With the globalization of the economy,maritime trade has surged,posing challenges in the supervision of marine vessel activities.An automatic identification system(AIS)is an effective means of shipping traffic service...With the globalization of the economy,maritime trade has surged,posing challenges in the supervision of marine vessel activities.An automatic identification system(AIS)is an effective means of shipping traffic service,but many uncertainties exist regarding its data quality.In this study,the AIS data from Haiyang(HY)series of satellites were used to assess the data quality,analyze the global ship trajectory distribution and update frequencies from 2019 to 2023.Through the analysis of maritime mobile service identity numbers,we identified 340185 unique vessels,80.1%of which adhered to the International Telecommunication Union standards.Approximately 49.7%of ships exhibit significant data gaps,and 1.1%show inconsistencies in their AIS data sources.In the central Pacific Ocean at low latitudes and along the coast of South America(30°-60°S),a heightened incidence of abnormal trajectories of ships has been consistently observed,particularly in areas associated with fishing activities.According to the spatial distribution of ship trajectories,AIS data exhibit numerous deficiencies,particularly in high-traffic regions such as the East China Sea and South China Sea.In contrast,ship trajectories in the polar regions,characterized by high latitudes,are relatively comprehensive.With the increased number of HY satellites equipped with AIS receivers,the quantity of trajectory points displays a growing trend,leading to increasingly complete trajectories.This trend highlights the significant potential of using AIS data acquired from HY satellites to increase the accuracy of vessel tracking.展开更多
In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation anal...In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation analysis on two typical co-pollution events in Beijing,occurring from July 22 to July 28,2019,and from April 25 to May 2,2020.These events were categorized into pre-trough southerly airflow type(Type 1)and post-trough northwest flow type(Type 2).Subsequently,sensitivity analyses using the GRAPES-CUACE adjoint model were performed to quantify the contributions of precursor emissions from Beijing and surrounding areas to PM_(2.5)and O_(3) concentrations in Beijing for two types of co-pollution.The results indicated that the spatiotemporal distribution of sensitive source region varied among different circulation types.Primary PM_(2.5)(PPM_(2.5))emissions from Hebei contributed the most to the 24-hour average PM_(2.5)(24-h PM_(2.5))peak concentration(41.6%-45.4%),followed by Beijing emissions(31%-35.7%).The maximum daily 8-hour average ozone peak concentration was primarily influenced by the emissions from Hebei and Beijing,with contribution ratios respectively of 32.8%-44.8% and 29%-42.1%.Additionally,NO_(x)emissions were the main contributors in Type 1,while both NO_(x)and VOCs emissions contributed similarly in Type 2.The iterative emission reduction experiments for two types of co-pollution indicated that Type 1 required emission reductions in NO_(x)(52.4%-71.8%)and VOCs(14.1%-33.8%)only.In contrast,Type 2 required combined emission reductions in NO_(x)(37.0%-65.1%),VOCs(30.7%-56.2%),and PPM_(2.5)(31%-46.9%).This study provided a reference for controlling co-pollution events and improving air quality in Beijing.展开更多
Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when f...Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.展开更多
Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the trans...Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the transducer to produce an extremely narrow beam,greatly improving the resolution and sensitivity.In this work,we summarize the fundamental properties and biological effects of the ultrasound field,aiming to establish a correlation between device design and application.Focusing techniques for piezoelectric transducers are highlighted,including material selection and fabrication methods,which determine the final performance of piezoelectric transducers.Numerous examples,from ultrasound imaging,neuromodulation,tumor ablation to ultrasonic wireless energy transfer,are summarized to highlight the great promise of biomedical applications.Finally,the challenges and opportunities of focused ultrasound transducers are presented.The aim of this review is to bridge the gap between focused ultrasound systems and biomedical applications.展开更多
基金The National Key R&D Program of China under contract Nos 2021YFC2803305 and 2024YFC2816301the Fundamental Research Funds for the Central Universities of China under contract No.2042022dx0001.
文摘With the globalization of the economy,maritime trade has surged,posing challenges in the supervision of marine vessel activities.An automatic identification system(AIS)is an effective means of shipping traffic service,but many uncertainties exist regarding its data quality.In this study,the AIS data from Haiyang(HY)series of satellites were used to assess the data quality,analyze the global ship trajectory distribution and update frequencies from 2019 to 2023.Through the analysis of maritime mobile service identity numbers,we identified 340185 unique vessels,80.1%of which adhered to the International Telecommunication Union standards.Approximately 49.7%of ships exhibit significant data gaps,and 1.1%show inconsistencies in their AIS data sources.In the central Pacific Ocean at low latitudes and along the coast of South America(30°-60°S),a heightened incidence of abnormal trajectories of ships has been consistently observed,particularly in areas associated with fishing activities.According to the spatial distribution of ship trajectories,AIS data exhibit numerous deficiencies,particularly in high-traffic regions such as the East China Sea and South China Sea.In contrast,ship trajectories in the polar regions,characterized by high latitudes,are relatively comprehensive.With the increased number of HY satellites equipped with AIS receivers,the quantity of trajectory points displays a growing trend,leading to increasingly complete trajectories.This trend highlights the significant potential of using AIS data acquired from HY satellites to increase the accuracy of vessel tracking.
基金supported by the National Key Research and Development Program of China(No.2022YFC3701205)the National Natural Science Foundation of China(No.41975173)the Science and Technology Development Fund of the Chinese Academy of Meteorological Sciences(No.2021KJ011)。
文摘In recent years,incidents of simultaneous exceedance of PM_(2.5)and O_(3) concentrations,termed PM_(2.5)and O_(3) co-pollution events,have frequently occurred in China.This study conducted atmospheric circulation analysis on two typical co-pollution events in Beijing,occurring from July 22 to July 28,2019,and from April 25 to May 2,2020.These events were categorized into pre-trough southerly airflow type(Type 1)and post-trough northwest flow type(Type 2).Subsequently,sensitivity analyses using the GRAPES-CUACE adjoint model were performed to quantify the contributions of precursor emissions from Beijing and surrounding areas to PM_(2.5)and O_(3) concentrations in Beijing for two types of co-pollution.The results indicated that the spatiotemporal distribution of sensitive source region varied among different circulation types.Primary PM_(2.5)(PPM_(2.5))emissions from Hebei contributed the most to the 24-hour average PM_(2.5)(24-h PM_(2.5))peak concentration(41.6%-45.4%),followed by Beijing emissions(31%-35.7%).The maximum daily 8-hour average ozone peak concentration was primarily influenced by the emissions from Hebei and Beijing,with contribution ratios respectively of 32.8%-44.8% and 29%-42.1%.Additionally,NO_(x)emissions were the main contributors in Type 1,while both NO_(x)and VOCs emissions contributed similarly in Type 2.The iterative emission reduction experiments for two types of co-pollution indicated that Type 1 required emission reductions in NO_(x)(52.4%-71.8%)and VOCs(14.1%-33.8%)only.In contrast,Type 2 required combined emission reductions in NO_(x)(37.0%-65.1%),VOCs(30.7%-56.2%),and PPM_(2.5)(31%-46.9%).This study provided a reference for controlling co-pollution events and improving air quality in Beijing.
文摘Pore pressure is essential data in drilling design,and its accurate prediction is necessary to ensure drilling safety and improve drilling efficiency.Traditional methods for predicting pore pressure are limited when forming particular structures and lithology.In this paper,a machine learning algorithm and effective stress theorem are used to establish the transformation model between rock physical parameters and pore pressure.This study collects data from three wells.Well 1 had 881 data sets for model training,and Wells 2 and 3 had 538 and 464 data sets for model testing.In this paper,support vector machine(SVM),random forest(RF),extreme gradient boosting(XGB),and multilayer perceptron(MLP)are selected as the machine learning algorithms for pore pressure modeling.In addition,this paper uses the grey wolf optimization(GWO)algorithm,particle swarm optimization(PSO)algorithm,sparrow search algorithm(SSA),and bat algorithm(BA)to establish a hybrid machine learning optimization algorithm,and proposes an improved grey wolf optimization(IGWO)algorithm.The IGWO-MLP model obtained the minimum root mean square error(RMSE)by using the 5-fold cross-validation method for the training data.For the pore pressure data in Well 2 and Well 3,the coefficients of determination(R^(2))of SVM,RF,XGB,and MLP are 0.9930 and 0.9446,0.9943 and 0.9472,0.9945 and 0.9488,0.9949 and 0.9574.MLP achieves optimal performance on both training and test data,and the MLP model shows a high degree of generalization.It indicates that the IGWO-MLP is an excellent predictor of pore pressure and can be used to predict pore pressure.
基金National Natural Science Foundation of China(12072189,82171011)Shanghai Jiao Tong University‘Deep Blue Program’Fund(Grant No.SL2103)+1 种基金Project of Biobank(No.YBKB202117)from Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of Medicine and Science Foundation of National Key Laboratory of Science and Technology on Advanced Composites in Special Environments(No.6142905223704)。
文摘Piezoelectric ultrasonic transducers have shown great potential in biomedical applications due to their high acoustic-to-electric conversion efficiency and large power capacity.The focusing technique enables the transducer to produce an extremely narrow beam,greatly improving the resolution and sensitivity.In this work,we summarize the fundamental properties and biological effects of the ultrasound field,aiming to establish a correlation between device design and application.Focusing techniques for piezoelectric transducers are highlighted,including material selection and fabrication methods,which determine the final performance of piezoelectric transducers.Numerous examples,from ultrasound imaging,neuromodulation,tumor ablation to ultrasonic wireless energy transfer,are summarized to highlight the great promise of biomedical applications.Finally,the challenges and opportunities of focused ultrasound transducers are presented.The aim of this review is to bridge the gap between focused ultrasound systems and biomedical applications.
