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Relationship extraction is a crucial step in the construction of a knowledge graph. In this research, the grid field entity relationship extraction was performed via a labeling approach that used span representation. The subject entity and object entity were used as training instances to bolster the linkage between them. The embedding layer of the RoBERTa pre-training model included word embedding, position embedding, and paragraph embedding information. In addition, semantic dependency was introduced to establish an effective linkage between different entities. To facilitate the effective linkage, an additional lexically labeled embedment was introduced to empower the model to acquire more profound semantic insights. After obtaining the embedding layer, the RoBERTa model was used for multi-task learning of entities and relations. The multi-task information was then fused using the parameter hard sharing mechanism. Finally, after the layer was fully connected, the predicted entity relations were obtained. The approach was tested on a grid field dataset created for this study. The obtained results demonstrated that the proposed model has high performance. [ABSTRACT FROM AUTHOR]

In view of the fact that entity nested and professional terms are difficult to identify in the field of power dispatch, a multi-task-based few-shot named entity recognition model (FSPD-NER) for power dispatch is proposed. The model consists of four modules: feature enhancement, seed, expansion, and implication. Firstly, the masking strategy of the encoder is improved by adopting whole-word masking, using a RoBERTa (Robustly Optimized BERT Pretraining Approach) encoder as the embedding layer to obtain the text feature representation, and an IDCNN (Iterated Dilated CNN) module to enhance the feature. Then the text is cut into one Chinese character and two Chinese characters as a seed set, the score for each seed is calculated, and if the score is greater than the threshold value ω, they are passed to the expansion module as candidate seeds; next, the candidate seeds need to be expanded left and right according to offset γ to obtain the candidate entities; finally, to construct text implication pairs, the input text is used as a premise sentence, the candidate entity is connected with predefined label templates as hypothesis sentences, and the implication pairs are passed to the RoBERTa encoder for the classification task. The focus loss function is used to alleviate label imbalance during training. The experimental results of the model on the power dispatch dataset show that the precision, recall, and F1 scores of the recognition results in 20-shot samples are 63.39%, 61.97%, and 62.67%, respectively, which is a significant performance improvement compared to existing methods. [ABSTRACT FROM AUTHOR]

With the aim of solving the current problems of low utilization of entity features, multiple meanings of a word, and poor recognition of specialized terms in the Chinese power marketing domain named entity recognition (PMDNER), this study proposes a Chinese power marketing named entity recognition method based on whole word masking and joint extraction of dual features. Firstly, word vectorization of the electricity text data is performed using the RoBERTa pre-training model; then, it is fed into the constructed dual feature extraction neural network (DFENN) to acquire the local and global features of text in a parallel manner and fuse them. The output of the RoBERTa layer is used as the auxiliary classification layer, the output of the DFENN layer is used as the master classification layer, and the output of the two layers is dynamically combined through the attention mechanism to weight the outputs of the two layers so as to fuse new features, which are input into the conditional random field (CRF) layer to obtain the most reasonable label sequence. A focal loss function is used in the training process to alleviate the problem of uneven sample distribution. The experimental results show that the method achieved an F1 value of 88.58% on the constructed named entity recognition dataset in the power marketing domain, which is a significant improvement in performance compared with the existing methods. [ABSTRACT FROM AUTHOR]

Named entity identification is an important step in building a knowledge graph of the grid domain, which contains a certain number of nested entities. To address the issue of nested entities in the Chinese power dispatching domain's named entity recognition, we propose a RoBERTa-Attention-FL model. This model effectively recognizes nested entities using the span representation annotation method. We extract the output values from RoBERTa's middle 4–10 layers, obtain syntactic information from the Transformer Encoder layers via the multi-head self-attention mechanism, and integrate it with deep semantic information output from RoBERTa's last layer. During training, we use Focal Loss to mitigate the sample imbalance problem. To evaluate the model's performance, we construct named entity recognition datasets for flat and nested entities in the power dispatching domain annotated with actual power operation data, and conduct experiments. The results indicate that compared to the baseline model, the RoBERTa-Attention-FL model significantly improves recognition performance, increasing the F1-score by 4.28% to 90.35%, with an accuracy rate of 92.53% and a recall rate of 88.12%. [ABSTRACT FROM AUTHOR]

Soft Magneto-active Elastomer (MAE) with soft magnetic particles arranged in chains (chain-like structure) can be driven by homogenous magnetic fields, which has great application potential in the area of soft robots. It is important to reveal the relationship between the magneto-actuation characteristics and the arrangement of chain angle, which is one of the essential issues for the practical application of the MAE with chain-like structure. In this study, the kinetic deformation properties of centimeter-scale soft MAE samples with different chain angles subjected to applied homogenous magnetic fields will be investigated based on the designed experimental set-up, and then a mathematical model will be proposed to describe the kinetic deformation properties. The presented research work will provide fundamental investigation on the application of soft robots based on MAE type materials. Graphical Abstract [ABSTRACT FROM AUTHOR]

Concussion syndrome is a common disease in neurosurgery, and its incidence ranks first among all traumatic brain injuries. Cognitive dysfunction is one of the most common functional impairments in concussion syndrome. Neuroimaging and content assessments on concussion patients and healthy control subjects are used in this study, which uses MRI technology to evaluate brain pictures of concussion patients. Moreover, this paper separately evaluates the scores of the concussion syndrome group and the healthy control group in multiple functional aspects and performs independent sample t -test after statistics of the two scores. In addition, this paper uses resting-state fMRI to study the changes in the functional connectivity of the medial prefrontal lobe in patients with PCS, which has certain significance in revealing cognitive dysfunction after concussion and has a certain effect on improving the clinical emergency diagnosis and treatment of concussion. [ABSTRACT FROM AUTHOR]

Objectives. To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods. Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88 ± 13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46 ± 11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. Results. The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. Conclusions. Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition. [ABSTRACT FROM AUTHOR]