Pattern recognition in software engineering trend adapting

Whether and when to adapt to certain software engineering trends are difficult questions to be answered by many decision-makers. The main reasons are due to the fact that evolution of software engineering trends itself is determined by various factors, many of which come from the fields outside of the software technology, thus hard to predict. So it is even harder to estimate the cost and benefit when adapting to certain trends. This paper is intended to study ways to decrease the risk involved in such decision making processes, by developing a pattern from past software engineering trends. While the pattern cannot answer all the questions by itself, it can relief the decision makers in a large extent by providing the most important information relevant to the software engineering trends. The pattern recognition is achieved by using neural networks. Our result seems to be very encouraging, which begins to prove that there does exist pattern between the input data that we can observe and the output data that we need to know. Although more trends need to be observed and analyzed before we can reach a more concrete conclusion, it does show that neural network may be a valid approach in future research

Observation of vacancy-induced suppression of electronic cooling in defected graphene

Previous studies of electron-phonon interaction in impure graphene have found that static disorder can give rise to an enhancement of electronic cooling. We investigate the effect of dynamic disorder and observe over an order of magnitude suppression of electronic cooling compared with clean graphene. The effect is stronger in graphene with more vacancies, confirming its vacancy-induced nature. The dependence of the coupling constant on the phonon temperature implies its link to the dynamics of disorder. Our study highlights the effect of disorder on electron-phonon interaction in graphene. In addition, the suppression of electronic cooling holds great promise for improving the performance of graphene-based bolometer and photo-detector devices.Comment: 13 pages, 4 figure