Due to their high wavelength selectivity and strong anti-interference capability, solar-blind UV photodetectors hold broad and important application prospects in fields like flame detection, missile warnings, and secure communication. Research on solar-blind UV detectors for amorphous Ga₂O₃ is still in its early stages. The presence of intrinsic defects related to oxygen vacancies significantly affects the photodetection performance of amorphous Ga₂O₃ materials. This paper focuses on growing high quality amorphous Ga₂O₃ films on silicon substrates through atomic layer deposition. The study investigates the impact of annealing atmospheres on Ga₂O₃ films and designs a blind UV detector for Ga₂O₃. Characterization techniques including atomic force microscopy (AFM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) are used for Ga₂O₃ film analysis. Ga₂O₃ films exhibit a clear transition from amorphous to polycrystalline after annealing, accompanied by a decrease in oxygen vacancy concentration from 21.26% to 6.54%. As a result, the response time of the annealed detector reduces from 9.32 s to 0.47 s at an external bias of 10 V. This work demonstrates that an appropriate annealing process can yield high-quality Ga₂O₃ films, and holds potential for advancing high-performance solar blind photodetector (SBPD) development.

Tongue diagnosis is an important part of the diagnostic process in traditional Chinese medicine (TCM). It primarily relies on the expertise and experience of TCM practitioners in identifying tongue features, which are subjective and unstable. We proposed a tongue feature classification framework based on convolutional neural networks to reduce the differences in diagnoses among TCM practitioners. Initially, we used our self-designed instrument to capture 482 tongue photos and created 11 data sets based on different features. Then, the tongue segmentation task was completed using an upgraded facial landmark detection method and UNET. Finally, we used ResNet34 as the backbone to extract features from the tongue photos and classify them. Experimental results show that our framework has excellent results with an overall accuracy of over 86 percent and is particularly sensitive to the corresponding feature regions, and thus it could assist TCM practitioners in making more accurate diagnoses.

Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.

Electronic skin (e-skin) aims to mimic human skin functions, which could profoundly affect medical care and robots. However, it still faces challenges in simultaneously detecting and distinguishing pressure and temperature signals in real-time. Herein, a simple and sustainable strategy is proposed to fabricate a reduced graphene oxide (rGO)-coated wood sponge (rGO@WS) for a multifunctional sensor with high pressure and temperature properties. The prepared porous wood sponge presents high elasticity and superior durability. The added rGO endows the sponge with high conductivity and excellent fatigue resistance. The proposed sensor can realize high sensitivity (1.18 kPa⁻¹), a large pressure range (0.2–100 kPa), a low detection limit and can also detect the temperature with a minimal detection limit of 0.6 K and a response time of 0.8 s. Due to the different response principles of piezoresistive and thermoelectric effects, pressure sensing performance and temperature sensing performance are independent. Meanwhile, a 4 × 4 pixel sensing array based on the rGO@WS sensor is fabricated to serve as e-skin to detect and distinguish pressure and temperature signals. This work sheds new light on the development of wearable devices and e-skin for health care.

Acne is a common skin disease typically diagnosed visually by dermatologists, involving manual localization and labeling. Instead of conventional manual marking, automated acne diagnosis can save medical time and avoid cross-contamination. This paper presents a new automatic detection model for acne based on improved YOLOv7. Specifically, the ELAN module in backbone is improved to enhance feature extraction functionality. Meanwhile, We added the EPSA module in Backbone to extract the multi-scale spatial feature information more effectively. And the concat node of Neck layer is adjusted to strengthen the feature fusion capability, thus improving the accuracy of target detection. Moreover, the activation function is replaced with ELU to improve the robustness of the model. Finally, the initial anchor boxes of YOLOv7 are adjusted by the K-Means algorithm to make the model more suitable for acne detection. We conducted our experiments using the publicly available ACNE04 dataset to train the model. The experimental results demonstrate the robust detection capabilities of the model, achieving an mean average accuracy(mAP) of 83.7% in acne detection. The mAP is 4.57% higher than the initial model and better than the general object detection model. This model holds promise for effectively managing patient health status and providing valuable assistance to physicians in the diagnostic process.