Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network.

Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model's comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network's proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network's ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.

Drought prediction based on an improved VMD-OS-QR-ELM model.

To overcome the low accuracy, poor reliability, and delay in the current drought prediction models, we propose a new extreme learning machine (ELM) based on an improved variational mode decomposition (VMD). The model first redefines the output of the hidden layer of the ELM model with orthogonal triangular matrix decomposition (QR) to construct an orthogonal triangular ELM (QR-ELM), and then introduces an online sequence learning mechanism (OS) into the QR-ELM to construct an online sequence OR-ELM (OS-QR-ELM), which effectively improves the efficiency of the ELM model. The mutual information extension method was then used to extend both ends of the original signal to improve the VMD end effect. Finally, VMD and OS-QR-ELM were combined to construct a drought prediction method based on the VMD-OS-QR-ELM. The reliability and accuracy of the VMD-OS-QR-ELM model were improved by 86.19% and 93.20%, respectively, compared with those of the support vector regression model combined with empirical mode decomposition. Furthermore, the calculation efficiency of the OS-QR-ELM model was increased by 88.65% and 85.32% compared with that of the ELM and QR-ELM models, respectively.

[Influence of semen preservation and processing methods on sperm DNA integrity].

OBJECTIVE: To investigate the influence of different methods of semen preservation and processing on sperm DNA integrity. METHODS: We collected semen samples from 100 normozoospermic male volunteers and, following homogeneous mixing, preserved them by means of snap freezing, slow freezing, or at the room temperature for 4 and 24 hours. Meanwhile we processed the semen by washing, swim-up, and density gradient centrifugation (DGC). Then we obtained the sperm DNA fragmentation index (DFI) by sperm chromatin dispersion test and measured total sperm motility and DFI after cultured for 24 hours following processing. RESULTS: The sperm DFIs after 4 hours of preservation by snap freezing, slow freezing, and at the room temperature were (27.3 ± 6.4)%, (26.9 ± 6.1)%, and (24.7 ± 6.8)%, respectively, and that after preserved at the room temperature for 24 hours was (35.6 ± 9.0)%, with statistically significant differences between the first three and the 24-hour room temperature preservation groups (P < 0.05) but not among the former three groups (P > 0.05). The sperm DFI was significantly higher in the samples processed by washing ([13.7 ± 2.0]%) than in those processed by swim-up ([9.1 ± 1.3]%) and DGC ([8.0 ± 2.5]%) (P < 0.05), and it was the lowest in the DGC group after 24-hour culture ([11.5 ± 4.2]%) as compared with the other groups (P < 0.05). CONCLUSION: Sperm DNA integrity is influenced by different semen preservation conditions and processing methods.

[Time interval from the end of sperm processing to artificial intrauterine in semination with husband's sperm correlates to the rate of clinical pregnancy].

OBJECTIVE: To investigate the influence of the time interval from the end of semen processing to artificial intrauterine in semination with husband's sperm (AIH-IUI) on the rate of clinical pregnancy. METHODS: This study involved 191 AIH-IUI cycles with the same ovulation induction protocol. After Percoll density gradient centrifugation, we divided the sperm into four groups based on the incubation time: 0-19, 20-39, 40-59, and 60-80 min, and again into another four groups according to the total progressively motile sperm count (TPMC): (0-9), (10-20), (21-30), and > 30 x 10(6). We analyzed the correlation of the clinical pregnancy rate with the time interval from the end of sperm processing to AIH-IUI and with other influencing factors, such as maternal age, infertility duration, and semen quality. RESULTS: The rate of clinical pregnancy was significantly higher in the 20-39 min group (18.3%) than in the 0-19, 40-59, and 60-80 min groups (12.7, 11.4 and 9.1%) (all P < 0.05). The (10-20) x 10(6) group achieved a remarkably higher pregnancy rate (16.7%) than the (0-9), (21-30), and > 30 x 10(6) groups (0, 11.4, and 8.3%) (all P < 0.05). Logistic multivariate analysis showed that the rate of clinical pregnancy was decreased with the increased age of the women (OR 0.89, 95% CI 0.83-0.94) but significantly elevated in the 20-39 min group (OR 2.11, 95% CI 1.34-3.13) and of (10-20) x 10(6) group (OR 2.06, 95% CI 1.32-3.46). CONCLUSION: The time interval from the end of sperm processing to AIH-IUI is a most significant factor influencing the rate of clinical pregnancy of AIH-IUI.