Water quality study of the Struma river basin,Bulgaria (1989–1998)

The present paper deals with an estimation of the water quality of the Struma river. Long-term trends, seasonal patterns and data set structures are studied by the use of statistical analysis. Nineteen sampling sites along the main river stream and different tributaries were included in the study. The sites are part of the monitoring net of the region of interest. Seventeen chemical indicators of the surface water have been measured in the period 1989–1998 in monthly intervals. It is shown that the water quality is relatively stable throughout the monitoring period, which is indicated by a lack of statistically significant trends for many of the sites and by chemical variables. Several seasonal patterns are observed at the sampling sites and four latent factors are identified as responsible for the data set structure.     

Ionic strength dependence in dilute common-ion electrolyte mixtures

The relation between the coefficients of the Harned's rule behavior and the mixing coefficients in the expression for the total excess free energy of a common-ion mixture is elucidated. The results are used to see if the strong ionic-strength dependence of theg ₁ coefficient is reflected in certain modified Harned's coefficients. This is the case for the system studied; moreover, in this case the experimental data are in agreement with the theoretical model calculation made previously.Contribution in memory of Prof. R. A. Robinson.     

多变量统计分类技术——磺胺甲基异口恶唑质量评价的研究

本文研究了近红外漫反射光谱法进行磺胺甲基异恶唑粉末药品质量评价的可能性，用多变量统计分类技术（系统聚类分析，逐步聚类分析，主成分分析和逐步判别分析），从磺胺甲基异恶唑末药品的一阶导数光谱，成功地鉴别了真药，劣药和假药，结果令人满意。     

The retention equation in reversed-phase ion-pair chromatography

In this paper, by combination of the statistical thermodynamic method with the Freundlich isotherm, the retention equations to describe the effects of the mobile phase composition, concentration of the ion-pair reagent and the ionic strength on the amount of adsorbed ion-pair reagent and the retention value of the ionic solute have been reported by simultaneously considering the electrostatic and molecular interaction between solutes, ion-pair reagent and molecule or ion in each phase in the reversed-phase ion-pair chromatography. The validity of these retention equations has been confirmed by calculation of capacity factor of the different phenylamine and naphthylamine sulphonic acids during systematic change of concentration of the strong solvent, the ion-pair reagent and the ionic strength in the mobile phase.Symbols a, b, c, d, d ₁,e, f, h, g anda, b, c, d, e, f', g, h parameters related to chromatographic system - A solute - B constant - B _i solventi - C _b andC _p concentration of strong solvent and ion-pair reagent - C _P ^a amount of adsorbed ion-pair reagent - D dielectric constant - E _AP , andE _pp molecular interaction of adjacent pair of solute-ion-pair reagent, solute-solventB _i , ion-pair reagent-solventB _i and ion-pair reagent-ion-pair reagent - E _p ^a andE _e ^a non-electrostatic and electrostatic adsorption energy of ion-pair reagent - F Faraday constant - h Planck constant - I ionic strength - j _A , andj _p internal partition function of solute, solventB _i and ion-pair reagent - k Boltzmann constant - k capacity factor - k _p ,n _p andk ₁,n ₁ parameters of Freundlich isotherm of ion-pair reagent and solventB ₁ - K _AP , and constants related with the molecular size of solute, ion-pair reagent and solventB _i - m _A ,m _P and molecular weight of solute, ion-pair reagent and solventB _i - N _A ,N _P and numbers of solute, ion-pair reagent and solventB _i in solution - N _A ^a ,N _P ^a and numbers of solute, ion-pair reagent and solventB _i adsorbed on the surface - N _s number of adsorbed sites on the surface - P ion-pair reagent - R gas constant - T absolute temperature - V ₀ volume of solution - V _p and volume of ion-pair reagent and solventB - X potential function - X _P ¹ andX _P ^l potential function of ion-pair reagent on the surface and in the solution - X _A ^a andX _A ^l potential function of solute on the surface and in the solution - Z _A ,Z _P andZ _i charge numbers of soluteA, ion-pair reagent and inorganic ioni - Z _AP and numbers of ion-pair reagentP and solventB _i surrounding the soluteA - ₀ permittivity in a vacuum - electrostatic potential of the surface Dedicated to Professor J. F. K. Huber on the occasion of his 65th birthday     

Limiting law for distinct diffusion coefficients in ionic solutions

The limiting laws for the distinct diffusion coefficient and the corresponding Onsager phenomenological coefficient are obtained from the cluster theory of electrolyte conductance. Results are given for single electrolytes and common-ion mixtures with unrestricted charges and friction coefficients in both cases.     

A twist on chiral Potts

We show that the chiral Potts model may be formulated so that the rapidity lines carry a second integer variable-an increment or twist in each bond crossing it. This modification does not affect those properties of the chiral Potts model which lead to integrability, since it is equivalent to one of the automorphisms allowed for in the theory. In particular, transfer matrices still form commuting families and still satisfy hierarchies of functional equations. Surprisingly, the superintegrable case with twists retains the special algebraic properties which lead to its Ising-like spectra. The formalism should be useful for considering systems with twisted boundary conditions or embedded interfaces.     

Statistical and systematic errors in Monte Carlo sampling

We have studied the statistical and systematic errors which arise in Monte Carlo simulations and how the magnitude of these errors depends on the size of the system being examined when a fixed amount of computer time is used. We find that, depending on the degree of self-averaging exhibited by the quantities measured, the statistical errors can increase, decrease, or stay the same as the system size is increased. The systematic underestimation of response functions due to the finite number of measurements made is also studied. We develop a scaling formalism to describe the size dependence of these errors, as well as their dependence on the bin length (size of the statistical sample), both at and away from a phase transition. The formalism is tested using simulations of thed=3 Ising model at the infinite-lattice transition temperature. We show that for a 96×96×96 system noticeable systematic errors (systematic underestimation of response functions) are still present for total run lengths of 10⁶ Monte Carlo steps/site (MCS) with measurements taken at regular intervals of 10 MCS.This paper is dedicated to Jerry Percus on the occasion of his 65th birthday.     

Causality and quantum mechanics

Statistical causality is recommended as the name of the generalized causality needed in quantum mechanics, instead of statistical correspondence used by Pauli.     

On the use of computational neural networks for the prediction of polymer properties

A general purpose computational paradigm using neural networks is shown to be capable of efficiently predicting properties of polymeric compounds based on the structure and composition of the monomeric repeat unit. Results are discussed for the prediction of the heat capacity, glass transition temperature, melting temperature, change in the heat capacity at the glass transition temperature, degradation temperature, tensile strength and modulus, ultimate elongation, and compressive strength for 11 different families of polymers. The accuracies of the predictions range from 1–13% average absolute error. The worst results were obtained for the mechanical properties (tensile strength and modulus: 13%, 7% elongation: 12%, and compressive strength: 8%) and the best results for the thermal properties (heat capacity, glass transition temperature, and melting point: <4%). A simple modification to the overall method is devised to better take into account the fact that the mechanical properties are experimentally determined with a fairly large range (due to variability in measurement procedures and especially the sample). This modification treats the bounds on the range for the mechanical properties as complex numbers (complex, modular neural networks) and leads to more rapid optimization with a smaller average error (reduced by 3%).Dedicated to Professor Bernhard Wunderlich on the occasion of his 65th birthdayThis research was sponsored by the Division of Materials Sciences, Office of Basic Energy Sciences, U.S. Department of Energy, under Contract No. DE-AC05-84R21400 with Lockheed Martin Energy Systems, Inc. We would like to express our gratitude for the continued collaboration, support, and interest of Prof. Wunderlich in our research. We would also like to thank participants of the 1st DOE Workshop on Applications of Neural Networks in Materials Sciences for useful discussion on materials properties and neural networks.