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Introduction: Grapes are prone to various diseases throughout their growth cycle, and the failure to promptly control these diseases can result in reduced production and even complete crop failure. Therefore, effective disease control is essential for maximizing grape yield. Accurate disease identification plays a crucial role in this process. In this paper, we proposed a real-time and lightweight detection model called Fusion Transformer YOLO for 4 grape diseases detection. The primary source of the dataset comprises RGB images acquired from plantations situated in North China. Methods: Firstly, we introduce a lightweight high-performance VoVNet, which utilizes ghost convolutions and learnable downsampling layer. This backbone is further improved by integrating effective squeeze and excitation blocks and residual connections to the OSA module. These enhancements contribute to improved detection accuracy while maintaining a lightweight network. Secondly, an improved dual-flow PAN+FPN structure with Real-time Transformer is adopted in the neck component, by incorporating 2D position embedding and a single-scale Transformer Encoder into the last feature map. This modification enables real-time performance and improved accuracy in detecting small targets. Finally, we adopt the Decoupled Head based on the improved Task Aligned Predictor in the head component, which balances accuracy and speed. Results: Experimental results demonstrate that FTR-YOLO achieves the high performance across various evaluation metrics, with a mean Average Precision (mAP) of 90.67%, a Frames Per Second (FPS) of 44, and a parameter size of 24.5M. Conclusion: The FTR-YOLO presented in this paper provides a real-time and lightweight solution for the detection of grape diseases. This model effectively assists farmers in detecting grape diseases. [ABSTRACT FROM AUTHOR]

Background: In 2016, the "universal two-child" policy, allowing each couple to have two children, was introduced in China. The characteristic change of the long-term period after the implementation of the universal two-child policy was unclear. We studied trends in the obstetric characteristics and their potential impact on the rates of cesarean section and preterm birth in the era of China's universal two-child policy. Methods: A tertiary center-based study (2010–2021) retrospectively focused single high-risk pregnancies who delivered from the one-child policy period (OCP, 2010–2015) to the universal two-child policy period (TCP, 2016–2021). A total of 39, 016 pregnancies were enrolled. Maternal demographics, complications, delivery mode and obstetric outcomes were analyzed. Furthermore, logistic regression analysis was used to explore the association between the cesarean section rate, preterm birth and implementation of the universal two-child policy, adjusting maternal age, parity, and fetal distress. Results: Ultimately a total of 39,016 pregnant women met the criteria and were included in this analysis. The proportion of women with advanced maternal age (AMA) increased from 14.6% in the OCP to 31.6% in the TCP. The number of multiparous women increased 2-fold in the TCP. In addition, the overall rate of cesarean section significantly decreased over the policy change, regardless of maternal age, whereas the risk of preterm birth significantly increased in the TCP. Adjusting for maternal age, parity and fetal distress, the universal two-child policy showed a significantly favorable impact on the cesarean section rate (RR 0.745, 95%CI (0.714–0.777), P < 0.001). Compared to the OCP group, a higher increase in fetal distress and premature rupture of membranes (PROM) were observed in the TCP group. In pregnancies with AMA, there was no increase in the risk of postpartum hemorrhage, whereas more women who younger than 35 years old suffered from postpartum hemorrhage in TCP. The logistic regression model showed that the universal two-child policy was positively associated with the risk of postpartum hemorrhage (RR: 1.135, 95%CI: 1.025–1.257, P = 0.015). Conclusions: After the implementation of the universal two-child policy in China, the rate of the cesarean section significantly decreased, especially for women under 35 years old. However, the overall risk of postpartum hemorrhage increased in women under 35 years old, while there was no change in women with AMA. Under the new population policy, the prevention of postpartum hemorrhage in the young women should not be neglected. [ABSTRACT FROM AUTHOR]

Background: Goose astrovirus (GoAstV) is an important pathogen that causes joint and visceral gout in goslings. It has been circulating in many provinces of China since 2017. Goose astrovirus genotypes 2 (GoAstV-2) is the main epidemic strain, and its high morbidity and mortality have caused huge economic losses to the goose industry. An accurate point-of-care detection for GoAstV-2 is of great significance. In this study, we developed a real-time reverse transcription recombinase polymerase amplification (RT-RPA) method for the on-site detection of GoAstV-2 infection. Results: The real-time RT-RPA reaction was carried out at a constant temperature of 39 °C, and the entire detection time from nucleic acid preparation to the end of amplification was only 25 min using the portable device. The results of a specificity analysis showed that no cross-reaction was observed with other related pathogens. The detection limit of the assay was 100 RNA copies/μL. The low coefficient of variation value indicated excellent repeatability. We used 270 clinical samples to evaluate the performance of our established method, the positive concordance rates with RT-qPCR were 99.6%, and the linear regression analysis revealed a strong correlation. Conclusions: The established real-time RT-RPA assay showed high rapidity, specificity and sensitivity, which can be widely applied in the laboratory, field and especially in the resource-limited settings for GoAstV-2 point-of-care diagnosis. [ABSTRACT FROM AUTHOR]

Highly accurate near-real-time satellite precipitation estimates (SPEs) are important for hydrological forecasting and disaster warning. The near-real quantitative precipitation estimates (REGC) of the recently developed Chinese geostationary meteorological satellite Fengyun 4A (FY4A) have the advantage of high spatial and temporal resolution, but there are errors and uncertainties to some extent. In this paper, a self-adaptive ill-posed least squares scheme based on sequential processing (SISP) is proposed and practiced in mainland China to correct the real-time biases of REGC hour by hour. Specifically, the scheme adaptively acquires sample data by setting temporal and spatial windows and constructs an error-correction model based on the ill-posed least squares method from the perspectives of climate regions, topography, and rainfall intensity. The model adopts the sequential idea to update satellite precipitation data within time windows on an hour-by-hour basis and can correct the biases of real-time satellite precipitation data using dynamically changing parameters, fully taking into account the influence of precipitation spatial and temporal variability. Only short-term historical data are needed to accurately rate the parameters. The results show that the SISP algorithm can significantly reduce the biases of the original REGC, in which the values of relative bias (RB) in mainland China are reduced from 11.2% to 3.3%, and the values of root mean square error (RMSE) are also reduced by about 17%. The SISP algorithm has a better correction in humid and semi-humid regions than in arid and semi-arid regions and is effective in reducing the negative biases of precipitation in each climate region. In terms of rain intensity, the SISP algorithm can improve the overestimation of satellite precipitation estimates for low rain intensity (0.2–1 mm/h), but the correction for high rain intensity (>1 mm/h) needs further improvement. The error component analysis shows that the SISP algorithm can effectively correct the hit bias. This study serves as a valuable reference for real-time bias correction using short-term accumulated precipitation data. [ABSTRACT FROM AUTHOR]

Changes in the opening of gates in open channel water transfer projects will cause fluctuations in the water level and flow of adjacent open channels and thus bring great challenges for real-time water level prediction. In this paper, a novel slope-similar shape method is proposed for real-time water level prediction when the change of gate opening at the next moment is known. The water level data points of three consecutive moments constitute the query. The slope similarity is used to find the historical water level datasets with similar change trend to the query, and then the best slope similarity dataset is determined according to the similarity index and the gate opening change. The water level difference of the next moment of the best similar data point is the water level difference of the predicted moment, and thus the water level at the next moment can be obtained. A case study is performed with the Middle Route of the South-to-North Water Diversion Project of China. The results show that 87.5% of datasets with a water level variation of less than 0.06 m have an error less than 0.03 m, 71.4% of which have an error less than 0.02 m. In conclusion, the proposed method is feasible, effective, and interpretable, and the study provides valuable insights into the development of scheduling schemes. [ABSTRACT FROM AUTHOR]

This article proposed a real-time short turning strategy based on passenger choice behavior to improve transit service for the stops with larger passenger demand in peak periods. A bi-level model is introduced to integrate the real-time control strategy and passenger choice. The upper-level model is an optimal model with the objective of maximizing the reduced waiting time of passengers with the strategy. The lower-level model is an allocation model to calculate passenger demand accurately. The optimization variables include which buses provide short turning services and the start and end stations of each bus. An algorithm is implemented to solve the problem and the effectiveness of the presented strategy is tested with the data of a transit route in Dalian city of China. The results indicate that the real-time short turning strategy based on passenger choice behavior can yield benefits in term of the reduction of passenger waiting time and even the vehicle capacity up. [ABSTRACT FROM AUTHOR]

In megacities, vehicle emissions face urgent challenges related to air pollution and CO 2 control. To achieve the refinement of vehicle control policies for the co-control of air pollutants and CO 2 , this study established a vehicle emission inventory with high spatial and temporal resolution based on the hourly traffic flow in Shanghai and analyzed the spatial and temporal distribution characteristics of the real-time vehicle emissions. Meanwhile, a policy evaluation framework was constructed by combining pollutant emission predictions with quantitative co-control effect assessments. The results indicated that spatio-temporal variations in different air pollutants and CO 2 could mainly be attributed to primary contributing vehicle types. The pollutants (CO 2 , CO and VOCs) primarily contributed by private cars exhibited a bimodal pattern in 24-h time series and their spatial distribution was concentrated in the urban city center. The spatial distribution of NOx and PM primarily contributed by heavy trucks was still obvious on non-urban center areas. Furthermore, the results of synergistic effect analysis revealed that the alternative energy replacement scenario demonstrated the most significant potential for the co-control. Based on temporal-spatial and co-benefit analysis, the precise control policy of vehicle emissions can be established through time-, region-, and model-control. This study provides references and research methods for the formulation of the vehicle refinement control policies in worldwide megacities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Tropospheric delay has always been an important issue in Global Navigation Satellite System (GNSS) processing. Empirical tropospheric delay models are difficult to simulate complex and volatile atmospheric environments, resulting in poor accuracy of the empirical model and difficulty in meeting precise positioning demand. In recent years, some scholars proposed to establish real-time tropospheric product by using real-time or near-real-time GNSS observations in a small region, and achieved some good results. This paper uses real-time observing data of 210 Chinese national GNSS reference stations to estimate the tropospheric delay, and establishes ZWD grid model in the country wide. In order to analyze the influence of tropospheric grid product on wide-area real-time PPP, this paper compares the method of taking ZWD grid product as a constraint with the model correction method. The results show that the ZWD grid product estimated based on the national reference stations can improve PPP accuracy and convergence speed. The accuracy in the north(N), east(E) and up(U) direction increase by 31.8%,15.6% and 38.3%, respectively. As with the convergence speed, the accuracy of U direction experiences the most improvement. [ABSTRACT FROM AUTHOR]

Ground-level fine particulate matter (PM 2.5) and ozone (O 3) are air pollutants that can pose severe health risks. Surface PM 2.5 and O 3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM 2.5 or O 3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014–2021, we found a strong relationship between PM 2.5 and O 3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM 2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM 2.5 and O 3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM 2.5 and O 3 based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019–2021 to construct the network, we found that simultaneous retrievals of PM 2.5 and O 3 improved the performance compared with retrieving them independently: the temporal R2 increased from 0.66 to 0.72 for PM 2.5 , and from 0.79 to 0.82 for O 3. The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM. [Display omitted] • The compound extremes of PM 2.5 and O 3 have clear spatial patterns in China. • Jointly learning PM 2.5 and O 3 information can improve retrieval performance. • The PM 2.5 and O 3 temporal features can be captured by the attention mechanism. • Joint PM 2.5 and O 3 retrieval can utilize more training samples than single modeling. [ABSTRACT FROM AUTHOR]

Ground-level fine particulate matter (PM 2.5 ) and ozone (O 3 ) are air pollutants that can pose severe health risks. Surface PM 2.5 and O 3 concentrations can be monitored from satellites, but most retrieval methods retrieve PM 2.5 or O 3 separately and disregard the shared information between the two air pollutants, for example due to common emission sources. Using surface observations across China spanning 2014-2021, we found a strong relationship between PM 2.5 and O 3 with distinct spatiotemporal characteristics. Thus, in this study, we propose a new deep learning model called the Simultaneous Ozone and PM 2.5 inversion deep neural Network (SOPiNet), which allows for daily real-time monitoring and full coverage of PM 2.5 and O 3 simultaneously at a spatial resolution of 5 km. SOPiNet employs the multi-head attention mechanism to better capture the temporal variations in PM 2.5 and O 3 based on previous days' conditions. Applying SOPiNet to MODIS data over China in 2022, using 2019-2021 to construct the network, we found that simultaneous retrievals of PM 2.5 and O 3 improved the performance compared with retrieving them independently: the temporal R 2 increased from 0.66 to 0.72 for PM 2.5 , and from 0.79 to 0.82 for O 3 . The results suggest that near-real time satellite-based air quality monitoring can be improved by simultaneous retrieval of different but related pollutants. The codes of SOPiNet and its user guide are freely available online at https://github.com/RegiusQuant/ESIDLM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Abstract: With aim to master early deformation stages and motion laws of landslide, the raw displacement data of landslide are very important for the landslide researchers and environmental scientists. With the recent advances of Global Position System (GPS) and computer science, the differential GPS technology now can be used for the remote displacement observation of landslide. Therefore, the paper developed a landslide displacement remote observation system based on differential GPS technology to get the raw displacement data continually. The system is divided into hardware and software functional segments. A real landslide in southwest of China was monitored for several months through observation using this GPS-based system placed around and the observed results are presented in this paper. The practical application shows that this system has such advantages as being highly-precise, nearly real-time, remote, continuous and automatic. [Copyright &y& Elsevier]

Abstract: LinAo Nuclear Power Plant (NPP) Phase II is a newly-built CPR1000 reactor in China, and many new technologies including the incorporation of digital control system (DCS) substituting traditional analog control systems have been applied. This is the first time for Chinese engineers to setup and adjust the DCS configurations. Both the lack of the operating experiences and the plant safety requirements from the government make a necessity of the closed-loop DCS test before commercial plant operation. The most practical way is to build a digital plant as the controlled target and this digital plant is used to provide the plant thermal–hydraulic parameters and feedbacks for the DCS. Though the RELAP5 code has been developed for the best-estimate transient simulation of light water reactor coolant systems and is used worldwide, its functionality is too limited to implement a digital plant, such as the simulation of the complicated plant control and protection systems, the 3-dimensional neutron kinetics and the fluid network for the plant auxiliary systems. To overcome these drawbacks, a RELAP5-based extensible simulator has been built to satisfy the new requirements for the implementation of a digital plant. Any simulation code of desired functionality can be integrated into this simulator as a simulation module once it applies a set of well-defined data exchange interfaces. At the present stage, a RELAP5 module, a control system modeling module, a software–hardware data bridge module and some other auxiliary modules have been integrated into the simulator. There are more than 60 systems that need to be tested with the DCS in LinAo Phase II, and the whole testing work is separated into several phases. In this paper, we take the testing of the pressure control system and water level control system of pressurizer as example. A typical transient of 10% load step change from 100%FP (full power) to 90%FP was performed for the closed-loop DCS test. The necessity and capability of this RELAP5-based engineering simulator has been demonstrated. [Copyright &y& Elsevier]

Ditylenchus destructor is a plant-parasitic nematode that seriously infests sweet potato crop in China. Thus, fast and accurate detection of D. destructor in soil and plant tissue samples is of great significance. In this study, a real-time recombinase polymerase amplification (RPA) assay was developed for the rapid and accurate detection of D. destructor in various samples. The RPA assay could be easily operated and detected as low as 1/500 individual J4 nematode DNA per reaction in 20 min at 39 °C with high specificity. The assay meets the requirements of rapid detection prior to port quarantine as well as on-site real-time detection and can be applied to detect the parasite in soil and plant samples. The modified gDNA extraction method for a single nematode established in this study significantly reduced the time of detection and improved the applicability of the real-time RPA assay for on-site detection in different environments. The real-time RPA assay to detect D. destructor will be useful for epidemiological investigations in the field as well as for quarantine processes in the sweet potato and potato trade. • A real-time RPA assay with high sensitivity and specificity was developed to detect Ditylenchus destructor. • The RPA assay can detect as low as 1/500 individual J4 nematode gDNA in 20 min. • The RPA assay can directly detect D. destructor from the DNA of soil and plant tissue. [ABSTRACT FROM AUTHOR]

With the popularity and development of mobile Internet, new transportation network services such as customized buses are expected to become a new way of popular transportation in crowded metropolises. In this paper, we propose a real-time customized bus routes design model. We divide the proposed model into two-stage problem to solve. The first stage problem is the vehicle route problem with time window (VRPTW), and the second stage problem is bilateral matching problem. For the first stage, the column generation algorithm is used to solve the problem. For the second stage, we solve it by the improved H-R bilateral matching algorithm. Finally, data on customized bus in Tianjin city of China are used to verify the accuracy of the real-time customized bus routes design model. The optimized results show that the average attendance rate has reached 70.8%, and the service rate of passenger has reached 88.5%. The results have proved that the real-time customized bus routes design model has a practical applicability in operation. [ABSTRACT FROM AUTHOR]

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online. [ABSTRACT FROM AUTHOR]

The socioeconomic benefits associated with informative water quality forecasts for large lakes are becoming increasingly evident. However, it remains an enormous challenge to produce forecasts of water quality variables that are accurate enough to meet public demand. In this study, we developed and evaluated a new forecast framework for real-time forecasting of daily dissolved oxygen (DO), ammonium nitrogen (NH), total phosphorus (TP) and total nitrogen (TN) concentrations at lead times from one to six days for Lake Chaohu, the fifth largest freshwater lake in China. The forecast framework is based on a 3-D hydrodynamic ecological model referred to as EcoLake. We used hydrological, meteorological and water quality data from multiple sources to generate initial conditions and forcing functions. Solar radiation and inflows from tributaries which are not readily available were calculated using forecasted cloud cover and rainfall. Forecast skill was evaluated based on 122 forecasts produced on different days in 2017 and for each of the 12 sampling sites. Results indicate that the skill of the forecast framework varies considerably across water quality variables, sampling sites, and lead times. Generally, the forecast framework is more skillful than the persistence forecasts, which use the most recent observations as forecasts. The TN forecasts tend to be the most skillful with a mean RMSE skill score of 28.5% averaged across the six lead times. The DO forecasts tend to have the lowest skill with an average value of 10.9%. Model sensitivity experiments further revealed that errors in the raw air temperature and wind speed forecasts have a noticeable impact on the overall skill of DO and NH forecasts. The forecast framework proposed here could be a useful operational forecasting tool to enhance the effectiveness of the drinking water supply and public health protection based on the water quality management of Lake Chaohu., (Copyright © 2019 Elsevier B.V. All rights reserved.)

A multiplex TaqMan real-time PCR platform was developed in this study for combined detection of opa and/or porA genes (identification of N. gonorrhoeae) and the key mutations (Ala501Val/Thr/Pro, and/or Gly545Ser) in penicillin-binding protein 2 (PBP2) associated with decreased susceptibility to extended-spectrum cephalosporins (ESCs). Firstly, the specificities of the TaqMan probes/primers for the multiplex TaqMan real time PCR platform were confirmed by Basic Local Alignment Search Tool (BLAST) analysis. Then the multiplex PCR platform was performed on 77 isolates with decreased susceptibility to ceftriaxone (CRO) and 100 isolates with full susceptibility to CRO under universal optimized reaction conditions. As a result, based on cultivation-based matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) and antimicrobial susceptibility testing in vitro, the multiplex platform had a sensitivity of 100% and a specificity of 95.0% for identifying cultured isolates of Neisseria gonorrhoeae (N. gonorrhoeae, NG, GC) with decreased susceptibility to CRO. When directly screening N. gonorrhoeae with decreased susceptibility to CRO from clinical urogenital swabs, the multiplex platform offered a sensitivity of 96.1% and a specificity of 95.0%. Therefore, on the basis of sample culture and antimicrobial susceptibility testing in vitro, the multiplex TaqMan real time PCR platform has been proven to be a sensitivity of 100% and a specificity of 95.0% useful tool for screening cultured isolates of N. gonorrhoeae with decreased susceptibility to CRO, which can be finished within 2 days., (Copyright © 2018. Published by Elsevier Inc.)