Optical effects of graphene addition on adhesives for orthodontic lingual retainers.

The objective of this study was to determine the effects on the colour of adding increasing concentrations of graphene to orthodontic fixed retainer adhesives and to evaluate changes in optical transmission during light curing and the resultant degree of conversion. Two different types of adhesives commonly used for fixed retainers were investigated: A packable composite (Transbond) and a flowable composite (Transbond Supreme). Graphene was added to the adhesives in three different concentrations (0.01, 0.05, and 0.1 wt%). Adhesives without graphene addition were set as control groups. A Minolta colourimeter was used to measure the colour and translucency parameters. Irradiance transmitted during curing was quantified using MARC Light Collector. Fourier-transform infrared spectroscopy was used to record degree of conversion. Data were statistically analysed with the Student's t-test and one-way ANOVA with Tukey's tests (α = 0.05). The findings showed that incorporating graphene darkened the adhesive colour significantly and reduced translucency. As the graphene concentration reached 0.1 wt%, samples became opaque; yet, no adverse effect on degree of conversion was observed. The addition of graphene reduces optical transmission of lingual retainer adhesives; the effect increases with graphene concentration.

High-transmittance double-layer frequency-selective surface based on interlaced multiring metallic mesh.

In this work, we proposed an optically transparent double-layer frequency-selective surface (FSS) based on interlaced multiring metallic mesh. By changing the large metal area of a conventional double-layer FSS into triangular-orthogonal distributed basic rings and nested rotated subrings, we achieved an FSS with high optical transmittance and low normalized high-order diffraction intensity while maintaining a flat passband and steep transition band. The results showed that our fabricated FSS had a normalized visible transmittance of 90.31%, stable filtering passband of ∼33.9 GHz, 3 dB bandwidth of 13.4 GHz, and uniform diffraction distribution, which are favorable characteristics for optically transparent FSS applications.

Highly thermal-stable heterodyne interferometer with minimized periodic nonlinearity.

Heterodyne interferometers suffer from thermal drift of optics (TDO), which may introduce error even up to several micrometers. In this paper, we propose a symmetric heterodyne interferometer with spatially separated beams, which realizes completely balanced optical paths between probe and reference beams and simultaneously avoids the optical mixing, and thereby is theoretically capable of eliminating TDO and the periodic nonlinearity (PNL). To validate the performance of the proposed interferometer, first a special experimental setup is constructed, with which the TDO test can be conducted in vacuum, and the result shows that a thermal coefficient of 1.2 nm/°C is achieved. Next, the PNL of the proposed interferometer is measured by both the frequency domain method and the phase quadrature method, which demonstrates an undetectable PNL at a noise level of 13 pm.

Measurement Method for Nonlinearity in Heterodyne Laser Interferometers Based on Double-Channel Quadrature Demodulation.

The phase quadrature measurement method is capable of measuring nonlinearity in heterodyne laser interferometers with picometer accuracy whereas it cannot be applied in the new kind of heterodyne interferometers with bidirectional Doppler frequency shift especially in the condition of non-uniform motion of the target. To solve this problem, a novel measurement method of nonlinearity is proposed in this paper. By employing double-channel quadrature demodulation and substituting the external reference signal with internal ones, this method is free from the type of heterodyne laser interferometer and the motion state of the target. For phase demodulation, the phase differential algorithm is utilized to improve the computing efficiency. Experimental verification is carried out and the results indicate that the proposed measurement method achieves accuracy better than 2 pm.

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy.

Background: A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool. Objective: In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians. Methods: According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers. Results: The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively. Conclusion: We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.