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The purpose of this research is to explore the effect of goal type of user-generated content (UGC) on consumers' intention of within-domain and across-domain compensatory consumption in closed social media platforms, the mediating effect of self-threat, self-enhancement and self-protection motivation and the moderating effect of self-efficacy.

Four between-subject experiments were conducted to verify the hypotheses presented in this research. Study 1a had a sample size of 60 respondents and examined the effect of goal type of UGC on compensatory consumption. Study1b replicated the effect by using a sample size of 60 respondents. Study 2 had a sample size of 70 respondents and verified the mediating role of self-threat, self-enhancement motivation and self-protection motivation. Study 3 examined self-efficacy as a critical moderator by using a sample of 255 participants.

Study 1a and Study 1b demonstrate that UGC with an attainment goal (vs maintenance goal) is more easily produced within-domain and across-domain compensatory consumption. Study 2 verifies that the effect of goal type of UGC on within-domain compensatory consumption is serially mediated by self-threat and self-enhancement motivation, and the effect of goal type of UGC on across-domain compensatory consumption is serially mediated by self-threat and self-protection motivation. Study 3 demonstrates that self-efficacy not only moderates the effects of self-threat on within-domain or across-domain compensatory consumption respectively, but also moderates the whole mediating path structure.

This research extends the understanding of UGC and addresses the inconclusive evidence of the impact of goal type of UGC on consumer compensatory behavior in the context of closed social media platforms. The authors identify the moderating role of self-efficacy, which explains why consumers adopt different compensatory strategies.

The purpose of this paper is to improve the precision of gangue detection. In the real production environment, some gangue features are not obvious, and it is difficult to distinguish between coal and gangue. The color of the conveyor belt is similar to the gangue, the background noise also brings challenge to gangue detection. To address the above problems, we propose a feature aggregation method based on optical flow (FAOF).

An FAOF is proposed. First, to enhance the feature representation of the current frame, FAOF applies the timing information of video stream, propagates the feature information of the past few frames to the current frame by optical flow. Second, the coordinate attention (CA) module is adopted to suppress the noise impact brought by the background of convey belt. Third, the Mish activation function is used to replace rectified linear unit to improve the generalization capability of our model.

The experimental results show that the gangue detection model proposed in this paper improve 4.3 average precision compared to baseline. This model can effectively improve the accuracy of gangue detection in real production environment.

The key contributions are as follows: this study proposes an FAOF; this study adds CA module and Mish to reduce noise from the background of the conveyor belt; and this study also constructs a large gangue data set.

This study aims to address the problem of the divergence of traditional inertial navigation system (INS)/celestial navigation system (CNS)-integrated navigation for ballistic missiles. The authors introduce Doppler navigation system (DNS) and X-ray pulsar navigation (XNAV) to the traditional INS/CNS-integrated navigation system and then propose an INS/CNS/DNS/XNAV deep integrated navigation system.

DNS and XNAV can provide velocity and position information, respectively. In addition to providing velocity information directly, DNS suppresses the impact of the Doppler effect on pulsar time of arrival (TOA). A pulsar TOA with drift bias is observed during the short navigation process. To solve this problem, the pulsar TOA drift bias model is established. And the parameters of the navigation filter are optimised based on this model.

The experimental results show that the INS/CNS/DNS/XNAV deep integrated navigation can suppress the drift of the accelerometer to a certain extent to improve the precision of position and velocity determination. In addition, this integrated navigation method can reduce the required accuracy of inertial navigation, thereby reducing the cost of missile manufacturing and realising low-cost and high-precision navigation.

The velocity information provided by the DNS can suppress the pulsar TOA drift, thereby improving the positioning accuracy of the XNAV. This reflects the “deep” integration of these two navigation methods.

The purpose of this paper is to handle the anaphors through anaphora resolution in aspect-oriented sentiment analysis. Sentiment analysis is one of the predictive analytics of social media. In particular, the social media platform Twitter is an open platform to post the opinion by subscribers on contextual issues, events, products, individuals and organizations.

The sentiment polarity assessment is not deterministic to conclude the opinion of the target audience unless the polarity is assessed under diversified aspects. Hence, the aspect-oriented sentiment polarity assessment is a crucial objective of the opinion assessment over social media. However, the aspect-oriented sentiment polarity assessment often influences by the curse of anaphora resolution.

Focusing on these limitations, a scale to estimate the aspects oriented sentiment polarity under anaphors influence has been portrayed in this article. To assess the aspect-based sentiment polarity of the tweets, the anaphors of the tweets have been considered to assess the weightage of the tweets toward the sentiment polarity.

The experimental study presents the performance of the proposed model by comparing it with the contemporary models, which are estimating the sentiment polarity tweets under anaphors impact.