A singular vector perspective of 4D-Var: Filtering and interpolation

Four-dimensional variational data assimilation (4D-Var) combines the information from a time sequence of observations with the model dynamics and a background state to produce an analysis. In this paper, a new mathematical insight into the behaviour of 4D-Var is gained from an extension of concepts that are used to assess the qualitative information content of observations in satellite retrievals. It is shown that the 4D-Var analysis increments can be written as a linear combination of the singular vectors of a matrix which is a function of both the observational and the forecast model systems. This formulation is used to consider the filtering and interpolating aspects of 4D-Var using idealized case-studies based on a simple model of baroclinic instability. The results of the 4D-Var case-studies exhibit the reconstruction of the state in unobserved regions as a consequence of the interpolation of observations through time. The results also exhibit the filtering of components with small spatial scales that correspond to noise, and the filtering of structures in unobserved regions. The singular vector perspective gives a very clear view of this filtering and interpolating by the 4D-Var algorithm and shows that the appropriate specification of the a priori statistics is vital to extract the largest possible amount of useful information from the observations. Copyright © 2005 Royal Meteorological Society

Effect of breed, gender, housing system and dietary crude protein content on performance of finishing beef cattle fed maize-silage-based diets

Maize silage-based diets with three dietary crude protein (CP) supplements were offered to 96 finishing cattle of contrasting breed (Holstein Friesian (HF) v. Simmental x HF (SHF)) and gender (bull v. steer) housed in two types of feeding system (group fed v. individually fed). The three protein supplements differed either in CP or protein degradability (degradable (LUDP) v. rumen undegradable (HUDP)) and provided CP concentrations of 142 (Con), 175 (LUDP) and 179 (HUDP) g/kg dry matter (DM) respectively, with ratios of degradable to undegradable of 3.0, 1.4 and 0.9:1 for diets Con, LOP and HUDP respectively. DM intakes were marginally higher (P = 0. 102) for LOP when compared with Con and HOP Rates of daily live-weight gain (DLWG) were higher (P = 0.005) in LUDP and HOP when compared with Con. HF had higher DM intakes than SHF although this did not result in any improvement in HF DLWG. Bulls had significantly better DM intakes, DLWG and feed conversion efficiency than steers. Conformation scores were better in SHF than HF (P < 0.001) and fat scores lower in bulls than steers (p < 0.001). There was a number of first order interactions established between dietary treatment, breed, gender and housing system with respect to rates of gain and carcass fat scores.

The DREAM Framework: using a network of scalable ontologies for intelligent indexing and retrieval of visual content

There are still major challenges in the area of automatic indexing and retrieval of digital data. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. Research has been ongoing for a few years in the field of ontological engineering with the aim of using ontologies to add knowledge to information. In this paper we describe the architecture of a system designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval.

EMG signal filtering based on Empirical Mode Decomposition

This paper introduces a procedure for filtering electromyographic (EMG) signals. Its key element is the Empirical Mode Decomposition, a novel digital signal processing technique that can decompose my time-series into a set of functions designated as intrinsic mode functions. The procedure for EMG signal filtering is compared to a related approach based on the wavelet transform. Results obtained from the analysis of synthetic and experimental EMG signals show that Our method can be Successfully and easily applied in practice to attenuation of background activity in EMG signals. (c) 2006 Elsevier Ltd. All rights reserved.

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

Performance of palm- based fat blends with a low saturated fat content in puff pastry

Four fat blends based on palm fractions in combination with high oleic sunflower oil (HOSF) with a relatively low saturated fatty acid content (29.2±0.85%, i.e. less than 50% of that of butter) were prepared. The saturated fat was located in different triacylglycerols (TAG) structures in each blend. Principal saturated TAG were derived from palm stearin (POs, containing tripalmitoyl glycerol - PPP), palm mid fraction (PMF, containing 1,3-dipalmitoyl-2-oleoyl glycerol - POP) and interesterified PMF (inPMF, containing PPP, POP and rac-1,2-dipalmitoyl-3-oleoyl glycerol - PPO). Thus, in blend 1, composed of POs and HOSF, the saturates resided principally in PPP. In blend 2, composed of POs, PMF and HOSF, the principal saturate-containing TAG were PPP and POP. Blend 3, composed of inPMF and HOSF, was similar to blend 2 except that the disaturated TAG comprised a 2:1 mixture of PPO:POP. Finally, blend 4, a mixture of PMF and HOSF, had saturates present mainly as POP. The physical properties and the functionality of blends, as shortenings for puff pastry laminated in a warm bakery environment (20-30°C), were compared with each other, and with butter. Puff pastry prepared with blend 1 (POs:HOSF 29:71) and blend 4 (PMF:HOSF 41:59), was very hard; blend 2 (POs:PMF:HOSF 13:19:68) was most similar to butter in the compressibility of the baked product and it performed well in an independent baking trial; blend 3 (inPMF:HOSF 40:60) gave a product that required a higher force for compression than butter.

Development of novel methods to determine crystalline glucose content of honey based on DSC, HPLC, and viscosity measurements and their use to examine the setting propensity of honey

Crystallization must occur in honey in order to produce set or creamed honey; however, the process must occur in a controlled manner in order to obtain an acceptable product. As a consequence, reliable methods are needed to measure the crystal content of honey (φ expressed as kg crystal per kg honey), which can also be implemented with relative ease in industrial production facilities. Unfortunately, suitable methods do not currently exist. This article reports on the development of 2 independent offline methods to measure the crystal content in honey based on differential scanning calorimetry and high-performance liquid chromatography. The 2 methods gave highly consistent results on the basis of paired t-test involving 143 experimental points (P > 0.05, r**2 = 0.99). The crystal content also correlated with the relative viscosity, defined as the ratio of the viscosity of crystal containing honey to that of the same honey when all crystals are dissolved, giving the following correlation: μr = 1 + 1398.8∅**2.318. This correlation can be used to estimate the crystal content of honey in industrial production facilities. The crystal growth rate at a temperature of 14 ◦C—the normal crystallization temperature used in practice—was linear, and the growth rate also increased with the total glucose content in the honey.

Multi-attribute collaborative filtering recommendation based on improved group decision-making

Currently researchers in the field of personalized recommendations bear little consideration on users' interest differences in resource attributes although resource attribute is usually one of the most important factors in determining user preferences. To solve this problem, the paper builds an evaluation model of user interest based on resource multi-attributes, proposes a modified Pearson-Compatibility multi-attribute group decision-making algorithm, and introduces an algorithm to solve the recommendation problem of k-neighbor similar users. Considering the characteristics of collaborative filtering recommendation, the paper addresses the issues on the preference differences of similar users, incomplete values, and advanced converge of the algorithm. Thus the paper realizes multi-attribute collaborative filtering. Finally, the effectiveness of the algorithm is proved by an experiment of collaborative recommendation among multi-users based on virtual environment. The experimental results show that the algorithm has a high accuracy on predicting target users' attribute preferences and has a strong anti-interference ability on deviation and incomplete values.

A dislocation based theory for the deformation hardening behavior of DP steels : Impact of martensite content and ferrite grain size

A dislocation model, accurately describing the uniaxial plastic stress-strain behavior of dual phase (DP) steels, is proposed and the impact of martensite content and ferrite grain size in four commercially produced DP steels is analyzed. It is assumed that the plastic deformation process is localized to the ferrite. This is taken into account by introducing a non-homogeneity parameter, f(e), that specifies the volume fraction of ferrite taking active part in the plastic deformation process. It is found that the larger the martensite content the smaller the initial volume fraction of active ferrite which yields a higher initial deformation hardening rate. This explains the high energy absorbing capacity of DP steels with high volume fractions of martensite. Further, the effect of ferrite grain size strengthening in DP steels is important. The flow stress grain size sensitivity for DP steels is observed to be 7 times larger than that for single phase ferrite.

Ontology-based metadata for e-learning content

Nowadays, the popularity of the Web encourages the development of Hypermedia Systems dedicated to e-learning. Nevertheless, most of the available Web teaching systems apply the traditional paper-based learning resources presented as HTML pages making no use of the new capabilities provided by the Web. There is a challenge to develop educative systems that adapt the educative content to the style of learning, context and background of each student. Another research issue is the capacity to interoperate on the Web reusing learning objects. This work presents an approach to address these two issues by using the technologies of the Semantic Web. The approach presented here models the knowledge of the educative content and the learner’s profile with ontologies whose vocabularies are a refinement of those defined on standards situated on the Web as reference points to provide semantics. Ontologies enable the representation of metadata concerning simple learning objects and the rules that define the way that they can feasibly be assembled to configure more complex ones. These complex learning objects could be created dynamically according to the learners’ profile by intelligent agents that use the ontologies as the source of their beliefs. Interoperability issues were addressed by using an application profile of the IEEE LOM- Learning Object Metadata standard.

Simulation-based smoothing and filtering in factor stochastic volatility models : two econometric applications

In this article we use factor models to describe a certain class of covariance structure for financiaI time series models. More specifical1y, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. We build on previous work by allowing the factor loadings, in the factor mo deI structure, to have a time-varying structure and to capture changes in asset weights over time motivated by applications with multi pIe time series of daily exchange rates. We explore and discuss potential extensions to the models exposed here in the prediction area. This discussion leads to open issues on real time implementation and natural model comparisons.

Simulation -based smoothing and filtering in factor stochastic volatility models : two econometric applications (July-2001)

The past decade has wítenessed a series of (well accepted and defined) financial crises periods in the world economy. Most of these events aI,"e country specific and eventually spreaded out across neighbor countries, with the concept of vicinity extrapolating the geographic maps and entering the contagion maps. Unfortunately, what contagion represents and how to measure it are still unanswered questions. In this article we measure the transmission of shocks by cross-market correlation\ coefficients following Forbes and Rigobon's (2000) notion of shift-contagion,. Our main contribution relies upon the use of traditional factor model techniques combined with stochastic volatility mo deIs to study the dependence among Latin American stock price indexes and the North American indexo More specifically, we concentrate on situations where the factor variances are modeled by a multivariate stochastic volatility structure. From a theoretical perspective, we improve currently available methodology by allowing the factor loadings, in the factor model structure, to have a time-varying structure and to capture changes in the series' weights over time. By doing this, we believe that changes and interventions experienced by those five countries are well accommodated by our models which learns and adapts reasonably fast to those economic and idiosyncratic shocks. We empirically show that the time varying covariance structure can be modeled by one or two common factors and that some sort of contagion is present in most of the series' covariances during periods of economical instability, or crisis. Open issues on real time implementation and natural model comparisons are thoroughly discussed.

Neural Network-Based Approach for Identification of the Harmonic Content of a Nonlinear Load in a Single-Phase System

In this paper an alternative method based on artificial neural networks is presented to determine harmonic components in the load current of a single-phase electric power system with nonlinear loads, whose parameters can vary so much in reason of the loads characteristic behaviors as because of the human intervention. The first six components in the load current are determined using the information contained in the time-varying waveforms. The effectiveness of this method is verified by using it in a single-phase active power filter with selective compensation of the current drained by an AC controller. The proposed method is compared with the fast Fourier transform.

Effect of ceria content on the sintering of ZrO2 based ceramics synthesized from a polymeric precursor

Zirconia-ceria powders with ceria concentration varying from 0 to 12 mol% were synthesized using a polymeric precursor route based on the Pechini process. Powder characteristics were evaluated with regard to the crystallite size, BET surface area, phase distribution, nitrogen adsorption/desorption behavior, and agglomeration state. Sintering was studied considering the shrinkage rate, densification, grain size, and phase evolution. It was demonstrated that the synthesis method is effective to prepare nanosized powders of tetragonal zirconia single-phase. Sinterability mainly depended on the agglomeration state of powders and the monoclinic phase content, fully tetragonal zirconia ceramic, with grain size of 2.4 mu m, was obtained after addition of at least 9 mol% ceria and sintering at 1500 degrees C for 4 h. (C) 2000 Elsevier B.V. Ltd. All rights reserved.

A neural networks-based method for single-phase harmonic content identification

A neural method is presented in this paper to identify the harmonic components of an ac controller. The components are identified by analyzing the single-phase current waveform. The method effectiveness is verified by applying it to an active power filter (APF) model dedicated to the selective harmonic compensation. Simulation results using theoretical and experimental data are presented to validate the proposed approach. © 2008 IEEE.