52 research outputs found

    Ultra-broadband near-field Josephson microwave microscopy

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    Advanced microwave technologies constitute the foundation of a wide range of modern sciences, including quantum computing, microwave photonics, spintronics, etc. To facilitate the design of chip-based microwave devices, there is an increasing demand for state-of-the-art microscopic techniques capable of characterizing the near-field microwave distribution and performance. In this work, we integrate Josephson junctions onto a nano-sized quartz tip, forming a highly sensitive microwave mixer on-tip. This allows us to conduct spectroscopic imaging of near-field microwave distributions with high spatial resolution. Leveraging its microwave-sensitive characteristics, our Josephson microscope achieves a broad detecting bandwidth of up to 200 GHz with remarkable frequency and intensity sensitivities. Our work emphasizes the benefits of utilizing the Josephson microscope as a real-time, non-destructive technique to advance integrated microwave electronics

    A‐to‐I RNA editing in Klebsiella pneumoniae regulates quorum sensing and affects cell growth and virulence

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    Millions of adenosine (A) to inosine (I) RNA editing events are reported and well-studied in eukaryotes; however, many features and functions remain unclear in prokaryotes. By combining PacBio Sequel, Illumina whole-genome sequencing, and RNA Sequencing data of two Klebsiella pneumoniae strains with different virulence, a total of 13 RNA editing events are identified. The RNA editing event of badR is focused, which shows a significant difference in editing levels in the two K. pneumoniae strains and is predicted to be a transcription factor. A hard-coded Cys is mutated on DNA to simulate the effect of complete editing of badR. Transcriptome analysis identifies the cellular quorum sensing (QS) pathway as the most dramatic change, demonstrating the dynamic regulation of RNA editing on badR related to coordinated collective behavior. Indeed, a significant difference in autoinducer 2 activity and cell growth is detected when the cells reach the stationary phase. Additionally, the mutant strain shows significantly lower virulence than the WT strain in the Galleria mellonella infection model. Furthermore, RNA editing regulation of badR is highly conserved across K. pneumoniae strains. Overall, this work provides new insights into posttranscriptional regulation in bacteria

    Fatigue crack growth prediction for shot-peened steel considering residual stress relaxation

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    The compressive residual stress field (CRSF) relaxes during the fatigue crack growth (FCG) process, and the pattern of this relaxation remains unclear. Residual stress (RS) relaxation leads to a change in residual stress intensity factor (RSIF), which significantly affects the fatigue crack growth rate (FCGR) and thus affects the assessment of fatigue crack growth rate. To this end, this work designed a series of experiments to explore the FCG behaviour in compressive residual stress fields (CRSFs) by considering RS relaxation. Firstly, shot peening(SP) treatments were conducted on the specimens. Then, residual stress fields (RSFs) at different crack propagation stages were measured to analyse the relationship between surface RS and inner RSF distribution. The derived weight function and the real-time RSF are combined to calculate the RSIF with/without considering RS relaxation. Finally, the effect of RS relaxation on the accuracy of FCGR assessment was investigated. The results show that the positive effect of CRS on the fatigue performance of the peened specimens is primarily embodied in the early stage of the FCG process. With the continuous relaxation of the CRSF, the difference in FCGR between the peened and unpeened specimens gradually decreases and tends to disappear. When the applied SIFs are large enough, the CRS would completely relax, and the inhibiting effect of CRSF on FCGR would fail. The proposed method in this work can effectively predict the FCGR of the peened specimen when considering the RS relaxation. This work could provide some meaningful insights for the assessment method of FCGR prediction in CRSF induced by surface strengthening treatment

    A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling

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    Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised learning requires both positive (flood) and negative (non-flood) samples in model training. Historical flood inventory data usually contain positive-only data, whereas negative data selected from areas without flood records are prone to be contaminated by positive data, which is referred to as case-control sampling with contaminated controls. In order to address this problem, we propose to apply a novel positive-unlabeled learning algorithm, namely positive and background learning with constraints (PBLC), in flood susceptibility modeling. PBLC trains a binary classifier from case-control positive and unlabeled samples without requiring truly labeled negative data. With historical records of flood locations and environmental covariates, including elevation, slope, aspect, plan curvature, profile curvature, slope length factor, stream power index, topographic position index, topographic wetness index, distance to rivers, distance to roads, land use, normalized difference vegetation index, and precipitation, we compared the performances of the traditional artificial neural network (ANN) and the novel PBLC in flood susceptibility modeling in the city of Guangzhou, China. Experimental results show that PBLC can produce more calibrated probabilistic prediction, more accurate binary prediction, and more reliable susceptibility mapping of urban flooding than traditional ANN, indicating that PBLC is effective in addressing the problem of case-control sampling with contaminated controls and it can be successfully applied in urban flood susceptibility mapping

    A Positive-Unlabeled Learning Algorithm for Urban Flood Susceptibility Modeling

    No full text
    Flood susceptibility modeling helps understand the relationship between influencing factors and occurrence of urban flooding and further provides spatial distribution of flood risk, which is critical for flood-risk reduction. Machine learning methods have been widely applied in flood susceptibility modeling, but traditional supervised learning requires both positive (flood) and negative (non-flood) samples in model training. Historical flood inventory data usually contain positive-only data, whereas negative data selected from areas without flood records are prone to be contaminated by positive data, which is referred to as case-control sampling with contaminated controls. In order to address this problem, we propose to apply a novel positive-unlabeled learning algorithm, namely positive and background learning with constraints (PBLC), in flood susceptibility modeling. PBLC trains a binary classifier from case-control positive and unlabeled samples without requiring truly labeled negative data. With historical records of flood locations and environmental covariates, including elevation, slope, aspect, plan curvature, profile curvature, slope length factor, stream power index, topographic position index, topographic wetness index, distance to rivers, distance to roads, land use, normalized difference vegetation index, and precipitation, we compared the performances of the traditional artificial neural network (ANN) and the novel PBLC in flood susceptibility modeling in the city of Guangzhou, China. Experimental results show that PBLC can produce more calibrated probabilistic prediction, more accurate binary prediction, and more reliable susceptibility mapping of urban flooding than traditional ANN, indicating that PBLC is effective in addressing the problem of case-control sampling with contaminated controls and it can be successfully applied in urban flood susceptibility mapping

    Polar iodate BiO(IO3): A two-dimensional ultrawide-bandgap semiconductor with high carrier mobility and robust piezoelectricity

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    Ultrawide-bandgap (UWBG) semiconductors, surpassing GaN with a bandgap of 3.4 eV, offer distinct advantages in high-temperature, high-frequency, and radiation-resistant applications. Specifically, two-dimensional (2D) UWBG materials play a crucial role in the miniaturization of practical devices. Here, employing first-principles calculations, we explore the properties of the polar iodate BiO(IO3) in its 2D monolayer configuration, boasting a bandgap of 3.61 eV. Our calculations confirm the feasibility of deriving 2D BiO(IO3) monolayer from its bulk counterpart while retaining structural stability at room temperature. The UWBG BiO(IO3) monolayer exhibits remarkable ultraviolet absorption, mechanical flexibility, and favorable electronic transport behavior. Notably, the estimated electron mobility reaches an impressive 1353.13 cm2 V−1 s−1. Importantly, the 2D structure of BiO(IO3) displays robust in-plane piezoelectricity without the odd–even effect commonly observed in other 2D piezoelectric materials. The piezoelectric coefficients d21 and d22 of monolayer reach high values of 13.87 and 16.66 pm V−1, respectively, surpassing or closely approaching those of most well-studied 2D systems. The direct stacking configuration enables 2D BiO(IO3) materials to maintain robust piezoelectricity at different thicknesses. Charge injection simulations validate the electromechanical conversion process, aligning well with its piezoelectric properties. This suggests the promising application potential of 2D BiO(IO3) in devices such as micro-electro-mechanical systems

    Winter Soil Respiration from Different Vegetation Patches in the Yellow River Delta, China

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    Vegetation type and density exhibited a considerable patchy distribution at very local scales in the Yellow River Delta, due to the spatial variation of soil salinity and water scarcity. We proposed that soil respiration is affected by the spatial variations in vegetation type and soil chemical properties and tested this hypothesis in three different vegetation patches (Phragmites australis, Suaeda heteroptera and bare soil) in winter (from November 2010 to April 2011). At diurnal scale, soil respiration all displayed single-peak curves and asymmetric patterns in the three vegetation patches; At seasonal scale, soil respiration all declined steadily until February, and then increased to a peak in next April. But, the magnitude of soil respiration showed significant differences among the three sites. Mean soil respiration rates in winter were 0.60, 0.45 and 0.17 mu mol CO2 m(-2) s(-1) for the Phragmites australis, Suaeda heteroptera and bare soil, respectively. The combined effect of soil temperature and soil moisture accounted for 58-68 % of the seasonal variation of winter soil respiration. The mean soil respiration revealed positive and linear correlations with total N, total N and SOC storages at 0-20 cm depth, and plant biomass among the three sites. We conclude that the patchy distribution of plant biomass and soil chemical properties (total C, total N and SOC) may affect decomposition rate of soil organic matter in winter, thereby leading to spatial variations in soil respiration.Vegetation type and density exhibited a considerable patchy distribution at very local scales in the Yellow River Delta, due to the spatial variation of soil salinity and water scarcity. We proposed that soil respiration is affected by the spatial variations in vegetation type and soil chemical properties and tested this hypothesis in three different vegetation patches (Phragmites australis, Suaeda heteroptera and bare soil) in winter (from November 2010 to April 2011). At diurnal scale, soil respiration all displayed single-peak curves and asymmetric patterns in the three vegetation patches; At seasonal scale, soil respiration all declined steadily until February, and then increased to a peak in next April. But, the magnitude of soil respiration showed significant differences among the three sites. Mean soil respiration rates in winter were 0.60, 0.45 and 0.17 mu mol CO2 m(-2) s(-1) for the Phragmites australis, Suaeda heteroptera and bare soil, respectively. The combined effect of soil temperature and soil moisture accounted for 58-68 % of the seasonal variation of winter soil respiration. The mean soil respiration revealed positive and linear correlations with total N, total N and SOC storages at 0-20 cm depth, and plant biomass among the three sites. We conclude that the patchy distribution of plant biomass and soil chemical properties (total C, total N and SOC) may affect decomposition rate of soil organic matter in winter, thereby leading to spatial variations in soil respiration
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