3D Hierarchical Network Derived from 2D Fe-doped NiSe Nanosheets/Carbon Nanotubes with Enhanced OER Performance for Overall Water Splitting

Designing an earth-abundant electrode material with high activity and durability is a major challenge for water splitting to produce clean and green hydrogen energy. In this study, we reported a...

Traffic Behavior Analysis During the Transition from One Level of the Network Hierarchy to Another

The hierarchical network model is the most preferable in the design of computer networks, as it allows you to create a more stable structure of network, rationally allocate available resources, and also provide a higher degree of data protection. In this work, the study of the behavior of the traffic during the transition from one level of the network hierarchy to another, based on the study of the values of the traffic self-similarity degree during this transition. For the study, a simulation model of a computer network with a hierarchical topology was developed using the NS-3 simulator. Also, a window application was developed in the Visual C# programming language. With the help of this application the degree of self-similarity of the traffic was investigated using the files obtained as a result of processing the trace file. Thus, as a result of the study, it can be stated that any changes in the degree of self-similarity of the network traffic when this traffic moves from one level of the hierarchy to another level depends on such a condition as the direction of traffic movement. The initial degree of selfsimilarity of network traffic also effects on the network traffic self-similarity degree.

Web-based Form as Expression of Networked Sociality in the Community-based Piano Piece Wiki-Piano.Net

Digital and virtual dimensions play an essential role throughout Alexander Schubert’s work, among audiovisual media, mechanisation of performative gestures, stage setting and computer tools for composing. Wiki-Piano.Net, based on the ‘wiki’ peer-production principle applied to the artistic field, is one of his first experiments with online communities’ interactivity. This article investigates the relationships between author, users and performer through the editable website. The intermedia approach extends and reflects human beings’ compositive and performative possibilities. Indeed, a wide range of internet sources communicate with the historical reference of the piano repertoire while also reflecting recent online habits. Nevertheless, the preset form anchors its expressivity to a specific communication, referring to the author’s informatics-digital attitude and a further staged representation. The man–machine dialectics is consequently expressed on different levels, also entailing acoustic-gestural and audiovisual contents. Questioning the authorship principle and generating a non-hierarchical network, Wiki-Piano.Net reflects Schubert’s aim to create a collaborative work towards which he has no control. However, the virtual environment results are strongly influenced by his settings and artistic attitude. The interaction, hence, derives not only from online users but also from the creator and performer through fundamental website mediation.

Text Data Mining Algorithm Combining CNN and DBM Models

The special text has a lot of features, such as professional words, abbreviations, large datasets, different themes, and uneven distribution of labels. While the existing text data mining classification methods use simple machine learning models, it has a bad performance on text classification. To solve this drawback, a text data mining algorithm based on convolutional neural network (CNN) model and deep Boltzmann machines (DBM) model is proposed in this paper. This method combines the CNN and DBM models with good feature extraction to realize the double feature extraction. It can realize the tag tree by constructing the tag tree and design the effective hierarchical network to achieve classification. At the same time, the model can suppress the input noise on the classification. Experimental results show that the improved algorithm achieves good classification results in special text data mining.

Identification and Optimization of the Spatial Structure of Urban and Rural Settlements from a Hierarchical Network Perspective

China has entered a new era of comprehensive promotion of rural revitalization and integrated urban–rural development. The identification and optimization of the spatial structure of urban and rural settlements are of great significance to the realization of rural revitalization and integrated urban–rural development. This study proposes a method framework for identifying and optimizing the spatial structure of urban and rural settlements from a hierarchical network perspective. This framework includes three parts—namely, the identification, analysis, and optimization of the hierarchical network structure of these settlements. The identification of this structure is the foundation of this study. To realize the identification, for each settlement, this study finds and retains its strongest interaction with other settlements which have a stronger capacity for development. This study used Hua County in Henan, China, as an example to verify the method framework. Results showed that the spatial structure of the urban and rural settlements in Hua County identified in this study was complete and continuous, with significant hierarchical and community characteristics. Based on this, a five-level optimization system of these settlements was constructed. This study expands the method used to study the spatial structure of urban and rural settlements from the network perspective and has a theoretical and practical significance for the optimization of the spatial structure of these settlements.

A Study on Hybrid Hierarchical Network Representation Learning

Network representation learning (NRL) aims to convert nodes of a network into vector forms in Euclidean space. The information of a network is needed to be preserved as much as possible when NRL converts nodes into vector representation. A hybrid approach proposed in this paper is a framework to improve other NRL methods by considering the structure of densely connected nodes (community-like structure). HARP [1] is to contract a network into a series of contracted networks and embed them from the high-level contracted network to the low-level one. The vector representation (or embedding) for a high-level contracted network is used to initialize the learning process of a low-level contracted graph hierarchically. In this method (Hybrid Approach), HARP is revised by using a well-designed initialization process on the most high-level contracted network to preserve more community-like structure information.