Correction: Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.

[This corrects the article DOI: 10.1371/journal.pone.0275998.].

Biomedical Argument Mining Based on Sequential Multi-Task Learning.

Biomedical argument mining aims to automatically identify and extract the argumentative structure in biomedical text. It helps to determine not only what positions people adopt, but also why they hold such opinions, which provides valuable insights into medical decision making. Generally, biomedical argument mining consists of three subtasks: argument component identification, argument component classification and relation identification. Current approaches employ conventional multi-task learning framework for jointly addressing the latter two subtasks, and achieve some success. However, explicit sequential dependency between these two subtasks is ignored, which is crucial for accurate biomedical argument mining. Moreover, relation identification is conducted solely based on the argument component pair without considering its potentially valuable context. Therefore, in this paper, a novel sequential multi-task learning approach is proposed for biomedical argument mining. Specifically, to model explicit sequential dependency between argument component classification and relation identification, an information transfer strategy is employed to capture the information of argument component type that is transferred to relation identification. Furthermore, graph convolutional network is employed to model dependency relation among the related argument component pairs. The proposed method has been evaluated on a benchmark dataset and the experimental results show that the proposed method outperforms the state-of-the-art methods.

Steam turbine power prediction based on encode-decoder framework guided by the condenser vacuum degree.

The steam turbine is one of the major pieces of equipment in thermal power plants. It is crucial to predict its output accurately. However, because of its complex coupling relationships with other equipment, it is still a challenging task. Previous methods mainly focus on the operation of the steam turbine individually while ignoring the coupling relationship with the condenser, which we believe is crucial for the prediction. Therefore, in this paper, to explore the coupling relationship between steam turbine and condenser, we propose a novel approach for steam turbine power prediction based on the encode-decoder framework guided by the condenser vacuum degree (CVD-EDF). In specific, the historical information within condenser operation conditions data is encoded using a long-short term memory network. Moreover, a connection module consisting of an attention mechanism and a convolutional neural network is incorporated to capture the local and global information in the encoder. The steam turbine power is predicted based on all the information. In this way, the coupling relationship between the condenser and the steam turbine is fully explored. Abundant experiments are conducted on real data from the power plant. The experimental results show that our proposed CVD-EDF achieves great improvements over several competitive methods. our method improves by 32.2% and 37.0% in terms of RMSE and MAE by comparing the LSTM at one-minute intervals.

Health issue identification in social media based on multi-task hierarchical neural networks with topic attention.

OBJECTIVE: Health issue identification in social media is to predict whether the writers have a disease based on their posts. Numerous posts and comments are shared on social media by users. Certain posts may reflect writers' health condition, which can be employed for health issue identification. Usually, the health issue identification problem is formulated as a classification task. METHODS AND MATERIAL: In this paper, we propose novel multi-task hierarchical neural networks with topic attention for identifying health issue based on posts collected from the social media platforms. Specifically, the model incorporates the hierarchical relationship among the document, sentences, and words via bidirectional gated recurrent units (BiGRUs). The global topic information shared across posts is incorporated with the hidden states of BiGRUs to obtain the topic-enhanced attention weights for words. In addition, tasks of predicting whether the writers suffer from a disease (health issue identification) and predicting the specific domain of the posts (domain category classification) are learned jointly in multi-task mechanism. RESULTS: The proposed method is evaluated on two datasets: dementia issue dataset and depression issue dataset. The proposed approach achieves 98.03% and 88.28% F-1 score on two datasets, outperforming the state-of-the-art approach by 0.73% and 0.4% respectively. Further experimental analysis shows the effectiveness of incorporating both the multi-task learning framework and topic attention mechanism.