Natural hyperbolic materials (HMs) in two dimensions (2D) have an extraordinarily high anisotropy and a hyperbolic dispersion relation. Some of them can even sustain hyperbolic polaritons with great directional propagation and light compression to deeply sub-wavelength scales due to their inherent anisotropy. Herein, the anisotropic optical features of 2D natural HMs are reviewed. Four hyperbolic polaritons (i.e., phonon polaritons, plasmon polaritons, exciton polaritons, and shear polaritons) as well as their generation mechanism are discussed in detail. The natural merits of 2D HMs hold promise for practical quantum photonic applications such as valley quantum interference, mid-infrared polarizers, spontaneous emission enhancement, near-field thermal radiation, and a new generation of optoelectronic components, among others. The conclusion of these analyses outlines existing issues and potential interesting directions for 2D natural HMs. These findings could spur more interest in anisotropic 2D atomic crystals in the future, as well as the quick generation of natural HMs for new applications.

Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved method for small target detection called YOLOv5s is proposed to enhance the detection accuracy for small targets such as apple fruits. By applying improvements to the RFA module, DFP module, and Soft-NMS algorithm, as well as integrating these three modules together, accurate detection of small targets in images can be achieved. Experimental results demonstrate that the integrated, improved model achieved a significant improvement in detection accuracy, with precision, recall, and mAP increasing by 3.6%, 6.8%, and 6.1%, respectively. Furthermore, the improved method shows a faster convergence speed and lower loss value during the training process, resulting in higher recognition accuracy. The results of this study indicate that the proposed improved method exhibits a good performance in apple fruit detection tasks involving UAV imagery, which is of great significance for fruit yield estimation. The research findings demonstrate the effectiveness and feasibility of the improved method in addressing small target detection tasks, such as apple fruit detection.

Impact damage to apples is one of the most crucial quality factors and needs to be detected in postharvest quality sorting processes. In this study, the impact damage of the ‘Red Fuji’ apple fruit was investigated quantitatively by hyperspectral imaging technology. A total of 240 samples were prepared with six groups for different damage degrees. The hyperspectral imaging technique based on near-infrared (NIR) spectrometry in the range of 900–1700 nm was used to measure mechanical parameters, such as the average pressure, contact load, damaged area, absorbed energy, and damaged firmness. Four types of spectral pre-treatment, including the standard normal variate, multiplicative scatter correction, first-order derivative, and second-order derivative, were adopted to improve the model’s predictive performance. The quantitative relationships between spectra and mechanical parameters were successfully modeled based on partial least squares (PLS) regression. For ‘Red Fuji’ apples, raw spectral data without pre-treatment performed better than those after spectral pre-treatments. In this model, the characteristic wavelengths were selected by the Savitzky–Golay second-order derivative (SG 2^nd Der) and competitive adaptive reweighted sampling (CARS) method. The results indicate that the CARS-PLS regression model produced better results than the SG 2^nd Der-PLS regression model. The good prediction performances were presented by the coefficient of determination (R_P²) and root mean square errors of prediction (RMSEP) values. The R_P² and RMSEP results of the average pressure, contact load, damaged area, absorbed energy, and damaged firmness are 0.66 and 0.02 MPa, 0.86 and 53.80 N, 0.83 and 116.37 mm², 0.81 and 0.24 J, and 0.64 and 0.19 N, respectively. This study demonstrates the potential of the NIR hyperspectral imaging technique as a highly accurate way to quantitatively predict the mechanical parameters of apples.

\ud Background: \ud The level of oral hygiene index among special need sudents is worse than normal ones due to their high rate of calculus and debris index. They need to improve their skills in maintaining their oral health, such as brushing their teeth. The parents assistance of brushing their child’s teeth might be one of the solution. The aim of this research is to know the effect of tooth brushing assistance among blind and deaf students towards debris index changes\ud Methods:\ud This study was quantitative descriptive with cross sectional approach. The population was the students of SLB Semarang. The samples were 20 blind and 20 deafs students who were 8-12 years old. Each group was divided into two sub groups with and without assistance. Parents given training of brushing teeth techniques would assist their child in brushing their teeth along 21 days and debris index would be observed every week. The data were analyzed by independent T test\ud Results: \ud There was signifficant difference in debris index changes between the group of deaf and blind (p value : 0,029). Tooth brushing assistance were more effective on deaf (p value : 0,042) than blind (p value : 0,067). The decrease of debris index among deaf students with parents assistance were 54.17% and 15.52% without parents assistance; among blind students with parents assistance increased 3.39% and 1.69% without parents assistance.\ud Conclusion: \ud The effect of tooth brushing assistance among deaf were more effective than blind students.\ud \ud Keywords: blind and deaf students, tooth brushing, debris inde