This study analyzed the spatiotemporal variations in PM_2.5 and O₃, and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM_2.5 and O₃ data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM_2.5 concentration and the lowest O₃ concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM_2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O₃ concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM_2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O₃ concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM_2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O₃ concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O₃ concentrations in winter.

In this Letter, we report a nano-optical conveyor belt containing a couple of graphene nano-ribbon pairs (GNRPs) with graded lengths for the realization of trapping and transport of nanoparticles through manipulating the Fermi energy of graphene. In the mid-infrared waveband, graphene behaves like metals, and even more importantly, it supports tunable surface plasmon resonance (SPR) via manipulating its Fermi energy. The spectral responses of these graded graphene nano-ribbons with individual dimensions are dominated by their geometric parameters and surface conductivity, which is tunable and connected with Fermi energy. Optically trapped nano-particles can be transferred from one GNR to another under a single-wavelength polarized excitation beam by sequentially switching the Fermi energy via electric gating. The feasibility of our design to deliver target nano-particles or biomolecules over a long distance with nanometer accuracy is confirmed by the three-dimensional finite-difference time-domain technique. With the capacity to trap and transfer, our design offers a versatile scheme for conducting mechanical sample manipulation in many on-chip optofluidic applications.

Image quality is significantly impacted by rain, posing challenges in fields like surveillance, autonomous driving, and outdoor robotics. The field of image deraining, particularly for single image, has attracted considerable attention to improve image clarity in inclement weather. To overcome the inherent complexity of the single‐image rain removal task, we proposed a novel architecture of the attention mechanism and gated recurrent network (AMGR‐Net) that combines spatial and channel attention mechanisms with gated recurrent units. AMGR‐Net contains multiple modules, each of which uses convolution kernels and attention mechanisms to enhance feature extraction. AMGR‐Net demonstrates superior performance over state‐of‐the‐art methods in both synthetic and real‐world image datasets, as evidenced by higher peak signal to noise ratio and structural similarity index measurement scores. The integration of spatial attention significantly enhances feature expression, enabling more effective rain streak removal and detail preservation. Furthermore, this method also shows promising results in the application of stripe noise removal from meteorological satellite cloud images.

In hazy weather, the quality of aerial images suffers severe degradation, which affects the imaging capabilities of advanced remote sensing applications. Most existing learning-based dehazing algorithms utilize manually designed deep structures to enhance model performance. However, these deep networks often contain redundant branches, leading to a significant decrease in computational efficiency. To address this issue, we first conduct experiments to explore the distribution of haze and introduce an improved dehazing strategy in the YUV space. Subsequently, we propose a Runge-Kutta (RK) method-inspired aerial image dehazing network called RKNet, which consists of two parts: luminance (Y) domain haze removal and chrominance (U,V) enhancement. From the perspective of dynamical systems and based on the 3rd-order RK method, we design an RK3 block and incorporate it into RKNet to improve computational accuracy. Experimental evaluations on both synthetic and real-world benchmarks demonstrate that RKNet outperforms current haze removal algorithms and achieves superior performance. In addition, RKNet can improve the detection accuracy and capability of high-level vision algorithms and is also applicable to the processing of sandstorm images and underwater images.

The synthesis of rain fields is essential in multiple research fields and applications, including Single‐image Derain. However, there is a lack of research on simulated rain fields, and the existing rain field generation models struggle to capture complex spatial distributions and generate truly random rain fields. To address this, the authors propose a generative adversarial networks‐based rain field generation network, which consists of a generator, a discriminator, and a feature extraction block that can produce realistic and complex rain fields. The authors’ experiments demonstrate that this method achieves an average Frechet Inception Distance score of 0.035, and user studies indicate that the generated rain distribution looks naturally.