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近年来，数字生成内容技术得到了极大的发展，数字内容的检测和取证技术面临新的挑战。首先从自然语言大模型、视觉生成技术、多模态生成技术3个方面介绍数字内容生成技术，从生成文本检测、生成图片检测、生成音视频检测3个方面介绍数字内容检测技术，从利用事实信息和伪造痕迹两方面介绍数字内容取证技术；接着介绍这些技术的应用场景；最后对该研究领域的未来工作进行展望，指出几个需要重点关注的方向。

风速-功率是风电机组发电性能的重要指标，对风电场的运行管理具有重要意义。风速-功率数据是通过安装在风电场的监视控制与数据采集（SCADA）系统采集得到的，原始数据存在大量噪声，给后续应用研究带来了很大的挑战。基于风速-功率数据的空间分布特征，将风速-功率数据分为3类，并改进了数据预处理方法bin算法，提出了基于分区域（dbin）算法的异常数据识别清洗方法及流程。实验结果表明，dbin算法识别异常数据的效率比传统算法更高，具有较强的通用性。

功能脑网络为理解大脑功能激活模式及大脑信息传递结构提供了一种有效的生物标记，如何更加有效地利用先验信息构建准确的脑网络模型尤为重要。提出了一种基于群体相似性约束的功能脑网络模型，通过引入张量低秩约束，利用张量的核范数，求解一个在组内具有低秩先验的脑网络组。这种方法利用组内的相似性约束，收缩脑网络的解空间，从而有效地构建更优的脑网络模型。将所构建的脑网络用于轻度认知障碍的判别任务，实验结果表明，所提出的基于群体相似性约束的脑网络建模方法，能够构建出更具判别性的脑网络，并得到了与以往研究一致的显著性连边，进一步验证了所提出方法的有效性。

脑血流自动调节（cerebral autoregulation，CA）是血压变化时大脑维持足够脑灌注的内在能 力。过去的30年，研究者已经提出了诸多分析CA的方法，但至今无公认的金标准，采用什么方法定量 CA仍是个人主观的选择。尽管如此，由于CA的概念代表了血压（刺激或者输入信号）与脑血流（反应 或者输出信号）之间的动态关系，故目前最通用的研究血压自发波动的分析方法是传递函数分析。虽 然理论上可行，但是文献显示，传递函数分析方法在实践中存在相当大的主观性，这限制了研究之 间的比较，也阻碍了其临床应用。因此，本白皮书旨在规范化研究动态CA的传递函数分析方法的参 数及设置，建立标准，以利于其临床应用，该推荐的研发始于（但不限于）脑血流自动调节研究网络 （CARNet-www.car-net.org）。

The motion behavior of dairy cows contains much of health information. The application of information and intelli‐ gent technology will help farms grasp the health status of dairy cows in time and improve breeding efficiency. In this paper, the development trend of intelligent morning technology of cow's motion behavior was mainly analyzed. Firstly, on the basis of ex‐ pounding the significance of monitoring the basic motion (lying, walking, standing), oestrus, breathing, rumination and limping of dairy cows, the necessity of behavior monitoring of dairy cows was introduced. Secondly, the current research status was summarized from contact monitoring methods and non-contact monitoring methods in chronological order. The principle and achievements of related research were introduced in detail and classified. It is found that the current contact monitoring methods mainly rely on acceleration sensors, pedometers and pressure sensors, while the non-contact monitoring methods mainly rely on video images, including traditional video image analysis and video image analysis based on deep learning. Then, the develop‐ ment status of cow behavior monitoring industry was analyzed, and the main businesses and mainstream products of representa‐ tive livestock farm automation equipment suppliers were listed. Industry giants, such as Afimilk and DeLaval, as well as their products such as intelligent collar (AfiCollar), pedometer (AfiActll Tag) and automatic milking equipment (VMS™ V300) were introduced. After that, the problems and challenges of current contact and non-contact monitoring methods of dairy cow motion behavior were put forward. The current intelligent monitoring methods of dairy cows' motion behavior are mainly wearable de‐ vices, but they have some disadvantages, such as bring stress to dairy cows and are difficult to install and maintain. Although the non-contact monitoring methods based on video image analysis technology does not bring stress to dairy cows and is low cost, the relevant research is still in its infancy, and there is still a certain distance from commercial use. Finally, the future de‐ velopment directions of relevant key technologies were prospected, including miniaturization and integration of wearable moni‐ toring equipment, improving the robustness of computer vision technology, multi-target monitoring with limited equipment and promoting technology industrialization. [ABSTRACT FROM AUTHOR]

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Copyright of Chinese Journal of Contemporary Pediatrics is the property of Xiangya Medical Periodical Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)