Preparation of CoFe@C composite modified electrode for neohesperidin dihydrochalcone sensing and its application in Chinese medicine.

CoFe@C was first prepared by calcining the precursor of CoFe-metal-organic framework-74 (CoFe-MOF-74), then an electrochemical sensor for the determination of neohesperidin dihydrochalcone (NHDC) was constructed, which was stemmed from the novel CoFe@C/Nafion composite film modified glassy carbon electrode (GCE). The CoFe@C/Nafion composite was verified by field-emission scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM). Electrochemical impedance spectroscopy (EIS) was used to evaluate its electrical properties as a modified material for an electrochemical sensor. Compared with CoFe-MOF-74 precursor modified electrode, CoFe@C/Nafion electrode exhibited a great synergic catalytic effect and extremely increased the oxidation peak signal of NHDC. The effects of various experimental conditions on the oxidation of NHDC were investigated and the calibration plot was tested. The results bespoken that CoFe@C/Nafion GCE has good reproducibility and anti-interference under the optimal experimental conditions. In addition, the differential pulse current response of NHDC was linear with its concentration within the range 0.08 ~ 20 µmol/L, and the linear regression coefficient was 0.9957. The detection limit was as low as 14.2 nmol/L (S/N = 3). In order to further verify the feasibility of the method, it was successfully used to determine the content of NHDC in Chinese medicine, with a satisfactory result, good in accordance with that of high performance liquid chromatography (HPLC).

Personnel Detection in Dark Aquatic Environments Based on Infrared Thermal Imaging Technology and an Improved YOLOv5s Model.

This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images collected from diverse aquatic environments. This dataset was further expanded through synthetic image generation using CycleGAN and a newly developed color gamut transformation technique, which significantly improves the data variance and model training effectiveness. Furthermore, we integrated the Convolutional Block Attention Module (CBAM) at the end of the last encoder's feedforward network. This integration maximizes the utilization of channel and spatial information to capture more intricate details in the feature maps. To decrease the computational demands of the network while maintaining model accuracy, Ghost convolution was employed, thereby boosting the inference speed as much as possible. Additionally, we applied hyperparameter evolution to refine the training parameters. The improved algorithm achieved an average detection accuracy of 85.49% on our proprietary dataset, significantly outperforming its predecessor, with a prediction speed of 23.51 FPS. The experimental outcomes demonstrate the proposed solution's high recognition capabilities and robustness, fulfilling the demands of intelligent lifesaving missions.