Especificaciones y estándares en e-learning.

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Unidades de Aprendizaje, una propuesta de complemento a los Objetos de Aprendizaje.

Monográfico con el título: 'Estado actual de los sistemas e-learning'. Resumen basado en el de la publicación

Measuring and modelling mixture toxicity of imidacloprid and thiacloprid on Caenorhabditis elegans and Eisenia fetida

While the standard models of concentration addition and independent action predict overall toxicity of multicomponent mixtures reasonably, interactions may limit the predictive capability when a few compounds dominate a mixture. This study was conducted to test if statistically significant systematic deviations from concentration addition (i.e. synergism/antagonism, dose ratio- or dose level-dependency) occur when two taxonomically unrelated species, the earthworm Eisenia fetida and the nematode Caenorhabditis elegans were exposed to a full range of mixtures of the similar acting neonicotinoid pesticides imidacloprid and thiacloprid. The effect of the mixtures on C. elegans was described significantly better (p<0.01) by a dose level-dependent deviation from the concentration addition model than by the reference model alone, while the reference model description of the effects on E. fetida could not be significantly improved. These results highlight that deviations from concentration addition are possible even with similar acting compounds, but that the nature of such deviations are species dependent. For improving ecological risk assessment of simple mixtures, this implies that the concentration addition model may need to be used in a probabilistic context, rather than in its traditional deterministic manner. Crown Copyright (C) 2008 Published by Elsevier Inc. All rights reserved.

Identification and characterisation of five novel miniature inverted-repeat transposable elements (MITEs) in amphioxus (Branchiostoma floridae)

As the sister group to vertebrates, amphioxus is consistently used as a model of genome evolution for understanding the invertebrate/vertebrate transition. The amphioxus genome has not undergone massive duplications like those in the vertebrates or disruptive rearrangements like in the genome of Ciona, a urochordate, making it an ideal evolutionary model. Transposable elements have been linked to many genomic evolutionary changes including increased genome size, modified gene expression, massive gene rearrangements, and possibly intron evolution. Despite their importance in genome evolution, few previous examples of transposable elements have been identified in amphioxus. We report five novel Miniature Inverted-repeat Transposable Elements (MITEs) identified by an analysis of amphioxus DNA sequence, which we have named LanceleTn-1, LanceleTn-2, LanceleTn-3a, LanceleTn-3b and LanceleTn-4. Several of the LanceleTn elements were identified in the amphioxus ParaHox cluster, and we suggest these have had important implications for the evolution of this highly conserved gene cluster. The estimated high copy numbers of these elements implies that MITEs are probably the most abundant type of mobile element in amphioxus, and are thus likely to have been of fundamental importance in shaping the evolution of the amphioxus genome.

RAMI4PILPS: An intercomparison of formulations for the partitioning of solar radiation in land surface models

[1] Remotely sensed, multiannual data sets of shortwave radiative surface fluxes are now available for assimilation into land surface schemes (LSSs) of climate and/or numerical weather prediction models. The RAMI4PILPS suite of virtual experiments assesses the accuracy and consistency of the radiative transfer formulations that provide the magnitudes of absorbed, reflected, and transmitted shortwave radiative fluxes in LSSs. RAMI4PILPS evaluates models under perfectly controlled experimental conditions in order to eliminate uncertainties arising from an incomplete or erroneous knowledge of the structural, spectral and illumination related canopy characteristics typical for model comparison with in situ observations. More specifically, the shortwave radiation is separated into a visible and near-infrared spectral region, and the quality of the simulated radiative fluxes is evaluated by direct comparison with a 3-D Monte Carlo reference model identified during the third phase of the Radiation transfer Model Intercomparison (RAMI) exercise. The RAMI4PILPS setup thus allows to focus in particular on the numerical accuracy of shortwave radiative transfer formulations and to pinpoint to areas where future model improvements should concentrate. The impact of increasing degrees of structural and spectral subgrid variability on the simulated fluxes is documented and the relevance of any thus emerging biases with respect to gross primary production estimates and shortwave radiative forcings due to snow and fire events are investigated.

Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography

This paper proposes a new reconstruction method for diffuse optical tomography using reduced-order models of light transport in tissue. The models, which directly map optical tissue parameters to optical flux measurements at the detector locations, are derived based on data generated by numerical simulation of a reference model. The reconstruction algorithm based on the reduced-order models is a few orders of magnitude faster than the one based on a finite element approximation on a fine mesh incorporating a priori anatomical information acquired by magnetic resonance imaging. We demonstrate the accuracy and speed of the approach using a phantom experiment and through numerical simulation of brain activation in a rat's head. The applicability of the approach for real-time monitoring of brain hemodynamics is demonstrated through a hypercapnic experiment. We show that our results agree with the expected physiological changes and with results of a similar experimental study. However, by using our approach, a three-dimensional tomographic reconstruction can be performed in ∼3  s per time point instead of the 1 to 2 h it takes when using the conventional finite element modeling approach

Generic construction of monitors for floating point unit designs

This paper proposes a set of well defined steps to design functional verification monitors intended to verify Floating Point Units (FPU) described in HDL. The first step consists on defining the input and output domain coverage. Next, the corner cases are defined. Finally, an already verified reference model is used in order to test the correctness of the Device Under Verification (DUV). As a case study a monitor for an IEEE754-2008 compliant design is implemented. This monitor is built to be easily instantiated into verification frameworks such as OVM. Two different designs were verified reaching complete input coverage and successful compliant results.

Upper-mantle seismic anisotropy from SKS splitting in the South American stable platform: A test of asthenospheric flow models beneath the lithosphere

Upper-mantle seismic anisotropy has been extensively used to infer both present and past deformation processes at lithospheric and asthenospheric depths. Analysis of shear-wave splitting (mainly from core-refracted SKS phases) provides information regarding upper-mantle anisotropy. We present average measurements of fast-polarization directions at 21 new sites in poorly sampled regions of intra-plate South America, such as northern and northeastern Brazil. Despite sparse data coverage for the South American stable platform, consistent orientations are observed over hundreds of kilometers. Over most of the continent, the fast-polarization direction tends to be close to the absolute plate motion direction given by the hotspot reference model HS3-NUVEL-1A. A previous global comparison of the SKS fast-polarization directions with flow models of the upper mantle showed relatively poor correlation on the continents, which was interpreted as evidence for a large contribution of ""frozen"" anisotropy in the lithosphere. For the South American plate, our data indicate that one of the reasons for the poor correlation may have been the relatively coarse model of lithospheric thicknesses. We suggest that improved models of upper-mantle flow that are based on more detailed lithospheric thicknesses in South America may help to explain most of the observed anisotropy patterns.

A multi-wavelet based technique for calculating dense 2D disparity maps from stereo

A vision based approach for calculating accurate 3D models of the objects is presented. Generally industrial visual inspection systems capable of accurate 3D depth estimation rely on extra hardware tools like laser scanners or light pattern projectors. These tools improve the accuracy of depth estimation but also make the vision system costly and cumbersome. In the proposed algorithm, depth and dimensional accuracy of the produced 3D depth model depends on the existing reference model instead of the information from extra hardware tools. The proposed algorithm is a simple and cost effective software based approach to achieve accurate 3D depth estimation with minimal hardware involvement. The matching process uses the well-known coarse to fine strategy, involving the calculation of matching points at the coarsest level with consequent refinement up to the finest level. Vector coefficients of the wavelet transform-modulus are used as matching features, where wavelet transform-modulus maxima defines the shift invariant high-level features with phase pointing to the normal of the feature surface. The technique addresses the estimation of optimal corresponding points and the corresponding 2D disparity maps leading to the creation of accurate depth perception model.

A loop-shaping approach to intelligent control

In this paper, we propose a loop-shaping approach to in telligent control with dynamically constructed neurocon troller. In the proposed control scheme, the process uncer tainly is reduced in the controller rather than in the process, without explicit identification of the process under control. The inherent noise/distrurbances in the process are utilized to satisfy persistency of excitation condition. The use of a reference model in form of a filter allow the frequency response of the closed-loop to be adapted in line with the changes in frequency response of the filter. The approach is evaluated on the example of control of polymerization reactor with promising results.

The application of soft systems methodology to supply chain management

This chapter reflects upon techniques that might facilitate improved strategic decision making in a supply chain management (SCM) environment. In particular, it presents the integration of a selection of techniques adapted from an approach to systems-based problem solving that has emerged primarily in the UK over the last 20-30 years—the soft systems methodology (SSM). The results reported indicate that SSM techniques can complement existing SCM decision-making tools. In particular, this chapter outlines a framework for integrating some SSM techniques with approaches based upon the supply-chain operations reference-model (SCOR) .

Eccentricity and hardness control in cold rolling mills with a dynamically constructed neural controller

The main objective of a steel strip rolling process is to produce high quality steel at a desired thickness.  Thickness reduction is the result of the speed difference between the incoming and the outgoing steel strip and the application of the large normal forces via the backup and the work rolls.  Gauge control of a cold rolled steel strip is achieved using the gaugemeter principle that works adequately for the input gauge changes and the strip hardness changes.  However, the compensation of some factors is problematic, for example, eccentricity of the backup rolls.  This cyclic eccentricity effect causes a gauge deviation, but more importantly, a signal is passed to the gap position control so to increase the eccentricity deviation.  Consequently, the required high product tolerances are severely limited by the presence of the roll eccentricity effects.
In this paper a direct model reference adaptive control (MRAC) scheme with dynamically constructed neural controller was used.  The aim here is to find the simplest controller structure capable of achieving an optimal performance.  The stability of the adaptive neural control scheme (i.e. the requirement of persistency of excitation and bounded learning rates) is addressed by using as the inputs to the reference model the plant's state variables.  In such a case, excitation is due to actual plant signals (states) affected by plant disturbances and noise.  In addition, a reference model in the form of a filter with a desired transfer function using Modulus Optimum design was used to ensure variance in the desired dynamic characteristics of the system.  The gradually decreasing learning rate employed by the neural controller in this paper is aimed at eliminating controller instability resulting from over-aggressive control.  The moving target problem (i.e. the difficulty of global neural networks to perfrom several separate computational tasks in closed -loop control) is addressed by the localized architecture of the controller.  The above control scheme and learning algorithm offers a method for automatic discovery of an efficient controller.
The resulting neural controller produces an excellent disturbance rejection in both cases of eccentricity and hardness disturbances, reducing the gauge deviation due to eccentricity disturbance from 33.36% to 4.57% on average, and the gauge deviation due to hardness disturbance from 12.59% to 2.08%.

Novel genes in the liver of diabetic Psammomys obesus

Type 2 diabetes mellitus is a metabolic disease characterised by defects in insulin secretion and insulin action and disturbances in carbohydrate, fat and protein metabolism. Hepatic insulin resistance contributes to hyperglycemia and also leads to disturbances in fat metabolism in type 2 diabetes. Psammomys obesus is a unique poly genie animal model of type 2 diabetes and obesity, ideally suited for studies examining physiological and genetic aspects of these diseases. To identify metabolic abnormalities potentially contributing to the obesity and diabetes phenotype in P. obesus, indirect calorimetry was used to characterise whole body energy expenditure and substrate utilisation. Lean-NGT, obese-IGT and obese-diabetic animals were examined in fed and fasted states and following 14 days of dietary energy restriction. Energy expenditure and fat oxidation were elevated in the obese-IGT and obese-diabetic groups in proportion to body weight. Glucose oxidation was not different between groups. Obese-diabetic P. obesus displayed elevated nocturnal blood glucose levels and fat oxidation. Following 14 days of dietary energy restriction, body weight was reduced and plasma insulin and blood glucose levels were normalised in all groups. Glucose oxidation was reduced and fat oxidation was increased. After 24 hours of fasting, plasma insulin and blood glucose levels were normalised in all groups. Energy expenditure and glucose oxidation were greatly reduced and fat oxidation was increased. Following either dietary energy restriction or fasting, energy expenditure, glucose oxidation and fat oxidation were not different between groups of P. obesus. Energy expenditure and whole body substrate utilisation in P. obesus was similar to that seen in humans. P. obesus responded normally to short term fasting and dietary energy restriction. Elevated nocturnal fat oxidation rates and plasma glucose levels in obese-diabetic P. obesus may be an important factor in the pathogenesis of obesity and type 2 diabetes in these animals. These studies have further validated P. obesus as an ideal animal model of type 2 diabetes and obesity. It was hypothesised that many genes in the liver of P. obesus involved in glucose and fat metabolism would be differentially expressed between lean-NGT and obese-diabetic animals. These genes may represent significant factors in the pathophysiology of type 2 diabetes. Two gene discovery experiments were conducted using suppression subtractive hybridisation (SSH) to enrich a cDNA library for differentially expressed genes. Experiment 1 used cDNA dot blots to screen 576 clones with cDNA derived from lean-NGT and obese-diabetic animals. 6 clones were identified as overexpressed in lean-NGT animals and 6 were overexpressed in obese-diabetic animals. These 12 clones were sequenced and SYBR-Green PCR was used to confirm differential gene expression. 4 genes were overexpressed (≥1.5 fold) in lean-NGT animals and 4 genes were overexpressed (≥1.5 fold) in obese-diabetic animals. To explore the physiological role of these genes, hepatic gene expression was examined in several physiological conditions. One gene, encoding thyroxine binding globulin (TBG), was confirmed as overexpressed in lean-NGT P. obesus with ad libitum access to food, relative to both obese-IGT and obese-diabetic animals. TBG expression decreased with fasting in all animals. Fasting TBG expression remained greater in lean-NGT animals than obese-IGT and obese-diabetic animals. TBG expression was not significantly affected by dietary energy restriction. TBG is involved in thyroid metabolism and is potentially involved in the regulation of energy expenditure. Fasting increased hepatic site 1 protease (SIP) expression in lean-NGT animals but was not significantly affected in obese-IGT and obese-diabetic animals. SIP expression was not significantly affected by dietary energy restriction. SIP is involved in the proteolytic processing of steroid response element binding proteins (SREBP). SREBPs are insulin responsive and are known to be involved in lipid metabolism. Gene expression studies found TBG and SIP were associated with obesity and diabetes. Future research will determine whether TBG and SIP are important in the pathogenesis of these diseases. Experiment 2 used SSH and cDNA microarray to screen 8064 clones. 223 clones were identified as overexpressed in lean-NGT P. obesus and 274 clones were overexpressed in obese-diabetic P. obesus (p ≤0.05). The 9 most significantly differentially expressed clones identified from the microarray screen were sequenced (p ≤0.01). 7 novel genes were identified as well as; sulfotransferase related protein and albumin. These 2 genes have not previously been associated with either type 2 diabetes or obesity. It is unclear why hepatic expression of these genes may differ between lean-NGT and obese-diabetic groups of P. obesus. Subsequent studies will explore the potential role of these novel and known genes in the pathophysiology of type 2 diabetes.

Fuzzy neural networks for control of dynamic systems

This thesis provides a unified and comprehensive treatment of the fuzzy neural networks as the intelligent controllers. This work has been motivated by a need to develop the solid control methodologies capable of coping with the complexity, the nonlinearity, the interactions, and the time variance of the processes under control. In addition, the dynamic behavior of such processes is strongly influenced by the disturbances and the noise, and such processes are characterized by a large degree of uncertainty. Therefore, it is important to integrate an intelligent component to increase the control system ability to extract the functional relationships from the process and to change such relationships to improve the control precision, that is, to display the learning and the reasoning abilities. The objective of this thesis was to develop a self-organizing learning controller for above processes by using a combination of the fuzzy logic and the neural networks. An on-line, direct fuzzy neural controller using the process input-output measurement data and the reference model with both structural and parameter tuning has been developed to fulfill the above objective. A number of practical issues were considered. This includes the dynamic construction of the controller in order to alleviate the bias/variance dilemma, the universal approximation property, and the requirements of the locality and the linearity in the parameters. Several important issues in the intelligent control were also considered such as the overall control scheme, the requirement of the persistency of excitation and the bounded learning rates of the controller for the overall closed loop stability. Other important issues considered in this thesis include the dependence of the generalization ability and the optimization methods on the data distribution, and the requirements for the on-line learning and the feedback structure of the controller. Fuzzy inference specific issues such as the influence of the choice of the defuzzification method, T-norm operator and the membership function on the overall performance of the controller were also discussed. In addition, the e-completeness requirement and the use of the fuzzy similarity measure were also investigated. Main emphasis of the thesis has been on the applications to the real-world problems such as the industrial process control. The applicability of the proposed method has been demonstrated through the empirical studies on several real-world control problems of industrial complexity. This includes the temperature and the number-average molecular weight control in the continuous stirred tank polymerization reactor, and the torsional vibration, the eccentricity, the hardness and the thickness control in the cold rolling mills. Compared to the traditional linear controllers and the dynamically constructed neural network, the proposed fuzzy neural controller shows the highest promise as an effective approach to such nonlinear multi-variable control problems with the strong influence of the disturbances and the noise on the dynamic process behavior. In addition, the applicability of the proposed method beyond the strictly control area has also been investigated, in particular to the data mining and the knowledge elicitation. When compared to the decision tree method and the pruned neural network method for the data mining, the proposed fuzzy neural network is able to achieve a comparable accuracy with a more compact set of rules. In addition, the performance of the proposed fuzzy neural network is much better for the classes with the low occurrences in the data set compared to the decision tree method. Thus, the proposed fuzzy neural network may be very useful in situations where the important information is contained in a small fraction of the available data.

Stereo vision and wavelets/multiwavelets analysis : 3d reconstruction using wavelets/multiwavelets theory and stereo vision

During the image formation process of the camera, explicit 3D information about the scene or objects in the scene are lost. Therefore, 3D structure or depth information has to be inferred implicitly from the 2D intensity images. This problem is com- monly referred to as 3D reconstruction. In this work a complete 3D reconstruction algorithm is presented, capable of reconstructing dimensionally accurate 3D models of the objects, based on stereo vision and multi-resolution analysis. The developed system uses a reference depth model of the objects under observation to improve the disparity maps, estimated. Only a few features are extracted from that reference model, which are the relative location of the discontinuities and the z-dimensional extremes of objects depth. The maximum error deviation of the estimated depth along the surfaces is less than 0.5mm and along the discontinuities is less than 1.5mm. The developed system is invariant to illuminative variations, and orientation, location and scaling of the objects under consideration, which makes the developed system highly robust.