Linear instrumental variables model averaging estimation

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in applications in which there is a large number of candidate instruments and, therefore, a considerable number of instrument sets arising from different combinations of the available instruments. The asymptotic properties of the estimator are derived under homoskedastic and heteroskedastic errors. A simple Monte Carlo study contrasts the performance of MA procedures with existing instrument selection procedures, showing that MA estimators compare very favorably in many relevant setups. Finally, this method is illustrated with an empirical application to returns to education.     

Automatic small blend recognition from B-rep models for analysis

The recognition and suppression of small features from B-rep models play an important role in effectively generating the qualified mesh for analysis. This paper presents a new algorithm for recognizing small blends automatically for the purpose of mesh generation. The algorithm first transforms the input B-rep model into an aggregation of faces. Then, each face is judged to be a blend or not based on matching conditions. At last, the blends with larger physical sizes than a threshold value are excluded. The innovations lie in the recognition conditions and the unique calculating procedure of the blend physical size. Several examples are given to demonstrate the validity and applicability of the algorithm.     

DETERMINATION OF MOISTURE DIFFUSIVITY OF PINE NUT SEEDS

The mechanism of drying and the diffusion of water in fresh pine nut seeds (2.5-2.62mm diameter) were successfully interpreted and modeled by using Fick's law. The initial moisture content of fresh pine nut seed was 23-24% and drying temperatures (45-60°C) were varied, but the drying air was kept at constant velocity and humidity.

The effective diffusivity was estimated from drying rate curves and expressed by an Arhenius relation. In addition, the absorption, and desorption-isotherms for fresh pine nuts were estimated at 20°C at water activity of 0.15-1.00. The isotherms of the fresh pine nuts were estimated in experimental runs.     

Timed verification of the generic architecture of a memory circuit using parametric timed automata

Using a variant of Clariso-Cortadella’s parametric method for verifying asynchronous circuits, we analyse some crucial timing behaviors of the architecture of SPSMALL memory, a commercial product of STMicroelectronics. Using the model of parametric timed automata and model checker HYTECH, we formally derive a set of linear constraints that ensure the correctness of the response times of the memory. We are also able to infer the constraints characterizing the optimal setup timings of input signals. We have checked, for two different implementations of this architecture, that the values given by our model match remarkably with the values obtained by the designer through electrical simulation. Partially supported by project MEDEA+ Blueberries. A preliminary version appeared in the Proceedings of 4th International Conference on Formal Modelling and Analysis of Timed Systems (FORMATS’06), Sept. 2006.     

Deposition and characterisation of vanadium oxide thin films: Linking single crystal and supported catalyst

Thin films can make a useful link between single crystal and supported vanadium oxide. The deposition of vanadium oxide thin films with physical vapour deposition techniques ensures clean and highly controllable synthesis. The resulting material is easily accessed with surface sensitive techniques. On flat TiO₂ anatase substrates, XPS–XPD and UPS indicated that the vanadia deposition was epitaxial, and fully oxidised if performed in situ. A step closer to typical industrial catalysts was achieved by sputter deposition onto sub-millimetre inert particles. In addition to surface characterisation, these model particle catalysts allow use in reactors for catalytic testing under relevant process conditions. On both silica and titania supports, sputter deposited vanadia of varying thickness proved to be equally well dispersed. Oxidative dehydrogenation (ODH) activity was higher over vanadia/titania (anatase) than over vanadia/silica, demonstrating the synergetic interaction between anatase and vanadia. Highest activity in alkane ODH was observed for vanadia a few monolayers thick, supported on titania-coated particles.     

A text-based visual notation for the unit testing of model-driven tools

During the unit testing of model-driven tools, a large number of models and test classes needs to be managed and maintained. Typically, some of these artifacts are specified manually, some are generated automatically. Existing approaches to test management rely on the available visual and textual modeling notations. As these notations are not tailored to unit testing, distinct maintainability trade-offs arise.In this paper, we propose a notation that aims to combine the benefits of visual and text-based approaches. The notation is at the same time visual and text-based, as it uses ASCII characters to emulate the familiar graphical notations. In our evaluation based on real models, we identify problematic model shapes challenging the scalability our notation, while finding that it is well-suited to capture typical test models.     

Robust model updating with insufficient data

The increasing need of many industrial fields for highly accurate predictions of performance and reliability gives rise to the need for enhanced underlying mathematical models. The thereby available test data are usually rather limited due to the high costs of experimental measurements. Therefore, decisions have to be made based on limited, incomplete information, which poses a challenging problem.Recently, an approach for coping with insufficient data has been introduced that attempts to extract the information delivered by the data and processes it using few additional assumptions. The underlying distribution is based on an appropriate confidence level providing a safeguard against severe underestimation of the variability of the measured quantities. This method has been applied within the field of statics involving the stochastic identification of one single structural parameter. The present paper shows the extension of this approach to the field of dynamics. It is shown how to deal with insufficient information by applying kernel densities on the stochastic representation of modal data. In addition, the problem of correlation of the established multi-dimensional probability density function will be addressed. As a numerical example the structural dynamics application of the Validation Challenge Workshop has been chosen.     

Dynamic optimization of the mashing process

This work aims to demonstrate the applicability of dynamic optimization to improve the time-temperature schedule of a brewery mashing process, based on kinetic models available in the literature. The mashing process consists in the enzymatic degradation of the polysaccharides present in the malt. This is a fundamental step within the brewery activity since the composition of the mashing wort determines the quality of the final product. The main reactions that take place in the mashing are the degradation of starch, β-glucans and arabinoxylans into small chain fermentable and non-fermentable carbohydrates. The manipulation of the temperature profile of the batch reactor is the main mechanism to control the extent of the ongoing reactions. Since high temperatures favor the production of fermentable matter but also increases the concentration of undesirable species in the wort, the choice of an adequate temperature profile is not obvious. Dynamic optimization studies with a complete mashing model demonstrate that profiles of “temperature averages” of about 51 °C are preferred over typical industrial mashings of 64 °C to optimize the operation.     

钻井过程中岩屑运移模型研究进展

随着斜井和水平井数量的不断增加，井眼清洁已成为钻井的关键技术和难题之一。在过去的60多年里，人们对钻井过程中的岩屑运移问题进行了大量的实验和理论研究，对这一课题有了较多的认识，建立了一些模型，为实际钻井过程中水力参数设计提供了有效的依据。本文对描述岩屑运移规律的经验模型和分层理论模型进行了系统的总结，并专门对泡沫携岩模型进行了总结，在此基础上对岩屑运移模型的发展进行展望，最后给出了研究建议，为今后深入开展岩屑运移研究提供参考。     

Multiphase modeling of intermittent drying using the spatial reaction engineering approach (S-REA)

Several schemes of energy minimization of drying process including intermittent drying have been attempted. Intermittent drying is conducted by applying different heat inputs in each drying period. An effective and physically meaningful drying model is useful for process design and product technology. The lumped reaction engineering approach (L-REA) has been shown previously to be accurate to model the intermittent drying In L-REA, the REA (reaction engineering approach) is used to describe the global drying rate. In this study, the REA is used to model the local evaporation/condensation rate and combined with the mechanistic drying models to yield the spatial reaction engineering approach (S-REA), a non-equilibrium multiphase drying model. The accuracy of the S-REA to model the intermittent drying under time-varying drying air temperature is evaluated here. In order to incorporate the effect of time-varying drying air temperature, the equilibrium activation energy and boundary condition of heat balance implement the corresponding drying settings in each drying period. The results of modeling using the S-REA match well with the experimental data. The S-REA can yield the spatial profiles of moisture content, concentration of water vapor, temperature and local evaporation/condensation rate so that better understanding of transport phenomena of intermittent drying can be obtained. It is argued here that the REA can describe the local evaporation rate under time-varying external conditions well. The S-REA is an effective non-equilibrium multiphase approach for modeling of intermittent drying process.