Junfeng He

Pre-wash sorting of medical textiles is essential for hospital infection control, yet accurate stain detection remains challenging because visually apparent stains often have blurred boundaries, whereas dried urine stains lack distinguishable optical features. This study proposes a medical textile stain detection method integrating chemically enhanced visualization with deep semantic segmentation. Dimethylaminocinnamaldehyde (DMACA) was used to convert latent urine stains into chemically developed stains with orange–red visual features. Based on the spatial color difference ΔE in the L*a*b* color space, 0.0183 mol/L was selected as the most suitable DMACA concentration among those tested. A dataset of 1974 images was constructed, including blood stains, chemically developed urine stains, medication stains, and uncontaminated textiles. A cascaded preprocessing strategy was applied to enhance stain boundaries and suppress textile texture noise, after which an Enhanced semantic segmentation model incorporating residual feature extraction, multiscale feature fusion, and transfer learning was used for pixel-level recognition. The IoU values for blood stains, chemically developed urine stains, and medication stains were 88.11%, 82.67%, and 89.62%, respectively. The average time required for image preprocessing and network inference was 15.39 ms per image. An input-level ablation comparison showed that DMACA-based color development increased the urine-stain IoU from 3.07% to 86.23%, demonstrating its substantial contribution to latent urine-stain detection. These results support the feasibility of integrating front-end chemical feature enhancement with back-end semantic segmentation for multiclass medical textile stain recognition under the current experimental conditions.

This paper presents a novel motion platform based on a π-shaped linear ultrasonic motor. Initially, a new preload device was designed in accordance with established criteria for high-power linear ultrasonic motors. Mounted on the base structure, this mechanism neither interferes with the stator’s high-frequency vibrations nor couples with the mover’s motion. Structural parameters were determined through theoretical modeling, while experimental validation confirmed the mechanism’s capability to deliver stable and appropriate preload throughout the motor’s complete operating cycle, thereby enhancing the platform’s operational stability and positioning accuracy. Subsequently, a novel mover was developed by replacing linear guides with a ceramic–ceramic mechanism. This mover features a compact structure and flexible design, facilitating both miniaturization and effective stroke amplification. The resulting platform achieves a 40% reduction in volume compared to conventional designs while extending the stroke to 150% of the original capacity. Finally, a prototype was fabricated and experimentally evaluated. Test results demonstrate output velocities exceeding 200 mm/s in both directions, with positioning accuracy reaching 1.1 μm.

Gallium nitride (GaN), as the core material of third-generation semiconductors, has important applications in high-temperature, high-frequency, and high-power devices, but its polishing process faces many challenges. In this work, a multifield synergistic material removal model is established to study the material removal behaviour by ultrasonic magnetorheological chemical compound polishing (UMCP) of gallium nitride wafers, and the polishing processing under different polishing solution compositions and processing conditions is used to examine the effects of the ultrasonic, chemical, and mechanical effects on the material removal rate. The results show that mechanical removal dominates during UMCP, the chemical enhancement is slightly greater than the ultrasonic action, and the synergistic interaction between the range of factors promotes better removal of the GaN materials. The percentage of mechanical removal by abrasives is about 25% to 44.63%, the mechanical removal by magnetorheological effect polishing pads is about 14.66% to 23.94%, the removal due to chemical action is about 15.52% to 23.41%, the removal due to ultrasonic action is about 11.73% to 14.66%, and the percentage of interactive removal is 6.47% to 14.36%. The abrasive composition significantly enhances the mechanical removal effect, and a higher abrasive concentration correlates to a stronger mechanical removal effect. The concentration of hydrogen peroxide has a superior effect on the chemical reaction, and too high or too low a concentration of hydrogen peroxide weakens the chemical action effect. The results of the study can provide a basis for further research on the material removal mechanism of the GaN UMCP process.

Magnetorheological–chemical-polishing tests are carried out on single-crystal silicon carbide (SiC) to study the influence of the process parameters on the polishing effect, predict the polishing results via a back propagation (BP) neural network, and construct a model of the processing parameters to predict the material removal rate (MRR) and surface quality. Magnetorheological–chemical polishing employs mechanical removal coupled with chemical action, and the synergistic effect of both actions can achieve an improved polishing effect. The results show that with increasing abrasive particle size, hydrogen peroxide concentration, workpiece rotational speed, and polishing disc rotational speed, the MRR first increases and then decreases. With an increasing abrasive concentration and carbonyl iron powder concentration, the MRR continues to increase. With an increasing machining gap, the MRR shows a continuous decrease, and the corresponding changes in surface roughness tend to decrease first and then increase. The prediction models of the MRR and surface quality are constructed via a BP neural network, and their average absolute percentage errors are less than 2%, which is important for the online monitoring of processing and process optimisation.

As the thickness of an ultra-thin flattened heat pipe (UTHP) decreases, the fabrication difficulty increases exponentially, and the thermal performance deteriorates rapidly. In this study, three types of composite wicks were developed for UTHPs with a 0.6 mm thickness: copper foam and mesh wick (CFMW), two layers of different mesh wick (TDMW), and three layers of the same mesh wick (TSMW). The startup and steady-state performances of the UTHPs with liquid filling ratios of 60% to 120% were investigated. The findings indicated that the CFMW UTHP with a filling ratio of 100% exhibited the best startup performance, with the highest equilibrium temperature of 58.37 °C. The maximum heat transport capacities of the CFMW, TDMW, and TSMW UTHP samples were 9, 8, and 8.5 W, respectively, at their corresponding optimum filling ratios of 110%, 90%, and 100%. The CFMW UTHP exhibited the lowest evaporation and condensation thermal resistances of 0.151 and 0.189 K/W, respectively, which were 24.67% and 41.85% lower than those of the TSMW UTHP. CFMW can be used to improve the thermal performance of UTHPs. This study provides important guidelines for the structural design, fabrication technology, and performance improvement of high-performance UTHPs used in portable electronic devices.