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New chemical sensors are needed for both present day expeditionary oceanography and an emerging new phase involving long term in situ ocean observations. Over the past four decades, a new spectrochemical technique, laser induced breakdown spectroscopy (LIBS), has been under development for the identification of the elemental constituents of materials. This technique uses a laser to create a spark or plasma on a sample. The plasma emission is then analyzed with a spectrometer to determine its elemental composition. Recently, LIBS has been identified as a viable tool for in situ field measurements because it is able to analyze all forms of matter (solids, liquids, and gases), can operate in a stand-off mode, and is non-invasive and non-destructive. A marine LIBS sensor would be a useful tool for studying many environments in the ocean, especially mid-ocean ridge hydrothermal vents where in situ measurements are difficult due to the presence of high-temperature, corrosive fluids. A feasibility assessment of oceanic LIBS in the laboratory has been initiated. A high pressure chamber was designed and built for investigating the effect of realistic ocean environments on the LIBS signal. Preliminary work shows that LIBS can successfully detect Li, Na, K, Ca, Mn, and Zn in bulk aqueous solutions at pressures up to 272 atm, making LIBS a viable technique for deep ocean chemical sensing.

Processor layout and data distribution are important to performance-oriented parallel computation, yet high-level language support that helps programmers address these issues is often inadequate. This paper presents a trio of abstract high-level language constructs - grids, distributions, and regions - that let programmers manipulate processor layout and data distribution. Grids abstract processor sets, regions abstract index sets, and distributions abstract mappings from index sets to processor sets; each of these is a first-class concept, supporting dynamic data reallocation and redistribution as well as dynamic manipulation of the processor set. This paper illustrates uses of these constructs in the solutions to several motivating parallel programming problems.

A model for searching algorithms is developed which includes most tree-like searching structures such as lists, binary trees, AVL trees and 2, 3-trees. It is shown that no searching algorithm employing a data structure that is uniquely represented (up to isomorphism) can provide search, insert and delete functions all operating faster than c√n time for every n key tree. The c√n bound is shown to be achievable for uniquely represented data structures.

Problems concerned with finding inscribing or circumscribing polygons that maximize some measurement are considered such as: Find an area maximizing triangle inscribed in a given convex polygon. Algorithms solving a number of these problems in linear time are presented. They use the common approach of finding an initial solution with respect to a fixed bounding point and then iteratively transforming this solution into a new solution with respect to a new point. The generality of this approach is discussed and several open problems are noted.