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To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module utilizing the DBSCAN clustering algorithm to effectively segment and extract critical features from the point cloud data. Secondly, we introduced a fusion attention mechanism that significantly improves the network’s capability to capture both global and local features, thereby enhancing object detection performance in complex environments. Finally, we incorporated a CSPNet downsampling module, which substantially boosts the network’s overall performance and processing speed while reducing computational costs through advanced feature map segmentation and fusion techniques. The proposed method was evaluated using the KITTI dataset. Under moderate difficulty, the BEV mAP for detecting cars, pedestrians, and cyclists achieved 87.74%, 55.07%, and 67.78%, reflecting improvements of 1.64%, 5.84%, and 5.53% over PointPillars. For 3D mAP, the detection accuracy for cars, pedestrians, and cyclists reached 77.90%, 49.22%, and 62.10%, with improvements of 2.91%, 5.69%, and 3.03% compared to PointPillars.

The aim of this study is to develop an antioxidant film based on pea protein isolate (PPI) without sacrificing the packaging properties. To achieve this, α-tocopherol was incorporated to impart antioxidant activity to the film. We investigated the effects on film properties resulting from the addition of α-tocopherol in a nanoemulsion form and pH-shifting treatment of PPI. The results revealed that direct addition of α-tocopherol into un-treated PPI film disrupted film structure and formed a discontinuous film with rough surface, and thereby significantly decreasing the tensile strength and elongation at break. However, pH-shifting treatment in combination with the α-tocopherol nanoemulsion, formed a smooth and compact film, which greatly improved the mechanical properties. It also significantly changed the color and opacity of PPI film, but had little effects on film solubility, moisture content, and water vapor permeability. After the addition of α-tocopherol, the DPPH scavenging ability of PPI film was greatly improved and the release of α-tocopherol was mainly within the first 6 h. Additionally, pH-shifting and nanoemulsion did not affect the film’s antioxidant activity nor the release rate. In conclusion, pH-shifting combined with nanoemulsion is an effective method to incorporate hydrophobic compounds such as α-tocopherol into protein-based edible films without negative impacts on film mechanical properties.

As a long-term, near real-time, and widely used satellite derived product, the summer performance of the Special Sensor Microwave Imager/Sounder (SSMIS)-based sea ice concentration (SIC) is commonly doubted when extensive melt ponds exist on the ice surface. In this study, three SSMIS-based SIC products were assessed using ship-based SIC and melt pond fraction (MPF) observations from 60 Arctic cruises conducted by the Ice Watch Program and the Chinese Icebreaker Xuelong I/II. The results indicate that the product using the NASA Team (SSMIS-NT) algorithm and the product released by the Ocean and Sea Ice Satellite Application Facility (SSMIS-OS) underestimated the SIC by 15% and 7–9%, respectively, which mainly occurred in the high concentration rages, such as 80–100%, while the product using the Bootstrap (SSMIS-BT) algorithm overestimated the SIC by 3–4%, usually misestimating 80% < SIC < 100% as 100%. The MPF affected the SIC biases. For the high MPF case (e.g., 50%), the estimated biases for the three products increased to 20% (SSMIS-NT), 7% (SSMIS-BT), and 20% (SSMIS-OS) due to the influence of MPF. The relationship between the SIC biases and the MPF observations established in this study was demonstrated to greatly improve the accuracy of the 2D SIC distributions, which are useful references for model assimilation, algorithm improvement, and error analysis.

Magnetic iron oxide nanoparticles are relatively advanced nanomaterials, and are widely used in biology, physics and medicine, especially as contrast agents for magnetic resonance imaging. Characterization of the properties of magnetic nanoparticles plays an important role in the application of magnetic particles. As a contrast agent, the relaxation rate directly affects image enhancement. We characterized a series of monodispersed magnetic nanoparticles using different methods and measured their relaxation rates using a 0.47 T low-field Nuclear Magnetic Resonance instrument. Generally speaking, the properties of magnetic nanoparticles are closely related to their particle sizes; however, neither longitudinal relaxation rate r 1 nor transverse relaxation rate r 2 changes monotonously with the particle size d . Therefore, size can affect the magnetism of magnetic nanoparticles, but it is not the only factor. Then, we defined the relaxation rates r i ′ (i = 1 or 2) using the induced magnetization of magnetic nanoparticles, and found that the correlation relationship between r 1 ′ relaxation rate and r 1 relaxation rate is slightly worse, with a correlation coefficient of R 2 = 0.8939, while the correlation relationship between r 2 ′ relaxation rate and r 2 relaxation rate is very obvious, with a correlation coefficient of R 2 = 0.9983. The main reason is that r 2 relaxation rate is related to the magnetic field inhomogeneity, produced by magnetic nanoparticles; however r 1 relaxation rate is mainly a result of the direct interaction of hydrogen nucleus in water molecules and the metal ions in magnetic nanoparticles to shorten the T 1 relaxation time, so it is not directly related to magnetic field inhomogeneity.

Weak voltage signals cannot be reliably measured using currently available logging tools when these tools are subject to high-temperature (up to 200 °C) environments for prolonged periods. In this paper, we present a digital lock-in amplifier (DLIA) capable of operating at temperatures of up to 200 °C. The DLIA contains a low-noise instrument amplifier and signal acquisition and the corresponding signal processing electronics. The high-temperature stability of the DLIA is achieved by designing system-in-package (SiP) and multi-chip module (MCM) components with low thermal resistances. An effective look-up-table (LUT) method was developed for the lock-in amplifier algorithm, to decrease the complexity of the calculations and generate less heat than the traditional way. The performance of the design was tested by determining the linearity, gain, Q value, and frequency characteristic of the DLIA between 25 and 200 °C. The maximal nonlinear error in the linearity of the DLIA working at 200 °C was about 1.736% when the equivalent input was a sine wave signal with an amplitude of between 94.8 and 1896.0 nV and a frequency of 800 kHz. The tests showed that the DLIA proposed could work effectively in high-temperature environments up to 200 °C.