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SubjectThis study aims to compare the clinical efficacy of aspiration thrombectomy and stent retriever thrombectomy as first-line approaches for anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke.MethodThis retrospective observational study included patients with anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke treated with endovascular therapy. Patients were grouped according to the first-line approach they received: aspiration thrombectomy or stent retriever thrombectomy. The primary outcome measure was the change in the National Institute of Health Stroke Scale (NIHSS) score from preoperative to immediate postoperative and from preoperative to discharge. Secondary indicators included the rate of favorable prognosis at discharge [Modified Rankin Scale (mRS) score ≤ 2], successful vessel recanalization rate [modified Treatment in Cerebral Ischemia (mTICI) score ≥ 2b], time from successful femoral artery puncture to successful vessel recanalization, and perioperative complications.ResultThe study included 127 cases, with 1 case withdrawal after enrollment due to a stroke of another determined cause, with 83 in the aspiration thrombectomy group and 43 cases in the stent retriever thrombectomy group. The change in NIHSS score from preoperative to immediate postoperative was 5 (1, 8) in the aspiration thrombectomy group and 1 (0, 4.5) in the stent retriever thrombectomy group. The change from preoperative to discharge was 8 (5, 12) in the aspiration thrombectomy group and 4 (0, 9) in the stent retriever thrombectomy group. The aspiration thrombectomy group exhibited significantly better prognosis rates and shorter time from successful femoral artery puncture to successful vessel recanalization. There were no significant differences between the two groups in terms of successful vessel recanalization rates and perioperative complications.ConclusionAs a first-line approach for anterior circulation large vessel cardiogenic cerebral embolism and cryptogenic stroke, aspiration thrombectomy leads to better improvement in neurological functional deficits and prognosis rates compared to stent retriever thrombectomy.

The current information exchange interface and human-computer interaction interface of secondary equipment operation and maintenance in substation are not unified,which seriously restricts the efficient operation and maintenance of substation. In view of this situation,the research on the efficient operation and maintenance technology of secondary equipment in substation based on power system general service protocol (GSP) is carried out. Firstly,the architecture of integrated operation and maintenance of secondary equipment in substation is designed,and the information modeling method of integrated operation and maintenance of secondary equipment in substation based on substation operation and maintenance configuration language (SOMCL) is proposed. Then,the service interface and information interaction mechanism of integrated operation and maintenance of secondary equipment in substation are designed based on object-oriented technology (OOT),which achieves the goal of plug and play information interaction between operation and maintenance tools and the object to be transported. Finally,the integrated operation and maintenance tool of secondary equipment in substation is developed to realize the standardization and visualization of operation and maintenance of intelligent secondary equipment in substation,and achieves the goal of integrated and efficient operation and maintenance of secondary equipment in substation. The proposed method has been piloted in several new smart substations,which verifies the feasibility of the integrated operation and maintenance technology of secondary equipment in substation.

In order to adapt to the trend of unattended substation, improve the debugging and maintenance efficiency of substation automation device, and at the same time meet the demand of flexible expansion of operation and maintenance business, the remote operation and maintenance technology of substation automation device based on micro-services architecture is studied. Firstly, the architecture and functions of the system are designed according to the operation and maintenance requirements. Secondly, the edge computing platform is developed, and the operation and maintenance business is realized by application (App) programs based on container technology. The plug and play process of device and Apps are designed, and active online interaction protocols are proposed. Finally, a operation and maintenance gateway based on micro-services technology is developed. The gateway has the characteristics of universal hardware, software decoupling and flexible functions. Apps such as virtual human-machine interaction, software version control, operation and maintenance debugging are developed. The system has been applied in a 220 kV substation in Nanjing, and the operation and maintenance efficiency is improved.

Background: Triple‐negative breast cancer (TNBC) is a particularly aggressive type of breast cancer, known for its lack of effective treatments and unfavorable prognosis. The G protein‐coupled estrogen receptor (GPER), a novel estrogen receptor, is linked to increased malignancy in various cancers. However, its involvement in the metabolic regulation of cancer‐associated fibroblasts (CAFs), a key component in the tumour microenvironment, remains largely unexplored. This study investigates how GPER influences the metabolic interaction between CAFs and TNBC cells, aiming to identify potential therapeutic targets. Methods: The co‐culture system is performed to examine the interaction between CAFs and TNBC cells, with a focus on GPER‐mediated glutamine production and release by CAFs and its subsequent uptake and utilization by TNBC cells. The definite roles of microenvironmental GPER/cAMP/PKA/CREB signalling in regulating the expression of glutamine synthetase (GLUL) and lactate dehydrogenase B (LDHB) are further investigated. Results: Our findings reveal that estrogen‐activated GPER in CAFs significantly upregulates the expression of GLUL and LDHB, leading to increased glutamine production. This glutamine is then secreted into the extracellular matrix and absorbed by TNBC cells, enhancing their viability, motility, and chemoresistance both in vitro and in vivo. TNBC cells further metabolize the glutamine through the glutamine transporter (ASCT2) and glutaminase (GLS1) axes, which, in turn, promote mitochondrial activity and tumour progression. Conclusions: The study identifies GPER as a critical mediator of metabolic coupling between CAFs and TNBC cells, primarily through glutamine metabolism. Targeting the estrogen/GPER/glutamine signalling axis in CAFs offers a promising therapeutic strategy to inhibit TNBC progression and improve patient outcomes. This novel insight into the tumour microenvironment highlights the potential of metabolic interventions in treating TNBC. Key points: Estrogen‐activated GPER in CAFs enhances GLUL and LDHB expression via the cAMP/PKA/CREB signalling, facilitating glutamine production and utilization.Microenvironmental GPER‐induced glutamine serves as a crucial mediator of metabolic coupling between CAFs and TNBC cells, boosting tumour progression by enhancing mitochondrial function.Targeting the glutamine metabolic coupling triggered by estrogen/GPER/GLUL signalling in CAFs is a promising therapeutic strategy for TNBC treatment. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed photovoltaic (PV) brings challenges to the safe and reliable operation of distribution networks, distributed PV access to the grid changes the characteristics of the traditional distribution grid. Therefore, the assessment of distributed PV carrying capacity is of great significance for distribution network planning. To this end, a differentiated scenario-based distributed PV carrying capacity assessment method based on a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed. Firstly, the meteorological characteristics affecting PV power are quantitatively analyzed using Pearson's correlation coefficient, and the influence of external factors on PV power characteristics is assessed by integrating the measured data. Then, for the problem of high blindness of clustering parameters and initial clustering centers in the K-means clustering algorithm, the optimal number of clusters is determined by combining the cluster Density Based Index (DBI) and hierarchical clustering. The improved K-means clustering method reduces the complexity of massive scenarios to obtain distributed PV power under differentiated scenarios. On this basis, a distributed PV power prediction method based on the CNN-GRU model is proposed, which employs the CNN model for feature extraction of high-dimensional data, and then the temporal feature data are optimally trained by the GRU model. Based on the clustering results, the solution efficiency is effectively improved and the accurate prediction of distributed PV power is realized. Finally, taking into account the PV access demand of the distribution network, combined with the power flow calculation of distribution network, the bearing capacity of distribution network considering node voltage in differentiated scenarios is evaluated. In addition, verified by source-grid-load measured data in IEEE 33-bus distribution system. The simulation results show that the proposed CNN-GRU fusion deep learning model can accurately and efficiently assess the distributed PV carrying capacity of the distribution network and provide theoretical guidance for realizing distributed PV access on a large scale. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model's predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations. [ABSTRACT FROM AUTHOR]

Small modular reactors (SMRs) are currently advancing towards increased degrees of automation and intelligence, with intelligent control emerging as a prominent trend in SMR development. SMRs exhibit significant variations in design specifications and safety auxiliary system design as compared to conventional commercial nuclear power reactors. Consequently, defect diagnostic techniques that rely on commercial nuclear power plants are not appropriate for SMRs. This study designed a defect detection system for the System-integrated Modular Advanced ReacTor SMR by utilizing the PCTRAN/SMR V1.0 software and a deep learning neural network structure. Through the comparison of several neural network designs, it was discovered that the CNN-BiLSTM model, which utilizes bidirectional data processing, obtained a fault diagnostic accuracy of 97.33%. This result confirms the accuracy and effectiveness of the fault diagnosis system. This strongly supports the eventual implementation of autonomous control for SMRs. [ABSTRACT FROM AUTHOR]

We delved into the intricate relationships of social support, ethnic identity, pride, and subjective well-being among Chinese athletes who belong to an ethnic minority. The study's focus was on 877 athletes from diverse economic regions in China, who participated in the 11th Zhengzhou National Minority Traditional Sports Games in 2019. The results showed that social support was positively correlated with ethnic identity, pride, and subjective well-being among Chinese athletes from ethnic minorities. Social support directly influenced the athletes' subjective well-being, as well as indirectly impacting it by affecting their ethnic identity and/or pride. By addressing critical research gaps, this study has contributed to a more nuanced understanding of well-being within the context of cultural diversity and sports participation. The insights derived from this study can inform interventions aimed at enhancing the overall experiences and achievements of athletes in ethnic minorities. [ABSTRACT FROM AUTHOR]

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) is the most prevalent malignancy of the digestive system. Glucose metabolism plays a crucial role in CRC development. However, the heterogeneity of glucose metabolic patterns in CRC is not well characterized. Here, we classified CRC into specific glucose metabolic subtypes and identified the key regulators. 2228 carbohydrate metabolism‐related genes were screened out from the GeneCards database, 202 of them were identified as prognosis genes in the TCGA database. Based on the expression patterns of the 202 genes, three metabolic subtypes were obtained by the non‐negative matrix factorization clustering method. The C1 subtype had the worst survival outcome and was characterized with higher immune cell infiltration and more activation in extracellular matrix pathways than the other two subtypes. The C2 subtype was the most prevalent in CRC and was characterized by low immune cell infiltration. The C3 subtype had the smallest number of individuals and had a better prognosis, with higher levels of NRF2 and TP53 pathway expression. Secreted frizzled‐related protein 2 (SFRP2) and thrombospondin‐2 (THBS2) were confirmed as biomarkers for the C1 subtype. Their expression levels were elevated in high glucose condition, while their knockdown inhibited migration and invasion of HCT 116 cells. The analysis of therapeutic potential found that the C1 subtype was more sensitive to immune and PI3K‐Akt pathway inhibitors than the other subtypes. To sum up, this study revealed a novel glucose‐related CRC subtype, characterized by SFRP2 and THBS2, with poor prognosis but possible therapeutic benefits from immune and targeted therapies. [ABSTRACT FROM AUTHOR]

Surface depressions are common defects in the continuous casting process of hypo-peritectic steel, categorised into transverse and longitudinal depressions. While the formation processes of these depressions are well understood, their influence on the propagation of longitudinal surface cracks remains unclear. This study developed a new method for large-area surface depth measurement of the strand based on optical focusing. Utilising this method, we investigated the impact of surface depressions on the three-dimensional propagation of longitudinal surface crack in terms of width, depth and casting direction through macrostructure characteristics and finite element static analysis. The results indicated that transverse and longitudinal depressions affect crack propagation differently. Specifically, the depth of longitudinal depressions increases crack width, while transverse depressions can inhibit it to some extent. Macrostructure analysis of the cross-section revealed a continuous segregation line at the bottom of the depression, closely related to heat transfer, thermal stress, and the flow of solute-enriched liquid steel. Statistical analysis indicated that the depth of longitudinal depressions significantly affects the length of the segregation line, which in turn influences crack depth. However, no surface cracks were found at the bottom of transverse depressions adjacent to longitudinal depressions. This may be due to the banded grains at the bottom of transverse depressions resisting tensile thermal stress parallel to the strand's width direction. Along the casting direction, crack propagation primarily occurs along the bottom of longitudinal depressions due to continuous thermal disturbances at the meniscus. Meanwhile, the transverse depression can restrict the propagation of the existing crack while promoting the formation of the new crack at the other site. Finally, the study proposed mechanisms by which depressions influence the propagation of the longitudinal surface crack. This research further elucidates the impact patterns of surface depressions on macrostructure and stress distribution and how these factors subsequently influence crack propagation in three dimensions. It underscores the importance of controlling surface depressions to mitigate the longitudinal surface crack. [ABSTRACT FROM AUTHOR]