The monitoring of acetone in exhaled breath is expected to provide a noninvasive and painless method for dynamic monitoring of summarized physiological metabolic status during obesity treatment. Although the commonly used Mass Spectrometry (MS) technology has high accuracy, the long detection time and large equipment size limit the application of daily bedside detection. As for the real-time and accurate detection of acetone, the gas sensor has become the best choice of gas detection technology, but it is easy to be disturbed by water vapor in breath gas. An integrated breath gas detection system based on cavity ring-down spectroscopy (CRDS) is reported in this paper, which is a laser absorption spectroscopy technique with high-sensitivity detection and absolute quantitative analysis. The system uses a 266 nm single-wavelength ultraviolet laser combined with a breath gas pretreatment unit to effectively remove the influence of water vapor. The ring-down time of this system was 1.068 μs, the detection sensitivity was 1 ppb, and the stability of the system was 0.13%. The detection principle of the integrated breath gas detection system follows Lambert–Beer’s law, which is an absolute measurement with very high detection accuracy, and was further validated by Gas Chromatography–Mass Spectrometer (GC-MS) testing. Significant differences in the response of the integrated breath gas detection system to simulated gases containing different concentrations of acetone indicate the potential of the system for the detection of trace amounts of acetone. Meanwhile, the monitoring of acetone during obesity treatment also signifies the feasibility of this system in the dynamic monitoring of physiological indicators, which is not only important for the optimization of the obesity treatment process but also promises to shed further light on the interaction between obesity treatment and physiological metabolism in medicine.

In piezoelectric drive, resonant drive is an important driving mode in which the external elastic force and electric drive signal are the key factors. In this paper, the effects of the coupling of external elastic force and liquid parameters with the structure on the vibrator resonance frequency and liquid drive are analyzed by numerical simulation. The fluid-structure coupling model for numerical analysis of the elastic force was established, the principle of microdroplet generation and the coupling method of the elastic force were studied, and the changes in the resonant frequency and mode induced by the changes in the liquid parameters in different cavities were analyzed. Through the coupled simulation and calculation of the pressure and deformation of the cavity, the laser vibration measurement test was carried out to test the effect of the vibration mode analysis. The driving model of the fluid jet driven by the elastic force on the piezoelectric drive was further established. The changing shape of the fluid jet under different elastic forces was analyzed, and the influence law of the external elastic force on the change in the droplet separation was determined. It provides reference support for further external microcontrol of droplet motion.

Microbial testing plays a crucial role in medical and biological research, serving as a significant direction in disease prevention and control studies. Traditional manual testing, which involves identifying colonies on Petri dishes is the best method, but it is not only expensive but also time consuming. However, the application and development of deep learning can address these issues. However, a crucial condition is that deep learning models require a large amount of labeled medical imaging data. Obtaining high-resolution images is time consuming and cannot generate a large amount of data in a short time. Therefore, we designed a Double Deep Convolutional Generative Adversarial Networks (D-DCGAN) to construct a dataset. Under the DCGAN adversarial network, synthetic colonies are generated by overlapping small single colonies and composite colonies to establish a random synthetic colony model, and a DCGANB generation model is established for the synthesis of complete colonies. In this study, we first evaluated the D-DCGAN model, obtaining an initial FID score of 38.9, and validated the effectiveness of the generated dataset using Faster R-CNN and YOLOv12. Subsequently, a second-order optimization model based on multiple evaluation metrics was developed to determine the optimal augmentation level, identifying 1320 synthetic images from the original 236 samples. With this optimized dataset, the FID score decreased to 37.1, while the detection accuracy reached 0.975. These results demonstrate the feasibility of the proposed framework for effective dataset augmentation and improved downstream detection performance.

Microbial testing plays a crucial role in medical and biological research, serving as a significant direction in disease prevention and control studies. Traditional manual testing, which involves identifying colonies on petri dishes, is the best method, but it is not only expensive but also time-consuming. However, the application and development of deep learning can address these issues. However, a crucial condition is that deep learning models require a large amount of labeled medical imaging data. Obtaining high-resolution images is time-consuming and cannot generate a large amount of data in a short time. Therefore, we designed a Double DCGAN (D-DCGAN) to construct a dataset. Under the DCGAN adversarial network, synthetic colonies are generated by overlapping small single colonies and composite colonies to establish a random synthetic colony model, and a DCGANB generation model is established for the synthesis of complete colonies. In this experiment, we evaluated the performance of the D-DCGAN model and verified the effectiveness of the generated dataset using Faster R-CNN and YOLOv12. A second-order evaluation model based on extensive evaluation metrics was constructed, and the optimal matching data volume was calculated to be 1320 out of the original 236 data points. We demonstrated that the proposed framework effectively enhances the image dataset and improves detection performance, achieving an FID score of 37.1 and an accuracy rate of 0.975, effectively proving the feasibility of dataset enhancement.