中医方剂与图集的转换方法

中药复方数据挖掘研究是将我国丰富的中药信息资源和现代最新信息技术相结合的重要内容,它意味着基于中医理论,利用知识发现技术,对传统中药新药、中医组方理论及规律、中药作用机制、有效成分构效关系等多个方面进行全面、系统的研究。图作为一种经典的数据结构,被广泛用于复杂数据结构分析与建模。文章探讨了图建模技术并实现了中医方剂与图集的转换。     

基于APCNN和BiGRU-Att的单词DGA域名检测方法

为了提高对基于单词的域名生成算法（domain generation algorithm,DGA）生成的恶意域名的检测准确率,提出了一种结合改进的并行卷积神经网络（APCNN）和融合简化注意力机制的双向门控循环单元（BiGRU-Att）的网络模型,该模型能充分学习单词特征、单词之间的组合关系和关键字符信息。实验结果表明,相比Bilbo和CL模型,APCNN-BiGRU-Att模型的分类准确率和F1值更高,表明该模型具有更好的检测效果、多分类效果和稳定性。     

基于智能垃圾桶的垃圾分类动态收运路径优化问题研究

针对城市生活垃圾分类收运过程中存在的环境二次污染和垃圾产生量不确定性等问题,提出了一种基于智能垃圾桶的动态收运车辆路径优化方法。建立以最小化碳排放成本、燃油消耗成本、固定成本和车辆延迟到达惩罚成本为目标的动态车辆路径优化模型。采用滚动时域的方式将动态问题转换为一系列静态问题,并设计两阶段算法进行求解。首先采用粒子群算法对收运车辆路径进行规划,而后在每个时域末,综合考虑待清运垃圾桶的位置和垃圾量、垃圾收运车辆的位置和装载量以动态调整现有车辆路径。研究结果表明,相较于传统的静态收运方案,动态垃圾收运方案能够在降低车辆运输成本和碳排放成本的同时,显著降低由于清运不及时造成的环境二次污染的风险。     

Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network

The application of cross‐corpus for speech emotion recognition (SER) via domain adaptation methods have gain high acknowledgment for developing good robust emotion recognition systems using different corpora or datasets. However, the issue of cross‐lingual still remains a challenge in SER and needs more attention to resolve the scenario of applying different language types in both training and testing. In this paper, we propose a triple attentive asymmetric convolutional neural network to address the recognition of emotions for cross‐lingual and cross‐corpus speech in an unsupervised approach. The proposed method adopts the joint supervision of softmax loss and center loss to learn high power discriminative feature representations for target domain via the use of high quality pseudo‐labels. The proposed model uses three attentive convolutional neural networks asymmetrically, where two of the networks are used to artificially label unlabeled target samples as a result of their predictions from training on source labeled samples and the other network is used to obtain salient target discriminative features from the pseudo‐labeled target samples. We evaluate our proposed method on three different language types (i.e., English, German, and Italian) data sets. The experimental results indicate that, our proposed method achieves higher prediction accuracy over other state‐of‐the‐art methods.     

基于等级保护的网络改造方法及异常情况分析与处理

本文介绍了在电力企业信息网中基于等级保护的网络安全防护改造方法。在具体案例中,针对网络改造过程中遇到的网络异常情况,技术人员通过逐一分析、排查,最终恢复正常网络,达到企业信息网络二级域改造的目的。网络安全防护改造方法以及异常情况分析排查案例对同类型企业在网络改造及异常排查工作中具有一定借鉴和指导意义。     

Adaptive load balancing algorithm for multiple homing mobile nodes

In places where mobile users can access multiple wireless networks simultaneously, a multipath scheduling algorithm can benefit the performance of wireless networks and improve the experience of mobile users. However, existing literature shows that it may not be the case, especially for TCP flows. According to early investigations, there are mainly two reasons that result in bad performance of TCP flows in wireless networks. One is the occurrence of out-of-order packets due to different delays in multiple paths. The other is the packet loss which is resulted from the limited bandwidth of wireless networks. To better exploit multipath scheduling for TCP flows, this paper presents a new scheduling algorithm named Adaptive Load Balancing Algorithm (ALBAM) to split traffic across multiple wireless links within the ISP infrastructure. Targeting at solving the two adverse impacts on TCP flows, ALBAM develops two techniques. Firstly, ALBAM takes advantage of the bursty nature of TCP flows and performs scheduling at the flowlet granularity where the packet interval is large enough to compensate for the different path delays. Secondly, ALBAM develops a Packet Number Estimation Algorithm (PNEA) to predict the buffer usage in each path. With PNEA, ALBAM can prevent buffer overflow and schedule the TCP flow to a less congested path before it suffers packet loss. Simulations show that ALBAM can provide better performance to TCP connections than its other counterparts.     

用Word邮件合并功能套打固定格式的毕业证书

邮件合并是Word提供的一种非常重要的实用功能,如能巧妙应用将大大减轻工作量。本文通过毕业证书的套打,根据Word邮件合并功能和Excel函数域功能,以解决问题的思路为顺序,论述了毕业证书套打设计工作的方法和步骤,详细介绍了邮件合并的使用,对实现批量打印非常有用。     

The GNAQ T96S Mutation Affects Cell Signaling and Enhances the Oncogenic Properties of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the most common malignant tumor in the liver, grows and metastasizes rapidly. Despite advances in treatment modalities, the five-year survival rate of HCC remains less than 30%. We sought genetic mutations that may affect the oncogenic properties of HCC, using The Cancer Genome Atlas (TCGA) data analysis. We found that the GNAQ T96S mutation (threonine 96 to serine alteration of the Gαq protein) was present in 12 out of 373 HCC patients (3.2%). To examine the effect of the GNAQ T96S mutation on HCC, we transfected the SK-Hep-1 cell line with the wild-type or the mutant GNAQ T96S expression vector. Transfection with the wild-type GNAQ expression vector enhanced anchorage-independent growth, migration, and the MAPK pathways in the SK-Hep-1 cells compared to control vector transfection. Moreover, cell proliferation, anchorage-independent growth, migration, and the MAPK pathways were further enhanced in the SK-Hep-1 cells transfected with the GNAQ T96S expression vector compared to the wild-type GNAQ-transfected cells. In silico structural analysis shows that the substitution of the GNAQ amino acid threonine 96 with a serine may destabilize the interaction between the regulator of G protein signaling (RGS) protein and GNAQ. This may reduce the inhibitory effect of RGS on GNAQ signaling, enhancing the GNAQ signaling pathway. Single nucleotide polymorphism (SNP) genotyping analysis for Korean HCC patients shows that the GNAQ T96S mutation was found in only one of the 456 patients (0.22%). Our data suggest that the GNAQ T96S hotspot mutation may play an oncogenic role in HCC by potentiating the GNAQ signal transduction pathway.     

基于图的语义网络构造算法研究

在自然语言处理的基础上,将中医领域本体与图的基本性质相结合,提出了构造中医诊疗知识语义网络算法。给出了图的基本性质、语义网络的图定义,并为知识的网络表示建立了相应的数据结构。将算法用于中医领域语义网络和语义场构建。试验结果表明,该算法具有较好的实用性。提出的基于图的语义网络构造算法,对于文本挖掘、知识获取技术研究有重要意义,也是中医专家知识获取的有效方法。     

基于改进DaNN的综合能源系统多能负荷预测

随着能源革命的推进及双碳目标的提出,综合能源系统越发受到广大研究者的重视,对综合能源系统进行高效的规划和控制离不开精准的多能负荷预测。基于上述需求,引入迁移学习理论,提出一种改进领域自适应神经网络（DaNN）负荷预测模型对综合能源系统中的冷、热、电负荷进行统一建模与预测。首先,通过历史数据分别构筑冷、热、电负荷特征图,随后输入改进DaNN的参数共享卷积层和全连接层;其次,基于冷、热、电负荷联合预测的特点改进传统神经网络的损失函数,加入最大均值差异指标,并优化训练模型;最后,通过3个各自独立的全连接层分别输出冷、热、电负荷的预测值。通过采用实际算例验证并与基准模型对比可知,所提改进DaNN模型能够有效提高综合能源多能负荷预测精度。