Earticle

Switching behavior refer to that users often move from one IT product to another. There are complex motivations and reasons to be explained behind the behavior. Understanding the factors that influence users’ switching behavior is important for SNS providers to consolidate existing user base and attract more users. In view of this, this study introduced the migration theory based on human migration studies from the perspective of social network services, and tried to find out the characteristics of SNSs switching. Based on the PPM framework, we had constructed a conceptual model in which we served social network fatigue as the push driver, three types of network externalities as pull drivers, and inertia as the mooring driver, and put forward relevant assumptions. After the reliability and validity test of the questionnaire, the model and related assumptions were tested using SmartPLS 2.0. We found that push effect of social network fatigue has positive impact on switching intention and is influenced by perceived overloads, pull effects of network size positively influenced switching intention as well. However, the positive associations of social network fatigue and network size with switching intention were alleviated by the mooring effect of inertia. And affective commitment and switching costs significantly promoted the inertia. Finally, the switching intention exerted positive impact on actual switching behavior. Our finding helps to further understand SNS users’ switching behavior, provides reference value for information processing and management of SNSs, and deepen theoretically the switching behaviors of conceptual model in the virtual community users.

This paper presented a fault detection method based on deep learning Convolutional Neural Networks(CNN) and Long Short-Term Memory. Using CNN we get more abstract features representation in the higher level to find the distributed characteristics of the data. After obtaining the features, use LSTM to further mining useful information in the time dimension. First, we presented a CNN model which has 9 layers to extract more abstract features. By comparing three different CNN models, we realized that the shape of the original data set is much important. 16×16 shape of data set has high accuracy, it is 95%. Also comparing with traditional fault detection model, it is much better than random forest and Deep Neutral network(DNN). And the results show that the proposed CNN model can extract the features automatically for fault detection intelligently. However, data has a complex time correlation with each other. How to get the most information in the data for fault detection? We presented LSTM to extract more useful information in the time dimension. The proposed CNN-LSTM method has the highest accuracy which up to 96.13%. The proposed CNN-LSTM exhibits the best performance in the electric vehicle charging pile diagnosis.

Power system security issue is becoming a vital and critical issue for efficient and effective operation of power system network. The power system engineers/operators may face various kinds of issues like outage of generators, transmission lines and loads during operation of the system. The operator should take an instant and correct decision to minimize the effect of such outages for reliable operation of network. This paper aims to solve N-1 contingency problem of transmission line using reactive power performance indexing method. The most vulnerable lines are screened by ranking in order to make ease to give high attention towards such lines during operation of network. For this purpose, two models IEEE-14 Test Bus and Integrated Nepalese Power System network were used.

The purpose of this study is to gain an understanding of the attitudes of male and female customers regarding internet-based brand loyalty and its determinants in the Pakistani garments context. This study proposes that perceived quality, perceived value, customer satisfaction, and brand image both directly and indirectly affects internet-based brand loyalty. A survey questionnaire with a convenience sampling approach was used to collect the primary data and it was collected from 197 customers of garment brands in Bahawalpur, Pakistan. The proposed conceptual model indicated that the perceived quality, perceived value, customer satisfaction, and brand image are important determinants of internet-based brand loyalty. All the hypotheses developed in this study were found statistically significant in the context of males and females. The study applies the regression analysis technique by using SPSS. The differences between male and female examined separately as well as together and the results showed that satisfied female customers have less internet-based brand loyalty than satisfied male customers (male customers are easier to retain as compare to female customers). This is because; loyalty and satisfaction of females are greatly shaped by the higher brand image and perceived quality, whereas male customers become more satisfied and loyal through attaining higher perceived value. The conclusions and the implications for internet-based brand loyalty are discussed.

Digital convergence and its importance to contemporary economies is undeniable. What does it mean to be a digital economy? This question is a big challenge to answer digital convergence disseminates new business models and brings about substantial changes across industrial activities worldwide. Convergence is becoming as a hot issue within the Information Technology (IT) industries. Several benefits come from converging communication networks, like increased productivity, reduction of transaction costs and a valuable impact on economic growth. Digital convergence of various industries has created new business opportunities. The net benefit of digitalization on a global basis could surpass $100 trillion by 2030. Industrial digitalization and internet access are projected to experience exponential growth. The digital economy is expected to grow from 15.5% to 24.3% of the global GDP by 2025. Digital technologies' long-term return on investments is 6.7 times more than that of non-digital investments. This study provides a look at digital convergence and its economic implications.

Software Defined Network (SDN) is a new network architecture for designing, building, and managing networks that separates the networks control plane and forwarding plane to better support the scalability and innovation in a network infrastructure. The overall network performance of an application is mostly affected by the two major factors, bandwidth, and latency. Between each pair of network elements in network infrastructure, there may exist multiple paths connecting them with different properties. A traditional network does not take this knowledge into consideration and may result in sub-optimal performance of applications and underutilization of a network resource. This research proposes a concept of Application aware routing which could improve the overall performance of a network by categorizing the application in bandwidth oriented and latency oriented and allocate the separate route for each type of traffic based on the application preferences using Software defined network architecture and OpenFlow protocol. The research also proposes a design of an application aware network topology by using software defined network architecture which uses open flow protocol and open flow protocol based controller. Routes for the application packets are chosen based on the application type. To verify the feasibility and practical implementation of the proposed concept SDN topology is implemented in emulated environment using mininet.