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With the rapid development of smart grids, the deployment number of transmission lines has significantly increased, posing significant challenges to the detection and maintenance of power facilities. Unmanned aerial vehicles (UAVs) have become a common means of power inspection. In the context of drone power inspection, drone clusters are used as relays for long-distance communication to expand the communication range and achieve data transmission between patrol drones and base stations. Most of the communication occurs in the air-to-air channel between UAVs, which requires high reliability of communication between drone relays. Therefore, the main focus of this paper is on decoding schemes for drone air-to-air channels. Given the limited computing resources and battery capacity of a drone, as well as the large amount of power data that needs to be transmitted between drone relays, this paper aims to design a high-accuracy and low-complexity decoder for LDPC long-code decoding. We propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization, applying deep learning to traditional LDPC decoding methods. In order to achieve high decoding performance while reducing complexity, this scheme utilizes codebook-based weight quantization and parameter sharing methods to improve the neural-network-normalized minimum sum (NNMS) decoding algorithm. Simulation experimental results show that the proposed method has a better BER performance and low computational complexity. Therefore, the LDPC decoding algorithm designed effectively meets the drone characteristics and the high channel decoding performance requirements. This ensures efficient and reliable data transmission on the data link between drone relays.

Predicting short-term passenger flow in urban rail transit is crucial for intelligent and real-time management of urban rail systems. This study utilizes deep learning techniques and multi-source big data to develop an enhanced spatial-temporal long short-term memory (ST-LSTM) model for forecasting subway passenger flow. The model includes three key components: (1) a temporal correlation learning module that captures travel patterns across stations, aiding in the selection of effective training data; (2) a spatial correlation learning module that extracts spatial correlations between stations using geographic information and passenger flow variations, providing an interpretable method for quantifying these correlations; and (3) a fusion module that integrates historical spatial-temporal features with real-time data to accurately predict passenger flow. Additionally, we discuss the model’s interpretability. The ST-LSTM model is evaluated with two large-scale real-world subway datasets from Nanjing and Chongqing. Experimental results show that the ST-LSTM model effectively captures spatial-temporal correlations and significantly outperforms other benchmark methods.

Background: The COVID-19 has spread to more than 200 countries and territories. But less is known about the knowledge, protection behavior and anxiety regarding the outbreak among the general population. Methods: A cross-sectional, population-based online survey was conducted in China and abroad from January 28 to February 1, 2020. Socio-demographic information was collected and knowledge scores, practice scores, anxiety scores and perceived risk were calculated. General linear model and binary logistic regression were used to identify possible associations. Results: We included 9,764 individuals in this study, and 156 (1.6%) were from Hubei Province. The average knowledge score was 4.7 (standard deviation, 1.0) (scored on a 6-point scale); 96.1% maintained hand hygiene, and 90.3% of participants had varying levels of anxiety. People in Hubei Province were the most anxious, followed by those in Beijing and Shanghai. People who had experienced risk behaviors did not pay more attention to wearing masks and hand hygiene. Conclusions: The public had high awareness on knowledge of COVID-19 outbreak, and a high proportion of people practiced good hand hygiene behavior. Many people claimed anxiety, especially in heavily affected areas during pandemic, suggesting the importance of closing the gap between risk awareness and good practice and conduct psychological counseling to public and patients.

Vaccination against coronavirus disease 2019 (COVID-19) has become an important public health solution. To date, there has been a lack of data on COVID-19 vaccination willingness, vaccine hesitancy, and vaccination coverage in China since the vaccine has become available. We designed and implemented a cross-sectional, population-based online survey to evaluate the willingness, hesitancy, and coverage of the COVID-19 vaccine among the Chinese population. 8742 valid samples were recruited and classified as the vaccine-priority group (n = 3902; 44.6%) and the non-priority group (n = 4840; 55.4%). The proportion of people’s trust in the vaccine, delivery system, and government were 69.0%, 78.0% and 81.3%, respectively. 67.1% of the participants were reportedly willing to accept the COVID-19 vaccination, while 9.0% refused it. 834 (35.5%) reported vaccine hesitancy, including acceptors with doubts (48.8%), refusers (39.4%), and delayers (11.8%). The current coverage was 34.4%, far from reaching the requirements of herd immunity. The predicted rate of COVID-19 vaccination was 64.9%, 68.9% and 81.1% based on the rates of vaccine hesitancy, willingness, and refusal, respectively. The COVID-19 vaccine rate is far from reaching the requirements of herd immunity, which will require more flexible and comprehensive efforts to improve the population’s confidence and willingness to vaccinate. It should be highlighted that vaccination alone is insufficient to stop the pandemic; further efforts are needed not only to increase vaccination coverage but also to maintain non-specific prevention strategies.

MiR-873/CDK3 has been shown to play a critical role in ERα signaling and tamoxifen resistance. Thus, targeting this pathway may be a potential therapeutic approach for the treatment of ER positive breast cancer especially tamoxifen resistant subtype. Here we report that Norcantharidin (NCTD), currently used clinically as an ani-cancer drug in China, regulates miR-873/CDK3 axis in breast cancer cells. NCTD decreases the transcriptional activity of ERα but not ERβ through the modulation of miR-873/CDK3 axis. We also found that NCTD inhibits cell proliferation and tumor growth and miR-873/CDK3 axis mediates cell proliferation suppression of NCTD. More important, we found that NCTD sensitizes resistant cells to tamoxifen. NCTD inhibits tamoxifen induced the transcriptional activity as well ERα downstream gene expressions in tamoxifen resistant breast cancer cells. In addition, we found that NCTD restores tamoxifen induced recruitments of ERα co-repressors N-CoR and SMRT. Knockdown of miR-873 and overexpression of CDK3 diminish the effect of NCTD on tamoxifen resistance. Our data shows that NCTD regulates ERα signaling and tamoxifen resistance by targeting miR-873/CDK3 axis in breast cancer cells. This study may provide an alternative therapy strategy for tamoxifen resistant breast cancer.

ObjectiveThe purpose was to explore the association between self-monitoring of blood glucose (SMBG) and hepatitis B virus (HBV) infection among people with diabetes.DesignA cross-sectional comparative study.SettingSix township hospitals in Gansu Province, China in October 2018.Participants408 patients with diabetes were systematically recruited, and based on their characteristics 408 people without diabetes were randomly matched 1:1.InterventionsVenous blood was collected for HBV serological testing and blood glucose testing.Primary and secondary outcome measuresThe primary outcome was comparison of hepatitis B surface antigen (HBsAg) positive rates between the two groups. The secondary outcome was the relationship between frequency of SMBG and HBsAg positivity.ResultsHBsAg positive rate in people without diabetes was 2.0% and in those with diabetes was 4.2%. Whether in people without diabetes or patients with diabetes, higher frequency of SMBG was associated with higher HBsAg positive rate. Increases in the duration of diabetes were correlated with increasing rates of HBsAg. Compared with people without diabetes, logistic regression identified an association between diabetes and HBV infection (OR=2.8; 95% CI 1.0 to 7.6), but impaired fasting glucose was not (OR=2.3; 95% CI 0.5 to 9.9).ConclusionRoutine blood glucose monitoring at home was associated with HBV infection, which meant people with diabetes may be at high risk of HBV infection. China is a country with high prevalence of both HBsAg and diabetes, and the increased risk of HBV infection in populations with diabetes needs more attention.

Short-term prediction of origin–destination (OD) flow is a primary but complex assignment to urban rail companies, which is the basis of intelligent and real-time urban rail transit (URT) operation and management. The short-term prediction of URT OD flow has three special characteristics: data lag, data dimensionality, and data malconformation, distinguishing it from other short-term prediction tasks. It is essential to propose a novel prediction algorithm that considers the special characteristics of the URT OD flow. For this purpose, based on deep learning methods and multi-source big data, a modified spatial–temporal long short-term memory (ST-LSTM) model is established. The proposed model comprises four components: (1) a temporal feature extraction module is devised to extract time information within network-wide historical OD data; (2) a spatial correlation learning module is introduced to address the data malconformation and data dimensionality problems, which provides an interpretable spatial correlation quantization method; (3) an input control-gated mechanism is originally proposed to solve the data lag problem, which combines the processed available OD flow and real-time inflow/outflow; (4) a fusion module combines historical spatial–temporal features with real-time information to achieve accurate OD flow prediction. We also further discuss the interpretability of the model in detail. The ST-LSTM model is evaluated by sufficient experiments on two large-scale actual subway datasets from Nanjing and Beijing, and the experimental results demonstrate that it can better learn the spatial–temporal correlations and exceed the rest benchmarking methods.

To assess chronic hepatitis B (CHB) patients' knowledge about hepatitis B and their experience of discrimination with regard to study, work, and daily life. We administered a questionnaire to 797 CHB patients in four provinces of China and used one-way analysis of variance (ANOVA) and a generalized linear model (GLM) to identify factors associated with discrimination. CHB patients had low levels of knowledge about hepatitis B. Patients under 40 years of age with a junior college education or above knew more about hepatitis B than CHB patients over 40 years of age who had only a high school education. Three-fourths of patients had experienced discrimination because of their hepatitis B infection, with no differences in the proportion experiencing discrimination by sex or age. People with more education reported less discrimination. Patients in Beijing and Henan province perceived less discrimination than those in Shaanxi and Guangdong provinces. Discrimination was significantly associated with negative emotions. CHB patients had little awareness of China's anti-discrimination laws and policies. Among patients who had experienced discrimination, fewer than 10% knew organizations or institutions that could offer help. Over 60% of CHB patients who experienced discrimination chose not to respond. CHB patients in China commonly experienced discrimination, which was associated with significant, negative emotional stress. To mitigate the damaging effects of discrimination, our study suggests raising general population knowledge about hepatitis B, raising awareness of the availability of legal protection and organizations that can fight discrimination, and providing psychological support for CHB patients.