During the time between 1980 and 2020, there were a lot of significant changes in the U.S. interest rate, the federal funds rate.Since the changes of interest rates in one country, especially in the United States, can affect fluctuations in the global economy, this paper focuses on how the U.S interest rate can affect the Chinese economy and what reasons or transmission mechanisms make those results.When discussing the relationship between the U.S. interest rate and the Chinese economy, this paper focuses on three fundamentals of the Chinese economy: Chinese macroeconomy, import and export, China-US exchange rate.The main result is that: the U.S interest rates and the Chinese economy are inversely related, especially in import, export, and China-US exchange rate.By exploring the causes of this result, the study finds out many determinant factors, and the degree of correlation depends on the net sum of the impacts of all factors.Eventually, based on the global economic environment, we get the conclusion that many key factors can make changes to the country's economy.Although Chinese economies will fluctuate as the federal reserve makes changes to the U.S. interest, the government can resist negative shocks by strengthening the internal driving force of economic growth.It can stabilize the economy through flexible monetary policy and intermediate variables, such as the commodity price level, consumption and investment, international capital flows, and so on.
With high morbidity and mortality, colon cancer (CC) is considered as one of the most often diagnosed cancers around the world. M7G-related lncRNA may provide a regulatory function in the formation of CC, but the principle of regulation is still unclear. The purpose of this research was to establish a novel signature that may be used to predict survival and tumour immunity in CC patients. Data about CC in TCGA was collected for analysis, coexpression analysis and univariate Cox analysis were used to screen prognostic m7G-related lncRNAs. A consensus clustering analysis based on prognostic m7G-related lncRNAs was applied, and a prognosis model based on least absolute shrinkage and selection operator (LASSO) regression analysis was established. Independent prognostic analysis, nomogram, PCA, clinicopathological correlation analysis, TMB, survival analysis, immune correlation analysis, qRT-PCR and clinical therapeutic compound prediction were also applied. 90 prognostic m7G-related lncRNAs were found, GO and KEGG analysis showed that prognostic m7G-related lncRNAs were mainly related to cell transcription and translation. The results of the consensus clustering analysis revealed substantial disparities in survival prognosis and tumour immune infiltration between two clusters. We built a risk model with 21 signature m7G-related lncRNAs, patients in the high-risk group had a considerably poorer prognosis than those in the low-risk group. Independent prognostic analysis confirmed that patients' prognosis was linked to their tumour stage and risk score. PCA, subgroups with distinct clinicopathological characteristics were studied for survival, multi-index ROC curve, c-index curve, the survival analysis of TMB, and model comparison tested the reliability of risk model. A tumour immunoassay revealed a substantial difference in immune infiltration between high-risk and low-risk individuals. Five chemicals were eliminated, and qRT-PCR indicated that the four lncRNAs were expressed differently. Overall, m7G-related lncRNA is closely related to colon cancer and the 21 signature lncRNAs risk model can efficiently evaluate the prognosis of CC patients, which has a possible positive consequence for the future diagnosis and therapy of CC.
ABSTRACT Urban water supply and drainage systems are a crucial component of urban infrastructure, directly affecting residents' livelihoods and industrial production. The normal operation of the water supply and the drainage pipeline is of great significance for conserving water resources and preventing water pollution. However, due to characteristics such as deep burial, diverse materials, and extensive lengths, the detection of defects becomes exceptionally complex. Traditional detection methods used in practical applications, such as ground excavation and destructive testing, typically require the shutdown of water pipelines. This process is time-consuming and labor-intensive, often resulting in significant economic losses. This paper proposes an effective technique for detecting defects in the water supply and the drainage pipeline. The method involves capturing images of the inner walls of water supply conduits and subsequently utilizing an artificial intelligence large-scale model approach (grounded language-image pre-training, GLIP) and a You Only Look Once version 5 (YOLOv5) model to detect defects within them. The experimental results show that GLIP demonstrates impressive detection performance in zero-shot scenarios, while YOLOv5 performs well on existing datasets. By combining these two models, we were able to achieve a balance between fast, flexible detection and high precision, making our approach both practical and efficient for real-world applications.
With the rapid development of computer electronic equipment, embedded devices have been applied in various fields of daily life. Such as intelligent home, intelligent agriculture, intelligent farming and so on. This article describes the intelligent turtle breeding system is a typical application. Different from the combination of traditional embedded devices and servers, this system is based on the combination of the Raspberry Pi and the virtualized container. It is designed as a platform of automatic detection of temperature, humidity, co 2 concentration, and light intensity at turtle breeding bases. Breaking through the limitations of traditional server monitoring, we use Docker to build a cloud environment and Docker Swarm as a container management technology, and adopt openFaas architecture to complete the serverless integration of virtual container cloud and embedded devices. Making the turtle aquaculture system more secure, more stable, less costly, and more resource efficient.
Optical–optical synchronization between independent mode-locked lasers with attosecond timing precision is essential for arbitrary electric-field waveform generation, subcycle optical pulse synthesis, optical frequency transfer as well as next-generation photon-science facilities, e.g., X-ray free-electron lasers. Long-term stable operation with low timing drift is highly desired for all above applications. Here, we present a five-day uninterrupted timing synchronization between two independent femtosecond Yb-fiber lasers via balanced optical correlation method. The out-of-loop residual timing drift over the entire time frame reaches 733 as rms, corresponding to $1.36\times 10^{{\rm -20}}$ instability at $1.31\times 10^{{\rm 5}}{\rm{\,s}}$ . To the best of our knowledge, it is the first characterization of 10 5 s instability for subfemtosecond optical–optical synchronization based on mode-locked lasers.
Traditional filtering methods can't eliminate the effect of interfering signals on steering wheel angle and extract useful signal effectively.Regarding to this, a new method based on wavelet transform is presented.According to the Mallat algorithm, firstly, the signal is decomposed into seven layers by selecting db7 wavelet.Aiming at every layer detail signal after wavelet decomposing, the soft-threshold filtering method is employed to eliminate noises, then the inverse wavelet transform is used to reconstruct the filtered signal.The experimental results show that this method can effectively eliminate awful noises and achieve a good performance.
We demonstrate a novel time domain timing jitter characterization method for ultra-low noise mode-locked lasers. An asynchronous optical sampling (ASOPS) technique is employed, allowing timing jitter statistics on a magnified timescale. As a result, sub femtosecond period jitter of an optical pulse train can be readily accessible to slow detectors and electronics (~100 MHz). The concept is applied to determine the quantum-limited timing jitter for a passively mode-locked Er-fiber laser. Period jitter histogram is acquired following an eye diagram analysis routinely used in electronics. The identified diffusion constant for pulse timing agrees well with analytical solution of perturbed master equation.
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