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Aim: To delineate the RNA-5-methylcytosine (m5C) modification of breast cancer brain metastasis (BCBM). Methods: Methylated RNA immunoprecipitation next-generation sequencing (MeRIP-seq) was performed to obtain RNA-m5C patterns of BCBM. Results: 1048 hypermethylation and 1866 hypomethylation m5C peaks were identified in BCBM compared with those in breast cancer. The most significant m5C hypermethylated genes included ENG, SHANK1, IGFN1, EVL and MMP9, whereas the most significant m5C hypomethylated genes included AREG, SAA2, TP53I11, KRT7 and LCN2. MeRIP-qPCR data were concordant with the corresponding MeRIP-seq results in terms of the observed m5C levels. Conjoint analysis identified 190 hyper-up genes characterized by concurrent m5C hypermethylation and up-regulation, alongside 284 hypo-down genes exhibiting both m5C hypomethylation and down-regulation. Conclusion: This study presents the first comprehensive analysis of RNA-m5C modification in BCBM. Article highlights Breast cancer (BC) is the second most common solid malignancy to involve the brain. However, the underlying molecular mechanism of breast cancer brain metastasis (BCBM) remains largely unknown. We attempted to delineate the RNA 5-methylcytosine (m5C) modification landscape of BCBM to elucidate its underlying molecular mechanism. We employed methylated RNA immunoprecipitation next-generation sequencing (MeRIP-seq) to obtain a comprehensive profile of m5C methylation patterns in the BCBM group (231-BR cells) in comparison to the BC group (MDA-MB-231 cells). A total of 1048 hypermethylation and 1866 hypomethylation m5C peaks were identified in the BCBM group compared with those in the BC group. The most significant m5C hypermethylated genes included ENG, SHANK1, IGFN1, EVL and MMP9, whereas the most significant m5C hypomethylated genes included AREG, SAA2, TP53I11, KRT7 and LCN2. MeRIP-qPCR results were consistent with the corresponding MeRIP-seq data regarding m5C methylation levels of the candidate molecules IGFN1, EVL, AREG and KRT7. Conjoint analysis identified 190 hyper-up genes characterized by concurrent m5C hypermethylation and up-regulation, alongside 284 hypo-down genes exhibiting both m5C hypomethylation and down-regulation. Protein-protein interaction (PPI) network analysis based on differentially m5C methylated and expressed genes identified MMP9, ICAM1, THBS1, LAMA5, PDGFRB, TGFB2, NOTCH1 and SAA2 as hub genes related to BCBM. The hub genes were associated with the positive regulation of cell migration, response to oxygen levels, regulation of epithelial cell migration and inflammatory response. An analysis of methylation regulators indicated that the methyltransferases NOP2, NSUN5, DNMT3A and DNMT3B and the demethylase TET2 may be implicated in the regulation of m5C RNA methylation related to BCBM. This study presents the first m5C modification profile of mRNAs in BCBM, providing a framework for a better understanding of the mechanisms that trigger brain metastasis and delineating innovative biomarkers and therapeutic targets for improving the diagnosis and treatment of BCBM. [ABSTRACT FROM AUTHOR]

Traditional stall prediction methods often rely on empirical formulas and models, which have certain limitations. The deep learning model was introduced to predict stall and surge under distorted inflow conditions in an axial flow compressor, and the model can learn from the dynamic pressure data containing stall processes measured on the casing wall by use of a long short-term memory neural network. In order to enhance the model's generalization capability and prediction accuracy, the model parameters are optimized through the Northern Goshawk optimization algorithm. In the experimental validation, the stall prediction model was first trained by using the collected stalling signal. Then, a step-by-step prediction method was used to verify the accuracy of the prediction model under uniform and distorted inflow conditions. Subsequently, the recursive prediction technology is used to predict the instability under different inflow conditions in both subsonic and supersonic axial flow compressors. By comparing with the measured stalling data under uniform and distorted inflow, the model accurately and timely predicts stall and surge signal through a self-learning mechanism when inputting non-stall pressure data. Regardless of whether the instability routes are spikes and modal-wave stall or surge, the model can predict the instability at least 1 s in advance, and it leaves enough time for the anti-surge actuator to operate. This study not only significantly improves the real-time and accuracy of predictions but also demonstrates the potential application value of deep learning in the field of aero engines, contributing to enhanced safety and reliability of aircraft engines. [ABSTRACT FROM AUTHOR]

Continuous cropping problems constitute threats to perennial plant health and survival. Soil conditioners have the potential to enhance plant disease resistance in continuous cropping systems. However, how microbes and metabolites of the rhizosphere respond to soil conditioner addition remains largely unknown, but this knowledge is paramount to providing innovative strategies to enhance plant adaptation in continuous cropping systems. Here, we found that a biochar conditioner significantly improved plant survival rates in a continuous cropping system. The biochar-induced rhizosphere significantly alters the fungal community, causing a decline in fungal diversity and the downregulation of soil microbial community functionality. Specifically, the biochar-induced rhizosphere causes a reduction in the relative abundance of pathogenic Fusarium sp. and phenolic acid concentration, whose variations are the primary causes of continuous cropping problems. Conversely, we observed an unexpected bacterial diversity increase in rhizospheric and non-rhizospheric soils. Our research further identified key microbial taxa in the biochar-induced rhizosphere, namely, Monographella, Acremonium, Geosmithia, and Funneliformis, which enhance soil nutrient availability, suppress Fusarium sp., mitigate soil acidification, and reduce phenolic acid concentrations. Collectively, we highlight the critical role of regular microbial communities and metabolites in determining plant health during continuous cropping and propose a synthetic microbial community framework for further optimizing the ecological functions of the rhizosphere. [ABSTRACT FROM AUTHOR]

We conduct experimental investigations of the effect of the tip air injection as a stability control method on a low-speed axial-flow compressor experiencing severe circumferential total pressure distortion. Eight Coanda-shaped injectors, uniformly distributed upstream of the rotor blade leading edge, were employed to counteract stall margin degradation caused by a flat-baffle circumferential distortion. Unsteady pressure data from the tip clearance and rotor wake regions were captured using time-resolved sensors and probes and analyzed through time-frequency transformation and proper orthogonal decomposition (POD) techniques. POD investigation under distorted inflow conditions reveals that the third-order POD mode, which characterizes unsteady tip leakage flow (TLF), plays a dominant role in triggering stall. Based on this insight, tip air injection with varying momentum ratios was applied to enhance the stall margin. The positive correlation between the injected momentum ratio and stall margin improvement was established, demonstrating the stabilizing effect of tip air injection under the circumferential distortion. The results indicate that tip air injection primarily acts on the rotor blade tip region to suppress the unsteady TLF at the center and downstream areas of the distortion, leading to an increase in tip blade load and a reduction in both the scale and number of pre-stall disturbances. However, the injection has a minimal impact on the unsteady TLF frequency band in the rotor wake region. Additionally, the POD analysis confirms that as the injection rate is increased, the energy percentage, flow field intensity, and power spectral density amplitude of the third-order POD mode are decreased, further reflecting the mitigation of unsteady TLF. The present work provides important insight into the stability control mechanisms of a tip air injection under circumferential distortion, offering design guidance for the implementation of active stability control strategies in axial-flow compressors. [ABSTRACT FROM AUTHOR]

A containerized edge data center (CEDC) is a critical node of the 5G telecommunication network. It is essential to maintain the functionality of CEDCs during and after earthquakes. A probabilistic seismic resilience assessment framework was developed to evaluate the postearthquake functionality and seismic resilience of a CEDC. Five subsystems ensure the CEDC's functionality: the structural, power distribution, air conditioning, computing, and monitoring subsystems. The subsystems' normal operation depends on several components. The functional relationships between the components of the five subsystems were analyzed. Fault tree models of the five subsystems were developed, and success path analysis was used to establish a state tree model of the CEDC. The seismic fragility and repair models of the CEDC's fundamental components were obtained from numerical analysis and data from the literature. State tree analysis and Monte Carlo simulations were conducted to quantify the postearthquake residual functionality and generate seismic resilience curves for the CEDC. The effects of different resilience improvement strategies were evaluated and incorporated into the proposed framework. A CEDC case study was conducted to demonstrate the proposed framework's feasibility. [ABSTRACT FROM AUTHOR]

In order to further understand the impact of tip air injection on compressor stability under circumferential pressure distortion, a series of experimental studies on tip air injection under uniform and circumferential distortion were conducted on a low-speed single rotor axial flow compressor. The unsteady measurements were carried out by use of microsensors and strain gauges bonded on the rotor blade surfaces, and a collection of dynamic pressure sensors installed on the casing wall. Results indicate that with the increase in the injected momentum ratio, the load at the leading edge near the rotor blade tip obviously decreases, thereby delaying the stall. By comparing the pressure distribution on the rotor blade surface under different injected momentum ratios, tip air injection can effectively suppress flow separation at the blade tip to enhance flow capability. In addition, it can also weaken the fluctuation of tip leakage flow and reduce the dynamic stress similar to natural vibration near the blade root both in stationary and rotating coordinate systems. Under the condition of inlet circumferential distortion, tip air injection can significantly reduce the load at the distorted region and suppress the low-frequency disturbances measured on the rotor blade surface caused by inlet distortion. In addition, the dynamic stress near the blade root can also be effectively reduced by tip air injection. It implies that tip air injection can both extend the stall margin and reduce blade vibration when suffering from circumferential distortion; thus, the compressor can safely operate. [ABSTRACT FROM AUTHOR]

To calculate the carrying capacity of spherical hydrostatic bearings, a numerical calculation model was presented. The influence law of dynamic pressure effect on the carrying characteristics of liquid hydrostatic spherical bearings is revealed. Under general working conditions, the dynamic pressure effect caused by the radial eccentricity of the bearing has little influence on the bearing load characteristics parameters; when the minimum width of the gap between the sealing edge is very small and the bearing rotational speed is high, the dynamic pressure effect is more obvious. [ABSTRACT FROM AUTHOR]

Hydrogen is increasingly recognized as a clean and reliable energy vector for decarbonization in the future. In the marine sector, marine solid oxide fuel cells (SOFCs) that employ hydrogen as an energy source have already been developed. In this study, a multi-channel plate-anode-loaded SOFC was taken as the research object. A three-dimensional steady-state computational fluid dynamics (CFD) model for anode-supported SOFC was established, which is based on the mass conservation, energy conservation, momentum conservation, electrochemical reactions, and charge transport equations, including detailed geometric shapes, model boundary condition settings, and the numerical methods employed. The polarization curves calculated from the numerical simulation were compared with experimental results from the literature to verify the model's accuracy. The curved model was applied by enlarging the flow channels or adding blocks. Numerical calculations were employed to obtain the current density, temperature distribution, and component concentration distribution under the operating conditions of the SOFC. Subsequently, the distribution patterns of various physical parameters during the SOFC operation were analyzed. Compared to the classical model, the temperature of the curved model was reduced by 1.3%, and the velocities of the cathode and anode were increased by 4.9% and 5.0%, respectively, with a 2.42% enhancement in performance. The findings of this study provide robust support for research into and the application of marine SOFCs, and offer they insights into how we may achieving "dual carbon" goals. [ABSTRACT FROM AUTHOR]

In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved. [ABSTRACT FROM AUTHOR]

Accurately quantifying the complexity of nodes in a network is crucial for revealing their roles and network complexity, as well as predicting network emergent phenomena. In this paper, we propose three novel complexity metrics for nodes to reflect the extent to which they participate in organized, structured interactions in higher-order networks. Our higher-order network is built using the BuildHON+ model, where communities are detected using the Infomap algorithm. Since a physical node may contain one or more higher-order nodes in higher-order networks, it may simultaneously exist in one or more communities. The complexity of a physical node is defined by the number and size of the communities to which it belongs, as well as the number of higher-order nodes it contains within the same community. Empirical flow datasets are used to evaluate the effectiveness of the proposed metrics, and the results demonstrate their efficacy in characterizing node complexity in higher-order networks. [ABSTRACT FROM AUTHOR]

Liner shipping accounts for over 80 % of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed "Multi-Scale Higher-Order Dependencies (MSHOD)" that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network's flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development. [ABSTRACT FROM AUTHOR]