sistema de Recomendación, Modelo de usuario o Sistemas de Información, solo toman en cuenta las preferencias y emociones del usuario [Velásquez, 1996, 1997; Goldspink, 2000; Conte and Paolucci, 2001; Urban and Schmidt, 2001; Dal Forno and Merlone, 2001, 2002; Berkovsky et al., 2007c]. Por lo tanto, el principal enfoque de nuestra investigación está basado en la creación de una metodología que permita la generación de una escala de valores humanos para el usuario desde el modelo de usuario. Presentamos resultados obtenidos de un estudio de casos utilizando las características objetivas, subjetivas y emocionales en las áreas de servicios bancarios y de restaurantes donde la metodología propuesta en esta investigación fue puesta a prueba.En esta tesis, las principales contribuciones son: El desarrollo de una metodología que, dado un modelo de usuario con atributos objetivos, subjetivos y emocionales, se obtenga la Escala de Valores Humanos del usuario. La metodología propuesta está basada en el uso de aplicaciones ya existentes, donde todas las conexiones entre usuarios, agentes y dominios que se caracterizan por estas particularidades y atributos; por lo tanto, no se requiere de un esfuerzo extra por parte del usuario.

Development of multi-electrode array screening for anticonvulsants in acute rat brain slices

The acute hippocampal brain slice preparation is an important in vitro screening tool for potential anticonvulsants. Application of 4-aminopyridine (4-AP) or removal of external Mg2+ ions induces epileptiform bursting in slices which is analogous to electrical brain activity seen in status epilepticus states. We have developed these epileptiform models for use with multi-electrode arrays (MEAs), allowing recording across the hippocampal slice surface from 59 points. We present validation of this novel approach and analyses using two anticonvulsants, felbamate and phenobarbital, the effects of which have already been assessed in these models using conventional extracellular recordings. In addition to assessing drug effects on commonly described parameters (duration, amplitude and frequency), we describe novel methods using the MEA to assess burst propagation speeds and the underlying frequencies that contribute to the epileptiform activity seen. Contour plots are also used as a method of illustrating burst activity. Finally, we describe hitherto unreported properties of epileptiform bursting induced by 100M4-AP or removal of external Mg2+ ions. Specifically, we observed decreases over time in burst amplitude and increase over time in burst frequency in the absence of additional pharmacological interventions. These MEA methods enhance the depth, quality and range of data that can be derived from the hippocampal slice preparation compared to conventional extracellular recordings. It may also uncover additional modes of action that contribute to anti-epileptiform drug effects

Development and illustrative outputs of the Community Integrated Assessment System (CIAS), a multi-institutional modular integrated assessment approach for modelling climate change.

This paper describes the development and first results of the “Community Integrated Assessment System” (CIAS), a unique multi-institutional modular and flexible integrated assessment system for modelling climate change. Key to this development is the supporting software infrastructure, SoftIAM. Through it, CIAS is distributed between the community of institutions which has each contributed modules to the CIAS system. At the heart of SoftIAM is the Bespoke Framework Generator (BFG) which enables flexibility in the assembly and composition of individual modules from a pool to form coupled models within CIAS, and flexibility in their deployment onto the available software and hardware resources. Such flexibility greatly enhances modellers’ ability to re-configure the CIAS coupled models to answer different questions, thus tracking evolving policy needs. It also allows rigorous testing of the robustness of IA modelling results to the use of different component modules representing the same processes (for example, the economy). Such processes are often modelled in very different ways, using different paradigms, at the participating institutions. An illustrative application to the study of the relationship between the economy and the earth’s climate system is provided.

How well do high resolution models reproduce tropical convection?

Cascade is a multi-institution project studying the temporal and spatial organization of tropical convective systems. While cloud resolving numerical models can reproduce the observed diurnal cycle of such systems they are sensitive to the chosen resolution. As part of this effort, we are comparing results from the Met. Office Unified Model to data from the Global Earth Radiation Budget satellite instrument over the African Monsoon Interdisciplinary Analyses region of North Africa. We use a variety of mathematical techniques to study the outgoing radiation and the evolution of properties such as the cloud size distribution. The effectiveness of various model resolutions is tested with a view to determining the optimum balance between resolution and the need to reproduce the observations.

A multi-scale framework for strategic management of diffuse pollution

A multi-scale framework for decision support is presented that uses a combination of experiments, models, communication, education and decision support tools to arrive at a realistic strategy to minimise diffuse pollution. Effective partnerships between researchers and stakeholders play a key part in successful implementation of this strategy. The Decision Support Matrix (DSM) is introduced as a set of visualisations that can be used at all scales, both to inform decision making and as a communication tool in stakeholder workshops. A demonstration farm is presented and one of its fields is taken as a case study. Hydrological and nutrient flow path models are used for event based simulation (TOPCAT), catchment scale modelling (INCA) and field scale flow visualisation (TopManage). One of the DSMs; The Phosphorus Export Risk Matrix (PERM) is discussed in detail. The PERM was developed iteratively as a point of discussion in stakeholder workshops, as a decision support and education tool. The resulting interactive PERM contains a set of questions and proposed remediation measures that reflect both expert and local knowledge. Education and visualisation tools such as GIS, risk indicators, TopManage and the PERM are found to be invaluable in communicating improved farming practice to stakeholders. (C) 2008 Elsevier Ltd. All rights reserved.

Estimating surface net solar radiation by use of Landsat-5 TM and digital elevation models

The ground surface net solar radiation is the energy that drives physical and chemical processes at the ground surface. In this paper, multi-spectral data from the Landsat-5 TM, topographic data from a gridded digital elevation model, field measurements, and the atmosphere model LOWTRAN 7 are used to estimate surface net solar radiation over the FIFE site. Firstly an improved method is presented and used for calculating total surface incoming radiation. Then, surface albedo is integrated from surface reflectance factors derived from remotely sensed data from Landsat-5 TM. Finally, surface net solar radiation is calculated by subtracting surface upwelling radiation from the total surface incoming radiation.

North Atlantic Oscillation response to transient greenhouse gas forcing and the impact on European winter climate: a CMIP2 multi-model assessment

This study investigates the response of wintertime North Atlantic Oscillation (NAO) to increasing concentrations of atmospheric carbon dioxide (CO2) as simulated by 18 global coupled general circulation models that participated in phase 2 of the Coupled Model Intercomparison Project (CMIP2). NAO has been assessed in control and transient 80-year simulations produced by each model under constant forcing, and 1% per year increasing concentrations of CO2, respectively. Although generally able to simulate the main features of NAO, the majority of models overestimate the observed mean wintertime NAO index of 8 hPa by 5-10 hPa. Furthermore, none of the models, in either the control or perturbed simulations, are able to reproduce decadal trends as strong as that seen in the observed NAO index from 1970-1995. Of the 15 models able to simulate the NAO pressure dipole, 13 predict a positive increase in NAO with increasing CO2 concentrations. The magnitude of the response is generally small and highly model-dependent, which leads to large uncertainty in multi-model estimates such as the median estimate of 0.0061 +/- 0.0036 hPa per %CO2. Although an increase of 0.61 hPa in NAO for a doubling in CO2 represents only a relatively small shift of 0.18 standard deviations in the probability distribution of winter mean NAO, this can cause large relative increases in the probabilities of extreme values of NAO associated with damaging impacts. Despite the large differences in NAO responses, the models robustly predict similar statistically significant changes in winter mean temperature (warmer over most of Europe) and precipitation (an increase over Northern Europe). Although these changes present a pattern similar to that expected due to an increase in the NAO index, linear regression is used to show that the response is much greater than can be attributed to small increases in NAO. NAO trends are not the key contributor to model-predicted climate change in wintertime mean temperature and precipitation over Europe and the Mediterranean region. However, the models' inability to capture the observed decadal variability in NAO might also signify a major deficiency in their ability to simulate the NAO-related responses to climate change.

El Nino-mean state-seasonal cycle interactions in a multi-model ensemble

The modelled El Nino-mean state-seasonal cycle interactions in 23 coupled ocean-atmosphere GCMs, including the recent IPCC AR4 models, are assessed and compared to observations and theory. The models show a clear improvement over previous generations in simulating the tropical Pacific climatology. Systematic biases still include too strong mean and seasonal cycle of trade winds. El Nino amplitude is shown to be an inverse function of the mean trade winds in agreement with the observed shift of 1976 and with theoretical studies. El Nino amplitude is further shown to be an inverse function of the relative strength of the seasonal cycle. When most of the energy is within the seasonal cycle, little is left for inter-annual signals and vice versa. An interannual coupling strength (ICS) is defined and its relation with the modelled El Nino frequency is compared to that predicted by theoretical models. An assessment of the modelled El Nino in term of SST mode (S-mode) or thermocline mode (T-mode) shows that most models are locked into a S-mode and that only a few models exhibit a hybrid mode, like in observations. It is concluded that several basic El Nino-mean state-seasonal cycle relationships proposed by either theory or analysis of observations seem to be reproduced by CGCMs. This is especially true for the amplitude of El Nino and is less clear for its frequency. Most of these relationships, first established for the pre-industrial control simulations, hold for the double and quadruple CO2 stabilized scenarios. The models that exhibit the largest El Nino amplitude change in these greenhouse gas (GHG) increase scenarios are those that exhibit a mode change towards a T-mode (either from S-mode to hybrid or hybrid to T-mode). This follows the observed 1976 climate shift in the tropical Pacific, and supports the-still debated-finding of studies that associated this shift to increased GHGs. In many respects, these models are also among those that best simulate the tropical Pacific climatology (ECHAM5/MPI-OM, GFDL-CM2.0, GFDL-CM2.1, MRI-CGM2.3.2, UKMO-HadCM3). Results from this large subset of models suggest the likelihood of increased El Nino amplitude in a warmer climate, though there is considerable spread of El Nino behaviour among the models and the changes in the subsurface thermocline properties that may be important for El Nino change could not be assessed. There are no clear indications of an El Nino frequency change with increased GHG.

A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Modeling small-scale dairy farms in central Mexico using multi-criteria programming

Milk supply from Mexican dairy farms does not meet demand and small-scale farms can contribute toward closing the gap. Two multi-criteria programming techniques, goal programming and compromise programming, were used in a study of small-scale dairy farms in central Mexico. To build the goal and compromise programming models, 4 ordinary linear programming models were also developed, which had objective functions to maximize metabolizable energy for milk production, to maximize margin of income over feed costs, to maximize metabolizable protein for milk production, and to minimize purchased feedstuffs. Neither multicriteria approach was significantly better than the other; however, by applying both models it was possible to perform a more comprehensive analysis of these small-scale dairy systems. The multi-criteria programming models affirm findings from previous work and suggest that a forage strategy based on alfalfa, rye-grass, and corn silage would meet nutrient requirements of the herd. Both models suggested that there is an economic advantage in rescheduling the calving season to the second and third calendar quarters to better synchronize higher demand for nutrients with the period of high forage availability.

The relationship between multi-species catch and effort: among fishery comparisons

The control of fishing mortality via fishing effort remains fundamental to most fisheries management strategies even at the local community or co-management level. Decisions to support such strategies require knowledge of the underlying response of the catch to changes in effort. Even under adaptive management strategies, imprecise knowledge of the response is likely to help accelerate the adaptive learning process. Data and institutional capacity requirements to employ multi-species biomass dynamics and age-structured models invariably render their use impractical particularly in less developed regions of the world. Surplus production models fitted to catch and effort data aggregated across all species offer viable alternatives. The current paper seeks models of this type that best describe the multi-species catch–effort responses in floodplain-rivers, lakes and reservoirs and reef-based fisheries based upon among fishery comparisons, building on earlier work. Three alternative surplus production models were fitted to estimates of catch per unit area (CPUA) and fisher density for 258 fisheries in Africa, Asia and South America. In all cases examined, the best or equal best fitting model was the Fox type, explaining up to 90% of the variation in CPUA. For lake and reservoir fisheries in Africa and Asia, the Schaefer and an asymptotic model fitted equally well. The Fox model estimates of fisher density (fishers km−2) at maximum yield (iMY) for floodplain-rivers, African lakes and reservoirs and reef-based fisheries are 13.7 (95% CI [11.8, 16.4]); 27.8 (95% CI [17.5, 66.7]) and 643 (95% CI [459,1075]), respectively and compare well with earlier estimates. Corresponding estimates of maximum yield are also given. The significantly higher value of iMY for reef-based fisheries compared to estimates for rivers and lakes reflects the use of a different measure of fisher density based upon human population size estimates. The models predict that maximum yield is achieved at a higher fishing intensity in Asian lakes compared to those in Africa. This may reflect the common practice in Asia of stocking lakes to augment natural recruitment. Because of the equilibrium assumptions underlying the models, all the estimates of maximum yield and corresponding levels of effort should be treated with caution.

A comparison of three different models for estimating relative risk in meta-analysis of clinical trials under unobserved heterogeneity

We focus on the comparison of three statistical models used to estimate the treatment effect in metaanalysis when individually pooled data are available. The models are two conventional models, namely a multi-level and a model based upon an approximate likelihood, and a newly developed model, the profile likelihood model which might be viewed as an extension of the Mantel-Haenszel approach. To exemplify these methods, we use results from a meta-analysis of 22 trials to prevent respiratory tract infections. We show that by using the multi-level approach, in the case of baseline heterogeneity, the number of clusters or components is considerably over-estimated. The approximate and profile likelihood method showed nearly the same pattern for the treatment effect distribution. To provide more evidence two simulation studies are accomplished. The profile likelihood can be considered as a clear alternative to the approximate likelihood model. In the case of strong baseline heterogeneity, the profile likelihood method shows superior behaviour when compared with the multi-level model. Copyright (C) 2006 John Wiley & Sons, Ltd.