Performance of the suspension method in large cross-section shallow-buried tunnels

Large cross-section tunnel construction induces ground surface settlements, potentially endangering both subterranean projects and nearby above-ground structures. A novel tunnel construction method, known as the suspension method, is introduced in this paper to mitigate surface settlement. The suspension method employs vertical tie rods to establish a structural connection between the initial tunnel support system and the surface steel beam, thereby exerting effective control settlements. To analyze the performance of the proposed method, systematic numerical simulations were conducted based on the practical engineering of Harbin Subway Line 3. The surface settlement and vault settlement characteristics during construction are investigated. The results show a gradual increment in both surface and vault settlement throughout the construction process, culminating in a stabilized state upon the completion of construction. In addition, compared to the double-side drift method and the Cross Diaphragm Method (CRD) method, the suspension method can obviously reduce the surface settlement and vault settlement. Moreover, the surface settlements and the axial force of tie rods were continuously monitored during the construction process at the trial tunnel block. These specific monitoring measurements are illustrated in comparison to numerical analysis results. The monitored results show great agreement with the numerical predictions, confirming the success of the project. This research can serve as a valuable practical reference for similar projects, offering insights and guidance for addressing ground surface settlements and enhancing construction safety in the domain of large cross-section tunneling

Comprehensive transcriptional profiling of aging porcine liver

Background Aging is a major risk factor for the development of many diseases, and the liver, as the most important metabolic organ, is significantly affected by aging. It has been shown that the liver weight tends to increase in rodents and decrease in humans with age. Pigs have a genomic structure, with physiological as well as biochemical features that are similar to those of humans, and have therefore been used as a valuable model for studying human diseases. The molecular mechanisms of the liver aging of large mammals on a comprehensive transcriptional level remain poorly understood. The pig is an ideal model animal to clearly and fully understand the molecular mechanism underlying human liver aging. Methods In this study, four healthy female Yana pigs (an indigenous Chinese breed) were investigated: two young sows (180-days-old) and two old sows (8-years-old). High throughput RNA sequencing was performed to evaluate the expression profiles of messenger RNA, long non-coding RNAs, micro RNAs, and circular RNAs during the porcine liver aging process. Gene Ontology (GO) analysis was performed to investigate the biological functions of age-related genes. Results A number of age-related genes were identified in the porcine liver. GO annotation showed that up-regulated genes were mainly related to immune response, while the down-regulated genes were mainly related to metabolism. Moreover, several lncRNAs and their target genes were also found to be differentially expressed during liver aging. In addition, the multi-group cooperative control relationships and constructed circRNA-miRNA co-expression networks were assessed during liver aging. Conclusions Numerous age-related genes were identified and circRNA-miRNA co-expression networks that are active during porcine liver aging were constructed. These findings contribute to the understanding of the transcriptional foundations of liver aging and also provide further references that clarify human liver aging at the molecular level

Insight-HXMT observations of Swift J0243.6+6124 during its 2017-2018 outburst

The recently discovered neutron star transient Swift J0243.6+6124 has been monitored by {\it the Hard X-ray Modulation Telescope} ({\it Insight-\rm HXMT). Based on the obtained data, we investigate the broadband spectrum of the source throughout the outburst. We estimate the broadband flux of the source and search for possible cyclotron line in the broadband spectrum. No evidence of line-like features is, however, found up to $\rm 150~keV$. In the absence of any cyclotron line in its energy spectrum, we estimate the magnetic field of the source based on the observed spin evolution of the neutron star by applying two accretion torque models. In both cases, we get consistent results with $B\rm \sim 10^{13}~G$, $D\rm \sim 6~kpc$ and peak luminosity of $\rm >10^{39}~erg~s^{-1}$ which makes the source the first Galactic ultraluminous X-ray source hosting a neutron star.Comment: publishe

Overview to the Hard X-ray Modulation Telescope (Insight-HXMT) Satellite

As China's first X-ray astronomical satellite, the Hard X-ray Modulation Telescope (HXMT), which was dubbed as Insight-HXMT after the launch on June 15, 2017, is a wide-band (1-250 keV) slat-collimator-based X-ray astronomy satellite with the capability of all-sky monitoring in 0.2-3 MeV. It was designed to perform pointing, scanning and gamma-ray burst (GRB) observations and, based on the Direct Demodulation Method (DDM), the image of the scanned sky region can be reconstructed. Here we give an overview of the mission and its progresses, including payload, core sciences, ground calibration/facility, ground segment, data archive, software, in-orbit performance, calibration, background model, observations and some preliminary results.Comment: 29 pages, 40 figures, 6 tables, to appear in Sci. China-Phys. Mech. Astron. arXiv admin note: text overlap with arXiv:1910.0443

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages