Named entity recognition of rice genes and phenotypes based on BiGRU neural networks.

Named Entity Recognition (NER) is a fundamental but crucial task in natural language processing (NLP) and big data analysis, with wide application range. NER for rice genes and phenotypes is a technique to identify genes and phenotypes from a large amount of text. NER for rice genes and phenotypes can facilitate the acquisition of information in the field of crops and provide references for our research on higher quality crops.

A Magnetic-Multiplier-Enabled Hybrid Generator with Frequency Division Operation and High Energy Utilization Efficiency.

The hybrid electromagnetic-triboelectric generator (HETG) is a prevalent device for mechanical energy harvesting. However, the energy utilization efficiency of the electromagnetic generator (EMG) is inferior to that of the triboelectric nanogenerator (TENG) at low driving frequencies, which limits the overall efficacy of the HETG. To tackle this issue, a layered hybrid generator consisting of a rotating disk TENG, a magnetic multiplier, and a coil panel is proposed.

Efficient and accurate identification of ear diseases using an ensemble deep learning model.

Early detection and appropriate medical treatment are of great use for ear disease. However, a new diagnostic strategy is necessary for the absence of experts and relatively low diagnostic accuracy, in which deep learning plays an important role. This paper puts forward a mechanic learning model which uses abundant otoscope image data gained in clinical cases to achieve an automatic diagnosis of ear diseases in real time.