Isochoric thermal conductivity of solid nitrogen

The isochoric thermal conductivity of solid nitrogen has been investigated on four samples of different densities in the temperature interval from 20 K to the onset of melting. In alfa-N2 the isochoric thermal conductivity exhibits a dependence weaker than 1/T; in beta-N2 it increases slightly with temperature. The experimental results are discussed within a model in which the heat is transported by low-frequency phonons or by "diffusive" modes above the mobility boundary. The growth of the thermal conductivity in beta-N2 is attributed to the decreasing "rotational" component of the total thermal resistance, which occurs as the rotational correlations between the neighboring molecules become weaker.Comment: Postscript 12 pages, 3 figures, 1 table. To be published in 200

On the Analysis of Chemical Composition of Moon's Surface by Direct Methods

Proportional counter for X ray emission detection and chemical analysis of lunar surface element

Revision of Agraptocoris Reuter (Heteroptera: Miridae: Phylinae), with description of five new species and a review of aedeagal terminology

The predominantly Central Asian genus Agraptocoris Reuter is revised. Eight valid species are recognized, five of those being described as new to science, namely A. eugeniae, A. nigrisetosus, A. pallescens, A. subconcolor (all Mongolia), and A. pamiricus (Tajikistan and Kyrgyzstan). A phylogenetic analysis based on 37 morphological characters is presented for all Agraptocoris species and 13 outgroup taxa. This analysis establishes Agraptocoris as monophyletic and rendered the subtribes Phylina and Oncotylina as non-monophyletic. The differential diagnosis for the genus and a key to all species are given. Habitus photographs, illustrations of male genitalic structures, scanning micrographs of morphological structures, host and distributional information are provided for all species. Homologies and terminology of the aedeagal structures in the subfamily Phylinae are discussed

A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints

A new computationally simple method of imposing hard convex constraints on the neural network output values is proposed. The key idea behind the method is to map a vector of hidden parameters of the network to a point that is guaranteed to be inside the feasible set defined by a set of constraints. The mapping is implemented by the additional neural network layer with constraints for output. The proposed method is simply extended to the case when constraints are imposed not only on the output vectors, but also on joint constraints depending on inputs. The projection approach to imposing constraints on outputs can simply be implemented in the framework of the proposed method. It is shown how to incorporate different types of constraints into the proposed method, including linear and quadratic constraints, equality constraints, and dynamic constraints, constraints in the form of boundaries. An important feature of the method is its computational simplicity. Complexities of the forward pass of the proposed neural network layer by linear and quadratic constraints are O(n*m) and O(n^2*m), respectively, where n is the number of variables, m is the number of constraints. Numerical experiments illustrate the method by solving optimization and classification problems. The code implementing the method is publicly available