Photonic crystals and photonic structures

This paper reviews the state-of-the-art of photonic band gap materials, addressing separately the spectral ranges of microwaves and optics. In the microwave domain, applications seem to emerge in particular, thanks to the recent breakthrough of metallo-dielectric structures. In the optical domain, 3D structures have been recently demonstrated in the infrared. In the mean time, refractive index engineering of a variety of complex structures involving photonic band gap reflections as well as more classical Fresnel confinement of light is now underway for the study of quantum electrodynamics in the solid state.     

芘荧光探针应用于狭鳕鱼皮凝胶形成过程的可行性研究

在狭鳕鱼皮弹性水凝胶的动态形成过程中引入了芘荧光探针。结果表明,在6.67%的鱼皮明胶溶液中,芘单体的荧光强度和I1/I3值均随芘浓度的增大先升高后降低,当芘浓度为40μmol/L时,单体荧光强度达到最大值,而当芘浓度为20～60μmol/L时,I1/I3值均处于较高水平,数值相近;探针的IE/IM值随芘浓度的增大一直升高,当芘浓度为2～40μmol/L时,比值偏低。综合考虑上述三个因素,选用浓度为40μmol/L的芘作为荧光探针应用于狭鳕鱼皮水凝胶的动态形成过程。实验确定芘I1/I3值能够用来表征溶胶中分子间的交联情况,其凝胶时间点为7h;但是IE/IM值则无法表征分子间的交联。这为表征其它鱼皮凝胶的动态形成过程提供了重要基础。     

基于自适应混沌粒子群优化的防空目标分配

粒子群优化算法存在早熟收敛和搜索精度较低的问题.为此,提出一种基于自适应混沌粒子群的优化算法.采用自适应权重和遗传算法中的交叉、变异操作更新粒子群,增加种群粒子的多样性,运用早熟判断机制判断粒子的当前状态,当粒子处于早熟状态时,利用混沌搜索的方法引导群体快速跳出局部最优.仿真结果表明,该算法可以有效解决粒子群算法的早熟问题,提高搜索精度和收敛速度.     

Ion-selective electrodes with superhydrophobic polymer/carbon nanocomposites as solid contact

Possibility of using superhydrophobic polymer/carbon nanocomposites as a new type of solid-contact material for solid-state ion-selective electrodes has been presented for example potassium-sensitive electrodes. The solid contact layers were prepared with the highly porous graphene/carbon black – fluorinated acrylic copolymer. Potassium-selective electrodes were exhibited a good Nernstian response with a slope of 59.10 mV/dec in the range from 10^−6.5 to 10⁻¹ M KCl. The stability of the electrical potential of the new solid-contact electrodes was tested by performing current-reversal chronopotentiometry, and the electrodes capacitance is 1471 μF. Due to the large capacitance and super hydrophobic character of the solid contact the developed electrodes exhibit excellent a long-term potential stability.     

基于深度学习的无人机影像夜光藻赤潮提取方法

针对目前卫星遥感中夜光藻赤潮识别精度低、实时性差的问题,提出一种基于深度学习的无人机（UAV）影像夜光藻赤潮提取方法。首先,以UAV采集的高分辨率夜光藻赤潮RGB视频影像作为监测数据,在原有UNet++网络基础上,通过修改主干模型为VGG-16,并引入空间dropout策略,分别增强了特征提取能力并防止过拟合;然后,使用ImageNet数据集预先训练的VGG-16网络进行迁移学习,以提高网络收敛速度;最后,为评估所提方法的性能,在自建的赤潮数据集Redtide-DB上进行实验。所提方法的夜光藻赤潮提取总体精度（OA）为94.63%,F1评分为0.955 2,Kappa为0.949 6,优于K近邻（KNN）、支持向量机（SVM）和随机森林（RF）这3种机器学习方法及3种典型语义分割网络（PSPNet、 SegNet和U-Net）。在模型泛化能力测试中,所提方法对不同拍摄设备和拍摄环境的夜光藻赤潮影像表现出一定泛化能力,OA为97.41%,F1评分为0.965 9,Kappa为0.938 2。实验结果表明,所提方法可以实现夜光藻赤潮自动化、高精度的提取,可为夜光藻赤潮监测和研究工作提供参考。     

内蒙古乌梁素海N、P的变化趋势研究

为研究乌梁素海芦苇对水体营养盐的净化作用,采集湖区南部湖水、底泥和芦苇样本,分析了其总氮、总磷的浓度、氮磷比变化趋势。结果表明:湖水由湖心向岸边流动过程中,总氮呈现先增大再减小的变化规律,湖水和芦苇中总磷含量相对平稳,底泥总磷持续增大;总氮浓度的变化范围是1.44~19.31 mg/L,总磷浓度变化范围0.024~0.057 mg/L,氮磷比远远大于16,乌梁素海处于氮过剩的富营养化状态;芦苇等挺水植物的生物量随着营养盐含量的升高而增加,对营养盐具有一定的吸收作用,通过合理的收割芦苇,可以促进乌梁素海氮磷的去除及当地经济的发展。     

Application of a generalized additive model (GAM) for estimating chlorophyll-a concentration from MODIS data in the Bohai and Yellow Seas,China

In optically complex waters, it is important to evaluate the accuracy of the standard satellite chlorophyll-a (chl-a) concentration algorithms, and to develop accurate algorithms for monitoring the dynamics of chl-a concentration. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote-sensing reflectance and concurrent in situ measured chl-a (2010–2013) were used to evaluate the standard OC3M algorithm (ocean chlorophyll-a three-band algorithm for MODIS) and Graver–Siegel–Maritorena model version 1 (GSM01) algorithm for estimating chl-a concentration in the Bohai and Yellow Seas (BYS). The results showed that the chl-a algorithms of OC3M and GSM01 with global default parameters presented poor performance in the BYS (the mean absolute percentage difference (MAPD) and coefficient of determination (R²) of OC3M are 222.27% and 0.25, respectively; the MAPD and R² of GSM01 are 118.08% and 0.07, respectively). A novel statistical algorithm based on the generalized additive model (GAM) was developed, with the aim of improving the satellite-derived chl-a accuracy. The GAM algorithm was established using the in situ measured chl-a concentration as the output variable, and the MODIS above water remote-sensing reflectance (visible bands at 412, 443, 469, 488, 531, 547, 555, 645, 667, and 678 nm) and bathymetry (water depth) as input variables. The MAPD and R² calculated between the GAM and the in situ chl-a concentration are 39.96% and 0.67, respectively. The results suggest that the GAM algorithm can yield a superior performance in deriving chl-a concentrations relative to the standard OC3M and GSM01 algorithms in the BYS.     

Retrieval of remotely sensed sea surface salinity using MODIS data in the Chinese Bohai Sea

Salinity dominates seawater density and directly affects physical and biochemical processes. Having a reliable retrieval model is essential to providing frequent and accurate sea surface salinity (SSS) data for marine research. Remote-sensing techniques provide alternatives for SSS data retrieval with its advantages of wide area surveys and real-time monitoring. In the present study, inverse relationship between SSS and coloured dissolved organic matter (CDOM) concentration in the Chinese Bohai Sea was verified. Thus, four simple band ratios of the original remote-sensing reflectance (R_rs) used to retrieve the CDOM concentration were compared and tested during SSS retrieval. R_rs (531)/R_rs (551) performed best among the four given band ratios. The model employed here can be applied to derive SSS with a root mean square error (RMSE) of 0.26 practical salinity units (psu) (R² = 0.76). A calibration model was verified using a discrete dataset of the measured SSS and was tested further during mapping of SSS in the Chinese Bohai Sea during 2010–2014. The yielded spatial patterns of SSS were satisfactory and an inverse relationship between SSS and the Yellow River discharge was confirmed.     

Remote-sensing estimation of dissolved inorganic nitrogen concentration in the Bohai Sea using band combinations derived from MODIS data

Remote sensing has been widely used for water quality monitoring, but most monitoring studies have only focused on a few water quality variables, such as chlorophyll-a, turbidity, and total suspended solids, which have typically been considered optically active variables. Remote sensing presents a challenge in estimating dissolved inorganic nitrogen (DIN) concentration in water. DIN in inland waters and estuaries had been estimated from remotely sensed observations. However, remote-sensing estimation of DIN in seawater over a large area had not yet been performed. Moreover, the bands used to estimate DIN in water were limited to 4 or 7 anterior bands of Moderate Resolution Imaging Spectroradiometer (MODIS) data at high spatial resolution rather than high spectral resolution. In this study, we attempted to establish a model to estimate DIN concentration in the Bohai Sea using band combinations derived from all the visible/near-infrared (Vis-NIR) bands of MODIS data. The results showed that regional multiple stepwise regression analysis (MLSR) yields a highly significant positive relationship between DIN concentration and certain remotely sensed combination variables. The modelling yielded higher accuracy for DIN concentration estimation in the Bohai Sea compared with previous studies. DIN concentration values showed a clear spatial variability, being high in coastal waters and relatively low further out. These results strongly suggest that the modelling demonstrates advantages for estimating DIN concentration in the Bohai Sea and has major potential for universal application in DIN concentration estimation in other waters.     

Evaluation of standard and regional satellite chlorophyll-a algorithms for moderate-resolution imaging spectroradiometer (MODIS) in the Bohai and Yellow Seas,China: a comparison of chlorophyll-a magnitude and seasonality

Retrieval of satellite remotely sensed chlorophyll-a (chl-a) concentrations in coastal regions such as the Bohai and Yellow Seas (BYS) is challenging due to their complex oceanic and atmospheric optical properties. The standard OC3M (ocean chl-a three-band algorithm for MODIS (moderate-resolution imaging spectroradiometer)) algorithm has been widely used in the BYS, despite well-known uncertainties about its accuracy in terms of absolute magnitude. This was based on the belief that OC3M chl-a is capable of representing reliable relative spatial and temporal patterns of sea surface chl-a concentrations. In this study, the ability of the standard OC3M chl-a algorithm to reproduce accurate seasonality patterns was evaluated, based on comparisons with in situ chl-a measurements in the BYS. The results quantified the overestimation by the standard OC3M algorithm with a median absolute percentage difference of 98.48% and a median relative difference of 1.13 mg m^?3.More importantly, the seasonality from OC3M chl-a was significantly biased relative to the seasonal patterns of in situ chl-a. In addition, a regional GAM (generalized additive model)-based satellite chl-a algorithm was evaluated and compared with OC3M chl-a. The results showed the GAM chl-a improved accuracy in both magnitude and seasonality when compared with in situ chl-a, relative to that from OC3M chl-a.