Transport phenomena in laser surface alloying

A three dimensional, transient model is developed for studying heat transfer, fluid flow and mass transfer for the case of a single-pass laser surface alloying process. The numerical study is performed in a co-ordinate system fixed to the laser which moves with a constant scanning speed. The coupled momentum, energy and species conservation equations are solved using a finite volume technique. Phase change processes are modelled using a fixed-grid enthalpy-porosity technique, which is capable of predicting the continuously evolving solid-liquid interface. The three-dimensional model is able to predict the species concentration distribution inside the molten pool during alloying, as well as in the entire cross section of the solidified alloy. Corresponding experimental results show a good qualitative agreement with the numerical predictions with regard to pool shape and final composition distribution.     

Formation of a geometric pattern with a mobile wireless sensor network

Mobile wireless sensor networks (MWSNs) will enable information systems to gather detailed information about the environment on an unprecedented scale. These self‐organizing, distributed networks of sensors, processors, and actuators that are capable of movement have a broad range of potential applications, including military reconnaissance, surveillance, planetary exploration, and geophysical mapping. In many of the foreseen applications, the MWSN will need to form a geometric pattern without assistance from the user. In military reconnaissance, for example, the nodes will be dropped onto the battlefield from a plane and land at random positions. The nodes will be expected to arrange themselves into a predetermined formation in order to perform a specific task. Thus, we present algorithms for forming a line, circle, and regular polygon from a given set of random positions. The algorithms are distributed and use no communication between the nodes to minimize energy consumption. Unlike past studies of geometric problems where algorithms are either tested in simulations where each node has global knowledge of all the other nodes or implemented on a small number of robots, the robustness of our algorithms has been studied with simulations that model the sensor system in detail. The simulations demonstrate that the algorithms are robust against random errors in the sensors and actuators. © 2004 Wiley Periodicals, Inc.     

Bounds on the number of samples needed for neural learning.

The relationship between the number of hidden nodes in a neural network, the complexity of a multiclass discrimination problem, and the number of samples needed for effect learning are discussed. Bounds for the number of samples needed for effect learning are given. It is shown that Omega(min (d,n) M) boundary samples are required for successful classification of M clusters of samples using a two-hidden-layer neural network with d-dimensional inputs and n nodes in the first hidden layer.     

Intravaginal Controlled Administration of Flurogestone Acetate: (III) Development of Rate-Control Vaginal Devices

A Rate-Control vaginal device was developed which overcomes the low bioavailability and unpredictable Q - t^1/2 type release and absorption rate profiles of flurogestone acetate delivered by the currently marketed Syncro-Mate pessary.

The in vitro release and vaginal absorption profiles from the Rate-Control vaginal device were run simultaneously, a linear Q - t relationship was obtained with a significant improvement in bioavailability. A mathematical model was developed to correlate the in vitro drug release and the vaginal absorption profiles of flurogestone acetate from the vaginal devices.

The design, development and the simultaneous release and absorption profiles of flurogestone acetate from this new vaginal device were outlined and discussed.     

The TSIMMIS Approach to Mediation: Data Models and Languages

TSIMMIS—The Stanford-IBM Manager of Multiple InformationSources—is a system for integrating information. It offers a datamodel and a common query language that are designed to support thecombining of information from many different sources. It also offerstools for generating automatically the components that are needed tobuild systems for integrating information. In this paper we shalldiscuss the principal architectural features and their rationale.     

Distributed memory systems for simulating artificial neural networks

In executing tasks involving intelligent information processing, the human brain performs better than the digital computer. The human brain derives its power from a large number [O(10¹¹)] of neurons which are interconnected by a dense interconnection network [O(10⁵) connections per neuron]. Artificial neural network (ANN) paradigms adopt the structure of the brain to try to emulate the intelligent information processing methods of the brain. ANN techniques are being employed to solve problems in areas such as pattern recognition, and robotic processing. Simulation of ANNs involves implementation of large number of neurons and a massive interconnection network. In this paper, we discuss various simulation models of ANNs and their implementation on distributed memory systems. Our investigations reveal that communication-efficient networks of distributed memory systems perform better than other topologies in implementing ANNs.