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For the extraction of judicial events for Tibetan, a low-resource language, traditional simple neural network approaches struggle to adequately capture the deep semantics and features of the texts because Tibetan texts are usually lengthy and contain numerous judicial-related entities. To overcome this limitation, this research presents an event extraction model combining deep word representation with hybrid neural networks for the Tibetan judicial domain. The model introduces the Chinese minority pre-trained language model (CINO), which generates dynamic word vector representations, addressing the challenge of modeling the deep semantics inherent in Tibetan texts. During feature extraction, a bidirectional long short-term memory network (BiLSTM) is applied to extract the temporal and contextual dependencies, while a convolutional neural network (CNN) is utilized to capture the local semantic features to construct a comprehensive global semantic representation. Finally, the sequences are decoded through conditional random field (CRF) to generate optimal prediction results, thus achieving the efficient extraction of Tibetan judicial events. The experimental findings indicate that the model outperforms the baselines by achieving F1 scores of 70.47% for trigger detection and 62.99% for argument recognition, with improvements of 16.6% and 16.42%, respectively. These results confirm the effectiveness and superiority of the proposed model.

Abstract Uneven lighting conditions often occur during real-life photography, such as images taken at night that may have both low-light dark areas and high-light overexposed areas. Traditional algorithms for enhancing low-light areas also increase the brightness of overexposed areas, affecting the overall visual effect of the image. Therefore, it is important to achieve differentiated enhancement of low-light and high-light areas. In this paper, we propose a network called correcting uneven illumination network (CUI-Net) with sparse attention transformer and convolutional neural network (CNN) to better extract low-light features by constraining high-light features. Specifically, CUI-Net consists of two main modules: a low-light enhancement module and an auxiliary module. The enhancement module is a hybrid network that combines the advantages of CNN and Transformer network, which can alleviate uneven lighting problems and enhance local details better. The auxiliary module is used to converge the enhancement results of multiple enhancement modules during the training phase, so that only one enhancement module is needed during the testing phase to speed up inference. Furthermore, zero-shot learning is used in this paper to adapt to complex uneven lighting environments without requiring paired or unpaired training data. Finally, to validate the effectiveness of the algorithm, we tested it on multiple datasets of different types, and the algorithm showed stable performance, demonstrating its good robustness. Additionally, by applying this algorithm to practical visual tasks such as object detection, face detection, and semantic segmentation, and comparing it with other state-of-the-art low-light image enhancement algorithms, we have demonstrated its practicality and advantages.

The expression changes of high-mobility group box 1 (HMGB1) in the mouse cochlea have recently been implicated in noise-induced hearing loss, suggesting that HMGB1 participates in regulating cochlear function. However, the precise role of HMGB1 in the auditory system remains largely unclear. This study aimed to investigate its function in the developing mouse cochlea by examining the expression pattern of HMGB1 in the mouse cochlea from embryonic day (E) 18.5 to postnatal day (P) 28 using double immunofluorescence on frozen sections. Our findings revealed that HMGB1 was extensively expressed in the cell nucleus across various regions of the mouse cochlea, including the organ of Corti. Furthermore, its expression underwent developmental regulation during mouse cochlear development. Specifically, HMGB1 was found to be localized in the tympanic border cells at each developmental stage, coinciding with the gradual anatomical in this region during development. In addition, HMGB1 was expressed in the greater epithelial ridge (GER) and supporting cells of the organ of Corti, as validated by the supporting cell marker Sox2 at P1 and P8. However, at P14, the expression of HMGB1 disappeared from the GER, coinciding with the degeneration of the GER into the inner sulcus cells. Moreover, we observed that HMGB1 co-localized with Ki-67-positive proliferating cells in several cochlear regions during late embryonic and early postnatal stages, including the GER, the tympanic border cells, cochlear lateral wall, and cochlear nerves. Furthermore, by dual-staining Ki-67 with neuronal marker TUJ1 and glial marker Sox10, we determined the expression of Ki-67 in the neonatal glial cells. Our spatial-temporal analysis demonstrated that HMGB1 exhibited distinct expression patterns during mouse cochlear development. The co-localization of HMGB1 with Ki-67-positive proliferating cells suggested that HMGB1 may play a role in cochlear development.

Field-programmable gate array (FPGA)-based real-time simulation plays a crucial role in testing power–electronic dominated systems with the formation of controller hardware-in-the-loop (CHIL) or power hardware-in-the-loop (PHIL). This work describes an efficient implementation of computation time and resource usage in the FPGA-based study of a modular multilevel converter (MMC) with detailed electromagnetic transients. The proposed modeling technique can be used in continuous control mode (CCM) and discontinuous control mode (DCM) for high-switching frequency semiconductor technologies. An FPGA-based designed solver structure is also presented to take advantage of the parallel features of FPGAs to achieve an ultra-fast calculation speed. In addition, two different switch modeling techniques are discussed with a five-level MMC case study. Experimental results on the NI PXIe platform show the feasibility of the proposed implementation, and a time step of 100 nanoseconds is achieved.

Mitogen-activated protein kinases (MAPKs and MPKs) are important in the process of resisting plant stress. In this study, 21, 12, 18, 16, and 10 MPKs were identified from Musa acuminata, Musa balbisiana, Musa itinerans, Musa schizocarpa, and Musa textilis, respectively. These MPKs were divided into Group A, B, C, and D. Phylogenetic analysis revealed that this difference in number was due to the gene shrinkage of the Group B subfamily of Musa balbisiana and Musa textilis. KEGG annotations revealed that K14512, which is involved in plant hormone signal transduction and the plant–pathogen interaction, was the most conserved pathway of the MPKs. The results of promoter cis-acting element prediction and focTR4 (Fusarium oxysporum f. sp. cubense tropical race 4) transcriptome expression analysis preliminarily confirmed that MPKs were relevant to plant hormone and biotic stress, respectively. The expression of MPKs in Group A was significantly upregulated at 4 °C, and dramatically, the MPKs in the root were affected by low temperature. miR172, miR319, miR395, miR398, and miR399 may be the miRNAs that regulate MPKs during low-temperature stress, with miR172 being the most critical. miRNA prediction and qRT-PCR results indicated that miR172 may negatively regulate MPKs. Therefore, we deduced that MPKs might coordinate with miR172 to participate in the process of the resistance to low-temperature stress in the roots of the banana. This study will provide a theoretical basis for further analysis of the mechanism of MPKs under low-temperature stress of bananas, and this study could be applied to molecular breeding of bananas in the future.

Polymetallic nodules, found abundantly in deep-ocean deposits, possess significant economic value and represent a valuable resource due to their high metal enrichment, crucial for the high-tech industry. However, accurately evaluating these valuable mineral resources presents challenges for traditional image segmentation methods due to issues like color distortion, uneven illumination, and the diverse distribution of nodules in seabed images. Moreover, the scarcity of annotated images further compounds these challenges, impeding resource assessment efforts. To overcome these limitations, we propose a novel two-stage diffusion-based model for nodule image segmentation, along with a linear regression model for predicting nodule abundance based on the coverage obtained through nodule segmentation. In the first stage, we leverage a diffusion model trained on predominantly unlabeled mineral images to extract multiscale semantic features. Subsequently, we introduce an efficient segmentation network designed specifically for nodule segmentation. Experimental evaluations conducted on a comprehensive seabed nodule dataset demonstrate the exceptional performance of our approach compared to other deep learning methods, particularly in addressing challenging conditions like uneven illumination and dense nodule distributions. Our proposed model not only extends the application of diffusion models but also exhibits superior performance in seabed nodule segmentation. Additionally, we establish a linear regression model that accurately predicts nodule abundance by utilizing the coverage calculated through seabed nodule image segmentation. The results highlight the model’s capacity to accurately assess nodule coverage and abundance, even in regions beyond the sampled sites, thereby providing valuable insights for seabed resource evaluation.

Abstract Background Fibrinogen (FIB) has been found to be a promising marker in diagnosing periprosthetic joint infection (PJI), however, the value of FIB in predicting reinfection of PJI is unknown. The purpose of this study was to evaluate the value of FIB in predicting reinfection after debridement, antibiotics, and implant retention (DAIR) for PJI. Methods We retrospectively analyzed the clinical data of patients who were diagnosed with PJI and underwent DAIR from 2013 to 2019. The levels of the FIB, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) were measured before DAIR. After DAIR, patients were followed and reinfections were identified. For both acute and chronic PJI, the predictive value of FIB was evaluated by calculating the sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve (ROC), and was compared with traditional inflammatory markers including ESR and CRP. Results The expression of FIB differed between patients reinfected and those not reinfected in both acute and chronic PJI (p

Considering that the motion state and the stress of floating hose are complex under the combined action of winds, waves and currents, the dynamic simulation analysis of offshore floating hose oil transportation system was thereby carried out through the method of joint simulation based on AQWA and OrcaFlex.The numerical model was established to analyze the frequency domain of oil tanker by AQWA, and to perform the time domain analysis on the coupling motion between floating hose, oil tanker, buoy and mooring line by OrcaFlex.The results show that the influence of wind, waves and currents on the floating hose tension increases nonlinearly.The maximum tension points of the floating hose oil transportation system are at both ends of the hose.The tension acting on the hose can be obviously reduced and the drift distance can be shortened by setting securing anchors along the line.Through the analysis on the offshore floating hose oil transportation system with the transportation distance of 1 100 m, based on this method and model, it can be concluded that the optimal hose length is 1 300 m, the hose drift distance is about 300 m when no securing anchor is set along the line, and the safety requirements can be met under 4-level sea state.The research results can provide certain reference for the dynamic simulation analysis of such offshore floating hose oil transportation system.