Background: Sequoia sempervirens (D. Don) Endl.) (redwood) has the potential to be grown in New Zealand in commercial forestry operations and is valued for its naturally durable heartwood. A viable redwood industry based on planted forests can only be achieved if the timber produced meets quality expectations, in particular durability. Natural durability is highly variable among trees. Also, a within-tree pattern of low durability close to the pith has been observed. Natural durability is preliminarily caused by secondary metabolites deposited into the cell walls during heartwood formation. The exact nature of the compounds responsible for natural durability in redwood is unknown. Methods: Samples of heartwood from 22 different trees were obtained, ground and extracted using a range of solvents. The ability of some of these extracts to reduce the growth of two fungi (Gloeophyllum trabeum and Trametes versicolor) was tested in vitro. Information on the composition of the extracts was obtained using infrared spectroscopy and gas chromatography.

In recent years, the U.S. Marine Corps has begun developing an infrastructure for applying agent-based models and simulation, computing power, and data analysis and visualization technologies to help answer complex questions in military operations. Factor screening approaches are of particular interest, since even relatively simple agent-based models may have hundreds (or even thousands) of inputs that can be varied. We describe a new experimental design, called a frequency-based design, that can be used for exploring the behavior of terminating simulations. We apply this to a model of a peace-enforcement operation. We examine the behavior of four performance measures (including two attrition ratios) and discuss how the results confirm and complement earlier findings. We conclude with a brief discussion of issues that merit further investigation.

This paper discusses a congestion avoidance and control scheme based on the application of fuzzy logic theory and its neural network implementation. It is mainly concerned with high-speed wide area networks where propagation delays can have significant effects on closed-loop traffic control. In order to overcome the detrimental effects, a fuzzy logic predictor is proposed at the switching node to estimate the queue length in advance. This information together with current queue length and the growth rate is fed into a fuzzy inference system for the generation of a traffic rate factor. This factor can he used alone or in conjunction with other schemes such as Explicit Rate Indication for Congestion Avoidance (ERICA) to calculate Available Bit Rate (ABR) traffic bandwidth allocation, and ultimately affects the Explicit Rate (ER) field in Backward Resource Management (BRM) cells. This paper also discusses on the neural network implementation of the fuzzy predictor. This will greatly reduce the amount of computation while maintaining high prediction accuracy. Simulation results indicate that the overall Quality of Service (QoS) is also comparable with the original fuzzy logic predictor.

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. ABSTRACT (Maximum 200 words.)Students and faculty in the Operations Research Department at the Naval Postgraduate School and researchers at the Naval Surface Warfare Center in Dahlgren, Virginia, have been developing an automated decision support tool for the Navy to optimally allocate to firing units tasks requiring Tomahawk Land Attack Missiles (TLAMs). A new type of TLAM firing unit, the nuclear-powered cruise-missile submarine (SSGN), will soon be operational. An SSGN will be able to carry a maximum of 154 TLAMs. This report describes how to incorporate the SSGN into the existing TLAM allocation decision support algorithm. SUBJECT TERMS ABSTRACTResearchers at the Naval Postgraduate School and Naval Surface Warfare Center in Dahlgren, Virginia, have been developing an automated decision-support tool for the Navy to optimally allocate to firing units tasks requiring Tomahawk Land Attack Missiles (TLAMs). A new type of TLAM firing unit, the nuclear-powered cruise-missile submarine (SSGN), capable of carrying 154 TLAMs, will soon be operational. We consider how to adjust the data structures and model of the existing TLAM allocation decision-support algorithm to incorporate the SSGN, and find that only minor modifications are necessary. Furthermore, based on interviews with submarine officers, we validate certain SSGN operational constraints and discard irrelevant ones.Specifically, the algorithm must account for constraints on the maximum number of missiles that can concurrently be powered up, the minimum amount of time required to open and close a hatch from which a Tomahawk missile is fired, and the minimum amount of time required between missile launches.