Coinductive definitions, such as that of an infinite stream, may often be described by elegant logic programs, but ones for which SLD-refutation is of no value as SLD-derivations fall into infinite loops. Such definitions give rise to questions of lazy corecursive derivations and parallelism, as execution of such logic programs can have both recursive and corecursive features at once. Observational and coalgebraic semantics have been used to study them abstractly. The programming developments have often occurred separately and have usually been implementation-led. Here, we give a coherent semantics-led account of the issues, starting with abstract category theoretic semantics, developing coalgebra to characterise naturally arising trees, and proceeding towards implementation of a new dialect, CoALP, of logic programming, characterised by guarded lazy corecursion and parallelism.

The experimental results reported in many papers suggest that making an appropriate a priori choice of an evolutionary method for a nonlinear parameter optimization problem remains an open question. It seems that the most promising approach at this stage of research is experimental, involving a design of a scalable test suite of constrained optimization problems, in which many features could be easily tuned. Then it would be possible to evaluate merits and drawbacks of the available methods as well as test new methods eciently. In this paper we propose such a test-case generator for constrained parameter optimization techniques. This generator is capable of creating various test problems with dierent characteristics, like (1) problems with dierent relative size of the feasible region in the search space; (2) problems with dierent number and types of constraints; (3) problems with convex or non-convex objective function, possibly with multiple optima; (4) problems with highly non-convex constraints consisting of (possibly) disjoint regions. Such a test-case generator is very useful for analyzing and comparing dierent constraint-handling techniques.

Thermodynamic data are a key resource in the search for new relationships between properties of chemical systems that constitutes the basis of the scientific discovery process. In addition, thermodynamic information is critical for development and improvement of all chemical process technologies. Historically, peer-reviewed journals are the major source of this information obtained by experimental measurement or prediction. Technological advances in measurement science have propelled enormous growth in the scale of published thermodynamic data (almost doubling every 10 years). This expansion has created new challenges in data validation at all stages of the data delivery process. Despite the peer-review process, problems in data validation have led, in many instances, to publication of data that are grossly erroneous and, at times, inconsistent with the fundamental laws of nature. This article describes a new global data communication process in thermodynamics and its impact in addressing these challenges as well as in streamlining the delivery of the thermodynamic data from "data producers" to "data users". We believe that the prolific growth of scientific data in numerous and diverse fields outside thermodynamics, together with the demonstrated effectiveness and versatility of the process described in this article, will foster development of such processes in other scientific fields.