禹门口提水工程基坑开挖控制爆破

该项工程的开挖爆破中,采用塑料导爆管非电起爆技术等措施,成功地解决了复杂环境条件下的爆破安全问题,为同类工程施工提供了可借鉴的经验。     

Newton法对应单参有理函数族的广义M-J集

讨论了Newton法对应单参数有理函数族的广义Mandelbrot集和Julia集,给出了它们的构造算法,证明了其广义Mandelbrot集的有界性,并给出了其周期点个数的计算公式.利用数学实验的方法研究了广义Mandelbrot集周期芽苞分布规律,并通过对比分析得到了它们与z~n+c的Mandelbrot集和Julia集之间的族相似性类似的新的族相似关系.文中算法为Mandelbrot集和Julia集的发展提供了新的思路.     

Molecular Dynamics of CYFIP2 Protein and Its R87C Variant Related to Early Infantile Epileptic Encephalopathy

The CYFIP2 protein (cytoplasmic FMR1-interacting protein 2) is part of the WAVE regulatory complex (WRC). CYFIP2 was recently correlated to neurological disorders by the association of the R87C variant with early infantile epileptic encephalopathy (EIEE) patients. In this set of syndromes, the epileptic spasms and seizures since early childhood lead to impaired neurological development in children. Inside the WRC, the variant residue is at the CYFIP2 and WAVE1 protein interface. Thus, the hypothesis is that the R87C modification weakens this interaction, allowing the WRC complex’s constant activation. This work aimed to investigate the impacts of the mutation on the structure of the WRC complex through molecular dynamics simulation. For that, we constructed WRC models containing WAVE1-NCKAP1 proteins complexed with WT or R87C CYFIP2. Our simulations showed a flexibilization of the loop comprising residues 80–110 due to the loss of contacts between internal residues in the R87C CYFIP2 as well as the key role of residues R/C87, E624, and E689 in structural modification. These data could explain the mechanism by which the mutation impairs the stability and proper regulation of the WRC.     

综合体内儿童活动场所执行消防规范的问题探讨

近年来,我国城市商业综合体的总数和规模出现了前所未有的增长,各种儿童活动场所在商业综合体内遍地开花,不少商业综合体甚至主打儿童品牌,成为满足儿童游乐、教育、摄影、零售、美食等多种需求的一站式基地。但此类场所的快速发展也带来了极大的火灾风险,相对消防法规和技术标准也出现了一些不适应、难执行的情况。文章对在商业综合体内设置的儿童活动场所执行现行消防法规和技术标准存在的问题及相应的解决措施进行分析,希望可以起到借鉴作用。     

基于文本挖掘的WUI火灾致灾因子网络构建及特征分析

为预防和减少WUI火灾的发生, 挖掘WUI火灾关键致灾因子, 厘清致灾因子间的作用机制. 本文首先基于本文挖掘技术从WUI火灾事故案例中得到致灾因子, 使用Apriori算法得到致灾因子之间的关联规则. 然后使用复杂网络理论构建WUI火灾致灾因子网络, 计算网络拓扑特征参数, 探析WUI火灾致灾因子网络特征. 最后引入WUI火灾致因链风险度指标, 挖掘出高风险连边, 并提出断链措施. 结果表明: WUI火灾致灾因子网络具有小世界特性, 高温、强风、干旱等对其他致灾因子影响较大. 燃烧废弃物、植物起火、应急响应速度、人为纵火、强风在不同致灾因子转换中具有重要作用, 应加强管控. 网络中风险度最高的边是燃烧废弃物→植物起火, 通过颁布禁止擅自燃烧废弃物等规定, 即可切断该风险链, 实现对WUI火灾的预防和主动控制.     

基于复小波变换全监督学习的微震波形降噪与相位拾取

由于井下微震信号有着更低的信噪比, 导致信号拾取精度降低. 现阶段基于小波阈值的信号降噪算法等在面对信噪比较低的信号时存在泛化性差, 阈值难以衡量等问题. 为解决这一问题, 本文研究了一种复小波变换全监督学习的微震波形降噪方法. 该方法首先利用复小波变换结合卷积自编器设计一个具有多个卷积和反卷积操作的编码-解码器完成图像的降噪过程. 为验证此方法的有效性, 首先在Stanford的Earthquake数据集上构建了Earthquake2023进行训练和测试, 并有着较好地拟合效果和训练结果. 同时基于该方法降噪后信号设计了一种震相拾取方法, 并达到了较高的拾取精度. 本文设计了多组对比实验, 结果表明此降噪方法能有效提高信号的峰值信噪比和均方根误差, 两者分别提高了16 dB和24%, P波、S波初至到时拾取的误差相较于STA/LTA减小了0.3 ms.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

中国大陆Internet物理特征的测量与分析

结合网络测量技术和复杂网络理论对中国大陆地区Internet物理特征展开测量与分析。首先阐述了Internet网络测量的研究现状,然后形式化地定义了Internet物理特征的3个重要表征量：Internet直径、聚类系数、度分布。其次,研究人员经过18个月的测量与分析中国大陆地区Internet物理特征,计算出未加权Internet直径是14.9跳,聚类系数为0.32,度值为5.68。最后,对Internet直径进行了加权处理,计算出加权的Internet直径为15.6±3.1跳,加权后的Internet直径分布特征能更加精确地反映Internet特性。     

Sugeno测度空间基于复样本的统计学习理论

引入复g_λ随机变量、准范数的定义,给出了复g_λ随机变量的期望和方差的概念及若干性质;证明了基于复g_λ随机变量的马尔可夫不等式、契比雪夫不等式和辛钦大数定律;提出了Sugeno测度空间中复经验风险泛函、复期望风险泛函以及复经验风险最小化原则严格一致性等定义;证明并构建了基于复g_λ随机样本的统计学习理论的关键定理和学习过程一致收敛速度的界,为系统建立基于复g_λ随机样本的统计学习理论奠定了理论基础。     

串联补偿线路SSR分析研究

随着我国电力系统的发展,串联电容补偿在输电系统的应用越来越广泛,但其引起的次同步谐振（SSR）问题也随之而来。针对输电网中串联电容补偿引发的次同步谐振,文章采用基于时域仿真实现的复转矩系数法分析了不同发电机出力以及不同串补度对系统次同步谐振的影响。仿真验证了故障情况下,较小串补度系统对次同步谐振的稳定性。