Investigation of the energy performance of a novel modular solar building envelope

The major challenges for the integration of solar collecting devices into a building envelope are related to the poor aesthetic view of the appearance of buildings in addition to the low efficiency in collection, transportation, and utilization of the solar thermal and electrical energy. To tackle these challenges, a novel design for the integration of solar collecting elements into the building envelope was proposed and discussed. This involves the dedicated modular and multiple-layer combination of the building shielding, insulation, and solar collecting elements. On the basis of the proposed modular structure, the energy performance of the solar envelope was investigated by using the Energy-Plus software. It was found that the solar thermal efficiency of the modular envelope is in the range of 41.78–59.47%, while its electrical efficiency is around 3.51% higher than the envelopes having photovoltaic (PV) alone. The modular solar envelope can increase thermal efficiency by around 8.49% and the electrical efficiency by around 0.31%, compared to the traditional solar photovoltaic/thermal (PV/T) envelopes. Thus, we have created a new envelope solution with enhanced solar efficiency and an improved aesthetic view of the entire building

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report