Improving a technology for treating pig iron outside the furnace with magnesium

Institute of Ferrous Metallurgy. Translated from Metallurg, No. 4, p. 24, April, 1989.     

The multi-effect boiling desalting system and its comparison with the multi-stage flash system

The basic characteristics of the multi-effect boiling (MEB) desalination system are presented. Recent developments are considered, and a thermodynamic analysis and evaluation of the heat transfer areas required for the MEB system are given. In comparing the heat transfer areas, water pretreatment and salt concentration, pumping power requirement and stability of the MEB and multi-stage flash (MSF) systems, MEB seems to have definite advantages over MSF. However, in the absence of well-proven large capacity MEB units, and in view of the good experience with and reliability of MSF systems, large producers of desalted water favor continued installation of MSF systems.     

A Semantic Future for AI

Computing must become much more human centered--for example, by presenting personalized information to users and by respecting personal preferences when controlling multiple devices or invoking various services. Appropriate representation of the information's semantics and of the functionality of devices and services will be critical to such personalized computing. Symbolic artificial intelligence techniques provide the method of choice for the required semantic-representation and reasoning capabilities.     

Kinematic motion model for jumping scout robots

/sup T/he University of Minnesota's Scout is a small cylindrical robot capable of rolling and jumping. Models describing the robot's motion are developed. These models can be employed for motion prediction and simulation. The results suggest that the determining factor of the Scout's behavior is the length of the winch cable.     

Representation as a fluent: an AI challenge for the next half century

The authors argued that AI systems must be able to manipulate their own internal representations automatically to deal with an infinitely complex and ever-changing world and to scale up to rich, complex applications. Such manipulation must go beyond changing beliefs and learning new concepts in terms of old concepts; it must be able to change ontology's underlying syntax and semantics. Initial progress has been made, but further progress is urgently needed owing to the demands of autonomous multiagent systems. Understanding and implementing this ability must be a major focus of AI for the next 50 years.