Infrared ocean ships detection still faces great challenges due to the low signal-to-noise ratio and low spatial resolution resulting in a severe lack of texture details for small infrared targets, as well as the distribution of the extremely multiscale ships. In this paper, we propose a CAA-YOLO to alleviate the problems. In this study, to highlight and preserve features of small targets, we apply a high-resolution feature layer (P2) to better use shallow details and the location information. In order to suppress the shallow noise of the P2 layer and further enhance the feature extraction capability, we introduce a TA module into the backbone. Moreover, we design a new feature fusion method to capture the long-range contextual information of small targets and propose a combined attention mechanism to enhance the ability of the feature fusion while suppressing the noise interference caused by the shallow feature layers. We conduct a detailed study of the algorithm based on a marine infrared dataset to verify the effectiveness of our algorithm, in which the AP and AR of small targets increase by 5.63% and 9.01%, respectively, and the mAP increases by 3.4% compared to that of YOLOv5.

PCR is indispensable in basic science and biotechnology for in-orbit life science research. However, manpower and resources are limited in space. To address the constraints of in-orbit PCR, we proposed an oscillatory-flow PCR technique based on biaxial centrifugation. Oscillatory-flow PCR remarkably reduces the power requirements of the PCR process and has a relatively high ramp rate. A microfluidic chip that could perform dispensing, volume correction, and oscillatory-flow PCR of four samples simultaneously using biaxial centrifugation was designed. An automatic biaxial centrifugation device was designed and assembled to validate the biaxial centrifugation oscillatory-flow PCR. Simulation analysis and experimental tests indicated that the device could perform fully automated PCR amplification of four samples in one hour, with a ramp rate of 4.4 ∘C/s and average power consumption of less than 30 W. The PCR results were consistent with those obtained using conventional PCR equipment. Air bubbles generated during amplification were removed by oscillation. The chip and device realized a low-power, miniaturized, and fast PCR method under microgravity conditions, indicating good space application prospects and potential for higher throughput and extension to qPCR.

Inputting text is a prevalent requirement among various virtual reality (VR) applications, including VR-based remote collaboration. In order to eliminate the need for complex rules and handheld devices for typing within virtual environments, researchers have proposed two mid-air input methods—the trace and tap methods. However, the specific impact of these input methods on performance in VR remains unknown. In this study, typing tasks were used to compare the performance, subjective report, and cognitive load of two mid-air input methods in VR. While the trace input method was more efficient and novel, it also entailed greater frustration and cognitive workload. Fortunately, the levels of frustration and cognitive load associated with the trace input method could be reduced to the same level as those of the tap input method via familiarity with VR. These findings could aid the design of virtual input methods, particularly for VR applications with varying text input demands.