Impact of vaccination on the COVID-19 pandemic in U.S. states

Governments worldwide are implementing mass vaccination programs in an effort to end the novel coronavirus (COVID-19) pandemic. Here, we evaluated the effectiveness of the COVID-19 vaccination program in its early stage and predicted the path to herd immunity in the U.S. By early March 2021, we estimated that vaccination reduced the total number of new cases by 4.4 million (from 33.0 to 28.6 million), prevented approximately 0.12 million hospitalizations (from 0.89 to 0.78 million), and decreased the population infection rate by 1.34 percentage points (from 10.10 to 8.76%). We built a Susceptible-Infected-Recovered (SIR) model with vaccination to predict herd immunity, following the trends from the early-stage vaccination program. Herd immunity could be achieved earlier with a faster vaccination pace, lower vaccine hesitancy, and higher vaccine effectiveness. The Delta variant has substantially postponed the predicted herd immunity date, through a combination of reduced vaccine effectiveness, lowered recovery rate, and increased infection and death rates. These findings improve our understanding of the COVID-19 vaccination and can inform future public health policies

Endogenous cross-region human mobility and pandemics

We study infectious diseases using a Susceptible-Infected-Recovered-Deceased model with endogenous cross-region human mobility. Individuals weigh the risk of infection against economic opportunities when moving across regions. The model predicts that the mobility rate of susceptible individuals declines with a higher infection rate at the destination. With cross-region mobility, a decrease in the transmission rate or an increase in the removal rate of the virus in any region reduces the global basic reproduction number (R0). Global R0 falls between the minimum and maximum of local R0s. A new method of Normalized Hat Algebra is developed to solve the model dynamics. Simulations indicate that a decrease in global R0 does not always imply a lower cumulative infection rate. Local and central governments may prefer different mobility control policies

Identification of Rice Transcription Factors Associated with Drought Tolerance Using the Ecotilling Method

The drought tolerance (DT) of plants is a complex quantitative trait. Under natural and artificial selection, drought tolerance represents the crop survival ability and production capacity under drought conditions (Luo, 2010). To understand the regulation mechanism of varied drought tolerance among rice genotypes, 95 diverse rice landraces or varieties were evaluated within a field screen facility based on the ‘line–source soil moisture gradient’, and their resistance varied from extremely resistant to sensitive. The method of Ecotype Targeting Induced Local Lesions in Genomes (Ecotilling) was used to analyze the diversity in the promoters of 24 transcription factor families. The bands separated by electrophoresis using Ecotilling were converted into molecular markers. STRUCTURE analysis revealed a value of K = 2, namely, the population with two subgroups (i.e., indica and japonica), which coincided very well with the UPGMA clusters (NTSYS-pc software) using distance-based analysis and InDel markers. Then the association analysis between the promoter diversity of these transcription factors and the DT index/level of each variety was performed. The results showed that three genes were associated with the DT index and that five genes were associated with the DT level. The sequences of these associated genes are complex and variable, especially at approximately 1000 bp upstream of the transcription initiation sites. The study illuminated that association analysis aimed at Ecotilling diversity of natural groups could facilitate the isolation of rice genes related to complex quantitative traits

Survey on Backdoor Attacks and Countermeasures in Deep Neural Network

The neural network backdoor attack aims to implant a hidden backdoor into the deep neural network, so that the infected model behaves normally on benign test samples, but behaves abnormally on poisoned test samples with backdoor triggers. For example, all poisoned test samples will be predicted as the target label by the infected model. This paper provides a comprehensive review and the taxonomy for existing attack methods according to the attack objects, which can be categorized into four types, including data poisoning attacks, physical world attacks, model poisoning attacks, and others. This paper summarizes the existing backdoor defense technologies from the perspective of attack and defense confrontation, which include poisoned sample identifying, poisoned model identifying, poisoned test sample filtering, and others. This paper explains the principles of deep neural network backdoor defects from the perspectives of deep learning mathematical principles and visualization, and discusses the difficulties and future development directions of deep neural network backdoor attacks and countermeasures from the perspectives of software engineering and program analysis. It is hoped that this survey can help researchers understand the research progress of deep neural network backdoor attacks and countermeasures, and provide more inspiration for designing more robust deep neural networks

Performance Analysis of Ku/Ka Dual-Band SAR Altimeter from an Airborne Experiment over South China Sea

Satellite radar altimeters have been successfully used for sea surface height (SSH) measurement for decades, gaining great insight in oceanography, meteorology, marine geology, etc. To further improve the observation precision and spatial resolution, radar altimeters have evolved from real aperture to synthetic aperture, from the Ku-band to Ka-band. Future synthetic aperture radar (SAR) altimeter of the Ka-band is expected to achieve better performance than its predecessors. To verify the SAR altimeter data processing method and explore the system advantage of the Ka-band, a Ku/Ka dual-band SAR altimeter airborne experiment was carried out over South China Sea on 6 November 2021. Through dedicated hardware design, this campaign has acquired the Ku and Ka dual-band echo data simultaneously. The airborne data are processed to estimate the SSH retrieval precision after a series of procedures (including height compensation, range migration correction, multi-look processing, waveform re-tracking). To accustom to the airborne experiment design, a SAR echo model that fully considers both the attitude variation of the aircraft and the elliptical footprint of radar beam is established. The retrieved SSH data are compared with the public SSH data along the flight path at the experiment day, showing good consistence for both bands. By calculating the theoretical precision of waveform re-tracking and re-processing the dual-band airborne data into different bandwidths, it is demonstrated that the Ku/Ka precision ratio is possible to achieve 1.4 within the 27 km offshore area, which indicates that Ka-band has better performance

The Impact of Systematic Attitude Error on the Measurement of Interferometric Radar Altimeter

Altimetric error has always been the significant performance parameter of the interferometric radar altimeter (IRA), particularly in the observation of sub-mesoscale ocean dynamic processes in which a higher accuracy of sea surface elevation (SSE) measurement is needed. The systematic attitude error of IRA associated with platform altitude, roll, pitch, and yaw errors is a remarkable source of altimetric error. However, the coupling attitude altimetric error is still less discussed up to now. In this paper, we focus on the study of the coupling attitude altimetric error and its related position-shifting, which are all induced by the attitude errors. The theoretical formulas of the coupling attitude altimetric error were derived, and the theoretical analysis demonstrates that the coupling attitude altimetric error is no longer along the range direction of the IRA image rigorously due to the change of the radar beam pointing. Based on theoretical formulas proposed by this study, the coupling attitude altimetric error and its related position-shifting are simulated and verified by using attitude data recorded by an airborne position and orientation system (POS) of three airborne experiments. The experimental results illustrate that the simulated coupling attitude altimetric errors are consistent with the measurements of the airborne experiments

Quasi-Static Influence Line Identification and Damage Identification of Equal-Span Bridges Based on Measured Vehicle-Induced Deflection

The bridge influence line (IL) reflects the response of a certain section due to varying load positions. As a result, IL has a wide application prospect in damage identification and condition assessment. Up to date, studies regarding IL have been focused on the structure condition evaluation. A feasible and practical method for damage identification is still not yet available. The present paper proposes a comprehensive damage identification methodology based on IL under a moving vehicle is composed of data pre-processing, IL extraction, and damage detection. Firstly, a thorough review of existing IL identification methods based on signal processing is provided. Then three quasi-static IL identification methods based on measured data are discussed. Consequently, the study proposes a two-stage damage identification approach for simply supported bridges with equal span length. Also, the effectiveness of this approach is verified through field tests on a real girder bridge. At last, conclusions are drawn, and potential issues for the application of the proposed method in practice are discussed

Modeling of Temperature Time-Lag Effect for Concrete Box-Girder Bridges

It is common to assume the relationship between temperature and temperature response is instantaneous in bridge health monitoring systems. However, a time-lag effect between temperature and thermal strain response has been documented by the analysis of monitored field data of concrete box-girder s. This effect is clearly reflected by the ring feature in the temperature-strain correlation curve. Inevitably, the time-lag effect has an adverse impact on the accuracy and reliability of state assessment and real-time warning for structural health monitoring (SHM) systems. To mitigate the influence of the time-lag effect, a phase-shifting method is proposed based on the Fourier series expansion fitting method. The time-domain signal is firstly converted into the frequency domain signal to compute the phase difference between temperature data and response strain data at each decomposed order. Subsequently, the total phase difference can be obtained by weighted summation. The signal processing effectively reduces the hysteresis loop area and enhances the correlation between the structural response data and the temperature data. When processing the daily data in different seasons, it is found that after subtraction by the proposed method, the linear feature becomes dominant in the relationship between temperature and the strain during long-term observation