Background Depression is a serious personal and public mental health problem. Self-reporting is the main method used to diagnose depression and to determine the severity of depression. However, it is not easy to discover patients with depression owing to feelings of shame in disclosing or discussing their mental health conditions with others. Moreover, self-reporting is time-consuming, and usually leads to missing a certain number of cases. Therefore, automatic discovery of patients with depression from other sources such as social media has been attracting increasing attention. Social media, as one of the most important daily communication systems, connects large quantities of people, including individuals with depression, and provides a channel to discover patients with depression. In this study, we investigated deep-learning methods for depression risk prediction using data from Chinese microblogs, which have potential to discover more patients with depression and to trace their mental health conditions. Objective The aim of this study was to explore the potential of state-of-the-art deep-learning methods on depression risk prediction from Chinese microblogs. Methods Deep-learning methods with pretrained language representation models, including bidirectional encoder representations from transformers (BERT), robustly optimized BERT pretraining approach (RoBERTa), and generalized autoregressive pretraining for language understanding (XLNET), were investigated for depression risk prediction, and were compared with previous methods on a manually annotated benchmark dataset. Depression risk was assessed at four levels from 0 to 3, where 0, 1, 2, and 3 denote no inclination, and mild, moderate, and severe depression risk, respectively. The dataset was collected from the Chinese microblog Weibo. We also compared different deep-learning methods with pretrained language representation models in two settings: (1) publicly released pretrained language representation models, and (2) language representation models further pretrained on a large-scale unlabeled dataset collected from Weibo. Precision, recall, and F1 scores were used as performance evaluation measures. Results Among the three deep-learning methods, BERT achieved the best performance with a microaveraged F1 score of 0.856. RoBERTa achieved the best performance with a macroaveraged F1 score of 0.424 on depression risk at levels 1, 2, and 3, which represents a new benchmark result on the dataset. The further pretrained language representation models demonstrated improvement over publicly released prediction models. Conclusions We applied deep-learning methods with pretrained language representation models to automatically predict depression risk using data from Chinese microblogs. The experimental results showed that the deep-learning methods performed better than previous methods, and have greater potential to discover patients with depression and to trace their mental health conditions.
The accumulation process of sulfate reducing bacteria (SRB) biofilms established in anaerobic stagnant batch bioreactors on the surface of carbon steel and the nutrient transport and corrosion products distribution in it were characterized by X-ray Photoelectron Spectroscopy (XPS). In addition, the corrosion occurrence and development of carbon steel under SRB biofilm was investigated by Electrochemical Impedance Spectroscopy (EIS) in-situ. The results show that the thickness of SRB biofilms increases exponentially with time in the beginning and after 14 days reaches a maximum.From then on, the accumulation rate decreases to zero. In mature biofilms, SRB dispersed throughout the biofilm. In the inner layer near the substrate, due to the high sulfate-reducing activity of SRB, corrosion products such S 2À , H 2 S and organic acid are present, which lead to corrosion occurrence and development. In the outer layer of the biofilm SRB can also reduce the SO 2À 4 to SO 2À 3 and S 2 O 2À 3 . This metabolism process enhances the Fe 2þ transfer from the inner to the outer side. The activity of SRB in the biofilm plays a key role in the initial corrosion process.
Inhibition of corrosion of mild steel by sodium n,n-diethyl dithiocarbamate in hydrochloric acid solution T he eVect of sodium n,n-diethyl dithiocarbamate ( SDEDT C) on the corrosion of mild steel H.-B. FAN in 0•5 mol L -1 HCl solution was studied using weight loss and potentiodynamic polarisation C.-Y. FU methods. Ex perimental data revealed that SDEDT C acted as an inhibitor in the acid H.-L. WANG environment and, furthermore, that the compound was a mixed type inhibitor, predominating X.-P. GUO as a cathodic inhibitor. It was found that the inhibition eYciency increased with an increase J.-S. ZHENGin SDEDT C concentration at temperatures in the range 303-333 K . T he ex perimental data for the inhibitor tted the Flory-H uggins adsorption isotherm and the El-Awady thermodynamic kinetic model. T he corrosion inhibition mechanism of SDEDT C on mild steel in H Cl solution was also investigated by infrared spectra and electron probe microanalysis methods. T he process of inhibition is attributed to the formation of an adsorbed lm on the metal surface, which protects the metal against corrosive agents.
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