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991.
建立了同时测定5种不同基质类型化妆品中8种替丁类药物的超高效液相色谱-三重四极杆质谱法（UHPLC-MS/MS）。不同基质类型化妆品样品经水-乙腈（2∶8,体积比）涡旋混匀、超声提取后过滤上机分析。选用YMC-TriartC18色谱柱（100mm×2.1mm,3.0μm）,以0.2%甲酸溶液-乙腈为流动相进行梯度洗脱分离,质谱分析采用电喷雾正离子模式和多反应监测模式。实验表明,8种替丁类药物在各自质量浓度范围内线性关系良好,相关系数（r 2）为0.9975~0.9986,检出限为0.06~0.3μg/L,定量下限为0.2~1.0μg/L。在3个不同加标水平下,水剂类、膏霜类、乳液类、凝胶类和粉剂类样品的平均回收率分别为80.7%~106%、90.1%~102%、82.6%~101%、82.0%~110%和86.6%~101%;相对标准偏差为0.70%~7.9%。该法前处理过程简单高效,易操作,灵敏度高,适用于上述5种类型化妆品中8种替丁类药物的同时快速定性定量分析。 相似文献
992.
Phase Generated Carrier （PGC） demodulation algorithm has been widely used in laser coherent vibration measurement and gained widespread interest due to its high accuracy, large dynamic range, good linearity, and low hardware overhead. However, the PGC demodulation technology is always accompanied by nonlinear distortions induced by phase modulation depth deviation, light intensity disturbance, carrier phase delay, etc. Therefore, an improved PGC demodulation algorithm is urgently required, which can effectively suppress the nonlinear distortions.In this study, we propose an improved PGC demodulation algorithm based on low frequency modulation and iteratively reweighted ellipse specific fitting, which suppresses the nonlinear distortions in the laser vibration measurement. The ellipse specific fitting is realized by introducing a 6×6 ellipse constraint matrix in the direct least square fitting of ellipse, which avoids getting a hyperbola solution, consequently. The iteratively reweighted ellipse specific fitting uses iteratively reweighted optimization technology to improve the precision of the ellipse specific fitting and reduce the weight of the outlier data, it has the advantages of ellipse-specificity, high robustness and high precison. In the imrpoved demodulation algorithm, the iteratively reweighted ellipse specific fitting is used to correct the original quadrature signals into a pair of perfect quadrature signals, which eliminates the nonlinear errors. Furthermore, to overcome the drawback of the ellipse fitting algorithm that it fails to work correctly under small phase signals, a low frequency modulation with a large amplitude is added in the carrier modulation and it guarantees the ellipse fitting accuracy regarless of the desired signal amplitudes. Finally, differential cross multiplying is used to extract the desired phase shift signal from the corrected quadrature signals.The simulations of ellipse specific fiting and iteratively reweighted ellipse specific fitting are performed and the results show that the iteratively reweighted ellipse specific fitting is superior. Then the proposed algorithm is verified in a Michelson inteferometer and the experimental results show that the Lissajous figure of the quadrature signals without a stimulus is observed to be a 1/4 ellipse arc, a 1/2 ellipse arc, a 3/4 ellipse arc and a full ellipse when the amplitude of the low frequency modulation is set as 0.035 V, 0.085 V, 0.013 5 V, and 0.185 V, respectively. Then, a 1 kHz stimulus with the amplitude of 100 mV is set, it is found that the fitted Lissajous figure deviates from the standard circle when there is no low frequency modulation while it overlaps well with the circle when the low frequency modulation amplitude is larger than 0.085 V. Thus, the accuracy of the ellipse fitting results can be gurantted by introducing a appropriate low frequency modulation. The frequency spectra of the demodulated signals under the low frequency modulation of 0 V, 0.085 V, 0.0135 V, and 0.185 V are compared, nonlinear distortions are well supressed when the low amplitude is larger than 0.085 V. The demodulation algorithms based on ellipse sepcfific fitting and iteratively reweighted ellipse specific fitting are also compared in the experiment, the Signal-to-Noise-And Distortion ratio （SINAD） and Total Harmonic Distortion （THD） of the demodulated signal based on iteratively reweighted ellipse specific fitting are improved by 1.99 dB and 0.27%, respectively. The demodulated signals of the improved algorithm at the phase modulation depth range of 0.8~3.4 rad show a high stability, the mean SINAD and THD are 42.99 dB and 0.44% with the corresponding standard deviations of 0.55 dB and 0.03%, respectively. The stimulating response linearity of the system is better than 99.99% and the dynamic range reaches 103.90 dB @ 500 Hz & THD=1%. The operating frequency band of the system is 20~8 000 Hz and two vibration signals are successfully demodulated in the experiment.The improved PGC demodulation algorithm has a promising application prospect in the field of laser vibration measurement because of the advantages of high precision, good linearity, strong robustness and high computational efficiency. 相似文献
993.
HUANG Hong;YANG Yichuan;WANG Long;ZHENG Fujian;WU Jian 《光子学报》2024,53(1):78-90
In clinical practice, segmentation and quantitative evaluation of target objects in pathological images provide valuable information for histopathological analysis, which is of great significance to auxiliary diagnosis and subsequent treatment. However, due to the dense distribution of cells and great morphological similarities between the cancer cells and normal cells, there are some challenges such as difficulty in feature extraction and unclear segmentation boundaries in the segmentation task of pathological images. At the same time, the traditional image segmentation methods are time-consuming and labor-intensive. They can only extract low level manual features, and the expression ability of deep discrimination features is insufficient, resulting in limited performance of traditional methods. Meanwhile, previous deep learning algorithms still suffer from two significant problems. Firstly, most networks ignore pixels that are difficult to segment, such as the boundaries of targets, which is particularly important for accurate segmentation. In addition, the problem of inconsistent semantic levels between different features are not solved, leading to low training efficiency. To address the above-mentioned problems, an end-to-end histopathological image segmentation network called Boundary Perception Network （BPNet） is proposed for improving the segmentation accuracy of histopathological images. Based on encoder-decoder structure, the encoder performs the convolutional downsampling operation to extract the feature information of the image through the Convolutional Neural Network （CNN）. And the encoding process uses the feature encoder based on the EfficientNet-B4 network which is specifically used for pathological image segmentation. The decoder mainly consists of decooding blocks, Boundary Perception Module （BPM） and Adative Shuffle Channel Attention Moudule （ASCAM）. In detail, the decoding block performs deconvolution operation to complete the decoding process of the feature information. Then, the BPM in the decoder stage aims to strengthen the ability of mining for difficult segmentation regions, so that the network focuses on the higher uncertainty as well as more complex edge regions, achieving feature complementarity and precision prediction results. For implementation, the BPM extracts the edge from the decoder output of each layer, and superimposes the edge information onto the encoded feature to strengthen the boundary feature information extracted from pathological images, outputting the enhanced edge perception feature map. Subsequently, the ASCAM is an improved chanel attention moudule which is used to make up the semantic gap between different levels of features, extrated by encoder, decoder and BPM, so as to further strengthens the feature understanding ability of the BPNet. This module exploits adaptive kernel size one-dimensional convolusion to capture the interactive information of local channels, at the same time ensures the efficiency and effectiveness of the training process. The obtained channel attention coefficient is multiplied by the module input feature layer to obtain the fusion feature, helping effectively learn the channel interaction information between features to improve the feature representation ability. Furthermore, a joint loss function based on structure and boundary is designed to optimize the targeting and detail processing capabilities of this method, achieving the better segmentation result of pathological images. Experiments are carried out on the Gland segmentation （GlaS） and MoNuSeg dataset, respectively. Both of the two datasets are devided into 4∶1 for training and validation. At the same time, in order to make up for the overfitting caused by the lack of training data, two kinds of online data enhancement methods of horizontal flipping and vertical flipping were carried out on the training set data in the experiment. And the four evaluation index, the Dice coefficient score, Intersection Over Union （IoU）, Accuracy （ACC） and Precision （PRE）, are used to evaluate the performance of this method propsed in this paper. The Dice coefficient score of the proposed method is 92.21% and 81.18%, the IoU is 85.55% and 68.34%, the ACC is 92.14% and 92.50%, the PRE is 92.07% and 75.46% on the GlaS and MoNuSeg datasets, respectively. Compared with the previous classical methods, such as U-Net, UNet++, MultiResUNet, TransUNet, UCTransNet and so on, the BPNet proposed gets the best segmentation result, especially retains more details in the segmentation boundary. Moreover, ablation experiments are carried out on the same two datasets for indicating the impacts of BPM and ASCAM. The results shows that the proposed BPM significantly optimizes the segmentation effect of the network for the edge, as well as the ASCAM makes up the semantic gap between features at different levels and further strengthens the feature understanding ability of the network. In conclusion, the BPNet proposed in this paper exploits BPM to generate edge enhancement feature maps, and uses ASCAM to seize crucial features. Finally, a joint loss function is used to capture the information of features at different levels in the output layer to achieve optimal segmentation performance. The experimental results have demonstrated that the effectiveness of each part of proposed method in the segmentation task of pathological images. 相似文献
994.
针对目前炸药切槽爆破存在非切槽方向上岩石破坏的问题,研究了双基火药切槽爆破特性。基于火药燃气释放规律,计算了双基火药被激发后密闭炮孔内的压力变化情况。结合高速摄影和数字图像相关（digital image correlation, DIC）方法,开展了炮孔的火药装填密度分别为0.84和0.96 g/cm3的2组实验,探究了火药作用下花岗岩板的动态破坏过程。结果表明：火药点火后,2组实验中花岗岩板均在100 μs时沿切槽方向起裂,200 μs时裂纹贯穿石板;当装填密度为0.96 g/cm3时,试件在断裂后上下石板分离速度较大,在封堵橡胶的摩擦力和试件惯性的共同作用下,2500 μs时上下石板被横向拉裂,裂纹沿垂直方向。炮孔预制切槽为火药燃气的气楔作用提供了空间,很好地引导裂纹的扩展,孔壁周围没有形成压碎区。双基火药燃烧产生的准静态压力是裂纹起裂、扩展的主要动力。研究结果为双基火药在岩体定向爆破上的应用提供了参考。
相似文献995.
在高温高压的极端条件下,分子晶体氮会打破传统三键机制向单键聚合态转变。氮在高压下的独特解离机制使聚合氮的研究意义超越了含能材料范畴,在基础物理学领域亦有深刻的科学意义。继立方偏转聚合氮cg-N（空间群
996.
为研究高原环境对不同敏化方式乳化炸药的影响程度,选取3种典型的敏化材料制备乳化炸药样品,模拟并对比分析了高原环境（−20 ℃、约0.05 MPa）储存后乳化炸药的微观结构和爆轰性能变化。结果表明：低温低压下的高原环境主要从加剧破坏乳化体系稳定性与热点分布两方面影响炸药的性能,在该环境下化学敏化炸药相较于物理敏化炸药的析晶程度较低,但热点变化较大,导致爆轰性能下降;物理敏化中,膨胀珍珠岩炸药晶体的生长方式更加复杂,因此更易破乳析晶,储存稳定性与爆轰性能均有明显降低;相对而言,树脂微球炸药在低温低压下的析晶程度与爆轰性能均较稳定。综合上述结果,树脂微球乳化炸药具有更好的高原适应性。
相似文献997.
998.
采用密度泛函理论(DFT)方法,在LDA+U水平下详细研究了电场和应力作用下氮钝化扶手型氧化锌纳米带(NA8-ZnONRs)的电子结构和磁特性。对体系的电子结构和磁性进行详细的计算,结果表明：本征扶手型氧化锌纳米带(A8-ZnONRs)是无磁性P型半导体。氮钝化后NA8-ZnONRs具有铁磁金属性,其磁性主要来源于N2p轨道(2.56μB)和O2p轨道(0.69μB)电子的自旋极化,总磁矩为3.21μB。NA8-ZnONRs体系对X方向电场有较强的响应,通过调节X方向电场的幅度,可以有效调节体系的磁矩。在X方向电场作用下体系仍具有铁磁金属性,磁性也主要来源于N2p和O2p轨道电子的自旋极化。施加X方向应力作用后,体系仍表现为铁磁金属性。与NA8-ZnONRs纳米带磁矩相比,体系的总磁矩均发生了较大幅度的增长,表明体系对应力作用具有较明显的相应。但随着应力幅度的调节,总磁矩的变化较平坦。表明施加应力可以有效调节体系的磁矩,但在较小应力范围内,体系对应力变化的相应不明显。 相似文献
999.
以泡沫镍(NF)为基底,采用一步水热法制备了主晶相为CoS2、夹杂少量NiO相、具有三维多孔风叶结构的CoS2/NF电极材料。当溶液中钴硫物质的量之比为1∶5时,在140℃下保温18 h,获得了由10 nm厚度的纳米片构成的三维风叶结构的晶态CoS2/NF电催化剂。CoS2/NF在肼氧化及析氢反应中均表现出优异的催化性能,在水合肼碱性介质中,获得-10 mA·cm-2的析氢电流密度时,需要的过电势仅为83 mV,获得50 mA·cm-2的氧化电流密度时,需要的肼氧化电位仅为51 mV(vs RHE);在水合肼辅助电解水双功能电解槽中,获得100 mA·cm-2的电流密度时,需要的分解槽压仅为0.550 V,远低于其在同条件下全水分解的2.075 V,大幅减小了电能消耗,极大地提高了电解水产氢效率。无论在三电极体系还是双电极体系,CoS2/NF均表现出优异的长效稳定性及耐用性。分析认为,电极表面多孔风叶结构的形成,... 相似文献
1000.
在少量水的存在下,由于NH4[CuCl3]对水不稳定,CuCl2·2H2O与氯化铵的配位化学反应只能得到1∶2产物(NH4)2[CuCl4(H2O)2];但在无水乙醇中,无水CuCl2与氯化铵按1∶1或1∶2的物质的量之比混合时,能相应地得到NH4[CuCl3]或(NH4)2[CuCl4]。CuCl2·2H2O与2,2''-联吡啶(bipy)的少溶剂固态化学反应仍能像固态化学反应一样,根据反应物的配比生成相应的唯一产物,但在物质的量之比为1∶1的反应中发现1∶2产物曾经形成。热力学分析说明,如果连串反应的后一步的ΔrG⊖大于前一步的,则上述反应现象是可能的。 相似文献