The MPE/UCB far-infrared imaging Fabry-Perot interferometer (FIFI)

FIFI is an imaging spectrometer with two or three Fabry-Perot interferometers (FPI) in series for airborne astronomical observations in the far-infrared range (=40...200m). It employs 5×5 arrays of photoconducting detectors and offers spectral resolutions as small as 2km/s. Resolution and bandwidth can be set over a wide range to match a variety of astronomical sources. Cryogenic optics minimizes thermal background radiation and provides for in-flight step tunable spatial resolution. At 158 m wavelength the background-limited NEP is 3 × 10^-15W/Hz at 40 km/s resolution and with two FPI's; with three FPI's the expected NEP is 10^-15WHz at 5 km/s resolution.The frequency-chopping mode of the high-resolution Fabry-Perot allows for line detection in extended objects. Absolute internal flux calibration ensures adequate flat fielding of the array elements.     

神经网络的函数逼近能力分析

本文综述了多层前传网络（ＭＬＰ）及径向基函数网络（ＲＢＦ）对函数任意精度逼近的能力，比较了两种网络的最佳逼近特性。对激活函数类的扩充作了介绍，并说明有限数值精度对函数逼近能力实现的影响。     

Extensions of algorithmic senstivity calculations nonlinear programming using barrier and penalty functions

We provide a theoretical basis for approximating the sensitivity of a perturbed solution and the local optimalvalue function, using information generated by a sequential unconstrained minimization technique in the normal course of solving a mathematical program. We show that various algorithmic sensitivity results can be obtained without other assumptions than those needed for the corresponding nonalgorithmic results. Our results extend the algorithmic calculation of sensitivity information introduced by Fiacco, utilizing the logarithmic barrier function and quadratic penalty function     

Generalizations of a Theorem of Datko and Pazy

This note gives necessary and sufficient conditions for exponential stability of semigroups of linear operators in Banach spaces. Generalizations of a well-known result due to Datko, Pazy and Neerven are obtained for the case of semigroups of operators that are not strongly continuous.     

On Controllability And Maximum Principle for Discrete Inclusions ∗ ∗A part of the paper was written while the first author was visiting the laboratory of Prof.S.Suzuki,Department of Mechanical Engineering,Sophia University,Tokyo,Japan$ef:

The paper develops sufficient conditions for controllability and necessary conditions in different forms of the maximum principle for extremal solutions of discrete inclusions. The objective function is assumed to be locally lipschitzian only     

Dew Points of Binary Methane + Water: Measurement and Correlation

Experimental measurements of dew points for four methane + water mixtures between 2.6 2 10 5 Pa and 100.4 2 10 5 Pa and the temperature range from 225.5 K to 272.7 K were carried out. The experimental results were analysed in terms of both an equation of state model and an excess function-equation of state method, which reproduced the experimental data within AAD from 1.8 to 3.1 K and from 0.9 to 1.8 K, respectively.     

Similar Transposed Sets of Polynomials

This paper studies the effectiveness of another kind of transposed sets of polynomials of one complex variable in closed regions, open discs, at the origin and for all entire functions. In addition, an upper bound for the order of these sets in this case is obtained.     

Phase Stability and Elastic Properties of Chromium Borides with Various Stoichiometries

Phase stability is important to the application of materials. By first‐principles calculations, we establish the phase stability of chromium borides with various stoichiometries. Moreover, the phases of CrB₃ and CrB₄ have been predicted by using a newly developed particle swarm optimization (PSO) algorithm. Formation enthalpy–pressure diagrams reveal that the MoB‐type structure is more energetically favorable than the TiI‐type structure for CrB. For CrB₂, the WB₂‐type structure is preferred at zero pressure. The predicted new phase of CrB₃ belongs to the hexagonal P‐6m2 space group and it transforms into an orthorhombic phase as the pressure exceeds 93 GPa. The predicted CrB₄ (space group: Pnnm) phase is more energetically favorable than the previously proposed Immm structure. The mechanical and thermodynamic stabilities of predicted CrB₃ and CrB₄ are verified by the calculated elastic constants and formation enthalpies. The full phonon dispersion calculations confirm the dynamic stability of WB₂‐type CrB₂ and predicted CrB₃. The large shear moduli, large Young’s moduli, low Poisson ratios, and low bulk and shear modulus ratios of CrB₄? PS_C and CrB₄? PS_D indicate that they are potential hard materials. Analyses of Debye temperature, electronic localization function, and electronic structure provide further understanding of the chemical and physical properties of these borides.     

Artificial Neural Networks in water analysis: Theory and applications

Artificial Neural Networks (ANNs) have seen an explosion of interest over the last two decades and have been successfully applied in all fields of chemistry and particularly in analytical chemistry. Inspired from biological systems and originated from the perceptron, i.e. a program unit that learns concepts, ANNs are capable of gradual learning over time and modelling extremely complex functions. In addition to the traditional multivariate chemometric techniques, ANNs are often applied for prediction, clustering, classification, modelling of a property, process control, procedural optimisation and/or regression of the obtained data. This paper aims at presenting the most common network architectures such as Multi-layer Perceptrons (MLPs), Radial Basis Function (RBF) and Kohonen's self-organisations maps (SOM). Moreover, back-propagation (BP), the most widespread algorithm used today and its modifications, such as quick-propagation (QP) and Delta-bar-Delta, are also discussed. All architectures correlate input variables to output variables through non-linear, weighted, parameterised functions, called neurons. In addition, various training algorithms have been developed in order to minimise the prediction error made by the network. The applications of ANNs in water analysis and water quality assessment are also reviewed. Most of the ANNs works are focused on modelling and parameters prediction. In the case of water quality assessment, extended predictive models are constructed and optimised, while variables correlation and significance is usually estimated in the framework of the predictive or classifier models. On the contrary, ANNs models are not frequently used for clustering/classification purposes, although they seem to be an effective tool. ANNs proved to be a powerful, yet often complementary, tool for water quality assessment, prediction and classification.