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To enhance the low-carbon construction level of subway stations, this paper, based on life cycle theory and the carbon emission factor method, summarizes the carbon emission calculation methods and relevant standards. Preliminary versions of the ''Carbon Emission Database Software for subway station Civil Engineering'' and ''Carbon Emission Calculation Software for subway station Civil Engineering'' were developed, enabling rapid calculation of carbon emissions for subway stations. From the perspective of low-carbon construction, a subway station in Guangzhou was used as an example to calculate the carbon emissions of three low-carbon construction methods. The results indicate that: (1) The carbon emission intensity of the materialization phase of the case station using the cover excavation reverse method is 3.46 tCO2e/m², demonstrating a lower carbon advantage compared to the other two methods; (2) The carbon emission of the building materials production phase accounts for over 84 % of the total carbon emission during the materialization phase for all three methods, making it the main source of carbon emission in the materialization phase of subway stations; (3) For subway stations of the same cost, the carbon emission of the open cut method is 13.77 % higher than that of the cover-excavation method and 10.18 % higher than that of the cover excavation reverse method.

Abstract Objective To explore the potential association between dietary live microbes and the prevalence of Chronic Obstructive Pulmonary Diseases (COPD). Methods In this cross-sectional study, data of 9791 participants aged 20 years or older in this study were collected from the National Health and Nutrition Examination Survey (NHANES) between 2013 and 2018. Participants in this study were classified into three groups according to the Sanders’ dietary live microbe classification system: low, medium, and high dietary live microbe groups. COPD was defined by a combination of self-reported physician diagnoses and standardized medical status questionnaires. Logistic regression and subgroup analysis were used to assess whether dietary live microbes were associated with the risk of COPD. Results Through full adjustment for confounders, participants in the high dietary live microbe group had a low prevalence of COPD in contrast to those in low dietary live microbe group (OR: 0.614, 95% CI: 0.474–0.795, and p

This study presents a novel risk-assessment methodology for deep foundation pit projects in karst regions, aimed at enhancing project safety and decision-making processes. This approach amalgamates fuzzy dynamic Bayesian networks with a refined Dempster–Shafer (DS) evidence theory to tackle the intricate uncertainties present in such contexts. A comprehensive risk index system, derived from historical accident cases, relevant standards, and the literature, encompasses environmental, design, construction, and management factors. Initial probabilities for each risk factor are determined through the integration of expert knowledge and fuzzy theory. The enhanced Dempster–Shafer theory is utilized to fuse diverse information sources, culminating in a robust and dynamic risk evaluation model. This model leverages real-time monitoring data to dynamically assess and adjust risk levels throughout the construction process. The validation of the proposed method is demonstrated through a detailed case study of the Guangzhou Tangxi Section 1 deep foundation pit project, which effectively identified critical risk factors and facilitated proactive construction strategy adjustments. To further evaluate the reliability of the methodology, comparisons were made with three alternative methods, and applications were conducted on three additional deep foundation pit projects. These comparative analyses confirm the superior reliability and applicability of the proposed methodology across varied scenarios.

The stability of underground diaphragm walls is crucial for ensuring the safety and integrity of trench excavations in geotechnical engineering. This study addresses this critical issue by proposing a novel destabilization mechanism based on a sliding body model specifically designed for diaphragm wall trenching operations. The research employs an analytical framework rooted in soil mechanics and plasticity theory, utilizing limit equilibrium analysis to develop a method for calculating the minimum required slurry density and corresponding safety factor for trench stability. The study compares two distinct approaches to slurry density computation, analyzing their sensitivity to various influencing factors. Theoretical findings are validated through multiple real-world engineering case studies. Comparative analysis demonstrates the superiority of the proposed method, particularly in assessing trench stability within clay layers. Key variables influencing the safety factor are identified, including trench length, slurry density, soil friction angle, and the relative height difference between slurry and groundwater levels. Results indicate that actual slurry densities observed in practice consistently fall within the bounds predicted by the theoretical calculations. This research contributes a valuable theoretical framework to the field of diaphragm wall construction, offering improved accuracy in stability assessments and potentially enhancing safety in geotechnical engineering projects.

In the simulation of foundation pit excavation, the traditional element birth–death method commonly used tends to encounter issues such as uncoordinated deformation and changes in the constitutive model, affecting the accuracy of the prediction results. To address these issues, this study proposes the use of element tracking. By duplicating elements for temporary supports or structures requiring changes in material properties and appropriately activating or deactivating them at the right moments, the simulation of the foundation pit excavation process can be achieved more precisely. Using the construction process of the Tangxi Passenger Transport Station’s comprehensive transportation hub foundation pit as an example, this study applied the proposed simulation method and compared the results with actual measurements, demonstrating its effectiveness. This research offers a more accurate approach for simulating foundation pit excavation and provides a reference for similar numerical simulation problems.

The mechanical and mechanochemical synergistic micro-grinding technology was proposed for grinding hard and brittle materials, especially for fused silica micro parts in this work. Compounded abrasive micro-grinding tools with diamond abrasives on the outer layer and cerium oxide abrasives on the inner layer were designed and fabricated. Efficient grinding was initially achieved by mechanical processing of super hard abrasives, and then ultra-precision processing was obtained by mechanochemical process to remove surface or subsurface damage. This study firstly analyzed the contact model of single diamond abrasive particle and theoretically evaluated ground surface roughness. The mechanism of abrasive layer wear was investigated for accurate error compensation. The formulation of cerium oxide abrasive layer and its processing efficiency were further analyzed. The material removal mechanisms of fused silica during mechanochemical grinding were also studied. Results showed that the material removal rate was 1.924 μm/min and the surface roughness Ra was 0.248 μm after mechanical processing. Subsequently, the surface roughness Ra was improved to 38 nm after mechanochemical processing. The study indicates that this technique is a promising approach for high-efficiency and high-quality grinding of fused silica.

Enhancing the naturalness and rhythmicity of generated audio in end-to-end speech synthesis is crucial. The current state-of-the-art (SOTA) model, VITS, utilizes a conditional variational autoencoder architecture. However, it faces challenges, such as limited robustness, due to training solely on text and spectrum data from the training set. Particularly, the posterior encoder struggles with mid- and high-frequency feature extraction, impacting waveform reconstruction. Existing efforts mainly focus on prior encoder enhancements or alignment algorithms, neglecting improvements to spectrum feature extraction. In response, we propose BERTIVITS, a novel model integrating BERT into VITS. Our model features a redesigned posterior encoder with residual connections and utilizes pre-trained models to enhance spectrum feature extraction. Compared to VITS, BERTIVITS shows significant subjective MOS score improvements (0.16 in English, 0.36 in Chinese) and objective Mel-Cepstral coefficient reductions (0.52 in English, 0.49 in Chinese). BERTIVITS is tailored for single-speaker scenarios, improving speech synthesis technology for applications like post-class tutoring or telephone customer service.

Abstract The immunity of patients who recover from coronavirus disease 2019 (COVID-19) could be long lasting but persist at a lower level. Thus, recovered patients still need to be vaccinated to prevent reinfection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or its mutated variants. Here, we report that the inactivated COVID-19 vaccine can stimulate immunity in recovered patients to maintain high levels of anti-receptor-binding domain (RBD) and anti-nucleocapsid protein (NP) antibody titers within 9 months, and high neutralizing activity against the prototype, Delta, and Omicron strains was observed. Nevertheless, the antibody response decreased over time, and the Omicron variant exhibited more pronounced resistance to neutralization than the prototype and Delta strains. Moreover, the intensity of the SARS-CoV-2-specific CD4+ T cell response was also increased in recovered patients who received COVID-19 vaccines. Overall, the repeated antigen exposure provided by inactivated COVID-19 vaccination greatly boosted both the potency and breadth of the humoral and cellular immune responses against SARS-CoV-2, effectively protecting recovered individuals from reinfection by circulating SARS-CoV-2 and its variants.

Background: Programmed death ligand-1 (PD-L1) is a biomarker for assessing the immune microenvironment, prognosis, and response to immune checkpoint inhibitors in the clinical treatment of lung adenocarcinoma (LUAD), but it does not work for all patients. This study aims to discover alternative biomarkers.Methods: Public data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) and gene ontology (GO) were used to determine the gene modules relevant to tumor immunity. Protein–protein interaction (PPI) network and GO semantic similarity analyses were applied to identify the module hub genes with functional similarities to PD-L1, and we assessed their correlations with immune infiltration, patient prognosis, and immunotherapy response. Immunohistochemistry (IHC) and hematoxylin and eosin (H&E) staining were used to validate the outcome at the protein level.Results: We identified an immune response–related module, and two hub genes (PSTPIP1 and PILRA) were selected as potential biomarkers with functional similarities to PD-L1. High expression levels of PSTPIP1 and PILRA were associated with longer overall survival and rich immune infiltration in LUAD patients, and both were significantly high in patients who responded to anti–PD-L1 treatment. Compared to PD-L1–negative LUAD tissues, the protein levels of PSTPIP1 and PILRA were relatively increased in the PD-L1–positive tissues, and the expression of PSTPIP1 and PILRA positively correlated with the tumor-infiltrating lymphocytes.Conclusion: We identified PSTPIP1 and PILRA as prognostic biomarkers relevant to immune infiltration in LUAD, and both are associated with the response to anti–PD-L1 treatment.

Excavations in a soft soil area are usually associated with substantial difficulties. Taking a special-shaped deep foundation pit in Hangzhou soft clay as the research object, the excavation performances, including groundwater level height, axial force, lateral wall, and soil deflection, and ground surface settlement were monitored and summarized based on the data published in the literature on similar excavations in Hangzhou, P. R. China. The following conclusions are drawn: (1) The axial forces of the struts dynamically change during the excavation and construction or removal of adjacent braces. (2) The ratio between the measured maximum wall deflection and excavation depth δh−max/He is 0.14–0.17%, larger than those in Shanghai. (3) The surface settlement behind the wall has an obvious primary influence zone and secondary influence zone, characterized by a “groove shape” and “triangle shape,” respectively. The maximum ground surface settlement δv−max ranges from 0.29% to 0.5% of the excavation depth. (4) The distribution of the ground settlement was analyzed. The relationship between the maximum settlements δv−max is between 1.28 δh−max and 3.72 δh−max. Moreover, ABAQUS software with Mohr–Coulomb soil models was used for model analysis of the construction process. The research results have important significance for the effective prevention of foundation pit accidents and the optimal design of deep foundation pit projects.

Most haptic devices generate haptic sensation using mechanical actuators. However, the workload and limited workspace handicap the operator from operating freely. Electrical stimulation is an alternative approach to generate haptic sensations without using mechanical actuators. The light weight of the electrodes adhering to the body brings no limitations to free motion. Because a real haptic sensation consists of feelings from several areas, mounting the electrodes to several different body areas can make the sensations more realistic. However, simultaneously stimulating multiple electrodes may result in “noise” sensations. Moreover, the operators may feel tingling because of unstable stimulus signals when using the dry electrodes to help develop an easily mounted haptic device using electrical stimulation. In this study, we first determine the appropriate stimulation areas and stimulus signals to generate a real touch sensation on the forearm. Then, we propose a circuit design guideline for generating stable electrical stimulus signals using a voltage divider resistor. Finally, based on the aforementioned results, we develop a wearable haptic glove prototype. This haptic glove allows the user to experience the haptic sensations of touching objects with five different degrees of stiffness.

Small-diameter jacked piles are widely used in civil engineering. The formation and development of the soil-plugging effect and surface frictional behavior of jacked piles have a high impact on the construction process and pile quality. Clarifying the developmental pattern of the soil-plugging effect and the change law of frictional force forms the premise of scientific construction and construction quality. Firstly, we carried out two groups of in situ tests on the small-diameter jacked piles, recording the relationship between penetration depth and resistance force. Then, the discrete element method (DEM) was used to analyze the mechanical behavior of the small-diameter jacked piles during the construction process. The particle flow code (PFC) 2D was used to carry out the DEM simulation. The research results show that pile resistance exhibited an irregular development trend as the construction process proceeded. There is a sudden change in pile resistance when the pile tip reaches the interface of certain soil layers. Both tests revealed the same phenomenon, yet both occurred at different depths. The DEM analysis showed that plug sliding was the main reason for the above phenomenon. The difference in strength and stiffness of adjacent soil layers causes the soil plug to slide, leading to a sudden change in pile resistance. When the upper layer is soft and the layer below is hard, this phenomenon is especially obvious. This also leads to a difference in the location of the sudden change in pile resistance between the two groups of tests. The research results of this paper can be helpful for revealing the relationship between the soil-plugging effect of small-diameter jacked piles and the development of pile resistance and also provides a reference for relevant engineering construction and design.

Due to the large cross-section design of large-diameter shield tunnels, most of the rocks and soils it crosses are composite strata with upper soft and lower hard. In order to reduce the construction cost of shield working shafts, large-diameter shield launching is usually buried at a shallow depth. Based on the typical large-diameter slurry shield tunnel, the following research results were obtained according to field monitoring and PLAXIS 3D finite element simulation. (1) The electronic level is used to monitor the surface settlement, and the field monitoring data were obtained; the surface settlement duration curve at the axis of the shield tunnel during the construction period can be divided into four stages: pre-deformation, shield passing, shield tail exit and shield moving away, of which the surface settlement accounts for the largest proportion during the shield passing. (2) In order to ensure the accuracy of the numerical simulation results, the linear shrinkage of the shield needs to be considered in the modeling. (3) The maximum surface settlement value at the center of the tunnel increases with the increase of the support pressure; when the support pressure exceeds 300 kPa, the surface uplift and the settlement caused by the formation loss will offset, and the surface settlement will decrease instead. The maximum surface settlement value is inversely proportional to the grouting pressure, but with the increase of the grouting pressure, the maximum uplift of the surface continues to increase. (4) With the numerical simulation of excavation step construction, the surface uplift increases with the increase of grouting pressure and shield radius, and decreases with the increase of shield buried depth.

When large-diameter slurry shields are tunneling in a composite stratum with a high clay content, the cutter head may form a mud cake. After the mud cake is formed, it will cover the cutter and reduce the opening rate of the cutter head, thus reducing the penetration of the cutter and the driving speed. Based on a road upgrading project, this paper studies the prevention and treatment of a mud cake and forms a set of comprehensive treatment methods. For a large mud cake, through theoretical analysis, two calculation methods of the cutter head sealing coefficient are obtained, and the cutter head sealing coefficient relationship model is established. Comparing the calculated cutter head’s actual opening coefficient with the required cutter head opening coefficient can provide a judgment basis for the time of manual warehousing. For medium and small mud cakes, the numerical simulation is carried out based on the cutter head and the scouring system, the distribution characteristics of the flow field near the cutter head are analyzed, and the angle of the central scouring hole is optimized. For a small mud cake, hydrogen peroxide is selected as an additive through microscopic mechanism analysis. The effect of the hydrogen peroxide solution concentration on dissolving a mud cake is obtained through laboratory tests, and its effectiveness is verified through field tests. The research results can provide a reference for the mud cake prevention of slurry shields in similar strata.

Remote sensing scene classification aims to automatically assign proper labels to remote sensing images. Most of the existing deep learning based methods usually consider the interclass and intraclass relationships of the image content for classification. However, these methods rarely consider the hierarchical information of scene labels, as a scene label may belong to hierarchically multi-grained levels. For example, multi-grained level labels may indicate that a remote sensing scene image may belong to the coarse-grained label “transportation land” while also belonging to the fine-grained label “airport”. In this paper, to exploit hierarchical label information, we propose an attention-embedding multi-task multi-grained network (AEMMN) for remote sensing scene classification. In the proposed AEMMN, we add a coarse-grained classifier as the first level and a fine-grained classifier as the second level to perform multi-task learning tasks. Additionally, a gradient control module is utilized to control the gradient propagation of two classifiers to suppress the negative transfer caused by the irrelevant features between tasks. In the feature extraction portion, the model uses an ECA module embedding Resnet50 to extract effective features with cross-channel interaction information. Furthermore, an external attention module is exploited to improve the discrimination of fine-grained and coarse-grained features. Experiments were conducted on the NWPU-RESISC45 and the Aerial Image Data Set (AID), and the overall accuracy of the proposed AEMMN is 92.07% on the NWPU-RESISC45 dataset and reached 94.96% on the AID. The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery.

The classification of urban land-use information has become the underlying database for a variety of applications including urban planning and administration. The lack of datasets and changeable semantics of land-use make deep learning methods suffer from low precision, which prevent improvements in the effectiveness of using AI methods for applications. In this paper, we first used GIS data to produce a well-tagged and high-resolution urban land-use image dataset. Then, we proposed a combined convolutional neural network named DUA-Net for complex and diverse urban land-use classification. The DUA-Net combined U-Net and Densely connected Atrous Spatial Pyramid Pooling (DenseASPP) to extract Remote Sensing Imagers (RSIs) features in parallel. Then, channel attention was used to efficiently fuse the multi-source semantic information from the output of the double-layer network to learn the association between different land-use types. Finally, land-use classification of high-resolution urban RSIs was achieved. Experiments were performed on the dataset of this paper, the publicly available Vaihingen dataset and Potsdam dataset with overall accuracy levels reaching 75.90%, 89.71% and 89.91%, respectively. The results indicated that the complex land-use types with heterogeneous features were more difficult to extract than the single-feature land-cover types. The proposed DUA-Net method proved suitable for high-precision urban land-use classification, which will be of great value for urban planning and national land resource surveying.