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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.

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.

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.

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.

The purpose of this study is to elucidate the effects of electric-arc-induced ablation on the corrosion behavior of pipeline steel in neutral and high pH environments.

Electrochemical testing, an atmospheric-pressure immersion experiment and various techniques (e.g. scanning electron microscopy, energy dispersive X-ray spectroscopy and X-ray photoelectron spectroscopy) were used to examine the effects of electric-arc-induced ablation on the corrosion behavior of pipeline steel in neutral and high pH environment.

Electric-arc-induced ablation occurred preferentially in areas of inclusion. The corrosion resistance of an ablation pit was lower than that of non-ablation areas. In the neutral soil solution, general corrosion was the dominant corrosion that affected pipeline steel; the effect of ablation was small but pitting corrosion could still be induced. In a high pH environment, the samples without ablation were passivated, whereas the samples with ablation pits could not be passivated; the ablation pits were likely to develop pitting corrosion.

Electric-arc-induced ablation can reduce the corrosion resistance of pipeline steel under high-voltage direct current interference.