Background: In coping with the coronavirus disease 2019 (COVID-19) epidemic, cities adopted social isolation and lockdown measures; however, little is known about the impacts of these restrictions on household food security. Objective: This study provides a timely assessment of household food insecurity (HFI) in the Chinese city of Wuhan during the COVID-19 epidemic period and also investigates its determinant factors. Design: We collected valid data on food insecurity from 653 households in Wuhan via an online questionnaire in March 2020. The Household Food Insecurity Access Scale Score (HFIASS) was used to measure HFI, and a multiple linear regression model was used to determine the HFIASS. Results: The mean HFIASS in Wuhan was 9.42 (standard deviation: 5.82), with more than 50% of the households had an HFIASS < 9. Compared with normal conditions, lockdown measures had a huge negative impact on household food security. The results revealed that socio-demographic characteristics remained the underlying determinants of HFIASS during the epidemic. Households in Wuhan with local Hukou (city household registration) and self-owned property had a lower risk of food insecurity. Discussion and conclusion: After the restriction of conventional food access channels, intermediary food purchase methods such as group purchasing, shopping with the help of neighborhood committees, property management agents, and volunteers became the most important or the only channel for residents to access food. There were similarities in the use of these intermediary channels. Based on the probability that the epidemic will continue and the probability of similar public health-related outbreaks in the future, the study calls for a more resilient and responsive sustainable food supply system by harnessing the capacity of communities, e-commerce and rapid logistics.

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It is difficult to interpret ultrasonic signals when the echoes that are reflected from the various target features overlap. In this paper, a pulse compression technique based on a combination of Wiener filtering and autoregressive (AR) spectral extrapolation is used to process ultrasonic signals to improve their temporal resolution. The influence of the AR order and the width of the frequency window in AR spectral extrapolation are discussed in detail, and optimal parameters are obtained. The technique is then applied to measure the size of the defects in a plate and discern two adjacent defects in a block using a phased array. It is shown that the widths of the defects in the plate were measured accurately and the two adjacent defects were distinguished well by the proposed technique.

To streamline fast-track processing of large data volumes, we have developed a deep learning approach to deblend seismic data in the shot domain based on a practical strategy for generating high-quality training data along with a list of data conditioning techniques to improve the performance of the data-driven model. We make use of unblended shot gathers acquired at the end of each sail line, to which the access requires no additional time or labor costs beyond the blended acquisition. By manually blending these data, we obtain training data with good control of the ground truth and fully adapted to the given survey. Furthermore, we train a deep neural network using multi-channel inputs that include adjacent blended shot gathers as additional channels. The prediction of the blending noise is added in as a related and auxiliary task with the main task of the network being the prediction of the primary-source events. Blending noise in the ground truth is scaled down during the training and validation process due to its excessively strong amplitudes. As part of the process, the to-be-deblended shot gathers are aligned by the blending noise. Implementation on field blended-by-acquisition data demonstrates that introducing the suggested data conditioning steps can considerably reduce the leakage of primary-source events in the deep part of the blended section. The complete proposed approach performs almost as well as a conventional algorithm in the shallow section and shows a great advantage in efficiency. It performs slightly worse for larger traveltimes, but still removes the blending noise efficiently.