What can we learn from the Monte Carlo simulation of the glass transition of polymer melts?

The use of coarse-grained lattice models for the modelling of the glassy freezing in of polymer melts is discussed. The bond-fluctuation model is shown to be a reasonable compromise between simulation efficiency and realistic chemical detail. The potential energy, favoring large values of the length of effective bonds, is at low temperatures in conflict with the tendency for dense packing of the effective monomers and this “geometrical frustration” is responsible for the glass transition of this model. It is shown that many physical properties of the model are in surprisingly close agreement with experiments. Open problems and directions for future research are briefly discussed.     

PAC-learnability of probabilistic deterministic finite state automata in terms of variation distance

We consider the problem of PAC-learning distributions over strings, represented by probabilistic deterministic finite automata (PDFAs). PDFAs are a probabilistic model for the generation of strings of symbols, that have been used in the context of speech and handwriting recognition, and bioinformatics. Recent work on learning PDFAs from random examples has used the KL-divergence as the error measure; here we use the variation distance. We build on recent work by Clark and Thollard, and show that the use of the variation distance allows simplifications to be made to the algorithms, and also a strengthening of the results; in particular that using the variation distance, we obtain polynomial sample size bounds that are independent of the expected length of strings.     

The effect of cyclic stress on the physical properties of a poly(dimethylsiloxane) elastomer

The dynamic creep behavior of a filled poly(dimethylsiloxane) elastomer was studied under cyclic stress. The stress level was chosen such that the increase in the internal temperature was small and that microcracks were not observed. This work has demonstrated that cyclic stress in combination with high temperature accelerates the degradation of the elastomer. The results suggest that because of the applied force, breaks in the load-bearing chains of the network occur. These breaks, while relieving the mechanical stress, create highly reactive ionic fragments. It is believed that because of the subsequent reactions of the ionic fragments, changes in the specific gravity, storage modulus, effective crosslink density, and length of the sample (creep) are observed. The observed decrease in the storage modulus is thought to occur because of the reaction of the ionic fragments with moisture, which results in the formation of silanol chain ends that reduce the effective crosslink density. The results also show that contrary to the prediction of the Boltzmann's Superposition Principle, the rate of creep is greatly enhanced when the sample is subjected to a sinusoidally varying dynamic load as compared to a comparable static load. The polymer weight loss was found to be linear with time and strongly dependent on the level of applied dynamic and static force. In addition, the weight loss and rate of creep were also found to be strongly dependent upon temperature.     

A case study in SQA audits

Software quality assurance (SQA) audits are becoming a standard practice in corporations seeking to enter contractual relations with other organizations, or just evaluating their own state of readiness. This paper describes one approach to conducting SQA audits. Excerpts from reports of actual audits conducted on several organizations engaged in software development are used to show how an audit is conducted and what can be achieved through the auditing process. The paper is not meant to be a definitive answer or guide to doing SQA audits. Rather, it serves as an annotated case study of how SQA audits can be conducted with an emphasis on data gathering and reporting.Dr Paul W. Oman is an associate professor of computer science at the University of Idaho, and an independent software consultant who specializes in software analysis. He is the director of the Software Engineering Test Lab within the UI College of Engineering, where he conducts research in software development methods, CASE tools, and software maintainability issues. Oman has published over 50 articles and reports on software tools, software development, and computer science education. He has a PhD in computer science and is a member of the IEEE, IEEE Computer Society, and ACM.     

Preexercise meal composition alters plasma large neutral amino acid responses during exercise and recovery

This study was designed to determine metabolic and physical performance responses to ingestion of pre-exercise meals with different macronutrient and fiber profiles. Twelve physically active subjects (6 males and 6 females) were used to investigate the metabolic and physical performance consequences of consuming pre-exercise meals consisting of oat, corn, or wheat cereals. A fasting trial served as the control, and all subjects received each treatment in a Latin-square design. Blood samples were drawn before and 85 min after meal ingestion, during 90 min of cycling exercise (60% VO2peak), after a 6.4 km performance ride, and during 60 min of recovery. Expired air samples were collected to determine nutrient utilization. Resting carbohydrate oxidation rates and plasma insulin concentrations after oat ingestion were less than after wheat, and corn and wheat ingestion, respectively (P < 0.05). During exercise, the change in plasma glucose from pre-exercise was greater after consuming wheat and corn compared with oat (P < 0.05), and it was inversely related to pre-exercise plasma insulin concentration (r = -0.55, P = 0.0001). Plasma free fatty acid concentrations were inversely related to plasma lactate concentrations (r = -0.58, P = 0.0001). Free fatty acid concentrations and fat oxidation were greater in fasting trials than all others, but performance ride times did not differ among treatments. Plasma branched-chain amino acid concentrations resembled their respective meal profiles throughout exercise, the performance ride, and recovery. These results indicate that pre-exercise meal composition can influence glucose homeostasis during early exercise and plasma branched-chain amino acid concentrations over a substantial range of metabolic demands.