Early monitoring and warning arrangements are effective ways to distinguish infectious agents and control the spread of epidemic diseases. Current testing technologies, which cannot achieve rapid detection in the field, have a risk of slowing down the response time to the disease. In addition, there is still no epidemic surveillance system, implementing prevention and control measures is slow and inefficient. Motivated by these clinical needs, a sample‐to‐answer genetic diagnosis platform based on light‐controlled capillary modified with a photocleavable linker is first developed, which could perform nucleic acid separation and release by light irradiation in less than 30 seconds. Then, on site polymerase chain reaction was performed in a handheld closed‐loop convective system. Test reports are available within 20 min. Because this method is portable, rapid, and easy to operate, it has great potential for point‐of‐care testing. Additionally, through multiple device networking, a real‐time artificial intelligence monitoring system for pathogens was developed on a cloud server. Through data reception, analysis, and visualization, the system can send early warning signals for disease control and prevention. Thus, anti‐epidemic measures can be implemented effectively, and deploying and running this system can improve the capabilities for the prevention and control of infectious diseases.

Although a number of fault diagnosis algorithms for inertial sensors have been proposed in previous decades, the performance of these algorithms needs to be improved with regard to small faults. In this paper, we introduce a data driven-based algorithm, namely, SaPD, for the anomaly detection and output reconstruction of a redundant inertial measurement unit (RIMU). SaPD implements the fault identification of an inertial apparatus by combining an artificial neural network with the Q contribution plots method in parity space. To improve the performance of the fault detection part, in particular for small faults, we introduce a novel hyperplane that measures the distances between inputs and the primary-neuron set obtained from a self-organizing incremental neural network (SOINN). We also employ the Q contribution plots of sensors in the fault isolation part by analyzing historical data with principal component analysis (PCA). We perform quantitative evaluations in a realistic simulation environment, which demonstrates that the proposed SaPD algorithm outperforms other related algorithms in terms of the fault identification accuracy of tiny faults with an acceptable computational complexity.

The tooth arrangements of human beings are challenging to accurately observe when relying on dentists’ naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients’ teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder–decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.

Solution coating of organic semiconductors offers great potential for achieving low‐cost and high‐throughput manufacturing of large‐area and flexible electronics. However, the solution processability of semiconducting small molecules for fabricating uniform and reliable thin‐film devices poses challenges due to the low viscosities of small‐molecule solutions. Here, we report a universal approach employing a primer template (PT) to enhance the spreadability of small‐molecule solutions on silicon wafers, enabling the spin‐coating fabrication of uniform thin films composed of millimeter‐scale grains with complete large‐area coverage and well‐ordered molecular packing. Using PT, we fabricated organic thin‐film transistors (OTFTs) using solutions containing various small molecules such as rubrene and 2‐decyl‐7‐phenyl‐[1]benzothieno[3,2‐b][1]benzothiophene. The device yield of all fabricated OTFTs is consistently 100% while achieving a high average mobility of 1.62 cm² V⁻¹ s⁻¹ with a device‐to‐device variation of 7.7% measured in ambient air condition. In addition, the utilization of PT resulted in a batch‐to‐batch variation of 12.5% in device performance over dozens of OTFT devices. The key industrial manufacturing metrics, such as device yield, reproducibility, and performance uniformity of the PT OTFTs, are among the best for devices fabricated using solution spin‐coating techniques.