细胞内镜需实现最大倍率约500倍的连续放大成像，受光纤照明及杂散光的影响，其图像存在不均匀光照，且光照分布会随放大倍率的变化而变化。这会影响医生对病灶的观察及判断。为此，本文提出一种基于细胞内镜光照模型的图像不均匀光照校正算法。根据图像信息由光照分量和反射分量组成这一基础，该算法通过卷积神经网络学习图像的光照分量，并基于二维Gamma函数实现不均匀光照校正。实验表明，经本文方法进行不均匀光照校正后，图像的光照分量平均梯度和离散熵分别为0.22和7.89，优于自适应直方图均衡化、同态滤波和单尺度Retinex等传统方法以及基于深度学习的WSI-FCN算法。

MAX/MAB相是一类非范德华三元层状材料, 具有丰富的元素组成和晶体结构, 兼具陶瓷和金属的物理性质, 在高温、强腐蚀、辐照等极端环境中极具应用潜力。近年来, 由MAX/MAB相衍生的二维(2D)材料(MXene和MBene)在材料物理与材料化学领域引起了广泛兴趣, 已经成为继石墨烯和过渡金属硫族化合物之后最受关注的二维范德华材料。MAX/MAB相材料结构调控不仅对这类非范德华层状材料本征性能产生重要影响, 而且对其衍生的二维范德华材料结构功能特性研究也具有重要价值。本文归纳和总结了MAX/MAB相层状材料在结构调控、理论计算和应用基础研究等方向的最新科研进展, 并展望了该类层状材料未来发展方向。

山区复杂的地形特征会导致地表反射率估算误差增加，降低地气解耦精度，进而影响气溶胶反演精度。当前应用较广泛的反演方法有基于波段关系估算地表反射的暗目标算法 (DT)、基于区域地表反射率库的深蓝算法 (DB) 和基于双向反射分布函数 (BRDF)、双向偏振分布函数 (BPDF) 模型估算地表反射率的GRASP (Generalized retrieval of atmosphere and surface properties) 算法。为探究适合山区气溶胶遥感的地气解耦方法，利用地面气溶胶自动观测网 (AERONET) 气溶胶产品 (AOD_A) 对比分析了2005年至2013年间POLDER-3 (Polarization and directionality of the earth's reflectances) 的GRASP气溶胶产品 (AOD_G)、中等分辨率成像光谱仪 (MODIS) 的DT气溶胶产品 (AOD_DT) 和DB气溶胶产品 (AOD_DB) 在中国区域的精度。结果显示，非山区站点处AOD_G与AOD_A整体相关性最高 (R = 0.921)，AOD_DT和AOD_DB总体精度差异不大，但山区AOD_G高于期望误差的比例达79.87%，AOD_DT和AOD_DB高估程度分别增加了近30%和20%。在河北兴隆和兰州大学半干旱气候与环境观测站 (SACOL) 两个山区站点分季节验证显示，植被覆盖度低的秋冬季节三种卫星产品精度均存在下降趋势，表明除去地表植被对反射率的影响后，山区地形影响了地气解耦精度。进一步分析显示，山区起伏地形对基于BRDF、BPDF模型的地气解耦方法影响较大；在山区等起伏地表上空，多角度观测的地表波段关系更有利于精确估算地表反射，而在城区BRDF、BPDF模型与波段关系估算地表反射的误差水平接近。研究结果为进一步优化多角度观测 (如高分五号DPC) 的山区气溶胶反演算法提供了新的方向。

针对目前变压器油中溶解气体分析装置体积庞大、抗电磁干扰能力弱、油路复杂等问题，将光纤传感和光声光谱技术结合，设计了一种基于光纤光声传感的变压器油中溶解气体分析系统，用于检测油中溶解的微量乙炔气体。通过谱线分析优化激光波长，并设计了恒温激光驱动模块。采用两根单模光纤分别将泵浦激光和探测光传输到光声探头中，气体吸收光能产生的光声信号由法布里-珀罗干涉型悬臂梁探测，并利用设计的光谱测量和信号处理模块进行解调。实验结果表明，设计的光纤光声传感系统可有效探测出变压器油中溶解的乙炔气体的光声信号，检出限达到0.5 μL/L。

针对H₂S气体在近红外波段的吸收系数低导致检测灵敏度难以提高的难题，提出了基于共振式多通光声池的SF₆分解产物H₂S气体检测技术。对赫里奥特型多通光声池进行优化设计，激光光束反射次数达到20次。近红外激光经功率放大后入射到多通光声池，通过多次光学反射大幅度提升了光声信号的激发效率，结合声学共振放大技术、光纤放大技术和波长调制-二次谐波检测技术，搭建了一套光声光谱气体检测系统，实现了SF₆背景下微量H₂S气体的高灵敏度检测。实验结果表明，归一化噪声等效吸收系数为2.23×10^-9 cm^-1·W·Hz^-1/2，在平均时间为100 s时，该检测系统对H₂S气体的检测极限达到2.7×10^-8。

针对真空脱气法脱气效率高但气量少的特点，提出了基于微型共振光声池的高灵敏度油中溶解气体检测技术。使用真空脱气法对变压器油中溶解的乙炔气体进行脱气，结合气室体积为12.4 mL的小体积H型共振式光声池，实现对油中溶解乙炔气体浓度的高灵敏度测量。利用掺铒光纤放大器对激光功率进行放大，并分析在近红外波段乙炔、水蒸气和二氧化碳的气体吸收光谱，选择较合适的气体吸收谱线进行光声信号激发。通过实验对激光调制电流和调制频率等参数进行优化。配置不同浓度的油样对系统的性能进行测试，得到系统对乙炔油样的最低检测限为0.2 μL/L。

搭建了一套气室容积仅为0.79 mL的非共振式光声光谱检测装置，在0至100 ppm浓度范围内，激发的光声信号幅度与SO₂/SF₆混合气体浓度之间有良好的线性关系，响应度为5.59 μV/ppm。系统的归一化噪声等效吸收系数为7.2×10^-8 cm^-1W·Hz^-1/2。Allan-Werle方差分析结果表明，当平均时间为100 s时，系统对SO₂的检测极限达到0.1 ppm。本文提出的高灵敏度SO₂气体原位检测技术为判断SF₆气体绝缘设备的故障程度提供了有效的解决方案。

The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.The threat posed to crop production by pests and diseases is one of the key factors that could reduce global food security. Early detection is of critical importance to make accurate predictions, optimize control strategies and prevent crop losses. Recent technological advancements highlight the opportunity to revolutionize monitoring of pests and diseases. Biosensing methodologies offer potential solutions for real-time and automated monitoring, which allow advancements in early and accurate detection and thus support sustainable crop protection. Herein, advanced biosensing technologies for pests and diseases monitoring, including image-based technologies, electronic noses, and wearable sensing methods are presented. Besides, challenges and future perspectives for widespread adoption of these technologies are discussed. Moreover, we believe it is necessary to integrate technologies through interdisciplinary cooperation for further exploration, which may provide unlimited possibilities for innovations and applications of agriculture monitoring.

Recently, the metasurfaces for independently controlling the wavefront and amplitude of two orthogonal circularly polarized electromagnetic (EM) waves have been demonstrated to open a way toward spin-multiplexing compact metadevices. However, these metasurfaces are mostly restricted to a single operation frequency band. The main challenge to achieving multiple frequency manipulations stems from the complicated and time-consuming design caused by multifrequency cross talk. To solve this problem, we propose a deep-learning-assisted inverse design method for designing a dual-spin/frequency metasurface with flexible multiplexing of off-axis vortices. By analyzing the cross talk between different spin/frequency channels based on the deep-learning method, we established the internal mapping relationship between the physical parameters of a meta-atom and its phase responses in multichannels, realizing the rapid inverse design of the spin/frequency multiplexing EM device. As a proof of concept, we demonstrated in the microwave region a dual-frequency arbitrary spin-to-orbit angular momentum converter, a dual-frequency off-axis vector vortex multiplexer, and a large-capacity (16-channel) vortex beam generator. The proposed method may provide a compact and efficient platform for the multiplexing of vortices, which may further stimulate their applications in wireless communication and quantum information science.