USING SOCIAL NETWORK ANALYSIS AS A STRATEGY FOR E-COMMERCE RECOMMENDATION

Recommender agents are being widely used by E-commerce business to help customers make decisions from a large amount of choices. To improve the performance of recommendation agents, three main approaches (content-based approaches, collaborative approaches and hybrid approaches) have been proposed to address recommendation problem whose basic idea is to discover similarity of items and users and predicate users’ preference toward a set of items. This provides potential for using social network analysis to make recommendations since social network analysis can be used to investigate the relationships of customers. In this research, we illustrate the concepts of social network analysis and how it can be employed to make better recommendations in E-commerce context. Application and research opportunities are presented

UNDERSTANDING USERS’ SATISFACTION WITH SOCIAL LEARNING NETWORK

The social learning network (SLN) constructed based on web 2.0 techniques demonstrates some unique characteristics comparing to traditional e-learning context based on 1.0 technique. In response to the new characteristics in SLN, we advance the theoretical understanding of user satisfaction by reconceptualising e-learning as a relational process among students. Based on that, we draw on network externalities and social capital theory to examine users’ satisfaction with social learning network. Considering that network externalities are involved in the process, we propose that two types of network externalities: direct network externality and indirect network externality moderate the relationship between social capital and user satisfaction. Theoretical, practical implications and future research are also discussed

Interpretable emotion recognition using EEG signals

Electroencephalogram (EEG) signal-based emotion recognition has attracted wide interests in recent years and has been broadly adopted in medical, affective computing, and other relevant fields. However, the majority of the research reported in this field tends to focus on the accuracy of classification whilst neglecting the interpretability of emotion progression. In this paper, we propose a new interpretable emotion recognition approach with the activation mechanism by using machine learning and EEG signals. This paper innovatively proposes the emotional activation curve to demonstrate the activation process of emotions. The algorithm first extracts features from EEG signals and classifies emotions using machine learning techniques, in which different parts of a trial are used to train the proposed model and assess its impact on emotion recognition results. Second, novel activation curves of emotions are constructed based on the classification results, and two emotion coefficients, i.e., the correlation coefficients and entropy coefficients. The activation curve can not only classify emotions but also reveals to a certain extent the emotional activation mechanism. Finally, a weight coefficient is obtained from the two coefficients to improve the accuracy of emotion recognition. To validate the proposed method, experiments have been carried out on the DEAP and SEED dataset. The results support the point that emotions are progressively activated throughout the experiment, and the weighting coefficients based on the correlation coefficient and the entropy coefficient can effectively improve the EEG-based emotion recognition accuracy

HOW TO ADVERTISE APPROAPRIATELY ON THE WORLD WIDE WEB? A MULTI-CONGRUITY ANALYSIS APPROACH

As a popular and important advertising style, Internet advertising has drawn substantial amount of scholarly attention. Previous studies focus on the independent effects of various factors, such as product, consumer, website and ad per se, but few studies consider the impacts of the congruities between these factors on consumer’s attitude toward the ads. In this paper, we propose an integrative model, product-consumer-website-ad model, to articulate how the congruity between factors exerts its effect. We propose that ad appeal (emotional vs. informational) should be designed consistent with the nature of the advertised product (hedonic vs. utilitarian), the nature of the website (hedonic vs. utilitarian) and the thinking styles of consumer (intuitive vs. rational). Personalization plays an important role in the process to achieve the congruity. We also propose that the ad on the website with high reputation will generate more favourable attitude toward it. Implications and future research are also discussed in the paper

Wettability Alteration Process at Pore-Scale during Engineered Waterflooding using Computational Fluid Dynamics

Engineered waterflooding modifies chemistry of injected brine to efficiently and environmentally friendly enhance oil recovery. The common practice of engineered waterflooding includes low salinity waterflooding (LSW) and carbonated waterflooding. Among these oil recovery methods, wettability alteration has been perceived as a critical physicochemical process for additional oil recovery. While extensive work has been conducted to characterize the wettability alteration, the existing theory cannot explain the conflict oil recovery between secondary mode (injecting engineered water at the very beginning of flooding) and tertiary mode (injecting engineered water after conventional waterflooding), where secondary engineered waterflooding always gives a greater incremental oil recovery than tertiary mode. To explain this recovery difference, a preferential flow channel was hypothesized to be created by secondary flooding, which likely reduces sweep efficiency of tertiary flooding. To test this hypothesis, computational fluid dynamic simulations were performed with finite volume method coupled with dynamic contact angles in OpenFOAM to represent wettability characteristics (from strongly oil-wet to strongly water-wet) at pore scale to quantify the role of pre-existing flow channel in the oil recovery at different flooding modes. The simulation results showed that secondary engineered waterflooding indeed generates a preferential flow pathway, which reduces recovery efficiency of subsequent tertiary waterflooding. Streamline analysis confirms that tertiary engineered waterflooding transports faster than secondary engineered waterflooding, implying that sweep efficiency of tertiary engineered waterflooding is lower than secondary engineered waterflooding. This work provides insights for a greater oil recovery at secondary mode than tertiary mode during engineered waterflooding at pore scale

Densovirus is a mutualistic symbiont of a global crop pest (Helicoverpa armigera) and protects against a baculovirus and Bt biopesticide

Mutualistic associations between symbiotic bacteria and their hosts are common within insect systems. However, viruses are often considered as pathogens even though some have been reported to be beneficial to their hosts. Herein, we report a novel densovirus, Helicoverpa armigera densovirus-1 (HaDNV-1) that appears to be beneficial to its host. HaDNV-1 was found to be widespread in wild populations of H. armigera adults (.67% prevalence between 2008 and 2012). In wild larval populations, there was a clear negative interaction between HaDNV-1 and H. armigera nucleopolyhedrovirus (HaNPV), a baculovirus that is widely used as a biopesticide. Laboratory bioassays revealed that larvae hosting HaDNV-1 had significantly enhanced resistance to HaNPV (and lower viral loads), and that resistance to Bacillus thuringiensis (Bt) toxin was also higher at low doses. Laboratory assays indicated that the virus was mainly distributed in the fat body, and could be both horizontally- and vertically-transmitted, though the former occurred only at large challenge doses. Densovirus-positive individuals developed more quickly and had higher fecundity than uninfected insects. We found no evidence for a negative effect of HaDNV-1 infection on H. armigera fitness-related traits, strongly suggesting a mutualistic interaction between the cotton bollworm and its densovirus