Advancements in the manufacture of Magneto-optic (M-O) media have led to the production of environmentally stable constructions. As the life expectancy of the media has increased, so has the need for more refined analysis techniques to estimate its reliability. A conventional Arrhenius model was examined for fit with actual media stability data. The time to failure was estimated by projecting Byte Error Rate (BER) increase as a function of time at various temperatures and 85% relative humidity. Assuming that actual failure may occur earlier due to other causes, a Weibull model is proposed as a more realistic model of current media. Hypothetical data is fitted to this model for illustration.

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We have made a stereoscopic viewing system for a large assembly of proteins using OpenRasmol. The stable version 2.7.1 of OpenRasmol is modified for the system, which uses an eye-ware instead of trained bare-eyes. Software rendering and other benefits in OpenRasmol are reserved. A 3-D graphic board is used just for the active stereo method, not for the acceleration of rendering. Our modification is simple one. In the results, an actin filament of 16-mers, where one actin monomer has about 400 residues, in space filling model can be rendered in stereoscopic viewing mode and can be made one turn within 10 seconds as quick as non-stereoscopic mode. Other 3-D molecular graphics programs with 3-D accelerator boards cannot render such a large assembly of molecules in stereoscopic usage mode as quickly as the modified OpenRasmol. An attractive application of our system is stereoscopic viewing with a large 200 inch screen in passive stereo method. Simultaneous usage is available for more than 100 persons with inexpensive eye-wares. The large screen allows us to investigate an interior of a groove in an actin filament in detail. Our modified OpenRasmol is distributed following the license, RASLIC, as an open source code at our web site (www.irisa-lab.bio.kyutech.ac.jp/openrasmol), where video files showing rendering speeds of our modified OpenRasmol are also available.

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A causal explanation of physical systems, what we call PCN, is proposed based on the discussion of the contrast between causal and teleological viewpoints. PCN (physical causal network) is a kind of qualitative explanation of physical systems. Its nodes denote labels of physical quantities and its arcs denote physical causalities between nodes. PCNs are generated in a bottom up manner from fragmentary and general pieces of knowledge on physical causalities. The resultant PCN involves not only the essential phenomena for a certain teleological viewpoint but also unexpected (unintentional) phenomena which will be beneficial in the case where PCNs are utilized to synthesis processes. After discussing the temporal and logical aspects of causal relations, we propose a form for representing fragmentary pieces of knowledge on causal relations and a method for generating PCN based on CMS (Clause Management System).

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