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Facial symmetry evaluation has always been a hot topic of concern for doctors who engage in the study of facial beauty disciplines such as orthodontics, dentistry, and plastic surgery. Although scholars at home and abroad have carried out much research on the evaluation of facial symmetry with a variety of emerging technologies and methods, there is still a lack of unified standards for the evaluation of facial asymmetry due to the complexity of the content and methods and individual subjectivity. Facial asymmetry involves changes in the length, width and height of the face. It is a complex dental and maxillofacial malformation whose early identification and accurate evaluation are particularly important. Clinically, in addition to the necessary dental and maxillofacial examinations, it is also necessary to evaluate facial asymmetry with the help of corresponding auxiliary methods. This paper gives a summary of the commonly used three-dimensional evaluation methods. The evaluation methods of facial asymmetry can be divided into 5 categories: qualitative analysis, quantitative analysis, dynamic analysis, mathematical analysis, and artificial intelligence analysis. After the analysis and summarization of the characteristics, advantages and limitations of each method in clinical applications, it is found that although these methods vary in accuracy, evaluation scope, diagnosis nature and calculation method, etc., the three-dimensional evaluation methods are more objective, more accurate and more convenient and will become the mainstream evaluation method for facial asymmetry with further development of three-dimensional measurement technologies.

This study examines the possible consequence of elder mistreatment on resilience and whether social support moderates this posited association, using a rural sample of 432 community-dwelling older Chinese adults aged 60 to 79 years. Elder abuse included verbal abuse, physical abuse, or financial exploitation. Social support was measured by The Multidimensional Scale of Perceived Social Support (MSPSS). Resilience was represented by a seven-item scale. Hierarchical regression models indicated that mistreatment is significantly related to low levels of resilience when confounding factors are adjusted. A significant interaction term (abuse × social support) was also detected. Mistreated respondents who reported higher levels of social support were less likely to experience low resilience compared to those with lower levels of social support. Social support buffers against the undesirable effect of mistreatment on resilience, especially for those who were abused. [ABSTRACT FROM AUTHOR]

In this work, N-doped and oxygen vacancy-rich NiCo2O4/NiO/Co3O4 composites are synthesized by the direct calcination method. Noticeably, by changing the additive concentrations of urea dissolved in DMF (N–N dimethylformamide), the electromagnetic wave (EMW) absorption abilities of NiCo2O4/NiO/Co3O4 composite effectively. A maximum reflection loss (RLmax) value at 12.94 GHz for a 2.8 mm thick sheet is −29.76 dB, while its effective absorption bandwidth (RL < −10 dB) reaches 4.21 GHz. In-depth research of possible mechanisms of EMW absorption enhancement. Owing to its simple preparation method and superb EMW absorption properties, the NiCo2O4/NiO/Co3O4 composites have a chance to be suitable candidates for high-property EMW absorbers. [ABSTRACT FROM AUTHOR]

Fiber gel dye‐sensitized solar cells (FGDSCs) are recognized as an effective solution for high‐performance, durable and safe wearable power supply. However, poor and unstable interfaces between fiber electrodes and gel electrolytes are bottleneck problems that have restricted the photovoltaic performances of FGDSCs and their stabilities during deformations. Here, an FGDSC with stable interlaced interfaces is designed by incorporating polymerized gel electrolyte into the aligned channels in fiber electrodes and the gaps between photoanode and counter electrode in situ. The interlaced structure of gel electrolyte in FGDSC improves the interfacial stability and provided stable channels for rapid ion diffusions, offering efficient charge transports at interfaces. The resulting FGDSC thus produced a high power conversion efficiency of 7.95%, and it is further maintained by over 90% after bending for 5000 cycles. These FGDSCs can be integrated with fiber batteries as a self‐charging power system, demonstrating an effective power solution for wearables. [ABSTRACT FROM AUTHOR]

Ternary I-III-VI quantum dots, unlike traditional binary counterparts, offer advantages like low toxicity, cadmium-free composition, tunable electronic properties, and near-infrared light emission, making them increasingly popular for their cost-effectiveness and versatile applications in light emission. In this study, monodispersive, water-soluble, highly emissive CuInS2@ZnS quantum dots were synthesized using cysteine and threonine as ligands by a simple one-pot solution method. By adjusting, the ratio of cysteine to threonine to 1:5 and the heating time during the synthesis process to 40 min, CuInS2@ZnS quantum dots achieved the highest fluorescence intensity. Then, CuInS2@ZnS quantum dots were applied for ciprofloxacin (CIP) detection in food sample, showing a linear range of 2–18 μg/ml and a lowest detection limit of 0.28 μg/ml. The fluorescent probe was successfully established by quenching process as CIP concentration increasing, due to photoinduced electron transfer. Experimental results demonstrated the detection method has high sensitivity and good selectivity. We, then utilized smartphones to visually distinguish CIP concentrations based on the colors emitted by the CuInS2@ZnS quantum dots under UV light. The method was finally effectively to detect residual CIP in porcine kidneys, with recoveries of 92%–98%. This approach has promising potential for enhancing food safety testing. [ABSTRACT FROM AUTHOR]

With advancements in computing technology and the rapid progress of data science, machine learning has been widely applied in various fields, showing great potential, especially in digital healthcare. In recent years, conversational diagnostic systems have been used to predict diseases through symptom checking. Early systems predicted the likelihood of a single disease by minimizing the number of questions asked. However, doctors typically perform differential diagnoses in real medical practice, considering multiple possible diseases to address diagnostic uncertainty. This requires systems to ask more critical questions to improve diagnostic accuracy. Nevertheless, such systems in acute medical situations need to process information quickly and accurately, but the complexity of differential diagnosis increases the system's computational cost. To improve the efficiency and accuracy of telemedicine diagnostic systems, this study developed an optimized algorithm for the Top-K algorithm. This algorithm dynamically adjusts the number of the most likely diseases and symptoms by real-time monitoring of case progress, optimizing the diagnostic process, enhancing accuracy (99.81%), and increasing the exclusion rate of severe pathologies. Additionally, the Top-K algorithm optimizes the diagnostic model through a policy network loss function, effectively reducing the number of symptoms and diseases processed and improving the system's response speed by 1.3–1.9 times compared to the state-of-the-art differential diagnosis systems. [ABSTRACT FROM AUTHOR]