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1.
机器译文自动评价是机器翻译中的一个重要任务。针对目前译文自动评价中完全忽略源语言句子信息,仅利用人工参考译文度量翻译质量的不足,该文提出了引入源语言句子信息的机器译文自动评价方法: 从机器译文与其源语言句子组成的二元组中提取描述翻译质量的质量向量,并将其与基于语境词向量的译文自动评价方法利用深度神经网络进行融合。在WMT-19译文自动评价任务数据集上的实验结果表明,该文所提出的方法能有效增强机器译文自动评价与人工评价的相关性。深入的实验分析进一步揭示了源语言句子信息在译文自动评价中发挥着重要作用。 相似文献
2.
本文提出一种基于K-means聚类与机器学习回归算法的预测模型以解决零售行业多个商品的销售预测问题,首先通过聚类分析识别出具有相似销售模式的商品从而实现数据集的划分,然后分别在每个子数据集上训练了支持向量回归、随机森林以及XGBoost模型,通过构建数据池的方式增加了用于训练模型的数据量以及预测变量的选择范围.在一家零售企业的真实销售数据集上对提出的模型进行了验证,实验结果表明基于K-means和支持向量回归的预测模型表现最优,且所提出的模型预测效果明显优于基准模型以及不使用聚类的机器学习模型. 相似文献
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4.
Corey Andrews Yiting Xu Michael Kirberger Jenny J. Yang 《International journal of molecular sciences》2021,22(1)
Calmodulin (CaM) is an important intracellular protein that binds Ca2+ and functions as a critical second messenger involved in numerous biological activities through extensive interactions with proteins and peptides. CaM’s ability to adapt to binding targets with different structures is related to the flexible central helix separating the N- and C-terminal lobes, which allows for conformational changes between extended and collapsed forms of the protein. CaM-binding targets are most often identified using prediction algorithms that utilize sequence and structural data to predict regions of peptides and proteins that can interact with CaM. In this review, we provide an overview of different CaM-binding proteins, the motifs through which they interact with CaM, and shared properties that make them good binding partners for CaM. Additionally, we discuss the historical and current methods for predicting CaM binding, and the similarities and differences between these methods and their relative success at prediction. As new CaM-binding proteins are identified and classified, we will gain a broader understanding of the biological processes regulated through changes in Ca2+ concentration through interactions with CaM. 相似文献
5.
Manuel Gentiluomo Alice Luddi Annapaola Cingolani Marco Fornili Laura Governini Ersilia Lucenteforte Laura Baglietto Paola Piomboni Daniele Campa 《International journal of molecular sciences》2021,22(8)
Over the past decade, telomeres have attracted increasing attention due to the role they play in human fertility. However, conflicting results have been reported on the possible association between sperm telomere length (STL) and leukocyte telomere length (LTL) and the quality of the sperm parameters. The aim of this study was to run a comprehensive study to investigate the role of STL and LTL in male spermatogenesis and infertility. Moreover, the association between the sperm parameters and 11 candidate single nucleotide polymorphisms (SNPs), identified in the literature for their association with telomere length (TL), was investigated. We observed no associations between sperm parameters and STL nor LTL. For the individual SNPs, we observed five statistically significant associations with sperm parameters: considering a p < 0.05. Namely, ACYP2˗rs11125529 and decreased sperm motility (p = 0.03); PXK˗rs6772228 with a lower sperm count (p = 0.02); NAF1˗rs7675998 with increased probability of having abnormal acrosomes (p = 0.03) and abnormal flagellum (p = 0.04); ZNF208˗rs8105767 and reduction of sperms with normal heads (p = 0.009). This study suggests a moderate involvement of telomere length in male fertility; however, in our analyses four SNPs were weakly associated with sperm variables, suggesting the SNPs to be pleiotropic and involved in other regulatory mechanisms independent of telomere homeostasis, but involved in the spermatogenic process. 相似文献
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7.
现有的双目同步定位与建图(SLAM)都使用标准立体相机,所处环境为静态的假设会影响其在动态环境中的精度。 提
出了一种多焦距动态立体视觉 SLAM 方法,它克服了标准立体相机无法兼顾远距离和宽视场感知场景的缺点,并去除了动态物
体对 SLAM 的影响。 具体来说,对传统的立体校正方法进行了改进,并使用校正参数修正了特征点的位置,而不是整张图像,还
提出了一种自适应特征提取和匹配方法以增加多焦距图像的特征匹配数量。 综合使用多视图几何、区域特征流和相对距离检
测动态对象,剔除动态对象上的特征点。 在公开数据集 KITTI 上,该方法相对 ORB-SLAM3 和 DynaSLAM 的定位精度都提高了
6. 97% ,在自建数据集中,该方法的定位精度比 ORB-SLAM3 提高了 26. 64% ,比 DynaSLAM 提高了 32. 09% 。 相似文献
8.
近年氢能已迅速成为能源领域“新宠”,正在迎来快速发展的战略机遇期,但氢安全问题仍然是制约其发展的关键,尤以高压氢气储运设施泄漏后引发喷射火灾害较为突出。为了探究高压氢气泄漏过程并对其引发喷射火灾特性参数变化进行评估,本文采用理论分析和实例验证相结合的方法对两起高压氢气泄漏实验案例(90 MPa氢气瓶和6 MPa氢气管道)进行了研究。结果表明:通过模型精度检验,Abel-Nobel气体状态方程适用于当前常用的多种高压氢气储运设施泄漏过程的描述。基于Abel-Nobel气体状态方程、火焰尺寸模型、辐射分数模型和热辐射模型构建的高压氢气泄漏喷射火过程预测模型对实验案例中的泄漏出口气体质量流量、氢喷射火焰长度和辐射热场等的模拟计算结果与实验测量数据基本一致,验证了模型有效性及所含假设合理性。另外在计算中还需要结合实际情况充分考虑高压氢气储运设施发生泄漏时产生的能量损失以及等温流动过程,从而对模型预测精度进行修正。上述结论对于工程实际、氢能安全利用以及灾害预防等具有重要现实意义。 相似文献
9.
针对煤矿进口锚杆机除尘马达频繁出现故障的情况,通过排查除尘装置的安装方式和监测马达工作时的压力状态,查找故障原因,并制定解决方案。通过马达外接单向阀,可满足马达进油口最低安全压力限制的要求,解决了马达频繁损坏的问题,保障了设备工作的稳定性,提高了工作效率。 相似文献
10.
Zihao Chen Long Hu Bao-Ting Zhang Aiping Lu Yaofeng Wang Yuanyuan Yu Ge Zhang 《International journal of molecular sciences》2021,22(7)
Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization. 相似文献