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面部表情识别是一种直接、有效的情绪识别模式。机器学习可以对图像表情进行形式化表征，但由于缺乏大脑的认知表征能力，在小样本数据集或复杂表情（伪装）数据集上的识别性能并不理想。针对此问题，将机器人工智能的形式化表征与人脑通用智能的情感认知能力结合，提出一种基于脑机协同智能的情绪识别方法。首先，从脑电图信号中提取脑电情感特征，以获取大脑对情绪的认知表征。其次，从情感图像中提取图像的视觉特征，以获取机器对情绪的形式化表征。为了增强机器模型的泛化能力，在特征学习中引入样本间的迁移适配。在得到图像视觉特征和脑电情感特征后，采用随机森林回归模型训练得到图像视觉特征与脑电情感特征之间的脑机映射关系。测试图像的图像视觉特征经过脑机映射关系产生虚拟脑电情感特征，然后将虚拟脑电情感特征与图像视觉特征进行融合，用于情绪识别。该方法已经在中国面孔表情图片系统上进行了验证，发现对7种情绪的平均识别准确率为88.51%，相比单纯基于图像的方法，提升了3%～5%。

In order to study the influence of different repair methods and different damage sizes on the tensile properties of composite laminates,experimental investigation on static tensile strength was designed and carried out in this paper with five groups of test specimens,including no-damage specimens,adhesive repair specimens and adhesive-rivet hybrid repair specimens with 60 mm and 30 mm circular hole damage for composite aircraft skin.The three-dimensional finite element simulation models of the test specimens were established,to analyze the difference between the five groups of specimens in terms of failure strength,failure mode,stress values and distribution,and critical sites.The results show that the adhesive-rivet hybrid repair method is a better choice which changes the stress distribution of composite repaired parts,reduces the stress concentration effectively in the critical sites,and improves the strength recovery rate of the structure.

针对电力公司在开展变压器设备运检过程中存在的非结构化文本数据难以利用、全口径数据难以深度融合、设备知识应用深度较浅等难题，基于语义网、知识图谱、自然语言处理等人工智能技术，对开展设备智能管理的关键技术进行了研究，提出了支撑变压器智能管理的智能技术框架，包括非结构化文本智能识别与提取、以设备为中心的设备知识表示与存储、设备知识服务应用三部分；形成了设备语义提取模型、设备语义相似度计算模型、基于深度神经网络的智能问答模型等三个智能模型；总结了该技术在变压器设备状态评价报告自动化审核、设备信息灵活查询、基于设备故障知识的辅助诊断三个场景的应用成果；提出了基于知识的设备智能技术下一步研究的方向。

目的本研究旨在探讨癌相关成纤维细胞（CAFs）对胃癌细胞迁移与侵袭能力的影响及作用机制。方法采用酶消化法分离培养正常成纤维细胞（NFs）及癌相关成纤维细胞（CAFs）并进行特征指标鉴定。收集两种细胞的条件培养基（NFs-CM、 CAFs-CM），分别作用于胃癌细胞系AGS及MGC803，利用细胞划痕实验检测胃癌细胞迁移能力；Transwell小室迁移及侵袭实验评价胃癌细胞迁移与侵袭能力；光学显微镜观察胃癌细胞形态变化；荧光定量PCR检测胃癌细胞中“上皮-间质”转化（EMT）相关指标的表达变化。结果我们成功分离培养NFs及CAFs；与NFs-CM 组相比，CAFs-CM组胃癌细胞的迁移与侵袭能力均增强（P < 0.05），胃癌细胞形态由扁圆的上皮细胞形态演变为梭形的间质细胞形态，胃癌细胞中上皮细胞标志E-钙粘蛋白（E-cadherin）表达降低，间质细胞标志波形蛋白（Vimentin）和 N-钙粘蛋白（N-cadherin）表达上升（P < 0.05）。结论CAFs可通过调节EMT进程促进胃癌细胞迁移与侵袭。

超表面材料在与光场相互作用时表现出独特的光学特性和许多有趣的物理现象,基于此材料制作的光电子功能器件对光场的调控能力有望被提升至新高。文章以光纤通信系统中的光功率分配应用为实例,提出了一种基于硅基超表面材料的新型光功率分配方案。首先,通过优化设计硅基超表面的单元结构,使其在1 4581 593nm波长范围内的相位偏振转化效率超过90%,而无偏振转化的效率则控制在1.2%以下;其次,研究了相邻硅纳米砖之间的光场耦合效应,将其对相位精度的影响降至最低;最后,利用优化设计的硅纳米砖结构,实现了4种不同的光功率分配方式,并通过仿真加以验证。设计和仿真结果表明,基于硅基超表面材料的光功率分配技术具有结构紧凑、效率高、偏振不敏感、宽带响应、设计灵活、制造上与半导体制造工艺兼容等突出优点,有望成为未来超密、任意、智能功率分配的首选技术途径。

电力调度数据网的服务质量（QoS）对电力系统的生产管理和调度运行有重要影响。QoS模型可评价电力调度数据网的运行状态,衡量电力调度数据网QoS对电网运行需求的满足程度。在分析了多个QoS相关概念的基础上,考虑了多QoS指标的相互影响,并依据电力调度数据网用户以及所承载的电力通信业务的特点,给出了基于电力调度数据网的以用户为中心面向业务的综合QoS模型。电力调度数据网综合QoS模型集成了用户观点,并以多个视点对电力调度数据网的QoS进行描述,可为电力调度数据网提供新的QoS维度。

为能依据振动检测要求合理选择FBG（光纤布拉格光栅）振动解调方案,提高FBG振动解调系统性能,文章深入剖析了FBG光强式振动解调系统中影响解调性能的各种因素,通过MATLAB软件对匹配光栅法、边缘滤波法及激光匹配法的系统灵敏度和解调范围进行了对比,分析了改变FBG 3dB带宽时三种方法灵敏度和解调范围的变化趋势,分析结果对合理搭建FBG振动解调系统具有参考价值。

从光脉冲在光纤中传输演变所满足的基本方程出发,深入研究了超高斯光脉冲在普通单模光纤中的传输特性,理论分析了影响最窄脉冲宽度位置的不同因素,仿真得到了最窄脉冲宽度位置与初始脉冲宽度、二阶群速度色散系数所符合的近似分布函数。仿真结果表明,无初始啁啾超高斯脉冲入射单模光纤后脉冲波形将不再保持单峰结构,而是经历一个从近平顶到多峰再到单峰的演变过程;脉冲宽度在色散的作用下将经历一个先窄化后变宽的过程。最窄脉冲宽度位置的研究对于实际光通信和光传感中色散补偿位置的确定具有一定的指导意义。

文章首先介绍了用 BOTDR测试光纤应变分布的原理 ,然后介绍了用 BOTDR测试、分析层绞式光缆的生产各工序过程所引入的光纤应变大小的一种方法 ,事实证明 ,这一方法对于分析光缆各生产工序的控制状态十分有效 .

文章介绍了用已成缆光纤偏振模色散（PMD）系数估算链路PMD系数分布的3种方法:蒙特卡罗数值法、伽马分布分析法和模式独立分析法,从而计算出光缆链路PMD系数设计值（PMDQ）。通过3组样品说明了3种方法及其近似解的计算过程和适用条件,分析了PMDQ值与已成缆光纤PMD系数的关系。结果表明,决定PMDQ值的关键因素是已成缆光纤PMD系数的统计分布而不是其平均值。

Nitrogen is a key nutrient for crop growth. Excessive or insufficient nitrogen affects crop growth, yield, and quality. Additionally, excessive nitrogen fertilizer can cause soil and water pollution. Applying panicle fertilizer during the late jointing stage can promote rice panicle growth. Therefore, accurately and timely monitoring of nitrogen status in rice fields during the late jointing stage and timely optimizing fertilization strategies is crucial for ensuring rice yield and environmental protection. This paper integrates multimodal data from unmanned aerial vehicle (UAV) remote sensing and ground observations to construct inversion models for leaf nitrogen content (LNC) and plant nitrogen content (PNC) of rice at the late jointing stage. The research was conducted at the Shapu Experimental Base of the Agricultural Science Research Institute in Zhaoqing City, Guangdong Province, with two field experiments carried out during the late rice seasons of 2021 and 2022. Each of Experiment 1 (2021) and Experiment 2 (2022) included 30 experimental plots, designed with 5 nitrogen fertilizer gradients, 2 planting densities, and 3 replications. Phosphorus and potassium fertilizers were applied uniformly across all plots. UAVs equipped with multispectral and RGB cameras were used to acquire remote sensing images of rice canopies during the late jointing stage. Vegetation indices (VIs) and texture feature values (TFVs) were extracted from the multispectral images, with TFVs derived using the gray level co-occurrence matrix (GLCM) method. Texture indices (TIs) were then constructed by combining TFVs. RGB images were used to generate digital surface models (DSM) for bare ground (pre-transplant) and rice fields (late jointing stage). These DSMs, combined with ground reference methods, were used to construct crop surface models (CSM) to derive estimated canopy heights (ECH) for each plot. Manually collected data included measured canopy height (MCH) and field nitrogen management data (FN) used as ground observations. For each experimental plot, three representative rice plants were selected as samples. After removing the roots, the leaves and stems were separated and dried at 85℃ to a constant weight, which was recorded as the aboveground biomass of the leaves and stems. The true values of leaf nitrogen content and stem nitrogen content were obtained using the Kjeldahl method. Combining these values with the dry weight data, the true values of plant nitrogen content were calculated. The maximal information coefficient (MIC) was used as an evaluation metric for feature assessment and selection. Random forest regression algorithms were employed to construct inversion models for rice LNC and PNC, respectively, using the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) as model evaluation metrics. The analysis and experimental results indicate: TIs constructed using combinations of TFVs significantly enhanced the correlation between texture information and LNC and PNC. When the UAV flight height was 100 m, the Ratio Texture Index constructed using a 9×9 sliding window size in the GLCM method showed the best performance, improving the MIC value by 11.48% compared to the best TFV. For conventional machine-transplanted rice planting density, the correlation between TIs and LNC and PNC was best when the GLCM sliding window size was set to 9×9 or 11×11 at a UAV flight height of 100 m. The ECH derived from the CSM showed a high correlation with the manual MCH in the field. Including canopy height (MCH or ECH) as an input feature in the random forest regression model significantly improved the inversion accuracy of rice nitrogen content. The ECH extracted from the CSM showed high estimation accuracy (R² = 0.77, RMSE = 3.4 cm, MAE = 2.8 cm). The inclusion of canopy height (MCH or ECH) in the model construction improved the inversion accuracy for PNC more significantly compared to LNC. Integrating UAV remote sensing and ground observation multimodal data, the random forest regression algorithm significantly improved the inversion accuracy of rice LNC and PNC at the late jointing stage. Considering both inversion accuracy and operational convenience, it is recommended to use a feature combination of VI+TI+ECH+FN in field production. Compared to using VI alone, this feature combination improved R² by 6.73%, and reduced RMSE and MAE by 13.62% and 14.68%, respectively, for LNC inversion. For PNC inversion, R2 was improved by 12.53%, and RMSE and MAE were reduced by 22.09% and 19.80%, respectively. The results demonstrate that constructing random forest regression models by integrating UAV remote sensing and ground observation multimodal data can accurately detect rice LNC and PNC, providing a scientific basis for rice field management and fertilization decision-making. [ABSTRACT FROM AUTHOR]

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PIT (passive integrated transponder) marker has been widely used in fish population dynamics, proliferation and release, habitat selection, fish passage facilities and other research fields due to its advantages of large information storage, durable and reliable, rapid identification, stable performance, uniqueness and small size. It has become a major vehicle for fish conservation research. In this paper, the working principle of PIT marking technology is introduced, and the applied research of PIT marking technology in fish resource protection at home and abroad from 1991 to 2020 is summarized. This study discusses the effectiveness of PIT marking technology in research on fish population dynamics, fish passage facilities, proliferation and release, and habitat selection. By comparing the application and research of PIT marking technology at home and abroad, combined with the actual needs and research of domestic fish resources protection, analyzes the reference effect of PIT marking technology application from abroad, points out the development direction of PIT marking technology application research in China and proposes to strengthen the long-term monitoring of PIT marking technology in fish passage facilities, fish proliferation and release, fish population dynamics, and fish habitat selection, improve the technical level of PIT marking technology in fish resource protection research, formulate technical specifications for PIT marking applications, and establish PIT marking monitoring network system to meet the needs of research, conservation, and management of fish. [ABSTRACT FROM AUTHOR]

Objectives: Integration of machine learning and multi-source data becomes a hot topic in soil moisture inversion, where relatively few studies are performed on L-band synthetic aperture radar (SAR) imagery. Methods: ALOS-2 PALSAR-2 and Landsat 8 images of Ejina Oasis are used to extract the radar and optical characteristic parameters which are then screened according to the importance score. Random forest is adopted to establish different soil moisture inversion models based on radar, optical, and radar-optical integrated parameters. Model accuracies are evaluated and soil moisture content in Ejina Oasis is inversed. Results: Compared with C-band, L-band SAR data is more sensitive to soil moisture content in arid desert oasis. With regard to radar characteristic parameters, surface and volume scattering components have higher important scores, while dihedral and helix scattering component are less important. As for optical characteristic parameters, vegetation water supply index takes the most important place while the enhanced vegetation index is the least important one. The determination coefficient R² and root mean square error (RMSE) of radar characteristic parameter scheme are 0.67 and 2.16%, respectively. The accuracy of optical characteristic parameter scheme model is generally low and the accuracy is equivalent, with R² and RMSE about 0.5 and 2.47%, respectively. R² and RMSE of the optimal radar-optical integrated parameter inversion model are 0.72 and 1.99%, respectively. Compared with either single data source, R² is increased by 7.46% and 38.4%, while RMSE is decreased by 8.54% and 22.6%. Conclusions: The research proves that the random forest model based on multi-source data fusion has higher prediction accuracy and better applicability in arid desert oasis area. [ABSTRACT FROM AUTHOR]

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Objective To explore the role of DExD/H box helicase 60(DDX60) in the development of systemic lu- pus erythematosus (SLE) by regulating the dsDNA-CGAS-IFN-I pathway. Methods Differences in DDX60 mRNA level in peripheral blood mononuclear cells (PBMCs) from SLE patients and healthy people were examined by analyzing RNA sequencing data and verified by RT-qPCR. The correlation between DDX60 mRNA level in PBMCs and disease activity of SLE patients was analyzed. Whether DDX60 was an interferon-stimulated gene (ISG) was clarified by interferon-alpha (IFN-α) stimulation of human acute monocyte leukemia cell line THP-1. The mRNA level of interferon-betal (IFNB1) was detected by RT-qPCR after silencing and knockdown of DDX60 and followed by double-stranded DNA (dsDNA) poly (dA:dT) transfection. Results RNA sequencing data and RT-qPCR result found high expression of DDX60 in PBMCs of SLE patients. DDX60 mRNA level in PBMCs was positively correlated with disease activity of SLE patients. IFN-a induced THP-1 cells to express DDX60, suggesting that DDX60 was an ISG. Silencing DDX60 resulted in a decrease in poly (dA; dT)-induced IFNB1 mRNA level. Conclusions DDX60 is highly expressed in PBMCs of SLE patients and involved in the development of SLE through the positive feedback loop of IFN-I-DDX60-dsDNA-CGAS-IFN-I, which suggests that DDX60 may be a target for the clinical diagnosis and treatment of SLE. The result of this research may support diagnosis and prognosis evaluation of SLE patients. [ABSTRACT FROM AUTHOR]

[Objective]　Nowadays most no contact body size measurement studies are based on point cloud segmentation method, they use a trained point cloud segmentation neural network to segment point cloud of pigs, then locate measurement points based on them. But point cloud segmentation neural network always need a larger graphics processing unit (GPU) memory, moreover, the result of the measurement key point still has room of improvement. This study aims to design a key point generating neural network to extract measurement key points from pig's point cloud. Reducing the GPU memory usage and improve the result of measurement points at the same time, improve both the efficiency and accuracy of the body size measurement. [Methods]　A neural network model was proposed using improved Transformer attention mechanic called Pig Back Transformer for generating key points and back orientation points which were related to pig body dimensions. In the first part of the network, it was introduced an embedding structure for initial feature extraction and a Transformer encoder structure with edge attention which was a self-attention mechanic improved from Transformer's encoder. The embedding structure using two shared multilayer perceptron (MLP) and a distance embedding algorithm, it takes a set of points from the edge of pig back's point cloud as input and then extract information from the edge points set. In the encoder part, information about the offset distances between edge points and mass point which were their feature that extracted by the embedding structure mentioned before incorporated. Additionally, an extraction algorithm for back edge point was designed for extracting edge points to generate the input of the neural network model. In the second part of the network, it was proposed a Transformer encoder with improved self-attention called back attention. In the design of back attention, it also had an embedding structure before the encoder structure, this embedding structure extracted features from offset values, these offset values were calculated by the points which are none-edge and down sampled by farthest point sampling (FPS) to both the relative centroid point and model generated global key point from the first part that introduced before. Then these offset values were processed with max pooling with attention generated by the extracted features of the points' axis to extract more information that the original Transformer encoder couldn't extract with the same number of parameters. The output part of the model was designed to generate a set of offsets of the key points and points for back direction fitting, than add the set offset to the global key point to get points for pig body measurements. At last, it was introduced the methods for calculating body dimensions which were length, height, shoulder width, abdomen width, hip width, chest circumference and abdomen circumference using key points and back direction fitting points. [Results and Discussions]　In the task of generating key points and points for back direction fitting, the improved Pig Back Transformer performed the best in the accuracy wise in the models tested with the same size of parameters, and the back orientation points generated by the model were evenly distributed which was a good preparation for a better body length calculation. A melting test for edge detection part with two attention mechanic and edge trim method both introduced above had being done, when the edge detection and the attention mechanic got cut off, the result had been highly impact, it made the model couldn't perform as well as before, when the edge trim method of preprocessing part had been cut off, there's a moderate impact on the trained model, but it made the loss of the model more inconsistence while training than before. When comparing the body measurement algorithm with human handy results, the relative error in length was 0.63%, which was an improvement compared to other models. On the other hand, the relative error of shoulder width, abdomen width and hip width had edged other models a little but there was no significant improvement so the performance of these measurement accuracy could be considered negligible, the relative error of chest circumference and abdomen circumference were a little bit behind by the other methods existed, it's because the calculate method of circumferences were not complicated enough to cover the edge case in the dataset which were those point cloud that have big holes in the bottom of abdomen and chest, it impacted the result a lot. [Conclusions]　The improved Pig Back Transformer demonstrates higher accuracy in generating key points and is more resource-efficient, enabling the calculation of more accurate pig body measurements. And provides a new perspective for non-contact livestock body size measurements. [ABSTRACT FROM AUTHOR]