The optical breakdown thresholds (OBTs) of typical dielectric and semiconductor materials are measured using double 40-fs laser pulses. By measuring the OBTs with different laser energy and different time delays between the two pulses, we found that the total energy of breakdown decrease for silica and increase for silicon with the increase of the first pulse energy.

The automatic intelligent acquisition of apple growth information in the long-term provides a promising benefit for growers to plan the application of nutrients and pesticides during apple maturation. The overall goal of this study was to develop an apple growth monitoring system in an orchard based on a deep learning edge detection network for apple size remote estimation throughout the entire growth period. A remote apple growth monitoring hardware system was built with a spherical video camera and two personal computers to regularly acquire apple images. For software, an edge detection network that fused convolutional features (FCF) was proposed to segment the apple images. To filter out irrelevant apples in the images, points on apples to be monitored were manually selected from the images as seed points, and the region growing method was conducted on the extracted edge maps. Then, the horizontal diameters of the apples were calculated. The experimental results showed that the F1 score of the FCF method was 53.1% on the apple test set, and the average run time was 0.075 s per image, which was better than the other five methods in comparison. The growth of the apples was monitored by our system from the date after apple thinning to apple ripening. The mean average absolute error of the apples' horizontal diameters detected by our system was 0.90 mm, and it decreased by 67.9% when compared with the circle fitting-based method (2.8 mm). These results suggest that our system provides an effective and accurate way to monitor the growth of apples on the trees. The proposed method provides a reference for monitoring the growth of other fruits during the growth period, and it can be used to optimize orchard management.

Dental caries is a widespread chronic infectious disease which may induce a series of oral and general problems if untreated. As a result, early diagnosis and follow-up following radiation-free dental caries therapy are critical. Terahertz (THz) waves with highly penetrating and non-ionizing properties are ideally suited for dental caries diagnosis, however related research in this area is still in its infancy. Here, we successfully observe the existence of THz birefringence phenomenon in enamel and demonstrate the feasibility of utilizing THz spectroscopy and birefringence to realize caries diagnosis. By comparing THz responses between healthy teeth and caries, the transmitted THz signals in caries are evidently reduced. Concomitantly, the THz birefringence is also unambiguously inhibited when caries occurs due to the destruction of the internal hydroxyapatite crystal structure. This THz anisotropic activity is position-dependent, which can be qualitatively understood by optical microscopic imaging of dental structures. To increase the accuracy of THz technology in detecting dental caries and stimulate the development of THz caries instruments, the presence of significant THz birefringence effect induced anisotropy in enamel, in combination with the strong THz attenuation at the caries, may be used as a new tool for caries diagnosis.

Terahertz (THz) technology lays the foundation for next-generation high-speed wireless communication, nondestructive testing, food safety inspecting, and medical applications. When THz technology is integrated by artificial intelligence (AI), it is confidently expected that THz technology could be accelerated from the laboratory research stage to practical industrial applications. Employing THz video imaging, we can gain more insights into the internal morphology of silkworm egg. Deep learning algorithm combined with THz silkworm egg images, rapid recognition of the silkworm egg development stages is successfully demonstrated, with a recognition accuracy of $98.5%. Through the fusion of optical imaging and THz imaging, we further improve the AI recognition accuracy of silkworm egg development stages to $99.2%. The proposed THz imaging technology not only features the intrinsic THz imaging advantages, but also possesses AI merits of low time consuming and high recognition accuracy, which can be extended to other application scenarios.