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Aerosol jet printing (AJP) is a technique that allows for high-resolution printing of complex circuits on flexible substrates. This method has the potential to be used in various applications, such as electronic sensors, antennas, and micro-coils. The selection of ink is crucial for achieving excellent performance, and different studies have explored the use of silver nanoparticle ink and aqueous silver ink for improved conductivity and printability. Additionally, optimizing process parameters and conducting post-treatments and testing can enhance the quality and reliability of printed circuits. Overall, these investigations highlight the importance of ink selection, parameter optimization, and post-processing in achieving high-performance printed electronics. [Extracted from the article]

Concrete wastewater from mixing stations leads to environment contamination due to its high alkalinity. The wastewater can be reused if its solid content is accurately and timely detected. However, investigations into the traditional methods for wastewater reuse have demonstrated that they are time consuming and not efficient. Therefore, the exact acquirement of solid content in concrete wastewater becomes a necessity. Recent studies have shown that deep learning has been successfully applied to detect the concentration of chemical solutions and the particle content of suspending liquid. Moreover, deep learning can also be used to recognize the accurate water level, which facilitates the detection of the solid–liquid separation surface after wastewater sedimentation. Therefore, in this article the feasibility and challenges of applying deep learning to detect the solid content of concrete wastewater were comprehensively evaluated and discussed. Finally, an experimental setup was proposed for future research, and it indicated that transfer learning, data augmentation, hybrid approaches, and multi-sensor integration techniques can be selected to facilitate future experimental performances. [ABSTRACT FROM AUTHOR]

Hence, UAVs are an effective additional platform for small-scale monitoring missions, effectively enhancing the monitoring accuracy of remote sensing despite the lack of continuous observation [[13]]. Deep learning techniques are capable of integrating large multi-source data in crop growth and hydrodynamic models to develop in situ monitoring equipment to detect fast-changing phenomena, as they can extract complex features and patterns from remote sensing data, such as spectral, spatial, temporal, and contextual information. 10.1007/s10489-021-02893-3 13 Al-Ali Z.M., Abdullah M.M., Asadalla N.B., Gholoum M. A comparative study of remote sensing classification methods for monitoring and assessing desert vegetation using a UAV-based multispectral sensor. Satellite remote sensing is a commonly used method that exploits sensors on satellites, aircrafts, or drones to collect real-time or near-real-time data on the Earth's surface without direct contact [[7]]. [Extracted from the article]

Background There is a lack of attention to screw placement techniques for surgical treatment of scoliosis in children and adolescents. This meta-analysis aims to compare the accuracy and safety of pedicle screw placement between the 3D-printed navigation template technique and the freehand technique during corrective surgery for scoliosis in children and adolescents. Methods A comprehensive search was conducted for relevant articles up to December 2021 in databases including PubMed, Embase, MEDLINE, Cochrane, and Web of Science. The systematic meta-analysis compared the efficacy of pedicle screw placement between the two techniques, including accuracy of pedicle screw placement, complication rate, operation time, blood loss, mean placement time per screw, and mean times for fluoroscopy. Results The seven articles analyzed in this study involved 229 patients altogether. A total of 2,805 pedicle screws were placed by the two methods. Our results revealed that the 3D-printed guide template technique was more accurate than the freehand technique in pedicle screw placement (odds ratio [OR] =2.96; 95% confidence interval [CI]: 2.24–3.91; p < 0.000) with a lower complication rate (OR = 0.21; 95% CI: 0.06–0.78; p = 0.02). The operation time (mean difference [MD] = –34.37; 95% CI: –67.47 to –1.28; p = 0.04) and mean placement time per screw (MD = –3.11; 95% CI: –6.13 to –0.09; p = 0.04) and mean times for fluoroscopy (MD = –6.60; 95% CI: –8.66 to –4.55; p < 0.000) significantly decreased among patients in the 3D-printed navigation template group compared with those in the freehand technique group. In addition, the two techniques had no significant statistical difference in blood loss. Conclusions Compared with the traditional freehand technique, the 3D-printed guide template is a promising technique with higher accuracy and safety in screw placement for surgical treatment of scoliosis in children and adolescents, and is worth popularizing and validating through more prospective clinical studies. [ABSTRACT FROM AUTHOR]