Sentinel-2卫星的多光谱轻量级船舶目标检测算法

近年来深度卷积神经网络在可见光船舶检测方面取得了显著的进展，然而，大多数相关研究是通过改进大型的网络结构来提高检测性能，因此加大了对更高计算机性能的需求。此外，可见光图像难以在云、雾、海杂波、黑夜等复杂场景检测到船舶。针对以上问题，提出了一种融合红（red, R）、绿（green, G）、蓝（blue, B）和近红外（NIR）4个波段光谱信息的由粗到精细的轻量型船舶检测算法。与现有的方法中根据光谱特性利用水体检测算法提取水体区域不同之处是该算法是利用改进的水体检测算法来提取船舶候选区域。为获取更准确的候选区域，对船舶、厚云、薄云、平静海面、杂波海面5种场景中4个波段的像素值进行了统计分析，选取近红外大于阈值作为辅助判断，并以其中心点获取候选区域32×32大小的切片，并对切片进行非极大值抑制，由此获得了船舶粗检测结果。随后构建了轻量级LSGFNet网络对船舶候选区域切片进行精细识别。构建的网络融合了1×1卷积提取的波谱特征与3×3的提取几何特征，为防止光谱特征与几何特征的信息在融合时“信息不流通”，在LSGFNet网络中引入了ShuffleNet中的通道打乱机制，并减小了模型结构，与典型的轻量级网络相比具有更好的效果且模型较小。最后，利用Sentinel-2卫星多光谱10 m分辨率数据构建了512×512大小的1 120组数据进行粗检测，以及32×32大小的6 014组数据进行精细网络训练，其中候选区域粗提取的查全率为98.99%，精细识别网络精确度为96.04%，不同场景下的平均精确度为92.98%。实验表明该算法在抑制云层、海浪杂波等干扰的复杂背景下具有较高的检测效率，且训练时间短、计算机性能需求低。     

从废水到新能源:光催化燃料电池的优化与应用

随着经济的飞速发展,社会对能源的需求日益扩大,对工业废水的无害化处理也提出了更高的要求。光催化燃料电池 (photocatalytic fuel cell, PFC) 在燃料电池中引入半导体光催化材料作为电极,实现了有机污染物高效降解和同步对外产电的双重功能,在废水无害化与资源化利用方面具有潜在的应用价值。半导体光催化电极是PFC系统高效运行的核心组件,增强其可见光响应和光生载流子分离是提高PFC性能的关键策略。反应器结构设计和运行参数优化也有利于改善PFC性能。本文从PFC基本原理和应用入手,综述了PFC在环境污染物资源化处理中的研究进展,并详细阐述了提高PFC的污染控制性能和产电效率的优化手段,为进一步设计高效稳定的PFC系统并实现其在水污染控制和清洁能源生产中的应用提供理论指导。     

Near-Ultraviolet Circular Dichroism and Two-Dimensional Spectroscopy of Polypeptides

A fully quantitative theory of the relationship between protein conformation and optical spectroscopy would facilitate deeper insights into biophysical and simulation studies of protein dynamics and folding. In contrast to intense bands in the far-ultraviolet, near-UV bands are much weaker and have been challenging to compute theoretically. We report some advances in the accuracy of calculations in the near-UV, which were realised through the consideration of the vibrational structure of the electronic transitions of aromatic side chains.     

An improved LC–MS/MS method for determination of docetaxel and its application to population pharmacokinetic study in Chinese cancer patients

Because of its unpredictable side effects and efficacy, the anticancer drug docetaxel (DTX) requires improved characterisation of its pharmacokinetic profiles through population pharmacokinetic studies. A sensitive and rugged LC–MS/MS method for the detection of DTX in human plasma was developed and optimised using paclitaxel as an internal standard (IS). The plasma samples underwent rapid extraction using hybrid solid-phase extraction-protein precipitation. The analyte and IS were separated with an isocratic system on a Zorbax Eclipse Plus C₁₈ column using water containing 0.05% acetic acid along with 20 μM of sodium acetate and methanol (30/70, v/v) as the mobile phase. Quantification was performed using a triple quadrupole mass spectrometer through multiple reaction monitoring in positive mode, using the m/z 830.3 → 548.8 and m/z 876.3 → 307.7 transitions for DTX and paclitaxel, respectively. The range of the calibration curve was 1–500 ng/mL for DTX, and the linear correlation coefficient was >0.99. The accuracies ranged from −4.6 to 4.2%, and the precision was no higher than 7.0% for the analytes. No significant matrix effect was observed. Both DTX and the IS showed considerable recovery. This method was finally applied to the establishment of a population pharmacokinetic model to optimise the clinical use of DTX.