江阴市"十四五"水利高质量发展规划的思考

总结江阴市"十三五"水利发展规划在水安全、水资源、水生态、农村水利、水管理服务及水利发展保障等方面的完成情况.分析江阴市水利发展存在的主要短板、问题.围绕建设高质量的水安全保障体系、水资源配置体系、水生态治理体系、水利管理体系、水利服务体系,系统提出江阴市"十四五"水利高质量发展规划思路.     

上覆水磷浓度对沉积物孔隙水中磷垂向分布的影响

为探究上覆水磷浓度对沉积物孔隙水中磷垂向分布的影响,通过室内实验,利用薄膜扩散平衡技术(DET)对孔隙水中磷浓度进行测量,分析孔隙水磷浓度的垂向变化规律,并揭示平衡时上覆水磷浓度与孔隙水磷浓度以及上覆水-沉积物界面孔隙水磷浓度的关系。结果表明:上覆水磷浓度越高,则沉积物孔隙水中磷垂向分布达到平衡所需的时间越长,相同垂向位置处孔隙水磷浓度及其浓度梯度越大;孔隙水磷浓度及其浓度梯度随深度增加而降低,表层沉积物孔隙水中磷的扩散作用最强,下层沉积物孔隙水磷浓度受上覆水磷浓度变化影响较小;吸附平衡后,沉积物孔隙水磷浓度和上覆水-沉积物界面孔隙水磷浓度均与上覆水磷浓度呈幂函数关系,而留存在沉积物孔隙水中的磷浓度仅占2.26%～3.80%,上覆水中减少的磷大部分被沉积物吸附。     

Instability of diatomic deuterium in fcc palladium

To clarify some of the solid-state aspects of cold fusion in deuterated transition metal electrodes, we have carried out first-principles self-consistent total energy calculations for various configurations of atomic and diatomic deuterium inside fcc palladium. We find that the stability of the Pd+D system is controlled by the relative position of the deuterium-inducedantibonding level with respect to the Fermi energy. The equilibrium D-D distance in dense PdD up to =3 is found to be much larger than the free space value. The calculated Born-Oppenheimer energy surface of diatomic D₂ in crystalline palladiuim is shown to have but metastable local minima whose internuclear separation is at least 0.2 Ålarger than that of the isolated D₂ molecule. We conclude that D₂ incrystalline Pd will have a substantially lower tunneling probability than hitherto thought and that explanation for fusion mechanisms should be sought elsewhere.     

CoGCN: Combining co‐attention with graph convolutional network for entity linking with knowledge graphs

Entity linking is a fundamental task in natural language processing. The task of entity linking with knowledge graphs aims at linking mentions in text to their correct entities in a knowledge graph like DBpedia or YAGO2. Most of existing methods rely on hand‐designed features to model the contexts of mentions and entities, which are sparse and hard to calibrate. In this paper, we present a neural model that first combines co‐attention mechanism with graph convolutional network for entity linking with knowledge graphs, which extracts features of mentions and entities from their contexts automatically. Specifically, given the context of a mention and one of its candidate entities' context, we introduce the co‐attention mechanism to learn the relatedness between the mention context and the candidate entity context, and build the mention representation in consideration of such relatedness. Moreover, we propose a context‐aware graph convolutional network for entity representation, which takes both the graph structure of the candidate entity and its relatedness with the mention context into consideration. Experimental results show that our model consistently outperforms the baseline methods on five widely used datasets.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

Image classification method on class imbalance datasets using multi-scale CNN and two-stage transfer learning

Neural Computing and Applications - Image classification tasks widely exist in many actual scenarios, including medicine, security, manufacture and finance. A major problem that hurts algorithm...     

Granular multiple kernel learning for identifying RNA-binding protein residues via integrating sequence and structure information

Neural Computing and Applications - RNA-binding proteins play an important role in the biological process. However, the traditional experiment technology to predict RNA-binding residues is...     

A deep multi-source adaptation transfer network for cross-subject electroencephalogram emotion recognition

Neural Computing and Applications - In real-world application of affective brain–computer interface (aBCI), individual differences across subjects and non-stationary characteristics of...     

云存储安全增强系统的设计与实现

云存储是一种新型的网络存储形式,并逐步为大家所接受,企业和个人用户都开始使用云存储作为其网络存储媒介。目前很多著名的IT企业都推出了云存储服务,其中Amazon公司推出的SimpleStorageService（S3）就是商用云存储服务的典型代表。随着云存储的广泛使用,云存储中数据的安全问题,如数据泄漏和数据篡改,也成了用户广泛关注的问题。文章基于Amazons3的云存储服务,设计并实现了一款云存储安全增强系统,对用户上传至Amazons3的数据进行加密保护,使得文件以密文形式存储于Amazons3,可以有效防止数据在传输和存储过程中的泄漏;同时系统还对从s3下栽的文件进行完整性校验,检测其内容与上传时是否一致,以防止文件被篡改;最后,系统还提供了多用户访问控制支持,多个用户可以共享同一个S3账号,同时保证各自存储的内容互相隔离,禁止一个用户非授权访问其他用户存储的文件。     

基于GSM的故障实时报警控制系统的设计与实现

针对实时监控系统中的运行设备或装置的异常报警信息,设计了基于GSM无线通讯的微机监测系统,实现了多故障远程短信息报警功能,介绍了系统的软硬件设计及主要功能;经应用测试,系统具有宴时性强、稳定性高、投入成本低等特点,能将信息同时发送至多个移动通信终端,已成功应用于农业温室大棚监控系统的实时故障报警,系统也可方便地应用于工厂生产线、金融、交通及家庭安全等方面的远程监测.