Chuangxin Guo, Yijia Cao, Yuezhong Tang and Zhenxiang Han
The purpose of this paper is to design an open architecture of an interconnected communication system (ICS) for multi‐level electric power control centers (EPCC) based on…
Abstract
Purpose
The purpose of this paper is to design an open architecture of an interconnected communication system (ICS) for multi‐level electric power control centers (EPCC) based on Tele‐control Application Service Element (TASE.2), which possesses specialties of high performances, robustness, cost‐efficiency, quick‐restoration, and easy‐maintenance.
Design/methodology/approach
Based on the hierarchy and structure of TASE.2, the overall architecture of the ICS for multi‐level EPCC is put forward at first. As the key devices in the system, the structures of the communication gateway (CG) and common interface are designed. Then, the logical procession flows in CG and the con modes, for both CG and IC are analyzed in detail. The web‐based software configuration of remote maintenance and fault diagnosis is discussed conceptually.
Findings
As a standardized, well‐developed, and efficient protocol, TASE.2 is considered to be the most suitable protocol to support the ICS for multi‐level EPCC.
Research limitations/implications
The performance of the ICS needs to be further simulated.
Practical implications
Practical architecture for ICS for multi‐level EPCC with robustness and cost‐efficient specialty is designed in principle, which is very useful for manufacturers to develop pilot devices or even products.
Originality/value
This paper proposes a new ICS scheme for multi‐level EPCC based on TASE.2 is proposed.
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Yanfang Qiu, Kun Ma, Weijuan Zhang, Run Pan and Zhenxiang Chen
Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most…
Abstract
Purpose
Fake news refers to intentionally fabricated or misleading information designed to deceive the public and manipulate opinions for personal, political or financial gain. Most existing detection methods primarily focus on capturing language features from news content. However, these methods neglect the varying importance of different news entities. Additionally, these methods tend to overlook the auxiliary role of external knowledge, resulting in an incomplete understanding of the entity. To address these issues, this paper aims to propose a Dual-layer Semantic Information Extraction Network with External Knowledge (DSEN-EK) for fake news detection.
Design/methodology/approach
This approach is proposed to comprise three parts: Dual-layer Semantic Information Extraction Network, Entity Integration Network with External Knowledge and Classifier. Specifically, Dual-layer Semantic Information Extraction Network is designed to enhance relationships between entities and the influence of important entity representations. The Entity Integration Network with External Knowledge is proposed to extract entity descriptions from external knowledge bases.
Findings
The DSEN-EK model performs well on the Liar, Constraint, Twitter15 and Twitter16 data sets, achieving accuracy of 98.02%, 94.61%, 90.09% and 93.65%, respectively. These results highlight its effectiveness in detecting fake news across different types of content.
Originality/value
The Dual-layer Semantic Information Extraction Network is proposed to capture the relationships between entities and enhance the continuous semantic information of the news. The Entity Integration Network with External Knowledge is designed to enrich entity descriptions, leading to a more comprehensive capture of semantic details.
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Ying Ye, Kwok Hung Lau and Leon Kok Yang Teo
The purpose of this paper is to explore the drivers and barriers to omni-channel retailing in China, and attempts to understand how companies formulate their business strategies…
Abstract
Purpose
The purpose of this paper is to explore the drivers and barriers to omni-channel retailing in China, and attempts to understand how companies formulate their business strategies during their transformations to omni-channel retailing.
Design/methodology/approach
This study adopts an exploratory case study approach to investigate the omni-channel retailing transformations of two well-established Chinese fashion apparel retailers. The study draws on multiple sources of evidence, comprising: interviews with eight business executives from three major operational departments; on-site observations in firm’s retail stores, factories and distribution centres; and secondary data review of firm business reports, news, whitepapers and archival records. The findings are established through a consistent within-case data analysis and cross-case comparison.
Findings
The study reveals that the two retailers formulated different strategies in developing their omni-channels, and exhibited different degrees of success. The similarities and differences in the drivers, as well as the barriers, were analyzed and compared in this study. Operational variations (i.e. enablers and inhibitors) due to the unique context of the Chinese market were also explored. The findings reveal that coherent leveraging firm resources and capabilities from the three perspectives – marketing, logistics and supply chain, and organizational management – is critical to the full implementation of omni-channel retailing. They provide relevant managerial insights that can assist firms in formulating appropriate strategic action plans during the transformations.
Originality/value
As a theoretical contribution, this paper identifies a set of drivers and barriers for omni-channel retailing in the developed market, and classifies them into three categories: marketing; logistics and supply chain; and organizational management. The empirical-based qualitative analysis reveals the key factors impacting on omni-channel retailing within the Chinese market, and suggests a series of practical implications for local retailers planning to embark on omni-channel retailing.
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Ran Li, Simin Wang, Zhe Sun, Aohai Zhang, Yuxuan Luo, Xingyi Peng and Chao Li
Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of…
Abstract
Purpose
Depression has become one of the most serious and prevalent mental health problems worldwide. The rise and popularity of social networks such as microblogs provides a wealth of psychological data for early depression detection. Language use patterns reflect emotional states and psychological traits. Differences in language use between depressed and general users may help predict and diagnose early depression. Existing work focuses on depression detection using users' social textual emotion expressions, with less psychology-related knowledge.
Design/methodology/approach
In this paper, we propose an RNN-capsule-based depression detection method for microblog users that improves depression detection accuracy in social texts by combining textual emotional information with knowledge related to depression pathology. Specifically, we design a multi-classification RNN capsule that enhances emotion expression features in utterances and improves classification performance of depression-related emotional features. Based on user emotion annotations over time, we use integrated learning to detect depression in a user’s social text by combining the analysis results with components such as emotion change vector, emotion causality analysis, depression lexicon and the presence of surprising emotions.
Findings
In our experiments, we test the accuracy of RNN capsules for emotion classification tasks and then validate the effectiveness of different depression detection components. Finally, we achieved 83% depression detection accuracy on real datasets.
Originality/value
The paper overcomes the limitations of social text-based depression detection by incorporating more psychological background knowledge to enhance the early detection success rate of depression.