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Objective In order to improve train operational efficiency, it is necessary to monitor metro traction power consumption and establish a relevant energy consumption model for prediction analysis of metro traction power consumption. Method The basic principles of grey prediction model and BP (backpropagation) neural network are introduced. Taking the traction daily electricity consumption data for a typical metro station in Tianjin in June 2021 as example, the grey correlation analysis method is used to select the influencing factors with high correlation to the daily traction power consumption of metro. Based on the GM (1,1) grey prediction model, the short-term traction daily power consumption is predicted. The selected influencing factors with high correlation, the short-term traction daily power consumption predicted by GM (1,1) grey model, and the adjacent historical traction daily power consumption data are used as input for training in BP neural network model to establish the GM-BP grey neural network model. The required short-term metro traction daily power consumption prediction data is generated. Result & Conclusion Compared with conventional GM (1,1) grey prediction model and BP neural network model, the prediction error of short-term traction daily power consumption predicted by the GM-BP grey neural network model shows significant improvement, and can be used as effective metro traction power consumption data for short-term prediction data analysis.

Objective In the ultra-close layered tunnel construction, the preceding excavated tunnel can cause unpredictable damage to the surrounding rock, which will affect the smooth construction of the following tunnel and increase the difficulties of engineering construction. Therefore, it is necessary to study the effect of the preceding tunnel on the following ones in the layered tunnel construction. Method Based on a tunnel project in Chongqing, a numerical simulation method is used to compare and analyze the stress distribution patterns and deformation characteristics of the surrounding rock caused by the preceding excavation of the lower layer tunnel. According to the stress characteristics and the deformation law of the upper layer tunnel under different working conditions, the relationship between the deformation control effect of the anchor rod reinforcement measures and the weakening effect of the surrounding rock caused by the lower layer tunnel excavation is analyzed. Result & Conclusion The preceding excavation of the lower layer tunnel causes the vault area settlement with a maximum of 4.1mm and the arch bottom area bulging with a maximum of 6.9 mm. According to the variation intensity of the stress distribution and the equivalent plastic strain range of the surrounding rock, the loosening area of the lower layer tunnel surrounding rock is equivalent to a circular range, and the average thickness of the loosening circle is 2.5m. The excavation unloading of the upper layer tunnel will cause the lower layer tunnel upfloating. After the anchor rod reinforcement measures are taken for the surrounding rock in the lower layer tunnel loosening circle,the strength coefficient of the corresponding surrounding rock is reduced to 0.70~0.75.

Anti-mitochondrial antibody (AMA) is not always present in patients with primary biliary cholangitis (PBC). We aimed to determine the additional value of anti-hexokinase 1 (anti-HK1) and anti-kelch-like 12 (anti-KLHL12) antibody in PBC and analyzed the biochemical and immunological parameters of 212 subjects, including PBC patients and healthy controls.

ObjectiveThe accurate estimation of terraced field areas is crucial for addressing issues such as slope erosion control, water retention, soil conservation, and increasing food production. The use of high-resolution remote sensing imagery for terraced field information extraction holds significant importance in these aspects. However, as imaging sensor technologies continue to advance, traditional methods focusing on shallow features may no longer be sufficient for precise and efficient extraction in complex terrains and environments. Deep learning techniques offer a promising solution for accurately extracting terraced field areas from high-resolution remote sensing imagery. By utilizing these advanced algorithms, detailed terraced field characteristics with higher levels of automation can be better identified and analyzed. The aim of this research is to explore a proper deep learning algorithm for accurate terraced field area extraction in high-resolution remote sensing imagery.MethodsFirstly, a terraced dataset was created using high-resolution remote sensing images captured by the Gaofen-6 satellite during fallow periods. The dataset construction process involved data preprocessing, sample annotation, sample cropping, and dataset partitioning with training set augmentation. To ensure a comprehensive representation of terraced field morphologies, 14 typical regions were selected as training areas based on the topographical distribution characteristics of Yuanyang county. To address misclassifications near image edges caused by limited contextual information, a sliding window approach with a size of 256 pixels and a stride of 192 pixels in each direction was utilized to vary the positions of terraced fields in the images. Additionally, geometric augmentation techniques were applied to both images and labels to enhance data diversity, resulting in a high-resolution terraced remote sensing dataset. Secondly, an improved DeepLab v3+ model was proposed. In the encoder section, a lightweight MobileNet v2 was utilized instead of Xception as the backbone network for the semantic segmentation model. Two shallow features from the 4th and 7th layers of the MobileNet v2 network were extracted to capture relevant information. To address the need for local details and global context simultaneously, the multi-scale feature fusion (MSFF) module was employed to replace the atrous spatial pyramid pooling (ASPP) module. The MSFF module utilized a series of dilated convolutions with increasing dilation rates to handle information loss. Furthermore, a coordinate attention mechanism was applied to both shallow and deep features to enhance the network's understanding of targets. This design aimed to lightweight the DeepLab v3+ model while maintaining segmentation accuracy, thus improving its efficiency for practical applications.Results and DiscussionsThe research findings reveal the following key points: (1) The model trained using a combination of near-infrared, red, and green (NirRG) bands demonstrated the optimal overall performance, achieving precision, recall, F1-Score, and intersection over union (IoU) values of 90.11%, 90.22%, 90.17% and 82.10%, respectively. The classification results indicated higher accuracy and fewer discrepancies, with an error in reference area of only 12 hm2. (2) Spatial distribution patterns of terraced fields in Yuanyang county were identified through the deep learning model. The majority of terraced fields were found within the slope range of 8º to 25º, covering 84.97% of the total terraced area. Additionally, there was a noticeable concentration of terraced fields within the altitude range of 1 000 m to 2 000 m, accounting for 95.02% of the total terraced area. (3) A comparison with the original DeepLab v3+ network showed that the improved DeepLab v3+ model exhibited enhancements in terms of precision, recall, F1-Score, and IoU by 4.62%, 2.61%, 3.81% and 2.81%, respectively. Furthermore, the improved DeepLab v3+ outperformed UNet and the original DeepLab v3+ in terms of parameter count and floating-point operations. Its parameter count was only 28.6% of UNet and 19.5% of the original DeepLab v3+, while the floating-point operations were only 1/5 of UNet and DeepLab v3+. This not only improved computational efficiency but also made the enhanced model more suitable for resource-limited or computationally less powerful environments. The lightweighting of the DeepLab v3+ network led to improvements in accuracy and speed. However, the slection of the NirGB band combination during fallow periods significantly impacted the model's generalization ability.ConclusionsThe research findings highlights the significant contribution of the near-infrared (NIR) band in enhancing the model's ability to learn terraced field features. Comparing different band combinations, it was evident that the NirRG combination resulted in the highest overall recognition performance and precision metrics for terraced fields. In contrast to PSPNet, UNet, and the original DeepLab v3+, the proposed model showcased superior accuracy and performance on the terraced field dataset. Noteworthy improvements were observed in the total parameter count, floating-point operations, and the Epoch that led to optimal model performance, outperforming UNet and DeepLab v3+. This study underscores the heightened accuracy of deep learning in identifying terraced fields from high-resolution remote sensing imagery, providing valuable insights for enhanced monitoring and management of terraced landscapes.

BackgroundNon-suicidal self-injury （NSSI） behavior is highly prevalent in middle school students， which poses a significant risk to the physical and mental health of middle school students， so finding ways to improve the NSSI behavior among middle school students is of great significance for promoting their healthy growth.ObjectiveTo investigate the relationship between psychological quality and NSSI behavior among middle school students in western Anhui， and to examine the pathway of coping style in the above relationship.MethodsFrom November to December 2020， 22 872 middle school students in western Anhui were selected using stratified cluster random sampling method， and were subjected to complete the assessment of Adolescent Non-suicidal Self-injury Assessment Questionnaire （ANSAQ）， Adolescent's Psychological Suzhi Scale-Simplified Version （APSS-SV） and Coping Style Scale for Middle School Students （CSSMSS）. Spearman correlation analysis was adopted to examine the correlation among scores of scales and the frequency of NSSI behavior in middle school students. Amos 23.0 was utilized to test the pathway of coping style in the relationship between psychological quality and NSSI behavior.ResultsA total of 21 718 （94.95%） middle school students completed the effective questionnaire survey. NSSI behavior was detected in 7 798 middle school students （35.91%）. ANSAQ total score and CSSMSS positive coping style score of middle school students were negatively correlated with the frequency of NSSI behavior （r=-0.219， -0.179， P

Objective To analyze the risk factors for dismal prognosis in patients with cryptococcal meningitis and construct a prediction scoring system Nomogram model. Methods A total of 100 patients with cryptococcal meningitis who treated with anticryptococcal therapy admitted to Affiliated Hospital of Zunyi Medical University from January 2010 to August 2022 were selected. The patients were divided into favorable prognosis group (n = 19) and dismal prognosis group (n = 81) according to the results of cerebrospinal fluid (CSF) cryptococcal culture during hospitalization and clinical symptoms and signs at the time of discharge. Risk factors were screened by using univariate and multivariate stepwise Logistic regression analyses. A Nomogram model was constructed based on the risk factors, the receiver operating characteristic (ROC) curve and calibration curves of the model were plotted, and Hosmer - Lemeshow goodness-of-fit test was performed. Results The proportion of patients in the dismal prognosis group with Nutritional Risk Screening 2002 (NRS 2002) score (Z = - 3.898, P = 0.000), CSF pressure > 250 mm H2O (χ2 = 9.512，P = 0.002) and duration of antifungal treatment < 14 d (χ2 = 17.847，P = 0.000) on admission were higher than those in the favorable prognosis group, and the blood routine red blood cell count (t = - 2.802, P = 0.006) and lymphocyte count (Z = - 2.878, P = 0.004), plasma albumin (t = - 4.332, P = 0.000), and the proportion of amphotericin B application (χ2 = 4.597, P = 0.032) were lower than those in the favorable prognosis group. Logistic regression analysis showed the admission high NRS 2002 score (OR = 3.258, 95%CI: 1.337-7.940; P = 0.009), CBF pressure > 250 mm H2O (OR = 0.108, 95%CI: 0.018-0.659; P = 0.016), and the duration of antifungal treatment < 14 d (OR = 0.092, 95%CI: 0.011-0.742; P = 0.025) were risk factors for dismal prognosis of cryptococcal meningitis. A Nomogram model was constructed based on the above 3 risk factors, and the area under the ROC curve was 0.927 (95%CI: 0.873-0.980, P = 0.000), which predicted a cut - off value of 53.50 points for dismal prognosis in cryptococcal meningitis; the calibration curve (with good consistency), and the Hosmer-Lemeshow goodness-of-fit test (χ2 = 2.694, P = 0.912) indicated that the model had good discrimination, calibration and stability. Conclusions Patients with cryptococcal meningitis with a high NRS 2002 score, CSF pressure > 250 mm H2O, and antifungal treatment < 14 d had a dismal prognosis, and the Nomogram model constructed accordingly has a high predictive value of dismal prognostic risk.

ObjectiveWheat lodging is one of the key isuess threatening stable and high yields. Lodging detection technology based on deep learning generally limited to identifying lodging at a single growth stage of wheat, while lodging may occur at various stages of the growth cycle. Moreover, the morphological characteristics of lodging vary significantly as the growth period progresses, posing a challenge to the feature capturing ability of deep learning models. The aim is exploring a deep learning-based method for detecting wheat lodging boundaries across multiple growth stages to achieve automatic and accurate monitoring of wheat lodging.MethodsA model called Lodging2Former was proposed, which integrates the innovative hierarchical interactive feature pyramid network (HI-FPN ) on top of the advanced segmentation model Mask2Former. The key focus of this network design lies in enhancing the fusion and interaction between feature maps at adjacent hierarchical levels, enabling the model to effectively integrate feature information at different scales. Building upon this, even in complex field backgrounds, the Lodging2Former model significantly enhances the recognition and capturing capabilities of wheat lodging features at multiple growth stages.Results and DiscussionsThe Lodging2Former model demonstrated superiority in mean average precision (mAP) compared to several mainstream algorithms such as mask region-based convolutional neural network (Mask R-CNN), segmenting objects by locations (SOLOv2), and Mask2Former. When applied to the scenario of detecting lodging in mixed growth stage wheat, the model achieved mAP values of 79.5%, 40.2%, and 43.4% at thresholds of 0.5, 0.75, and 0.5 to 0.95, respectively. Compared to Mask2Former, the performance of the improved model was enhanced by 1.3% to 4.3%. Compared to SOLOv2, a growth of 9.9% to 30.7% in mAP was achieved; and compared to the classic Mask R-CNN, a significant improvement of 24.2% to 26.4% was obtained. Furthermore, regardless of the IoU threshold standard, the Lodging2Former exhibited the best detection performance, demonstrating good robustness and adaptability in the face of potential influencing factors such as field environment changes.ConclusionsThe experimental results indicated that the proposed HI-FPN network could effectively utilize contextual semantics and detailed information in images. By extracting rich multi-scale features, it enabled the Lodging2Former model to more accurately detect lodging areas of wheat across different growth stages, confirming the potential and value of HI-FPN in detecting lodging in multi-growth-stage wheat.

In response to the challenge of sample data imbalance in fault diagnosis methods for photovoltaic power plants based on machine learning， the paper proposes a fault diagnosis method leveraging an enhanced BP-Bagging algorithm. Firstly， a mapping relationship between photovoltaic data and fault types is established using a BP neural network to achieve fault diagnosis in photovoltaic systems. Subsequently， the Bagging algorithm is enhanced by utilizing random under-sampling （RUS） to address the issue of class imbalance in samples. Furthermore， to tackle the problem of overfitting in the BP network， the paper introduces a fault diagnosis model for photovoltaic power plants based on the enhanced BP-Bagging. This involves parallel training of multiple BP networks and determining fault diagnosis results through a voting method. Finally， the paper conducts comparative experiments with different algorithms， calculates evaluation metrics related to model accuracy， and validates that the proposed method demonstrates high overall performance. To a certain extent， it effectively mitigates the challenge of sample class imbalance in fault diagnosis of photovoltaic power plants， thereby improving the accuracy of fault diagnosis in such systems.

To enhance the efficiency and sustainability, technical preparations were made for eliminating the Temperature, Sample, Oxygen test of basic oxygen furnace (BOF) steelmaking process in this work. Utilizing data from 13,528 heats and state-of-the-art (SOTA) machine learning (ML) and deep learning algorithms, data-driven models with different types of inputs were developed, marking the first use of time series data (off-gas profiles and blowing practice related curves) for BOF steelmaking’s endpoint prediction, and the tabular features were expanded to 45. The prediction targets are molten steel’s concentrations of phosphorus (Endpoint [P], %) and carbon (Endpoint [C], %), and temperature (Endpoint-Temp, °C). The optimal models for each target were implemented at a Hesteel Group’s BOF steelmaking facility. Initially, SOTA ML models (XGBoost, LightGBM, Catboost, TabNet) were employed to predict Endpoint [P]/[C]/Temp with tabular data. The best mean absolute errors (MAE) achieved were 2.276 × 10−3% (Catboost), 6.916 × 10−3% (Catboost), and 7.955°C (LightGBM), respectively, which surpassed the conventional models’ performance. The prediction MAEs of the conventional models with the same inputs for Endpoint [P]/[C]/Temp were 3.158 × 10−3%, 7.534 × 10−3%, and 9.150°C (Back Propagation neural network) and 2.710 × 10−3%, 7.316 × 10−3%, and 8.310°C (Support Vector Regression). Subsequently, predictions were explored to be made using SOTA time series analysis models (1D ResCNN, TCN, OmniScaleCNN, eXplainable Convolutional neural network (XCM), Time-Series Transformer, LSTM-FCN, D-linear) with the original time series data and SOTA image analysis models (Pre-activation ResNet, DenseNet, DLA, Dual path networks (DPN), GoogleNet, Vision Transformer) with resized time series data. Finally, the concat-model and the paral-model architectures were designed for making predictions with both tabular data and time series data. It was determined that the concat-Model with TCN and ResCNN as the backbone exhibited the highest accuracy. It’s MAE for predicting Endpoint [P]/[C]/Temp reaches 2.153 × 10−3%, 6.413 × 10−3%, and 5.780°C, respectively, with field test’s MAE at 2.394 × 10−3%, 6.231 × 10−3%, and 7.679°C. Detailed results of the importance analysis for tabular data and time series are provided.

Infection following inguinal hernia repair (IHR) is uncommon. Rational use of antibiotics can significantly improve the prognosis of patients. However, accurately identifying the pathogen involved is usually challenging. This case report describes a patient who developed intermittent fever after undergoing open preperitoneal tension-free repair of a bilateral inguinal hernia. The scrotal fluid specimen was cultured and subjected to metagenomic next-generation sequencing (mNGS). Culture revealed the presence of Enterococcus faecalis (a facultative anaerobe). However, mNGS detected E. faecalis along with multiple anaerobic bacteria including Bacteroides thetaiotaomicron, Parabacteroides distasonis, and Levyella massiliensis. The patient was finally diagnosed with a mixed infection of E. faecalis and multiple anaerobes, and his condition was effectively controlled after timely adjustment of the antibiotic regimen. Treating postoperative infections with multiple concurrent conditions can be challenging. mNGS is valuable for the accurate diagnosis and treatment of infections, as it not only can further verify the culture results, but also assist clinicians in ruling out pulmonary infection caused by hematogenous dissemination after IHR in patients.

Using polypropylene (PP) as a matrix and carbon nanotube (CNTs) as a modified material to prepare CNTs/PP composites, the effects of solid-phase drawing and CNTs on properties of PP-based composites after solid-phase drawing were analyzed. Crystallization, mechanical and electromagnetic shielding properties of the composites were studied by wide angle X-ray diffraction, 2D wide angle X-ray diffraction, small angle X-ray scattering, differential scanning calorimetry, polarizing microscope, scanning electron microscope, universal test machine, and vector analyzer. The results show that the solid-phase drawing and CNTs increase the tensile strength of the composite and reduce its elongation at break; the addition of CNTs plays a clear heterogeneous nucleation, refines the grain and improve the crystallinity, and the crystal formation of the CNTs/PP composite before and after the solid-phase drawing does not change, but the α-crystal for PP is improved significantly. The crystallinity of the composites is slightly decreased with increasing solid-phase drawing rate. The solid-phase drawing treatment and CNTs can improve the electromagnetic shielding performance, but make the electromagnetic shielding performances reduce after high drawing rate treatment.

In this article, we develop an efficient Legendre-Galerkin approximation based on a reduced-dimension scheme for the fourth-order equation with singular potential and simply supported plate (SSP) boundary conditions in a circular domain. First, we deduce the equivalent reduced-dimension scheme and essential pole condition associated with the original problem, based on which a class of weighted Sobolev spaces are defined and a weak formulation and its discrete scheme are also established for each reduced one-dimensional problem. Second, the existence and uniqueness of the weak solution and the approximation solutions are given using the Lax-Milgram theorem. Then, we construct a class of projection operators, give their approximation properties, and then prove the error estimates of the approximation solutions. In addition, we construct a set of effective basis functions in approximate space using orthogonal property of Legendre polynomials and derive the equivalent matrix form of the discrete scheme. Finally, a large number of numerical examples are performed, and the numerical results illustrate the validity and high accuracy of our algorithm.

Abstract Background Breast cancer (BC) patients tend to suffer from distant metastasis, especially bone metastasis. Methods All the analysis based on open-accessed data was performed in R software, dependent on multiple algorithms and packages. The RNA levels of specific genes were detected using quantitative Real-time PCR as a method of detecting the RNA levels. To assess the ability of BC cells to proliferate, we utilized the CCK8 test, colony formation, and the 5-Ethynyl-20-deoxyuridine assay. BC cells were evaluated for invasion and migration by using Transwell assays and wound healing assays. Results In our study, we identified the molecules involved in BC bone metastasis based on the data from multiple BC cohorts. Then, we comprehensively investigated the effect pattern and underlying biological role of these molecules. We found that in the identified molecules, the EMP1, ACKR3, ITGA10, MMP13, COL11A1, and THY1 were significantly correlated with patient prognosis and mainly expressed in CAFs. Therefore, we explored the CAFs in the BC microenvironment. Results showed that CAFs could activate multiple carcinogenic pathways and most of these pathways play an important role in cancer metastasis. Meanwhile, we noticed the interaction between CAFs and malignant, endothelial, and M2 macrophage cells. Moreover, we found that CAFs could induce the remodeling of the BC microenvironment and promote the malignant behavior of BC cells. Then, we identified MMP13 for further analysis. It was found that MMP13 can enhance the malignant phenotype of BC cells. Meanwhile, biological enrichment and immune infiltration analysis were conducted to present the effect pattern of MMP13 in BC. Conclusions Our result can improve the understanding of researchers on the underlying mechanisms of BC bone metastasis.

Epithelial-mesenchymal transformation (EMT) plays an important role in the progression of diabetic nephropathy. Dexmedetomidine (DEX) has shown renoprotective effects against ischemic reperfusion injury; however, whether and how DEX prevents high glucose-induced EMT in renal tubular epithelial cells is incompletely known. Here, we conduct in vitro experiments using HK-2 cells, a human tubular epithelial cell line. Our results demonstrate that high glucose increases the expressions of EMT-related proteins, including Vimentin, Slug, Snail and Twist, while decreasing the expression of E-cadherin and increasing Cdk5 expression in HK-2 cells. Both Cdk5 knockdown and inhibition by roscovitine increase the expressions of E-cadherin while decreasing the expressions of other EMT-related markers. DEX inhibits Cdk5 expression without affecting cell viability and changes the expressions of EMT-related markers, similar to effects of Cdk5 inhibition. Furthermore, Cdk5 is found to interact with Drp1 at the protein level and mediate the phosphorylation of Drp1. In addition, Drp1 inhibition with mdivi-1 could also restrain the high glucose-induced EMT process in HK-2 cells. Immunofluorescence results show that roscovitine, Mdivi-1 and DEX inhibit high glucose-induced intracellular ROS accumulation, while the oxidant H2O2 eliminates the protective effect of DEX on the EMT process. These results indicate that DEX mitigates high glucose-induced EMT progression in HK-2 cells via inhibition of the Cdk5/Drp1/ROS pathway.

The new power system at the station level is the phased product of the large-scale and high-proportion penetration development of wind and solar new energy. The source network load storage system with the station area as the smallest adjustable unit is constructed by using energy storage +. Different from the traditional substation area system, the architecture of the new power system at the substation area level becomes extremely complex due to the change of access terminal attributes, interaction relationship and various business types. In order to systematically describe the new power system architecture at the station district level, this paper first combs and summarizes the relevant literature at home and abroad, and summarizes the literature related to the current substation District, wind and new energy access and energy storage system regulation. Secondly, from the perspectives of demand differences, common functional requirements and non functional requirements, the requirements of station area level new power system under different application scenarios are classified and analyzed in detail. Then, a new power system for different scenarios is designed, which is divided into local equipment layer, edge control layer, station management layer and centralized control layer from bottom to top, and the functions of each layer are described in detail. Taking Zhangjiajie scenery storage and charging comprehensive energy demonstration project as an example, this paper expounds the ways of terminal data access, transmission and visualization under the framework of Taiwan district level new power system, so as to provide reference for the networking of other similar projects. Finally, the research contents of this paper are summarized, and the contents that need further research in the future are prospected.

To facilitate the application of the subsumption sorting algorithm within vocabulary instruction, this study develops a novel Learning Management System (LMS). This system adopts the ESP vocabulary teaching model as its primary instructional framework delves into the requisites of English vocabulary acquisition, the system’s architecture, and its instructional content. It employs an enhanced DTW pattern-matching algorithm to dynamically refine English vocabulary resources. Furthermore, it integrates the merging and sorting algorithm to augment the efficiency of the LMS operations, enabling precise management of student learning progression and vocabulary retention. Comparative analyses of various sorting algorithms were conducted alongside assessments of the impacts of the LMS and ESP vocabulary teaching models on vocabulary learning outcomes. The findings from a semester-long study of vocabulary instruction indicate significant improvements within the experimental group: increases of 6.3, 5.32, and 9.33 points in word form, meaning, and usage accuracy, respectively. Notably, the usage of vocabulary saw the most substantial enhancement. The ESP model-based English vocabulary LMS effectively ameliorates students’ vocabulary usage errors and enhances accuracy in vocabulary application.

In the “Internet+” era, Civic Party building programs at universities face the challenge of modernizing their pedagogical approach to meet students’ evolving needs. This paper examines the hurdles in updating course content and diversifying teaching methods, utilizing the Logit model to determine factors crucial to innovating these courses. Analysis reveals that political cognition, emotion, and participation play pivotal roles in the success of course innovation, especially political cognition with a coefficient of 0.784. Recommendations for course improvement include adopting internet technologies, updating content, varying teaching techniques, and enhancing student involvement. Ultimately, the study advocates for a strategic overhaul of Civic Party building courses to fulfill their educational goals.

Multi-exposure image fusion as a technical means to bridge the dynamic range gap between real scenes and image acquisition devices, which makes the fused images better quality and more realistic and vivid simulation of real scenes, has been widely concerned by scholars from various countries. In order to improve the adaptive fusion effect of multi-exposure images, this paper proposes a fusion algorithm based on multilayer perceptron (MLP) based on the perceptron model and verifies the feasibility of the algorithm by the peak signal-to-noise ratio (PSNR), correlation coefficient (PCC), structural similarity (SSMI) and HDR-VDR-2, an evaluation index of HDR image quality. Comparison with other algorithms revealed that the average PSNR of the MLP algorithm improved by 4.43% over the Ma algorithm, 7.88% over the Vanmail algorithm, 10.30% over the FMMR algorithm, 11.19% over the PMF algorithm, and 11.19% over the PMF algorithm. For PCC, the MLP algorithm improves by 20.14%, 17.46%, 2.31%, 11.24%, and 15.36% over the other algorithms in that order. For SSMI, the MLP algorithm improved by 16.99%, 8.96%, 17.17%, 14.41%, and 4.85% over the other algorithms, in that order. For HDR-VDR-2, the MLP algorithm improved by 3.02%, 2.79%, 6.84%, 4.90%, and 6.55% over the other algorithms, in that order. The results show that the MLP algorithm can avoid image artifacts while retaining more details. The MLP-based adaptive fusion method is a step further in the theoretical study of multi-exposure image fusion, which is of great significance for subsequent research and practical application by related technology vendors.

Biodegradable metals and their alloys have gained significant attention as promising candidates for biomedical applications. Notably, Magnesium, Zinc, and Iron stand out as the most prominent biodegradable materials, prompting extensive research into novel alloy development. These metals exhibit inherent biodegradability, acceptable biocompatibility, and commendable mechanical integrity, rendering them suitable for implant use. In this comprehensive review, we delve into recent advancements in biodegradable implants within the biomedical domain, with a primary focus on orthopedic and cardiovascular applications. The paper extensively explores the in vivo and in vitro degradation behavior, mechanical properties, and corrosion mechanism of these materials. Additionally, we discuss progress in surface modification and fabrication techniques, vital in optimizing implant performance. However, certain limitations associated with alloying materials, especially Zinc and Iron, have hindered their widespread implementation. We underscore the importance of overcoming these challenges to unlock the full potential of these materials. Furthermore, we present the promise of new metals, such as Molybdenum, which exhibit remarkable properties, opening doors to implants with superior mechanical characteristics. Overall, this review emphasizes the growing significance of biodegradable metallic implants, showcasing their potential to address diverse clinical needs. By addressing material limitations and harnessing innovative metals, we pave the way for the development of implants with superior mechanical properties and desirable degradation rates, thus revolutionizing the field of biomedical applications.

This study conducted a questionnaire survey on the physical condition of high school students based on FIT. The correlation between sedentary behavior, exercise level and physical fitness was studied. In this paper, the fuzzy C-average clustering method of the BP neural network is used to make statistics and classification of students’ physical condition. This paper collates the biological quality data of college students. Then this paper makes a comprehensive classification, classification and quantitative assessment of the data. The study found that college students’ sedentary habits varied from individual and family backgrounds. Family factors influence students’ physical exercise. Physical exercise was positively correlated with sedentary behavior and body mass index in adolescents. The accuracy of clustering combined with the BP neural network can reach 94%.

Objective To determine the effects and underlying mechanism of pterostilbene (PTE) on memory dysfunction in mice exposed to chronic unpredictable military stress (CUMS). Methods Thirty-six healthy 7 to 8 week-old C57BL/6J male mice were randomly divided into control group (CON), CUMS group (for 30 d) and PTE intervention group (100 mg/kg PTE, gastric infusion). Body weight and food intake of each mouse were recorded during the intervention. After the intervention, serum corticosterone concentrations were detected by ELISA, spatial memory function of mouse was detected by Morris water maze assay, ultrastructural changes of mice hippocampal neurons were observed by transmission electron microscopy, mitochondrial respiratory function of mouse hippocampus was detected by Seahorse XF HS Mini analyzer, the relative copy number of mtDNA and the expression of Pgc-1α and Tfam at mRNA level in the hippocampus were detected by RT-qPCR, and the protein level of PGC-1α was measured with Western blotting. Results Compared with the CON group, the CUMS group had significantly decreased body weight and food intake (P < 0.05), increased serum content of corticosterone (P < 0.000 1), while PTE intervention reversed above changes (P < 0.05 and P < 0.000 1, respectively). Morris water maze assay showed that the CUMS group were worse in the memory accuracy of the platform, while the PTE group had better performance. After exposure to CUMS, more lipofuscin, swollen mitochondria and disappearance of cristae were observed with transmission electron microscopy, but comparatively mild damages were found in the PTE group. Moreover, mitochondrial basal respiration (P < 0.05), maximal respiration (P < 0.001) and spare respiratory capacity (P < 0.01) in isolated hippocampal neurons were decreased after exposed to CUMS, whereas the maximal respiration (P < 0.05), ATP-linked respiration (P < 0.05) and respiratory control rate (P < 0.05) were significantly improved in the PTE group. Furthermore, the copy number of mtDNA (P < 0.01), and the expression of Pgc-1α (P < 0.000 1) and Tfam (P < 0.01) at mRNA level in the hippocampus of the CUMS group were decreased; while those phenomena were significantly elevated in the CUMS group (P < 0.05). Western blotting indicated the expression of PGC-1α was lower in the hippocampus of the CUMS group than the CON group (P < 0.01), whereas that was significantly higher in the PTE group than the CUMS group (P < 0.05). Conclusion PTE could improve the memory function of CUMS-exposed mice, which might be related with its enhancing PGC-1α expression and thus mitigating the CUMS-induced structural and functional damages in the hippocampal mitochondria.

Aiming at the problem of mounting adaptability evaluation of shipboard gun weapon system, a mounting adaptability evaluation model based on analytic hierarchy process is put forward. The mounting adaptability of guns, tracking sensors and fire control equipment are chosen as fuzzy variables to evaluate the mounting adaptability of shipboard gun weapon system in the model. And the evaluation ruler of them is given. And the calculation method of mounting adaptability based on analytic hierarchy process is put forward. The example analysis shows the model can realize the quantitative evaluation for the adaptability of shipboard gun weapon system. The model has good operability and provides a powerful support for type selection decision-making.

The threats of nonrenewable energy consumption and environmental disruption caused by the extensive use of metals and polymers derived from petroleum have prompted the development of eco-friendly, high-performance, and long-lasting structural materials. After various treatments, cellulose materials exhibit exceptional properties such as high strength, fire resistance, hydrophobic properties, and thermal stability. Cellulose-based structural materials have excellent mechanical strength and the distinct advantages of being lightweight, inexpensive, and energy efficient. This review summarizes the recent progress in the preparation methods and properties of high-performance cellulose structural materials such as high-strength cellulose structural materials, thermal insulation cellulose structural materials, flame-retardant cellulose structural materials, hydrophobic cellulose structural materials, cellulose structural material with electrical properties, and other cellulose structural materials. The future of high-performance cellulosic structural materials and the prospective of their development are concluded.

Fatty acid oxidation (FAO) has been proven to be an accomplice in tumor progression. Carnitine palmitoyltransferase 1C (CPT1C), a rate-limiting enzyme in FAO, mainly functions to catalyze fatty acid carnitinylation and guarantee subsequent entry into the mitochondria for FAO in colorectal cancer (CRC). Gene expression data and clinical information extracted from The Cancer Genome Atlas (TCGA) database show significantly higher expression of CPT1C in patients with metastatic CRC ( P=0.005). Moreover, overexpression of CPT1C is correlated with worse relapse-free survival in CRC (HR 2.1, P=0.0006), while no statistical significance is indicated for CPT1A and CPT1B. Further experiments demonstrate that downregulation of CPT1C expression leads to a decrease in the FAO rate, suppression of cell proliferation, cell cycle arrest and repression of cell migration in CRC, whereas opposite results are obtained when CPT1C is overexpressed. Furthermore, an FAO inhibitor almost completely reverses the enhanced cell proliferation and migration induced by CPT1C overexpression. In addition, analysis of TCGA data illustrates a positive association between CPT1C expression and HIF1α level, suggesting that CPT1C is a transcriptional target of HIF1α. In conclusion, CPT1C overexpression indicates poor relapse-free survival of patients with CRC, and CPT1C is transcriptionally activated by HIF1α, thereby promoting the proliferation and migration of CRC cells.

ZnO is currently the main coating material for piezoelectric bolt sensors, which exhibits excellent acoustic-electric signal conversion because of its unique piezoelectric effect, but its high-temperature structure and performance stability have been less studied. In this paper, ZnO piezoelectric coating that could generate ultrasonic longitudinal waves was prepared on （100）Si and industrial titanium bolts by RF magnetron sputtering, and annealed at different temperatures and different time durations to investigate the effects of high-temperature annealing treatment on the structure and properties of the coating. The results of scanning electron microscopy show that the annealing treatment below 600 °C does not affect the microscopic morphology of the coating surface, and the cross-sectional morphology of the coating shows a columnar crystal structure, and the columnar crystals tend to merge with the temperature increased. The results of atomic force microscopy show that the surface roughness of the coating varies by ±4 nm, and the X-ray diffraction results show that different annealing temperatures do not affect the crystal structure of the coating significantly. The acoustic signals of the bolt samples after annealing treatment at different temperatures and durations are examined, and the results show that the coating surface is intact after annealing treatment at 500 °C and below, and the bolt coating is completely peeled off after annealing treatment at 600 °C. The ultrasonic signals characterize that the structure of coating is stable after annealing at 500 °C or below; at 300 °C, after a long annealing treatment, none of the excitable ultrasonic properties of the bolt samples are destroyed, indicating that the ZnO coating can serve in the temperature range of 300 ℃ for a long time.

In this paper a novel method for fabrication of TiB2 coatings on graphite substrates by electrophoretic deposition of TiB2 nanoparticles in NaF-AlF3 melt at 980°C has been explored. With an applied cell voltage of 1.2 V (i.e. electric field 0.6 V/cm), a flat and dense TiB2 coating with a thickness of around 50 μm has been prepared. It was found that the surface consisted of TiB2 flakes with a nanoscale size. In addition, the average value of hardness of the TiB2 coating was determined as 3585 ± 139 HV0.1 (36.6 ± 1.4GPa) measured by a Vickers micro-hardness tester.

The antimicrobial substances produced by Bacillus velezensis 1-3, a strain with antibacterial activity isolated from oral samples, were separated and purified by ammonium sulfate precipitation, gel filtration chromatography and thin-layer chromatography and were analyzed by matrix-assisted laser desorption ionization-time of flight-mass spectrometry (MALDI-TOF-MS). These substances were identified as surfactin consisting of three cyclic lipopeptides with 14–16 carbon atoms, whose molecular masses were 1 030.642, 1 044.660 and 1 058.677 Da, respectively. Surfactin could function as a surfactant and exhibited good antimicrobial activity against common pathogenic bacteria in the food, medical and livestock industries. Meanwhile, the minimal inhibitory concentration (MIC) of surfactin against Listeria monocytogenes ATCC 19115 was 4 μg/mL. Surfactin did not show hemolytic activity against mouse red blood cells within the effective antibacterial concentration range. It could endure temperatures at 20–100 ℃ and withstand a wide pH range of 2.0–10.0. Hence, surfactin produced by B. velezensis 1-3 has great potential for applications in the food, medical, and livestock fields .

BackgroundThis research endeavors to examine the potential effects of human and societal interactions on individuals’ post-traumatic growth in the aftermath of the Corona outbreak. To achieve the aforementioned objective, the current research investigates the correlations between post-traumatic growth and group identity, while also examining the potential mediating function of social–emotional competence.MethodsA cross-sectional design included a representative sample of 2,637 high school students located in the capital territory of Pakistan using convenience sampling method. To explore the associations, correlation and mediation analyzes utilizing the group identification scale, the social–emotional competence scale, and the post-traumatic growth scale was performed with SPSS PROCESS 4 macro and AMOS.ResultsThe findings demonstrated that group identification emerged as a substantial predictor substantially associated with post-traumatic growth. Moreover, the relationship linking group identification and post-traumatic growth was found to be partially moderated by social–emotional competence.ConclusionThe phenomenon of group identification can exert influence on post-traumatic growth through both direct and mediating pathways, with the latter being essentially mediated by social–emotional competence. The aforementioned outcomes possess significant academic and practical implications concerning the promotion of post-traumatic growth and the improvement of psychological well-being after the Corona outbreak.

In order to study the strength performance of the airship envelope and the influence of overpressure bearing on its residual strength, the sample forms and experiments plans for the uniaxial and biaxial tensile of the envelope material were designed, and the tearing situation of the airship was analyzed with the help of a high-speed camera. Based on the experimental data, fitted the uniaxial tensile strength curve when the airship bears air pressure, and the longitudinal and latitudinal strength envelope line of the material when it was biaxially stretched. The experimental results demonstrate that there is a negative correlation between the residual strength of the envelope and the airship bearing air pressure when uniaxially stretched, the bearing strength of the envelope is comprehensively affected by the longitudinal and latitudinal strengths when biaxially stretched. This experiment also analyzes the failure situation of the airship envelope, which can provide an experimental guidance for its strength prediction.

Random Fourier features represent one of the most influential and wide-spread techniques in machine learning to scale up kernel algorithms. As the methods based on random Fourier approximation of the kernel function can overcome the shortcomings of machine learning methods that require a large number of labeled sample, it is effective to be applied to the practical areas where samples are difficult to obtain. Network traffic forwarding policy making is one such practical application, and it is widely concerned in the programmable networks. With the advantages of kernel techniques and random Fourier features, this paper proposes an application of network traffic forwarding policy making method based on random Fourier approximation of kernel function in programmable networks to realize traffic forwarding policy making to improve the security of networks. The core of the method is to map traffic forwarding features to Hilbert high-dimensional space through random Fourier transform, and then uses the principle of maximum interval to detect adversarial samples. Compared with the traditional kernel function method, it improves the algorithm efficiency from square efficiency to linear efficiency. The AUC on the data set from real-world network reached 0.9984, showing that the method proposed can realize traffic forwarding policy making effectively to improve the security of programmable networks.

Boosting is an ensemble learning method that combines a set of weak learners into a strong learner to minimize training errors. AdaBoost algorithm, as a typical boosting algorithm, transforms weak learners or predictors to strong predictors in order to solve problems of classification. With remarkable usability and effectiveness, AdaBoost algorithm has been widely used in many fields, such as face recognition, speech enhancement, natural language processing, and network intrusion detection. In the large-scale enterprise network environment, more and more companies have begun to build trustworthy networks to effectively defend against hacker attacks. However, since trustworthy networks use trusted flags to verify the legitimacy of network requests, it cannot effectively identify abnormal behaviors in network data packets. This paper applies Adaboost algorithm in trustworthy network for anomaly intrusion detection to improve the defense capability against network attacks. This method uses a simple decision tree as the base weak learner, and uses AdaBoost algorithm to combine multiple weak learners into a strong learner by re-weighting the samples. This paper uses the real data of trustworthy network for experimental verification. The experimental results show that the average precision of network anomaly detection method based on AdaBoost algorithm is more than 0.999, indicating that it has a significant detection effect on abnormal network attacks and normal network access. Therefore, the proposed method can effectively improve the security of trustworthy networks.

Electrical switching of nanophotonic structural color elements is a promising approach towards addressable color switching pixels for next generation reflective displays. However, electrical switching between the primary colors to colorless near-white state remains a challenge. Here, we present a reversible electrical switching approach, relying on the electrocoagulation of Ag nanoparticles between silicon nanostructures that support Mie resonances. The electrodeposited Ag nanoparticles enable the excitation of the hybrid plasmon-Mie resonance as supported on Ag-silicon nanostructures, resulting in a large spectral transformation. Importantly, this process is reversible. This device design outperforms other designs in terms of electrotonic color control since it is highly stable and reliable for use in high-resolution reflective displays, such as colored electronic papers and smart display glass, where the combination is scalable to other nanostructure designs and electrolytic solutions.

An accuracy synthesis approach for a 2UPS & UPR & UP parallel robot is proposed. Based on the nominal kinematic equations, an error mapping model of the robot is established using the perturbation method, and the compensable and uncompensable geometric error sources are sorted out. The standard deviation relationship between the uncompensable error sources and the terminal attitude errors is established to reveal the standard deviation distribution of the terminal attitude error in the workspace. A sensitivity analysis of the uncompensable error sources is carried out to determine the influence of each uncompensable geometric error source on the terminal accuracy. According to the 3σ principle, the sensitivity index is taken as the allocation coefficient, and the values of the uncompensable error sources under the given terminal accuracy are obtained through the accuracy synthesis. The proposed approach can provide a theoretical basis for the manufacturing of the robot, and the effectiveness of the proposed method is verified by simulation.

In order to further improve the coating microstructure and properties，a new surface micro-crystallization technique，high temperature assisted ultrasonic deep rolling (HT+UDR)， was used to treat the cermet coating， which could soften the cermet coating by high temperature treatment， thus reducing the strength and hardness of the coating，and large plastic deformation would occur on the coating surface during ultrasonic deep rolling. In this paper， SEM， EDS， hardness test and XRD were used to investigate the effect of different pressing amounts (0.20 mm，0.25 mm，0.30 mm) in high temperature assisted ultrasonic deep rolling on the microstructure，structure and properties of ceramic coating on 45 steel substrate. Results showed that after HT+UDR+0.25 mm treatment， the longitudinal cracks in the coating were closed or transformed into transverse cracks under the action of plastic flow， and the porosity was small. After work hardening and grain refinement， the surface hardness was further increased to 650.2 HV. The residual tensile stress on the coating surface was changed into residual compressive stress，with the maximum value of -337.9 Mpa. However， cracks appeared on the surface layer of the coating after HT+UDR+0.30 mm treatment， and the dynamic recrystallization behavior of the surface was intensified. The decrease of dislocation density led to the release of residual stress， which was 8.67%lower than that of HT+UDR+0.25 mm treatment.

In order to obtain the location and size information of the wear area of face gears, a method of gear surface wear inspection based on reverse engineering is proposed. According to the measurement principle, the conversion matrix of the measuring coordinates is deduced; by measuring the tooth profile deviation and with the error compensation algorithm, the compensation point of the measuring point is obtained and the control equation of NURBS surface fitting is presented; a test bench is built, and an example operation is carried out to compare and analyze the standard tooth surface and the worn surface. The data in the unworn area is well matched, and the data points in the worn area are clear and free of noise. Therefore the use of reverse engineering technology in the face gear wear detection method is correct and feasible, and it provides a prerequisite for the future of green remanufacturing.

Objective To investigate the influencing factors of postoperative meningioma recurrence. Methods A total of 176 patients with meningioma treated in Beijing Tiantan Hospital, Capital Medical University from May 2010 to January 2011 were included. The general information, imaging findings, extent of tumor resection and postoperative pathological results were analyzed. Kaplan⁃Meier survival curve comparison and Log⁃rank test were used to compare the related influencing factors of meningioma recurrence. Multifactor Cox's proportional hazards regression model was used for survival analysis. Results The median progression free survival (PFS) of patients with preoperative Karnofsky Performance Status (KPS)＜70 was lower than that with KPS≥70 [(8.04±1.98) years vs. (8.08±1.18) years; χ2=5.370, P=0.020]. The median PFS of patients with peritumoral edema grade 2-3 was lower than that with peritumoral edema grade 0-1 [(8.05±1.79) years vs. (8.06±1.09) years; χ2=9.805, P=0.002]. The median PFS of patients with dural mater invasion was lower than that without dural mater invasion [(7.97±1.70) years vs. (8.06±1.09) years; χ2=12.357, P=0.000]. The median PFS of patients with WHO grade 2-3 was lower than that with WHO grade 1 [(5.14±2.07) years vs. (8.12±0.87) years; χ2=113.774, P=0.000]. Multivariate Cox's proportional hazards regression model showed that WHO grade 2-3 could increase the risk of postoperative meningioma recurrence (RR=15.693, 95%CI: 5.905-41.707; P=0.000). Conclusions The preoperative KPS score, peritumoral edema, dura mater invasion and WHO grade may affect the recurrence of meningioma, and WHO grade 2-3 can increase the risk of meningioma recurrence.

Objective To evaluate brain plasticity by resting⁃state fMRI (rs⁃fMRI), and to explore the potential neuroendocrine mechanism of repetitive transcranial magnetic stimulation (rTMS) in improving post⁃stroke cognitive impairment (PSCI) in patients with low thyroid hormone (TH) level. Methods A total of 28 patients with PSCI with low TH level who received rehabilitation treatment in Shijiazhuang People's Hospital from August 2020 to December 2021 were randomly divided into rTMS group (n=15) and control group (n=13). Cognitive function was assessed by Montreal Cognitive Scale (MoCA) before and 4 weeks after rTMS, and serum levels of tri⁃iodothyronine (T3), thyroxine (T4) and thyroid stimulating hormone (TSH) were measured by double antibody sandwich method. rs⁃fMRI was used to calculate the fractional amplitude of low⁃frequency fluctuation (fALFF) and observe the activated brain regions after stimulation. Pearson correlation analysis was used to obtain the distribution of synchronous changes of activated brain areas based on whole⁃brain functional connection strength of hypothalamus and serum TH level in 2 groups. Results After 4 weeks of intervention, compared with control group, MoCA score (F=1163.054, P=0.000), and serum T3 (F=1111.685, P=0.000), T4 (F=847.333, P=0.000), TSH (F=251.316, P=0.000) in rTMS group was increased. In rTMS group, fALFF value increased mainly in posterior cerebellar lobe (t=4.699, P＜0.05), middle frontal gyrus (t=4.356, P＜0.05) and superior frontal gyrus (t=5.675, P＜0.05), while fALFF value decreased in superior temporal gyrus (t=⁃7.874, P＜0.05) and middle temporal gyrus (t=⁃5.776, P＜0.05). The enhanced functional connectivity of the whole brain with bilateral hypothalamus as the region of interest (ROI) and the simultaneous increase of serum T3 and TSH levels were mainly concentrated in the cerebellum, frontal lobe and parahippocampal gyrus. Conclusions rTMS can indirectly affect hypothalamic function through cerebral cortex, increase serum TH level and improve cognitive function in PSCI patients.

Natural gas hydrates (NGH) are widely found in seafloor sediments. In engineering, it is usually necessary to inject 60% of thermodynamic inhibitors, which makes hydrate extraction costly and polluting. Currently, kinetic inhibitors have attracted much attention due to their low injection dose and environmental friendliness, but the research is costly and time-consuming. In this study, we investigated the interaction between winter flounder antifreeze proteins (AFPs) and methane hydrate growth using molecular dynamics simulations. AFPs adsorbed on the hydrate surface and hindered the mass transfer of methane molecules. At the same time, the water molecules around the AFP adsorption surface are in a quasi-liquid state, a structure that facilitates the binding of AFPs to the hydrate surface. Analysis of the probability of amino acid adsorption showed that AFP was adsorbed to the hydrate surface through a combination of hydrophobic and hydrogen bonding interactions. Subsequent directional mutagenesis experiments showed that increasing the hydrophobicity of AFP rather weakens its adsorption capacity. This suggests that excessive hydrophobicity of AFP may be counterproductive to its adsorption on the hydrate surface. These findings deepen the understanding of the AFP mechanism and its potential for the development of novel hydrate inhalants.

25 models are designed according to the orthogonal table, to study the comprehensive relationship between heat transfer and sealing characteristics in straight-through labyrinth seals with different geometrical parameters. The experimental investigations are carried out at different pressure ratios of 1.2, 1.3, 1.4, 1.6, 1.8. The results show that different geometry parameters brought obvious different influence on both leakage efficiency and heat transfer characteristics, and in which the parameters (H, c,β) have the most significant influence on leakage efficiency, and heat transfer characteristics under different pressure ratios, whose small changes in size can lead to performance optimization in leakage and heat transfer. The geometrical parameters always show consistent influence on leakage efficiency and heat transfer characteristics, while heat transfer characteristics show larger vibration with the increase of pressure ratio.

In order to further strengthen the operation protection measures of the optical cable and realize real-time monitoring of the hidden danger of external force damage in the optical cable line area, this paper uses the principle of Rayleigh backscattering and coherent detection to design and construct an optical cable external damage event monitoring based on distributed optical fiber vibration sensing early warning system. Using Phase Sensitive Optical Time Domain Reflectometer (Φ-OTDR) technology, the optical fiber vibration signal is collected in real time. The key characteristics of vibration sensing are extracted, and the type of hidden danger is sent to the operation and maintenance staff to locate and warn the hidden danger of external force damage in real time. Field verification results show that the system can simultaneously detect and locate multiple vibration sources along the sensing fiber, and can identify various types of disturbance signals. The monitoring and early warning system has a complete system and a simple structure. The recognition rate of various construction signals is as high as 95%. It realizes double early warning of the type of external breakage and precise location, which can effectively prevent and stop external damage events and improve the work efficiency of anti-external breakage of optical cables.

This research studied the effect of cordyceps militaris noodles on alleviating physical fatigue and concentration of serum lipids in mouse. The mouse were divided into feed group, blank noodle group, low dose of C.militaris noodles group and high dose of C.militaris noodles group, and the animal model of swimming fatigue was established. The exhausted swimming time of mouse in each group was recorded, and the related indexes of alleviating physical fatigue and affecting lipid concentration in mouse were measured. The results show that there was no significant difference in body weight of mouse in each group. Compared with feed group and blank noodle group, cordyceps militaris noodles could effectively prolong the exhausted swimming time of mouse (P

An accurate estimation model of winding hot spot temperature is the key to assess the thermal state and insulation life of oil-immersed transformers. Based on the hot spot temperature and load current monitored by the substation,the genetic programming algorithm is applied to train the basic structure of the hot spot temperature estimation model. Then,the parameter identification of the hot spot temperature estimation model is performed by the normalized least square mean (NLMS) algorithm. Finally,an explicit prediction model of the hot spot temperature is established for oil-immersed transformers. The explicit winding hot spot temperature estimation model can effectively reflect the relationship between the load factor and the winding hot spot temperature. Moreover,the goodness of fit of the model under the test set is 0.998 8,and the maximum absolute error is only 1.36 ℃,which verify the correctness and effectiveness of the model. Furthermore,the strong generalization performance of the proposed model is proved by estimating the winding hot spot temperature for oil-immersed transformers with the same capacity and model in the same area.

Objective To preliminarily investigate the safety and effectiveness of robot-assisted kidney transplantation (RAKT) in the perioperative period. Methods Clinical data of 14 patients undergoing renal transplantation in our department from June 2018 to March 2022 were collected and retrospectively analyzed. They were 8 males and 6 females, and at an age of 22 to 45 years. There were 4 patients receiving robotic-assisted laparoscopic living-donor kidney transplantation (RAKT group), and 10 cases receiving open kidney transplantation (OKT group) with deceased donor kidney. Arterio-venous anastomoses (AVAs) time, length of post-operative stay, pre- and post-operative serum creatinine levels, intraoperative blood loss, and incidence of post-operative complications were retrospectively analyzed between the patients of the 2 groups. Results The surgeries went well in the 14 patients. The amount of intraoperative blood loss was 162.5±47.8 mL in the RAKT group and 245.1±68.5 mL in the OKT group(P < 0.05). The arterial anastomosis time was 32.5±6.5 min in the RAKT group and 24.3±5.3 min in the OKT group. The venous anastomosis time was 16.5±3.8 min in the RAKT group and 14.6±1.5 min in the OKT group. The blood creatinine level was 98.1±33.2 μmol/L in the RAKT group and 142.8±42.1 μmol/L in the OKT group in 7 d after surgery. The retention time of drainage tube was 10.0±1.2 d in the OKT group and 6.5±0.5 d in the RAKT group(P < 0.01). The length of stay after surgery was 13.0±2.5 d in the RAKT group and 16.3±2.1 d in the OKT group(P=0.01). Conclusion RAKT is safer than OKT in the perioperative period and causes fewer postoperative complications.

A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. Specifically, three categories of 2D diffractive chiral structures with different geometrical parameters, including widths, separation spaces, bridge lengths, and gold lengths are studied utilising both the conventional rigorous coupled wave analysis (RCWA) approach and DEIFS algorithm, with the former approach assisting the training process for the latter. The DEIFS algorithm can be divided into two main stages, namely data enhancement and iterations. Firstly, some “pseudo data” are generated by a forward prediction network that can efficiently predict the circular dichroism (CD) response of 2D diffractive chiral metamaterials to reinforce the dataset after necessary denoising. Then, the algorithm uses the CD spectra and the predictions of parameters with smaller errors iteratively to achieve accurate values of the remaining parameters. Meanwhile, according to the impact of geometric parameters on the chiroptical response, a new functionality is added to interpret the experimental results of DEIFS algorithm from the perspective of data, improving the interpretability of the DEIFS. In this way, the DEIFS algorithm replaces the time-consuming iterative optimization process with a faster and simpler approach that achieves accurate inverse design with dataset whose amount is at least one to two orders of magnitude less than most previous deep learning methods, reducing the dependence on simulated spectra. Furthermore, the fast inverse design of multiple shaped metamaterials allows for different light manipulation, demonstrating excellent potentials in applications of optical coding and information processing. This work belongs to one of the first attempts to thoroughly characterize the flexibility, interpretability, and generalization ability of DEIFS algorithm in studying various chiroptical effects in metamaterials and accelerating the inverse design of hypersensitive photonic devices.

Abstract Background Human milk banks (HMBs) are essential facilities for the selection, collection, testing, transportation,storage, and distribution of DHM for special medical needs. The aim of this analysis was to analyze the operation status and data over the last 8 years of operation of the first human milk bank (HMB) in East China. Methods Data related to the costs, donors, donation, pasteurization, and recipients were extracted from the web-based electronic monitoring system of the HMB for the period August 1, 2013 to July 31, 2021. Results Over the 8 years of operation, 1,555 qualified donors donated 7,396.5L of qualified milk at a cost of ¥1.94 million($306,051), with the average cost per liter of donor human milk being ¥262.3($41.4). The donors were between 25 and 30 years of age, and the majority (80.1%) were primipara. All the donated milk was pasteurized and subjected to bacteriological tests before and after pasteurization: 95.4% passed the pre-pasteurization tests, and 96.3% passed the post-pasteurization tests. A total of 9,207 newborns received 5,775.2L of pasteurized donor milk. The main reason for the prescription of donor human milk was preterm birth. As a result of continuous quality improvements, January 2016 witnessed a significant increase in the volume of qualified DHM and the number of qualified donors. However, in 2020, as a result of the restrictions related to the COVID-19 pandemic, the volume of qualified DHM and the number of qualified donors decreased. Conclusions Over its 8 years of operation, our HMB has made steady quality improvements in its screening and information processes. Continuous quality improvement is on ongoing need, along with recruiting more qualified donors and collecting donor human milk for vulnerable newborns.

The mu-opioid receptor (MOR), a membrane-bound G protein-coupled receptor, is implicated in progression and long-term outcome of several types of tumors. However, the expression and clinical significance of MOR in colorectal cancer (CRC) remain unclear. In this study, a total of 180 paraffin-embedded samples of paired tumors and normal tissues from CRC patients are used to explore expression levels of MOR by immunohistochemistry (IHC). Results show that MOR is highly expressed in tumors compared with that in paired normal tissues ( P