Improvements relating to the manufacture of coated substrates

This invention relates to the manufacture of coated substrates, and particularly, but not exclusively, to the deposition of multi-layer coatings in the manufacture of interference filters consisting of multiple thin films. An object of the invention is to allow accurate control of the deposition of a succession of layers having good uniformity, for example during the manufacture by vacuum evaporation of multilayer interference filters for use with infrared radiation of particularly long wavelength, using a method which is self calibrating and which avoids the repetitive use of individual control layers.

Neural networks for linear control: an analysis

This paper considers the use of radial basis function and multi-layer perceptron networks for linear or linearizable, adaptive feedback control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parameterization. A comparison is made with standard, nonneural network algorithms, e.g. self-tuning control.

A critique of neural networks for discrete-time linear control

This paper discusses the use of multi-layer perceptron networks for linear or linearizable, adaptive feedback.control schemes in a discrete-time environment. A close look is taken at the model structure selected and the extent of the resulting parametrization. A comparison is made with standard, non-perceptron algorithms, e.g. self-tuning control, and it is shown how gross over-parametrization can occur in the neural network case. Because of the resultant heavy computational burden and poor controller convergence, a strong case is made against the use of neural networks for discrete-time linear control.

Optimising memory usage in n-tuple neural networks

The use of n-tuple or weightless neural networks as pattern recognition devices is well known (Aleksander and Stonham, 1979). They have some significant advantages over the more common and biologically plausible networks, such as multi-layer perceptrons; for example, n-tuple networks have been used for a variety of tasks, the most popular being real-time pattern recognition, and they can be implemented easily in hardware as they use standard random access memories. In operation, a series of images of an object are shown to the network, each being processed suitably and effectively stored in a memory called a discriminator. Then, when another image is shown to the system, it is processed in a similar manner and the system reports whether it recognises the image; is the image sufficiently similar to one already taught? If the system is to be able to recognise and discriminate between m-objects, then it must contain m-discriminators. This can require a great deal of memory. This paper describes various ways in which memory requirements can be reduced, including a novel method for multiple discriminator n-tuple networks used for pattern recognition. By using this method, the memory normally required to handle m-objects can be used to recognise and discriminate between 2^m — 2 objects.

The control of dynamical systems by neural networks

In this paper the use of neural networks for the control of dynamical systems is considered. Both identification and feedback control aspects are discussed as well as the types of system for which neural networks can provide a useful technique. Multi-layer Perceptron and Radial Basis function neural network types are looked at, with an emphasis on the latter. It is shown how basis function centre selection is a critical part of the implementation process and that multivariate clustering algorithms can be an extremely useful tool for finding centres.

Cloud statistics and cloud radiative effect for a low-mountain site

In 2007, the Atmospheric Radiation Measurement (ARM) Mobile Facility (AMF) was operated for a nine-month period in the Murg Valley, Black Forest, Germany, in support of the Convective and Orographically-induced Precipitation Study (COPS). The synergy of AMF and COPS partner instrumentation was exploited to derive a set of high-quality thermodynamic and cloud property profiles with 30 s resolution. In total, clouds were present 72% of the time, with multi-layer mixed phase (28.4%) and single-layer water clouds (11.3%) occurring most frequently. A comparison with the Cloudnet sites Chilbolton and Lindenberg for the same time period revealed that the Murg Valley exhibits lower liquid water paths (LWPs; median = 37.5 g m−2) compared to the two sites located in flat terrain. In order to evaluate the derived thermodynamic and cloud property profiles, a radiative closure study was performed with independent surface radiation measurements. In clear sky, average differences between calculated and observed surface fluxes are less than 2% and 4% for the short wave and long wave part, respectively. In cloudy situations, differences between simulated and observed fluxes, particularly in the short wave part, are much larger, but most of these can be related to broken cloud situations. The daytime cloud radiative effect (CRE), i.e. the difference of cloudy and clear-sky net fluxes, has been analysed for the whole nine-month period. For overcast, single-layer water clouds, sensitivity studies revealed that the CRE uncertainty is likewise determined by uncertainties in liquid water content and effective radius. For low LWP clouds, CRE uncertainty is dominated by LWP uncertainty; therefore refined retrievals, such as using infrared and/or higher microwave frequencies, are needed.

Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment

The Along-Track Scanning Radiometers (ATSRs) provide a long time-series of measurements suitable for the retrieval of cloud properties. This work evaluates the freely-available Global Retrieval of ATSR Cloud Parameters and Evaluation (GRAPE) dataset (version 3) created from the ATSR-2 (1995�2003) and Advanced ATSR (AATSR; 2002 onwards) records. Users are recommended to consider only retrievals flagged as high-quality, where there is a good consistency between the measurements and the retrieved state (corresponding to about 60% of converged retrievals over sea, and more than 80% over land). Cloud properties are found to be generally free of any significant spurious trends relating to satellite zenith angle. Estimates of the random error on retrieved cloud properties are suggested to be generally appropriate for optically-thick clouds, and up to a factor of two too small for optically-thin cases. The correspondence between ATSR-2 and AATSR cloud properties is high, but a relative calibration difference between the sensors of order 5�10% at 660 nm and 870 nm limits the potential of the current version of the dataset for trend analysis. As ATSR-2 is thought to have the better absolute calibration, the discussion focusses on this portion of the record. Cloud-top heights from GRAPE compare well to ground-based data at four sites, particularly for shallow clouds. Clouds forming in boundary-layer inversions are typically around 1 km too high in GRAPE due to poorly-resolved inversions in the modelled temperature profiles used. Global cloud fields are compared to satellite products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, and a climatology of liquid water content derived from satellite microwave radiometers. In all cases the main reasons for differences are linked to differing sensitivity to, and treatment of, multi-layer cloud systems. The correlation coefficient between GRAPE and the two MODIS products considered is generally high (greater than 0.7 for most cloud properties), except for liquid and ice cloud effective radius, which also show biases between the datasets. For liquid clouds, part of the difference is linked to choice of wavelengths used in the retrieval. Total cloud cover is slightly lower in GRAPE (0.64) than the CALIOP dataset (0.66). GRAPE underestimates liquid cloud water path relative to microwave radiometers by up to 100 g m�2 near the Equator and overestimates by around 50 g m�2 in the storm tracks. Finally, potential future improvements to the algorithm are outlined.

Improvement of the GenTHREADER method for genomic fold recognition

Motivation: In order to enhance genome annotation, the fully automatic fold recognition method GenTHREADER has been improved and benchmarked. The previous version of GenTHREADER consisted of a simple neural network which was trained to combine sequence alignment score, length information and energy potentials derived from threading into a single score representing the relationship between two proteins, as designated by CATH. The improved version incorporates PSI-BLAST searches, which have been jumpstarted with structural alignment profiles from FSSP, and now also makes use of PSIPRED predicted secondary structure and bi-directional scoring in order to calculate the final alignment score. Pairwise potentials and solvation potentials are calculated from the given sequence alignment which are then used as inputs to a multi-layer, feed-forward neural network, along with the alignment score, alignment length and sequence length. The neural network has also been expanded to accommodate the secondary structure element alignment (SSEA) score as an extra input and it is now trained to learn the FSSP Z-score as a measurement of similarity between two proteins. Results: The improvements made to GenTHREADER increase the number of remote homologues that can be detected with a low error rate, implying higher reliability of score, whilst also increasing the quality of the models produced. We find that up to five times as many true positives can be detected with low error rate per query. Total MaxSub score is doubled at low false positive rates using the improved method.

Chitosan coated alginate beads for the survival of microencapsulated Lactobacillus plantarum in pomegranate juice

This work studied the effect of multi-layer coating of alginate beads on the survival of encapsulated Lactobacillus plantarum in simulated gastric solution and during storage in pomegranate juice at 4 °C. Uncoated, single and double chitosan coated beads were examined. The survival of the cells in simulated gastric solution (pH 1.5) was improved in the case of the chitosan coated beads by 0.5–2 logs compared to the uncoated beads. The cell concentration in pomegranate juice after six weeks of storage was higher than 5.5 log CFU/mL for single and double coated beads, whereas for free cells and uncoated beads the cells died after 4 weeks of storage. In simulated gastric solution, the size of the beads decreased and their hardness increased with time; however, the opposite trend was observed for pomegranate juice, indicating that there is no correlation between cell survival and the hardness of the beads.

The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems

To bridge the gaps between traditional mesoscale modelling and microscale modelling, the National Center for Atmospheric Research, in collaboration with other agencies and research groups, has developed an integrated urban modelling system coupled to the weather research and forecasting (WRF) model as a community tool to address urban environmental issues. The core of this WRF/urban modelling system consists of the following: (1) three methods with different degrees of freedom to parameterize urban surface processes, ranging from a simple bulk parameterization to a sophisticated multi-layer urban canopy model with an indoor–outdoor exchange sub-model that directly interacts with the atmospheric boundary layer, (2) coupling to fine-scale computational fluid dynamic Reynolds-averaged Navier–Stokes and Large-Eddy simulation models for transport and dispersion (T&D) applications, (3) procedures to incorporate high-resolution urban land use, building morphology, and anthropogenic heating data using the National Urban Database and Access Portal Tool (NUDAPT), and (4) an urbanized high-resolution land data assimilation system. This paper provides an overview of this modelling system; addresses the daunting challenges of initializing the coupled WRF/urban model and of specifying the potentially vast number of parameters required to execute the WRF/urban model; explores the model sensitivity to these urban parameters; and evaluates the ability of WRF/urban to capture urban heat islands, complex boundary-layer structures aloft, and urban plume T&D for several major metropolitan regions. Recent applications of this modelling system illustrate its promising utility, as a regional climate-modelling tool, to investigate impacts of future urbanization on regional meteorological conditions and on air quality under future climate change scenarios. Copyright © 2010 Royal Meteorological Society

Elaboration and optimization of (Y,Er)Al(3)(BO(3))(4) glassy planar waveguides through the sol-gel process

In this work, a sol-gel route was used to prepare Y(0.9)Er(0.1)Al(3)(BO(3))(4) glassy thin films by spin-coating technique looking for the preparation and optimization of planar waveguides for integrated optics. The films were deposited on silica and silicon substrates using stable sols synthesized by the sol-gel process. Deposits with thicknesses ranging between 520 and 720 nm were prepared by a multi-layer process involving heat treatments at different temperatures from glass transition to the film crystallization and using heating rates of 2 degrees C/min. The structural characterization of the layers was performed by using grazing incidence X-ray diffraction and Raman spectroscopy as a function of the heat treatment. Microstructural evolution in terms of annealing temperatures was followed by high resolution scanning electron microscopy and atomic force microscopy. Optical transmission spectra were used to determine the refractive index and the film thicknesses through the envelope method. The optical and guiding properties of the films were studied by m-line spectroscopy. The best films were monomode with 620 nm thickness and a refractive index around 1.664 at 980 nm wavelength. They showed good waveguiding properties with high light-coupling efficiency and low propagation loss at 632.8 and 1550 nm of about 0.88 dB/cm. (C) 2009 Elsevier B.V. All rights reserved.

Artificial Neural Networks and the Study of the Psychoactivity of Cannabinoid Compounds

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher`s weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

An Experimental Study of Submerged Entry Nozzles (SEN) Focusing on Decarburization and Clogging

The submerged entry nozzle (SEN) is used to transport the molten steel from a tundish to a mould. The main purpose of its usage is to prevent oxygen and nitrogen pick-up by molten steel from the gas. Furthermore, to achieve the desired flow conditions in the mould. Therefore, the SEN can be considered as a vital factor for a stable casting process and the steel quality. In addition, the steelmaking processes occur at high temperatures around 1873 K, so the interaction between the refractory materials of the SEN and molten steel is unavoidable. Therefore, the knowledge of the SEN behaviors during preheating and casting processes is necessary for the design of the steelmaking processes  The internal surfaces of modern SENs are coated with a glass/silicon powder layer to prevent the SEN graphite oxidation during preheating. The effects of the interaction between the coating layer and the SEN base refractory materials on clogging were studied. A large number of accretion samples formed inside alumina-graphite clogged SENs were examined using FEG-SEM-EDS and Feature analysis. The internal coated SENs were used for continuous casting of stainless steel grades alloyed with Rare Earth Metals (REM). The post-mortem study results clearly revealed the formation of a multi-layer accretion. A harmful effect of the SENs decarburization on the accretion thickness was also indicated. In addition, the results indicated a penetration of the formed alkaline-rich glaze into the alumina-graphite base refractory. More specifically, the alkaline-rich glaze reacts with graphite to form a carbon monoxide gas. Thereafter, dissociation of CO at the interface between SEN and molten metal takes place. This leads to reoxidation of dissolved alloying elements such as REM (Rare Earth Metal). This reoxidation forms the “In Situ” REM oxides at the interface between the SEN and the REM alloyed molten steel. Also, the interaction of the penetrated glaze with alumina in the SEN base refractory materials leads to the formation of a high-viscous alumina-rich glaze during the SEN preheating process. This, in turn, creates a very uneven surface at the SEN internal surface. Furthermore, these uneven areas react with dissolved REM in molten steel to form REM aluminates, REM silicates and REM alumina-silicates. The formation of the large “in-situ” REM oxides and the reaction of the REM alloying elements with the previously mentioned SEN´s uneven areas may provide a large REM-rich surface in contact with the primary inclusions in molten steel. This may facilitate the attraction and agglomeration of the primary REM oxide inclusions on the SEN internal surface and thereafter the clogging. The study revealed the disadvantages of the glass/silicon powder coating applications and the SEN decarburization. The decarburization behaviors of Al2O3-C, ZrO2-C and MgO-C refractory materials from a commercial Submerged Entry Nozzle (SEN), were also investigated for different gas atmospheres consisting of CO2, O2 and Ar. The gas ratio values were kept the same as it is in a propane combustion flue gas at different Air-Fuel-Ratio (AFR) values for both Air-Fuel and Oxygen-Fuel combustion systems. Laboratory experiments were carried out under nonisothermal conditions followed by isothermal heating. The decarburization ratio (α) values of all three refractory types were determined by measuring the real time weight losses of the samples. The results showed the higher decarburization ratio (α) values increasing for MgO-C refractory when changing the Air-Fuel combustion to Oxygen-Fuel combustion at the same AFR value. It substantiates the SEN preheating advantage at higher temperatures for shorter holding times compared to heating at lower temperatures during longer holding times for Al2O3-C samples. Diffusion models were proposed for estimation of the decarburization rate of an Al2O3-C refractory in the SEN. Two different methods were studied to prevent the SEN decarburization during preheating: The effect of an ZrSi2 antioxidant and the coexistence of an antioxidant additive and a (4B2O3 ·BaO) glass powder on carbon oxidation for non-isothermal and isothermal heating conditions in a controlled atmosphere. The coexistence of 8 wt% ZrSi2 and 15 wt% (4B2O3 ·BaO) glass powder of the total alumina-graphite refractory base materials, presented the most effective resistance to carbon oxidation. The 121% volume expansion due to the Zircon formation during heating and filling up the open pores by a (4B2O3 ·BaO) glaze during the green body sintering led to an excellent carbon oxidation resistance. The effects of the plasma spray-PVD coating of the Yttria Stabilized Zirconia (YSZ) powder on the carbon oxidation of the Al2O3-C coated samples were investigated. Trials were performed at non-isothermal heating conditions in a controlled atmosphere. Also, the applied temperature profile for the laboratory trials were defined based on the industrial preheating trials. The controlled atmospheres consisted of CO2, O2 and Ar. The thicknesses of the decarburized layers were measured and examined using light optic microscopy, FEG-SEM and EDS. A 250-290 μm YSZ coating is suggested to be an appropriate coating, as it provides both an even surface as well as prevention of the decarburization even during heating in air. In addition, the interactions between the YSZ coated alumina-graphite refractory base materials in contact with a cerium alloyed molten stainless steel were surveyed. The YSZ coating provided a total prevention of the alumina reduction by cerium. Therefore, the prevention of the first clogging product formed on the surface of the SEN refractory base materials. Therefore, the YSZ plasma-PVD coating can be recommended for coating of the hot surface of the commercial SENs.

Calibration Strategies Designed For Long Term Sediment Transport Modelling

The presented work deals with the calibration of a 2D numerical model for the simulation of long term bed load transport. A settled basin along an alpine stream was used as a case study. The focus is to parameterise the used multi fractional transport model such that a dynamically balanced behavior regarding erosion and deposition is reached. The used 2D hydrodynamic model utilizes a multi-fraction multi-layer approach to simulate morphological changes and bed load transport. The mass balancing is performed between three layers: a top mixing layer, an intermediate subsurface layer and a bottom layer. Using this approach bears computational limitations in calibration. Due to the high computational demands, the type of calibration strategy is not only crucial for the result, but as well for the time required for calibration. Brute force methods such as Monte Carlo type methods may require a too large number of model runs. All here tested calibration strategies used multiple model runs utilising the parameterization and/or results from previous run. One concept was to reset to initial bed elevations after each run, allowing the resorting process to convert to stable conditions. As an alternative or in combination, the roughness was adapted, based on resulting nodal grading curves, from the previous run. Since the adaptations are a spatial process, the whole model domain is subdivided in homogeneous sections regarding hydraulics and morphological behaviour. For a faster optimization, the adaptation of the parameters is made section wise. Additionally, a systematic variation was done, considering results from previous runs and the interaction between sections. The used approach can be considered as similar to evolutionary type calibration approaches, but using analytical links instead of random parameter changes.

Classificação de imagens de sensoriamento remoto baseada em textura por redes neurais

O objetivo principal deste trabalho é propor uma metodologia de classificação de imagens de sensoriamento remoto que integre a importância de atributos de textura na seleção de feições, através da utilização de freqüências espaciais de cada classe textural e sua direção, com a eficiência das redes neurais artificiais para classificá-las. O processo é composto por uma etapa de filtragem baseada nos filtros de Gabor, seguida de uma fase de classificação através de uma rede neural Multi-Layer Perceptron com algoritmo BackPropagation. A partir da transformada de Fourier são estimados os parâmetros a serem utilizados na constituição dos filtros de Gabor, adequados às freqüências espaciais associadas a cada classe presente na imagem a ser classificada. Desta forma, cada filtro gera uma imagem filtrada. O conjunto de filtros determina um conjunto de imagens filtradas (canais texturais). A classificação pixel a pixel é realizada pela rede neural onde cada pixel é definido por um vetor de dimensionalidade igual ao número de filtros do conjunto. O processo de classificação através da rede neural Multi-Layer Perceptron foi realizado pelo método de classificação supervisionada. A metodologia de classificação de imagens de sensoriamento remoto proposta neste trabalho foi testada em imagens sintética e real de dimensões 256 x 256 pixels. A análise dos resultados obtidos é apresentada sob a forma de uma Matriz de Erros, juntamente com a discussão dos mesmos.