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Objective The polishing effects of different compositions of chairside polishing systems on polyetheretherketone (PEEK) were compared to provide a reference for the rational selection of polishing systems for this material in clinical settings. Methods A total of 108 specimens were made from ShushijieTMPEEK crown and bridge materials, Bio-PAEK® dental PEEK, and BioHPP® PEEK. Thirty-six specimens of each material were randomly divided into 6 groups on average based on their polishing technique: OptiDisc®, Super-Snap® Rainbow Technique Kit, VITA ENAMIC®Clinical Polishing Set, Sof-LexTM Diamond Polishing System, Visio.lign Finishing Kit for Composite Veneers, and CERAMAGE Polishing Kit, LavaTM Ultimate ceramic and VITA ENAMIC ceramics were set as the control groups, and 6 specimens were made in each material, polished by Sof-LexTM Diamond Polishing System and VITA ENAMIC® Polishing Set clinical, respectively, according to the manufacturers’ suggestions. The surface roughness (Ra value and Sa value) and gloss of each set were measured. The surface morphologies of the polished specimens were observed by scanning electron microscopy. Results Surface roughness (Ra value and Sa value) and gloss were mainly influenced by the type of polishing system, and they varied between PEEK material types. The Sof-LexTM Diamond Polishing System produced the highest surface roughness and the lowest gloss values for PEEK materials, while the Super-Snap® Rainbow Technique Kit, Visio.lign Finishing Kit for Composite Veneers and CERAMAGE Polishing Kit resulted in low surface roughness values. The Visio.lign Finishing Kit for Composite Veneers and CERAMAGE Polishing Kit exhibited high gloss values. Polishing paste particles were observed embedded on the surfaces of the PEEK materials polished by the Visio.lign Finishing Kit for Composite Veneers; the surfaces of specimens polished by the CERAMAGE Polishing Kit were smooth, without polishing paste particle residue. Using the same polishing system, the best polishing results were obtained with BioHPP® among the PEEK materials tested. The surface roughness values of the three tested PEEK materials polished by the CERAMAGE Polishing Kit were similar to those of the LavaTM Ultimate ceramic and VITA ENAMIC ceramic, but the gloss was higher than that of both. Conclusion CERAMAGE Polishing Kit was recommended for polishing all three tested PEEK materials, achieving a good polishing effect, with similar surface roughness and higher gloss characteristics comparable to those of polished computer-aided design and manufacturing (CAD/CAM) resin-matrix ceramics.

Background: Adolescent weight problems have become a growing public health concern, making early prediction of non-normal weight status crucial for effective prevention. However, few temporal prediction tools for adolescent four weight status have been developed. This study aimed to predict the short- and long-term weight status of Hong Kong adolescents and assess the importance of predictors. Methods: A population-based retrospective cohort study of adolescents was conducted using data from a territory-wide voluntary annual health assessment service provided by the Department of Health in Hong Kong. Using diet habits, physical activity, psychological well-being, and demographics, we generated six prediction models for successive weight status (normal, overweight, obese and underweight) using multiclass Decision Tree, Random Forest, k-Nearest Neighbor, eXtreme gradient boosting, support vector machine, logistic regression. Model performance was evaluated by multiple standard classifier metrics and the overall accuracy. Predictors' importance was assessed using Shapley values. Results: 442,898 Primary 4 (P4, Grade 4 in the US) and 344,186 in Primary 6 (P6, Grade 6 in the US) students, with followed up until their Secondary 6 (Grade 12 in the US) during the academic years 1995/96 to 2014/15 were included. The XG Boosts model consistently outperformed all other model in predicting the long-term weight status at S6 from P4 or P6. It achieved an overall accuracy of 0.72 or 0.74, a micro-averaging AUC of 0.92 or 0.93, and a macro-averaging AUC of 0.83 or 0.86, respectively. XG Boost also demonstrated accurate predictions for each predicted weight status, surpassing the AUC values obtained by other models. Weight, height, sex, age, frequency and hours of aerobic exercise were consistently the most important predictors for both cohorts. Conclusions: The machine learning approaches accurately predict adolescent weight status in both short- and long-term. The developed multiclass model that utilizing easy-assessed variables enables accurate long-term prediction on weight status, which can be used by adolescents and parents for self-prediction when applied in health care system. The interpretable models may help to provide the early and individualized interventions suggestions for adolescents with weight problems particularly. [ABSTRACT FROM AUTHOR]

In order to solve the problems of difficult recognition of space-frequency block codes (SFBC) and high requirement of prior information in complex electromagnetic environment and non-cooperative communication, a blind recognition method of SFBC based on frequency cross-correlation sequence and peak detection is proposed. Firstly, by analyzing the coding characteristics of SFBC signals, the cross-correlation function of transmitter is calculated. Then, considering the influence of frequency selective channel, multipath propagation and noise, the cross-correlation function of the received signal is derived, and the frequency domain cross-correlation sequence is obtained by Fourier transform. Finally, based on the frequency domain cross- correlation sequence, the peak position is detected to identify the coding mode of the transmitter. The derivation and experimental results show that the algorithm has good recognition performance at low signal to noise ratio (SNR), strong adaptability to time deviation and frequency deviation, and has good recognition performance in complex electromagnetic environment. It can be applied to cognitive radio and spectrum detection. The algorithm does not need to know the priori information of channel and noise, the size of orthogonal frequency division multiplexing (OFDM) block and the synchronization information of receiver and transmitter, so it is very suitable for non-cooperative communication occasions such as electronic reconnaissance. [ABSTRACT FROM AUTHOR]

In this study, we utilized Selective Laser Melting (SLM) technology to fabricate a magnesium alloy, and subsequently subject it to micro-arc oxidation treatment. We analyzed and compared the microstructure, elemental distribution, wetting angle, and corrosion resistance of the SLM magnesium alloy both before and after the micro-arc oxidation process. The findings indicate that the SLM magnesium alloy exhibits surface porosity defects ranging from 2% to 3.2%, which significantly influence the morphology and functionality of the resulting film layer formed during the micro-arc oxidation process. These defects manifest as pores on the surface, leading to an uneven distribution of micropores with varying sizes across the layer. The surface roughness of the 3D-printed magnesium alloy exhibits a high roughness value of 180 nanometers. The phosphorus (P) content is lower within the film layer compared to the surface, suggesting that the Mg 3 (PO 4 ) 2 phase predominantly resides on the surface, whereas the interior is primarily composed of MgO. The micro-arc oxidation process enhances the hydrophilicity and corrosion resistance of the SLM magnesium alloy, thereby potentially endowing it with bioactivity. Additionally, the increased surface roughness post-treatment promotes cell proliferation. However, certain inherent defects present in the SLM magnesium alloy samples negatively impact the improvement of their corrosion resistance.

Background: Early identification of high-risk individuals for weight problems in children and adolescents is crucial for implementing timely preventive measures. While machine learning (ML) techniques have shown promise in addressing this complex challenge with high-dimensional data, feature selection is vital for identifying the key predictors that can facilitate effective and targeted interventions. This study aims to utilize feature selection process to identify a robust and minimal set of predictors that can aid in the early prediction of short- and long-term weight problems in children and adolescents., Methods: We utilized demographic, physical, and psychological wellbeing predictors to model weight status (normal, underweight, overweight, and obese) for 1-, 3-, and 5-year periods. To select the most influential features, we employed four feature selection methods: (1) Chi-Square test; (2) Information Gain; (3) Random Forest; (4) eXtreme Gradient Boosting (XGBoost) with six ML approaches. The stability of the feature selection methods was assessed by Jaccard's index, Spearman's rank correlation and Pearson's correlation. Model evaluation was performed by various accuracy metrics., Results: With 3,862,820 million student-visits were included in this population-based study, the mean age of 11.6 (SD = 3.64) for the training set and 10.8 years (SD = 3.50) for the temporal test set. From the initial set of 38 predictors, we identified 6, 9, and 13 features for 1-, 3-, and 5-year predictions, respectively, by the best performed feature selection method of Chi-Square test in XGBoost models. These feature sets demonstrated excellent stability and achieved prediction accuracies of 0.82, 0.73, and 0.70; macro-AUCs of 0.94, 0.86, and 0.83; micro-AUCs of 0.96, 0.93, and 0.92 for different prediction windows, respectively. Weight, height, sex, total score of self-esteem, and age were consistently the most influential predictors across all prediction windows. Additionally, several psychological and social wellbeing predictors showed relatively high importance in long-term weight status prediction., Conclusions: We demonstrate the potential of ML in identifying key predictors of weight status in children and adolescents. While traditional anthropometric measures remain important, psychological and social wellbeing factors also emerge as crucial predictors, potentially informing targeted interventions to address childhood and adolescence weight problems., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Leng, Wu, Chau, Chung and Fong.)