Continuing education in structural biology for science teachers

The present paper sought to identify what perception teachers from Natural Science fields have on the use of instructional strategies that make use of models to represent biomolecules. The data presented are related to two continuing education courses\ud carried out with teachers from public schools of the state of São Paulo (Brazil). Such data showed that the teachers approved the use of instructional materials such as the ones suggested in the courses (e.g., construction of a 3-D biomolecular structure) and\ud they pointed out some advantages and obstacles to the use of such materials.\ud © 2010 Elsevier Ltd. All rights reserved

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance