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In kagome metal CsV_{3}Sb_{5}, multiple intertwined orders are accompanied by both electronic and structural instabilities. These exotic orders have attracted much recent attention, but their origins remain elusive. The newly discovered CsTi_{3}Bi_{5} is a Ti-based kagome metal to parallel CsV_{3}Sb_{5}. Here, we report angle-resolved photoemission experiments and first-principles calculations on pristine and Cs-doped CsTi_{3}Bi_{5} samples. Our results reveal that the van Hove singularity (vHS) in CsTi_{3}Bi_{5} can be tuned in a large energy range without structural instability, different from that in CsV_{3}Sb_{5}. As such, CsTi_{3}Bi_{5} provides a complementary platform to disentangle and investigate the electronic instability with a tunable vHS in kagome metals.

Background: diabetices foot ulcer (DFU) are serious complications. It is crucial to detect and diagnose DFU early in order to provide timely treatment, improve patient quality of life, and avoid the social and economic consequences. Machine learning techniques can help identify risk factors associated with DFU development. Objective: The aim of this study was to establish correlations between clinical and biochemical risk factors of DFU through local foot examinations based on the construction of predictive models using automated machine learning techniques. Methods: The input dataset consisted of 566 diabetes cases and 50 DFU risk factors, including 9 local foot examinations. 340 patients with Class 0 labeling (low-risk DFU), 226 patients with Class 1 labeling (high-risk DFU). To divide the training group (consisting of 453 cases) and the validation group (consisting of 113 cases), as well as preprocess the data and develop a prediction model, a Monte Carlo cross-validation approach was employed. Furthermore, potential high-risk factors were analyzed using various algorithms, including Bayesian BYS, Multi-Gaussian Weighted Classifier (MGWC), Support Vector Machine (SVM), and Random Forest Classifier (RF). A three-layer machine learning training was constructed, and model performance was estimated using a Confusion Matrix. The top 30 ranking feature variables were ultimately determined. To reinforce the robustness and generalizability of the predictive model, an independent dataset comprising 248 cases was employed for external validation. This validation process evaluated the model's applicability and reliability across diverse populations and clinical settings. Importantly, the external dataset required no additional tuning or adjustment of parameters, enabling an unbiased assessment of the model's generalizability and its capacity to predict the risk of DFU. Results: The ensemble learning method outperformed individual classifiers in various performance evaluation metrics. Based on the ROC analysis, the AUC of the AutoML model for assessing diabetic foot risk was 88.48 % (74.44–97.83 %). Other results were found to be as follows: 87.23 % (63.33 %–100.00 %) for sensitivity, 87.43 % (70.00 %–100.00 %) for specificity, 87.33 % (76.66 %–95.00 %) for accuracy, 87.69 % (75.00 %–100.00 %) for positive predictive value, and 87.70 % (71.79 %–100.00 %) for negative predictive value. In addition to traditional DFU risk factors such as cardiovascular disorders, peripheral artery disease, and neurological damage, we identified new risk factors such as lower limb varicose veins, history of cerebral infarction, blood urea nitrogen, GFR (Glomerular Filtration Rate), and type of diabetes that may be related to the development of DFU. In the external validation set of 158 samples, originating from an initial 248 with exclusions due to missing labels or features, the model still exhibited strong predictive accuracy. The AUC score of 0.762 indicated a strong discriminatory capability of the model. Furthermore, the Sensitivity and Specificity values provided insights into the model's ability to correctly identify both DFU cases and non-cases, respectively. Conclusion: The predictive model, developed through AutoML and grounded in local foot examinations, has proven to be a robust and practical instrument for the screening, prediction, and diagnosis of DFU risk. This model not only aids medical practitioners in the identification of potential DFU cases but also plays a pivotal role in mitigating the progression towards adverse outcomes. And the recent successful external validation of our DFU risk prediction model marks a crucial advancement, indicating its readiness for clinical application. This validation reinforces the model's efficacy as an accessible and reliable tool for early DFU risk assessment, thereby facilitating prompt intervention strategies and enhancing overall patient outcomes.

The nitrogen and phosphorus in electroplating wastewater were recovered using struvite crystallization method. The effects of pH, concentration of heavy metal ions and mass concentration of fluoride ions on the removal of nitrogen and phosphorus in electroplating wastewater were investigated. The results showed that pH and concentration of heavy metal ions had significantly affect on the process of struvite crystallization, while the effect of low mass concentration（0-40 mg/L） of fluoride ions on the structure of struvite could be ignored. XRD, SEM and XPS characterization were conducted on struvite formed under different conditions, and the influence mechanism of heavy metal ions and fluorine ions on the crystallization process of struvite was analyzed. Heavy metal ions could co-precipitate with struvite and interfere with the formation of struvite crystals. When fluoride ions coexisted with heavy metal ions, fluoride ions would interfere the adsorption of heavy metal ions on the crystal, and generate amorphous phase coprecipitates, changing the crystal morphology of struvite. This study provides a theoretical basis for the recovery of nitrogen and phosphorus from electroplating wastewater by the struvite crystallization method.

Objective Fracture network analysis plays an important role in oil and gas exploration and development. However, complexity analysis of tight sandstone fracture networks and their control factors is relatively lagging. Based on an experimental study of the dynamic evolution of the complex fracture network in tight sandstone, the fractal and multifractal spectral characteristics of the fracture network were defined, and the complexity and main controlling factors of the fracture network were analyzed. Fracture network complexity analysis of tight sandstone plays an important role in hydraulic fracturing optimization, fracture network prediction, and fracture modeling. Methods Rock mechanics and X-ray computed tomography scan experiments determined the characteristics of rock mechanics and fracture networks . The microstructure and fracture network fractal characteristics of tight sandstone were quantitatively characterized by SEM and fracture network fractal analysis. Results The results showed that the quartz content of tight sandstone ranges from 28.08 to 52.88%, clay content ranges from 11.54 to 25.45%, particle size ranges from 61.18 to 184.55 μm, and porosity ranges from 8.125 to 10.296%. Uniaxial compressive strength ranges from 69.09 to 188.33 MPa, and the elastic modulus ranges from 31.69 to 92.76 GPa. The fractal dimension (DB) ranges from 1.28 to 2.35 and average spectral width (Δα) ranges from 1.0851 to 1.3638. Conclusion The initiation and propagation of fractures extend through the entire stress–strain process. The complexity of the fracture network of tight sandstone is mainly controlled by microscopic fabric characteristics, and has obvious confining pressure as well as scale effects. The DB of the three-dimensional fracture network and average Δα of the multifractal spectrum represents the complexity and heterogeneity of the fracture spatial distribution, respectively, and are relatively independent. As the content of quartz, feldspar, and other brittle minerals in sandstone increases, the porosity of the reservoir increases, particle size of the sandstone decreases, DB of the fracture network increases, and average Δα decreases. In the absence of confining pressure, the complexity of the sample fracture network is mainly controlled by the microscopic fabric characteristics, and the complexity increases with increase of axial pressure. When present confining pressure plays a leading role; the higher it is, the lower the DB value, and the higher the mean Δα value. Clay minerals are unconducive to complex fractures formation. The mean values of DB and Δα of small-scale samples are greater than those of large-scale samples. The elastic modulus and compressive strength of sandstone are positively correlated with DB and mean Δα.

Based on the fracture mechanics theory of the isotropic Hoek-Brown strength criterion, this paper established the initiation conditions of the bifurcated microcrack along bedding which begins with the meso-fracture mechanism of laminated rocks. Then the author substituted the critical angle which indicates that the initial crack most likely causes the fracture of the rock into above established conditions and derived the anisotropy expression of the parameter m1 for the weak plane of bedding leading to the crack deflection. Through introducing the correction coefficient for the mixed fracture of the rock along the bedding and the matrix, this paper obtained the anisotropic modified model of Hoek-Brown strength criterion. Compared with the established anisotropic Hoek-Brown model and shale triaxial test results, the validity of the model was proved. The modified model inherits the fracture mechanics analysis of the isotropic Hoek-Brown strength criterion for the selection of rock failure characteristic quantity, and reflects the microscopic failure mechanism of the rock, also considers the effect of bedding anisotropy. The parameters in the modified criterion have a clear physical meaning. The parameter m in the model is related to the angle of the bedding plane, the tensile strength, compressive strength and friction coefficient of bedding and matrix. The change in the friction coefficient of the bedding plane will cause the position of the minimum value of m and the rock strength characteristics changed.

Aiming at the fault overcurrent problem existing in the current arrangement scheme of the bridge arm reactor in the modular multilevel converter (MMC) of the bipolar flexible DC transmission system, the influence of the bridge arm reactor in different positions on the converter fault current is analyzed in this paper, and the mathematical expression of fault current component is derived. A scheme of placing the bridge arm reactor near the DC connection point is proposed. The study shows that the arrangement of bridge arm reactor near the DC connection point can effectively limit the rise rate of fault current, reducing the overcurrent level of the bridge arm module without increasing larger costs. However, when the bridge arm reactor is arranged close to the DC connection point, the short circuit fault of the bridge arm reactor wall bushing cannot be detected in time by the existing protection strategy, which brings potential threats to the MMC. A protection strategy, which can effectively identify the short circuit fault of the bridge arm reactor wall bushing is proposed in this paper. Finally, the correlation analysis and protection strategy are verified by PSCAD/EMTDC simulation.

[Introduction] Exploring the difference of quantities between composite cross arm tower and conventional tower in different design schemes can quickly carry out scheme comparison and selection in the planning and design stage, and provide reference for the application of composite cross arm tower in different scenarios. [Method] Taking the 500 kV tower as an example, the tower was divided into several parts, and compared with the design scheme of the composite cross arm tower. At the same time, the tower weight, the amount of foundation concrete, the quantities of fittings and insulators were calculated, and the differences between the quantities of each composite cross arm tower and the conventional tower were studied. [Result] The calculation results show that compared with the conventional tower, the design scheme 1 of composite cross arm tower has better economy, better corridor compression performance and poor emergency repair performance; Design scheme 2 of composite cross arm tower has general economy, good corridor compression performance and general emergency repair performance; Design scheme 3 of composite cross arm tower has poor economy, good corridor compression performance and good emergency repair performance. [Conclusion] The research results of this paper can provide reference for the selection of composite cross arm scheme in the future.

Pre-trained language models perform well in various natural language processing tasks. However, their large number of parameters poses significant challenges for edge devices with limited resources, greatly limiting their application in practical deployment. This paper introduces a simple and efficient method called Autocorrelation Matrix Knowledge Distillation (AMKD), aimed at improving the performance of smaller BERT models for specific tasks and making them more applicable in practical deployment scenarios. The AMKD method effectively captures the relationships between features using the autocorrelation matrix, enabling the student model to learn not only the performance of individual features from the teacher model but also the correlations among these features. Additionally, it addresses the issue of dimensional mismatch between the hidden states of the student and teacher models. Even in cases where the dimensions are smaller, AMKD retains the essential features from the teacher model, thereby minimizing information loss. Experimental results demonstrate that BERTTINY-AMKD outperforms traditional distillation methods and baseline models, achieving an average score of 83.6% on GLUE tasks. This represents a 4.1% improvement over BERTTINY-KD and exceeds the performance of BERT4-PKD and DistilBERT4 by 2.6% and 3.9%, respectively. Moreover, despite having only 13.3% of the parameters of BERTBASE, the BERTTINY-AMKD model retains over 96.3% of the performance of the teacher model, BERTBASE. [ABSTRACT FROM AUTHOR]