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Background The mechanisms underlying the relationship between workplace violence (WPV) and depressive symptoms in nurses have been less studied. This study aims to examine the mediating role of fear of future workplace violence (FFWV) and burnout in the association between WPV and depressive symptoms. Methods We conducted a cross-sectional web survey at 12 tertiary hospitals in Shandong province, China, in 2020. The Center for Epidemiologic Studies Depression Scale (CESD-10), the Chinese version of the Maslach Burnout Inventory-General Survey and the Fear of Future Violence at Work Scale were used to collect data. Descriptive statistics, independent sample t-test, one-way analysis of variance, Pearson’s correlation coefficient, and ordinary least squares regression with bootstrap resampling were used to analyze the data. Results The prevalence of depressive symptoms was 45.9% among nurses. The regression model showed that FFWV and burnout mediated the relationship between WPV and depressive symptoms. The total effects of WPV on depressive symptoms (3.109, 95% bootstrap CI:2.324 − 3.713) could be decomposed into direct (2.250, 95% bootstrap CI:1.583 − 2.917) and indirect effects (0.769, 95% bootstrap CI:0.543 − 1.012). Indirect effects mediated by FFWV and burnout were 0.203 (95% bootstrap CI:0.090 − 0.348) and 0.443 (95% bootstrap CI:0.262 − 0.642), respectively. Furthermore, serial multiple mediation analyses indicated that the indirect effect mediated by FFWV and burnout in a sequential manner was 0.123 (95% bootstrap CI:0.070 − 0.189). Conclusion The prevalence of depressive symptoms among Chinese nurses was high. The WPV was an important risk factor for depressive symptoms and its negative effect was mediated by FFWV and burnout. The importance of decreasing WPV exposure and level of FFWV and burnout was emphasized to prevent depressive symptoms among nurses. The findings implied that hospital managers and health policy makers should not only develop targeted interventions to reduce exposure to WPV in daily work among all nurses, but also provide psychological support to nurses with WPV experience to reduce FFWV and burnout.

Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transformer (ViT) model, with its capability of extracting global information, offers a significant advantage in contextual information compared to the limited receptive field of convolutional kernels in CNNs. Despite this, ViT models struggle to fully detect and extract high-frequency signals, such as textures and boundaries, in medical images. These high-frequency features are essential in medical imaging, as targets like tumors and pathological organs exhibit significant differences in texture and boundaries across different stages. Additionally, the high resolution of medical images leads to computational complexity in the self-attention mechanism of ViTs. To address these limitations, we propose a medical image segmentation network framework based on frequency domain decomposition using a Laplacian pyramid. This approach selectively computes attention features for high-frequency signals in the original image to enhance spatial structural information effectively. During attention feature computation, we introduce Singular Value Decomposition (SVD) to extract an effective representation matrix from the original image, which is then applied in the attention computation process for linear projection. This method reduces computational complexity while preserving essential features. We demonstrated the segmentation validity and superiority of our model on the Abdominal Multi-Organ Segmentation dataset and the Dermatological Disease dataset, and on the Synapse dataset our model achieved a score of 82.68 on the Dice metrics and 17.23 mm on the HD metrics. Experimental results indicate that our model consistently exhibits segmentation effectiveness and improved accuracy across various datasets.

Medical image segmentation is a fundamental task in computer-aided diagnosis, playing a crucial role in organ structure analysis, lesion delineation, and treatment planning. However, current Transformer-based segmentation networks still face two major challenges. First, the global self-attention in the encoder often introduces redundant connections, leading to high computational cost and potential interference from irrelevant tokens. Second, the decoder shows limited capability in reconstructing fine-grained boundary structures, resulting in blurred segmentation contours. To address these issues, we proposed an efficient and accurate framework for general medical image segmentation. Specifically, in the encoder, we introduce a frequency-domain similarity measure and construct a Key-Semantic Dictionary (KSD) via amplitude spectrum cosine similarity. This enables stage-wise sparse attention matrices that reduce redundancy and enhance semantic relevance. In the decoder, we design a learnable gradient-based operator that injects boundary-aware logits bias into the attention mechanism, thereby improving structural detail recovery along object boundaries. On ACDC, the framework delivers a 0.55% gain in average Dice and a 14.6% reduction in HD over the second-best baseline. On ISIC 2018, it achieves increases of 1.01% in Dice and 0.21% in ACC over the second-best baseline, while using 88.8% fewer parameters than typical Transformer-based models. On Synapse, it surpasses the strongest prior approach by 1.03% in Dice and 6.35% in HD, yielding up to 8.36% Dice improvement and 52.46% HD reduction compared with widely adopted Transformer baselines. Comprehensive results confirm that the proposed frequency-domain sparse attention and learnable edge-guided decoding effectively balance segmentation accuracy, boundary fidelity, and computational cost. This framework not only suppresses redundant global correlations and enhances structural detail reconstruction, but is also robust to different medical imaging modalities, providing a lightweight and clinically applicable solution for high-precision medical image segmentation.

Large-scale agricultural land management has become the obvious development trend of China’s rural land management. This paper focuses on large-scale agricultural land management in China and analyzes the influence mechanism of large-scale agricultural land management on the circulation of agricultural products. We use the methods of field investigation and empirical research, put forward the theoretical hypothesis through field investigation, and empirically test it. It is found that the impact of large-scale agricultural land management on the circulation efficiency of the agricultural products under the “input-output” index has a lag and shows a U-shaped characteristic of decreasing first and then increasing. For the modernization of agricultural product circulation under the comprehensive index system, large-scale agricultural land management has a significant positive promoting effect. This reflects the potential of large-scale agricultural land management in promoting the development of rural agriculture and agricultural product circulation. This suggests that in the process of promoting the modernization of agricultural product circulation, the government should pay special attention to the modernization of upstream agricultural production, promote large-scale agricultural land management in a standardized and orderly way, and realize the coordinated reform of agriculture and the agricultural product circulation industry. In addition, the Chinese government also needs to make up for the shortcomings in the upstream organization, the construction of wholesale markets for the agricultural products, and rural logistics infrastructure.

The lumbar spine and pelvis are critical stress-bearing structures of the human body, and their rapid and accurate segmentation plays a vital role in clinical diagnosis and intervention. However, conventional CT imaging poses significant challenges due to the low contrast of sacral and bilateral hip tissues and the complex and highly similar intervertebral space structures within the lumbar spine. To address these challenges, we propose a general-purpose segmentation network that integrates a cross-scale feature fusion strategy with a linear self-attention mechanism. The proposed network effectively extracts multi-scale features and fuses them along the channel dimension, enabling both structural and boundary information of lumbar and pelvic regions to be captured within the encoder-decoder architecture.Furthermore, we introduce a linear mapping strategy to approximate the traditional attention matrix with a low-rank representation, allowing the linear attention mechanism to significantly reduce computational complexity while maintaining segmentation accuracy for vertebrae and pelvic bones. Comparative and ablation experiments conducted on the CTSpine1K and CTPelvic1K datasets demonstrate that our method achieves improvements of 1.5% in Dice Similarity Coefficient (DSC) and 2.6% in Hausdorff Distance (HD) over state-of-the-art models, validating the effectiveness of our approach in enhancing boundary segmentation quality and segmentation accuracy in homogeneous anatomical regions.

This study focuses on the kinetics of a pure strain of bacterium Rhodococcus sp. SKC, isolated from phenol-contaminated soil, for the biodegradation of phenol as its sole carbon and energy source in aqueous medium. The kinetics of phenol biodegradation including the lag phase, the maximum phenol degradation rate, maximum growth rate (Rm) and maximum yield coefficient (Y) for each Si (initial phenol concentration, mg/L) were fitted using the Gompertz and Haldane models of substrate inhibition (R2 > 0.9904, RMSE < 0.00925). The values of these parameters at optimum conditions were μmax = 0.30 h−1, Ks = 36.40 mg/L, and Ki = 418.79 mg/L, and that means the inhibition concentration of phenol was 418.79 mg/L. By comparing with other strains of bacteria, Rhodococcus sp. SKC exhibited a high yield factor and tolerance towards phenol. This study demonstrates the potential application of Rhodococcus sp. SKC for the bioremediation of phenol contaminate.

Background Health economic evaluation is critical in supporting novel cardiovascular disease therapies. However, most clinical studies do not include preference-based questionnaires to calculate utilities for health economic evaluations. Thus, this study aimed to develop mapping algorithms that convert the Seattle Angina Questionnaire (SAQ) to EQ-5D-5L health utility scores for patients with coronary health disease (CHD) in China. Methods Data were obtained from a longitudinal study of patients with CHD conducted at the Tianjin Medical University General Hospital in China. Convenience sampling was used to recruit patients with CHD. The inclusion criteria were having been diagnosed with CHD through a medical examination and being aged 18 years or older. The exclusion criteria were a lack of comprehension ability, serious comorbidities, mental illness, and hearing or vision impairment. All eligible patients were invited to participate, and 305 and 75 patients participated at baseline and in the follow-up, respectively. Seven regression models were developed using a direct approach. Furthermore, we predicted the five EQ-5D items using ordered logit model and derived the utility score from predicted responses using an indirect approach. Model performances were evaluated using mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (ρ), and Lin’s concordance correlation coefficient (CCC). A five-fold cross-validation method was used to evaluate internal validation. Results The average age was 63.04 years, and 53.72% of the included patients were male. Most (70.05%) patients had unstable angina pectoris, and the mean illness duration was 2.50 years. The EQ-5D scores were highly correlated with five subscales of the SAQ, with Spearman’s rank correlation coefficients ranging from 0.6184 to 0.7093. The mixture beta model outperformed the other regression models in the direct approach, with the lowest MAE and RMSE and highest ρ and CCC. The ordered logit model in the indirect approach performed the same as the mixture beta regression with equal MAE, lower RMSE, and higher ρ and CCC. Conclusion Mapping algorithms developed using mixture beta and ordered logit models accurately converted SAQ scores to EQ-5D-5L health utility values, which could support health economic evaluations related to coronary heart disease.

Named entities are the main carriers of relevant medical knowledge in Electronic Medical Records (EMR). Clinical electronic medical records lead to problems such as word segmentation ambiguity and polysemy due to the specificity of Chinese language structure, so a Clinical Named Entity Recognition (CNER) model based on multi-head self-attention combined with BILSTM neural network and Conditional Random Fields is proposed. Firstly, the pre-trained language model organically combines char vectors and word vectors for the text sequences of the original dataset. The sequences are then fed into the parallel structure of the multi-head self-attention module and the BILSTM neural network module, respectively. By splicing the output of the neural network module to obtain multi-level information such as contextual information and feature association weights. Finally, entity annotation is performed by CRF. The results of the multiple comparison experiments show that the structure of the proposed model is very reasonable and robust, and it can effectively improve the Chinese CNER model. The model can extract multi-level and more comprehensive text features, compensate for the defect of long-distance dependency loss, with better applicability and recognition performance.

The task allocation of multiple robot chemical inspection plays a vital role in enhancing the inspection efficiency of robots, and it is crucial for the timely detection of hazardous factors in chemical enterprises and the elimination of potential production risks. In this paper, an integer programming model of multiple heterogeneous inspection robot task assignment (MHRTA) problem is established based on the consideration of the constraints on the applicability of sensors carried by robots to inspection tasks. Given the NP-hard nature of the MHRTA problem, this study proposes a multiple strategy enhanced artemisinin algorithm to solve the proposed MHRTA model. Improving the position update strategy of artemisinin molecules in the comprehensive elimination phase of the artemisinin algorithm by incorporating concepts from the slime mold algorithm, and incorporates a nonlinear curve as the probability factor during the later consolidation phase, while introducing self-adaptive t distribution mutation to enhance the quality of solutions. Furthermore, Considering the discrete combinatorial optimization characteristics of the MHRTA problem, a two-layer encoding scheme is adopted to create a connection between the encoding space and the solution space, specifically for addressing multiple robot task allocation and inspection Hamiltonian routing, a variable neighborhood search strategy is embedded in the artemisinin optimizer to improve its computational efficiency. In eight test cases, the proposed method was evaluated against other algorithms and CPLEX solvers. The experimental results verified that the proposed method has strong optimization ability and stability in solving MHRTA problems.

Background The fragmentation of health insurance schemes in China has undermined equity in access to health care. To achieve universal health coverage by 2020, the Chinese government has decided to consolidate three basic medical insurance schemes. This study aims to evaluate the effects of integrating Urban and Rural Residents Basic Medical Insurance schemes on health care utilization and its equity in China. Methods The data for the years before (2013) and after (2015) the integration were obtained from the China Health and Retirement Longitudinal Study. Respondents in pilot provinces were considered as the treatment group, and those in other provinces were the control group. Difference-in-difference method was used to examine integration effects on probability and frequency of health care visits. Subgroup analysis across regions of residence (urban/rural) and income groups and concentration index were used to examine effects on equity in utilization. Results The integration had no significant effects on probability of outpatient visits (β = 0.01, P  > 0.05), inpatient visits (β = 0.01, P  > 0.05), and unmet hospitalization needs (β =0.01, P  > 0.05), while it had significant and positive effects on number of outpatient visits (β = 0.62, P  < 0.05) and inpatient visits (β = 0.39, P  < 0.01). Moreover, the integration had significant and positive effects on number of outpatient visits (β = 0.77, P  < 0.05) and inpatient visits (β = 0.49, P  < 0.01) for rural residents but no significant effects for urban residents. Furthermore, the integration led to an increase in the frequency of inpatient care utilization for the poor (β = 0.78, P  < 0.05) among the piloted provinces but had no significant effects for the rich (β = 0.25, P  > 0.05). The concentration index for frequency of inpatient visits turned into negative direction in integration group, while that in control group increased by 0.011. Conclusions The findings suggest that the integration of fragmented health insurance schemes could promote access to and improve equity in health care utilization. Successful experiences of consolidating health insurance schemes in pilot provinces can provide valuable lessons for other provinces in China and other countries with similar fragmented schemes.