The full basis theorem does not imply analytic wellordering

A finite support product of ${\omega }_1$ clones of Jensen's minimal ${\Pi }_2^1$ singleton forcing is used to define a model in which any non-empty analytically definable set of reals contains an analytically definable real (the full basis theorem), but there is no analytically definable wellordering of the reals.     

Molecular Orbital Calculations on 2, 4, 6-Trinitrotoluene

Three ab initio calculations (HF/6-3IG, HF/6-3IG*, and HF/6-3IG**) on 2,4,6-trinitrotoluene were made, The results compare well with xray data, except dihedral angles of NO₂ relative to the plane of the benzene ring. The deviations are attributed to packing forces and steric effects in the crystal. The most stable structure was a torsional angle 10° of the methyl top with the benzene ring, unlike toluene. The rotational barriers of the methyl top and the 4-nitro group are small. Hydrogen bonding, dipole moments and total atomic charges arc calculated.     

A continuous function space with a Faber basis

Let be compact with #S=∞ and let C(S) be the set of all real continuous functions on S. We ask for an algebraic polynomial sequence (P_n)_n=0^∞ with deg P_n=n such that every fC(S) has a unique representation f=∑_i=0^∞ α_iP_i and call such a basis Faber basis. In the special case of , 0<q<1, we prove the existence of such a basis. A special orthonormal Faber basis is given by the so-called little q-Legendre polynomials. Moreover, these polynomials state an example with A(S_q)≠U(S_q)=C(S_q), where A(S_q) is the so-called Wiener algebra and U(S_q) is the set of all fC(S_q) which are uniquely represented by its Fourier series.     

THE THEORETICAL STUDY OF ADSORPTION OF METAL IONS ON CHITOSAN

1. INTRODUCTION Chitin, the most abundant natural amino polysaccharide and estimated to be produced annually almost as much as cellulose, is well known to consist of 2-acetamido-2-deoxy-β-D- glucose through α, β(1→4) linkage. Chitin is the major source of surface pollution in coastalareas. Chitosan is the N-deacetylated derivative of chitin and their structures are shown in Fig. 1. Fig. 1 Structures of Chitin and Chitosan Because of the excellent properties such as biocompatibility…     

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.     

Acoustic detection and classification of river boats

We present a robust algorithm to detect the arrival of a boat of a certain type when other background noises are present. It is done via the analysis of its acoustic signature against an existing database of recorded and processed acoustic signals. We characterize the signals by the distribution of their energies among blocks of wavelet packet coefficients. To derive the acoustic signature of the boat of interest, we use the Best Discriminant Basis method. The decision is made by combining the answers from the Linear Discriminant Analysis (LDA) classifier and from the Classification and Regression Trees (CART) that is also accompanied with an additional unit, called Aisles, that reduces false alarms rate. The proposed algorithm is a generic solution for process control that is based on a learning phase (training) followed by an automatic real time detection while minimizing the false alarms rate.     

On the validity of the counterpoise correction for the basis set superposition error including the fragment relaxation terms

It is demonstrated that relatively large geometrical changes make Emsley et al.'s assumption (J Am Chem Soc (1978) 100:3303) on the counterpoise correction for the basis set superposition error including the fragment relaxation terms unacceptable. Received: 23 September 1997 / Accepted: 31 October 1997     

Basis Sets Dependency in Constructing Spectroscopy-Accuracy Ab Initio Global Electric Dipole Moment Functions

Recently, more attention have been paid on the construction of dipole moment functions (DMF) using theoretical methods. However, the computational methods to construct DMFs are not validated as much as those for potential energy surfaces do. In this letter, using Ar...He as an example, we tested how spectroscopyaccuracy DMFs can be constructed using ab initio methods. We especially focused on the basis set dependency in this scenario, i.e., the convergence of DMF with the sizes of basis sets, basis set superposition error, and mid-bond functions. We also tested the explicitly correlated method, which converges with smaller basis sets than the conventional methods do. This work can serve as a pictorial sample of all these computational technologies behaving in the context of constructing DMFs.