The present study was motivated by problems at continuous casting plants where a variety of thermally induced defects were observed. The paper outlines an experimental method for the measurement of cooling intensity in the secondary cooling area where nozzles are applied. The precision of a variety of experimental methods is discussed. The nozzles have been investigated in terms of pressure setting, the influence of casting speed, and behaviour in the overlapping areas. The tests have provided information on heat transfer coefficient characteristics and heat flux distribution on the cooled steel surface. The paper presents new experimental findings regarding specification of the Leidenfrost temperature, which is the point between high and low surface temperature regions where a large difference in cooling intensity is observed. The paper also deals with the problems of homogeneity of cooling along the nozzle spray angle and in the overlapping area where thermal 'stripes' occur. The sensitivity of this problem to the pressure of the coolant is discussed.

By analyzing the highlights of the major activities reported by the technology transfer offices (TTOs) of twenty US major universities, the performances of TTO activities are quantitatively assessed and the associated scores are compared with each other. The key performance indicators, which govern the success of the university technology transfer, are specifically selected and examined. Two normalized metrics, overall performance metric (OPM) and patenting control ratio (PCR), which are the representing combined indicators for the TTO performance, are developed and demonstrated. The two metrics are evaluated for each university selected and compared to specifically provide a comprehensive overview of how good is the TTO of a university as compare to those of its peers. Finally, the factors for a successful TTO are described and the major unsolved issues are also discussed.

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Inverse problems deal with determining the causes on the basis of knowing their effects. The object of the inverse parameter estimation problem is to fix the thermal material parameters (the cause) on the strength of a given observation of the temperature history at one or more interior points (the effect). This paper demonstrates two novel approaches to the inverse problems. These approaches use two artificial intelligence mechanisms: neural network and genetic algorithm. Examples shown in this paper give a comparison of results obtained by both of these methods. The numerical technique of neural networks evolved from the effort to model the function of the human brain and the genetic algorithms model the evolutional process of nature. Both of the presented approaches can lead to a solution without having problems with the stability of the inverse task. Both methods are suitable for parallel processing and are advantageous for a multiprocessor computer architecture.

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