3D and simplified pseudo-2D modeling of single cell of a high temperature solid oxide fuel cell to be used for online control strategies

In the current study, a single cell of a planar SOFC is firstly modeled in 3D using commercial SOFC module of ANSYS Fluent and the results are validated against the experimental investigations in the literature. Many researchers have used ANSYS Fluent for simulating solid oxide fuel cells. However, there is a huge gap in the literature on explaining the detailed procedure that should be followed in order to use this software effectively. A thorough step-by-step approach is presented to provide a deep insight into the software. Thereafter, a simplified quasi-2D method with infinitely shorter computational time is developed and the results are compared with the 3D model. It is found that the reduced model is capable of being utilized as an alternate method for both online diagnosis and designing active control strategies.     

Capitalizing on the Koran to fuel online violent radicalization: A taxonomy of Koranic references in ISIS’s Dabiq

The current study set out to investigate to what extent ISIS is bolstering its jihadist ideology on a ‘cut-and-paste’ or ‘cherry-picked’ version of Islam in their renowned online propaganda magazine Dabiq. The main objective was to examine in a systematic and quantitative way to what extent ISIS utilizes the Koran in an atomistic, truncated and tailored manner to bolster its religious legitimacy. A total of 15 issues of Dabiq and 700 Koranic references were scrutinized. By means of a quantitative analysis we developed an innovative taxonomy of Koranic chapters and verses (i.e. surahs and ayat, respectively) on the basis of their appearance in Dabiq. Our large-scale data analysis provide consistent empirical evidence for severe decontextualization practices of the Koran in three ways: (1) a thin, Medinan-dominated religious layer, (2) ayah mutilation, and (3) clustered versus exclusive mentions. Limitations and implications for future research, policy makers and CVE initiatives are discussed.     

在线粒度分析仪的工业应用

催化裂化催化剂应具有良好的颗粒分布,以保证良好的流化状态。一般要求催化剂颗粒<40μm的不大于25%,40～111μm不小于50%,>111μm的不大于30%。目前催化裂化催化剂生产过程中通常采用压力式喷雾干燥成型工艺,用高压泵把制备好的胶体在与热空气的接触中,迅速脱去水分汽化,即得到粉状或颗粒状干燥产品。现有生产装置在生产过程中,因分析结果滞后,无法及时指导装置调整催化剂成品的粒度分布,特引进使用了在线粒度分析仪。通过对该分析仪的简单研究,介绍了该分析仪的使用现状,并对使用中出现的问题进行了简单总结。     

Value drivers of blockchain technology: A case study of blockchain-enabled online community

There is growing recognition that blockchain technology has significant potential to alter how organizations and people work and communicate. However, theoretical guidance concerning how organizations leverage blockchain technology to enhance value creation for users is still limited. Grounded in the socio-technical perspective and leveraging the rich data obtained from case analyses of blockchain-enabled online communities, this paper develops a theoretical model to identify the core value drivers that blockchain enables for online communities. The core value drivers include: a reputation-value system, data ownership mechanisms, and verification & tracking mechanisms. Our findings suggest that these three value drivers enhance value creation of online communities by motivating participation and protecting contributions.     

Using discussion logic in analyzing online group discussions: A text mining approach

We propose a Discussion Logic-based Text Analytics (DiLTA) framework, which combines theories developed in social science and text mining fields. The framework extracts features that uncover discussion logic and uses these features in analyzing online discussions. A series of models are proposed including conversation disentanglement, coherence analysis, and visualization. Validation experiments showed that DiLTA achieved significantly superior performance over existing text analytics methods in reconstructing internal structure. A case study using DiLTA-enabled visualization on a healthcare forum illustrates the great potential of DiLTA in assisting comprehension of the internal linkage, structure, and logic of online group discussions.     

Bracketing or reinforcing? Socio-economic status,network power,and online deliberation

The extent to which the deliberative scenario brackets socio-economic inequalities (i.e., functioning as an ideal public sphere) has been long-debating. This study empirically addresses this question in the online setting. We test whether offline advantages (i.e., economic and cultural capitals) can be translated into power in online deliberation and how their dynamic relations further influence interlocutors’ deliberation experience (i.e., perceived procedural fairness and validity claim). Power is conceptualized as network power, including prestige (indegree), leadership (outdegree), and brokering power (betweenness). A national sample of Singaporeans participated in a three-week online deliberation, and network power is measured based on the web log. The participants answered a two-wave (i.e., pre- and post-deliberation) survey to indicate individual attributes. Structural equation modeling finds that online deliberation brackets as well as reinforces offline status. Economic capital is suppressed while cultural capital translates into network power, which further influences interlocutors’ evaluation of the deliberation.     

Exchanging social support on online teacher groups: Relation to teacher self-efficacy

The present study explored the relation between exchanging social support on online teacher groups and teacher self-efficacy. An online survey was conducted inside a massive Facebook teacher group, and 584 elementary school teachers provided information regarding Facebook use, self-efficacy for creative teaching, and the extent to which they provided and received support on the online teacher group. Results showed that teacher self-efficacy varied as a function of years of teaching experience, length of group membership, and the extent to which teachers provided and received social support on the online teacher group. Furthermore, the providing of online support predicted teacher self-efficacy after taking into account the effects of teaching experience and group membership. The findings are discussed in terms of the features and importance of social support exchange on online teacher support groups.     

The introduction strategy of the emerging online marketplace considering risk attitude and channel power

With the development of online retailing, traditional online platforms have begun to offer a novel online marketplace format that brings many advantages but also gives rise to problems such as operational risks and changes in channel power. This paper studies whether the e-tailer and manufacturer can reach a consensus on introducing the marketplace channel and obtains the following insights. First, the manufacturer always prefers to introduce the online marketplace in the model where she acts as the leader. The e-tailer has the same preference for the online marketplace when he moves later or simultaneously with the manufacturer. Second, as the participants become more risk-averse, the manufacturer and e-tailer would be less willing to introduce the online marketplace, and it would be more difficult to realize the equilibrium strategy under all power structure models. Third, by combining the cross-effect of the attitude towards risks and the power structure, we find that when the participants prefer risks and the e-tailer has strong channel power, it would be much easier for the two parties to reach a consensus about introducing the online marketplace.     

Fault handling in large water networks with online dictionary learning

Fault detection and isolation in water distribution networks is an active topic due to the nonlinearities of flow propagation and recent increases in data availability due to sensor deployment. Here, we propose an efficient two-step data driven alternative: first, we perform sensor placement taking the network topology into account; second, we use incoming sensor data to build a network model through online dictionary learning. Online learning is fast and allows tackling large networks as it processes small batches of signals at a time. This brings the benefit of continuous integration of new data into the existing network model, either in the beginning for training or in production when new data samples are gathered. The proposed algorithms show good performance in our simulations on both small and large-scale networks.