This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.展开更多
Rowlands et al.1present an analysis of accelerometer data from the UK Biobank cohort,examining variations in the duration,intensity,and accumulation of moderate-intensity physical activity(MPA)and vigorous-intensity p...Rowlands et al.1present an analysis of accelerometer data from the UK Biobank cohort,examining variations in the duration,intensity,and accumulation of moderate-intensity physical activity(MPA)and vigorous-intensity physical activity(VPA)sufficient to reduce the risk of all-cause mortality.In this study,the authors questioned if shorter durations(i.e.,1,2,3,4,5,10,15,and 20 min/day)of MPA and VPA performed continuously or accumulated throughout the day would equally reduce the risks of all-cause mortality as longer duration MPA and VPA recommended in the physical activity(PA)guidelines.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The prese...Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.展开更多
Energy poverty in developing countries is a critical issue characterized by the lack of access to modern energy services,such as electricity and clean cooking facilities,as marked in SDG 7.This study explores the corr...Energy poverty in developing countries is a critical issue characterized by the lack of access to modern energy services,such as electricity and clean cooking facilities,as marked in SDG 7.This study explores the correlations between energy poverty,energy intensity,resource abundance,and income inequality,as these factors have been theorized to play important roles in influencing energy poverty in developing countries.By observing that the dataset is heterogeneous across the countries and over the time frame,we use the Method of Moments Quantile Regression(MMQR)to analyze our developing countries’data from 2000 to 2019.Our findings indicate that energy intensity is a significant factor influencing energy poverty,suggesting that higher energy consumption relative to the sample countries can exacerbate this issue.Additionally,we observe that income inequality within the sample countries is a critical determinant of energy poverty levels,highlighting the dynamics between economic disparity and access to energy resources.Interestingly,our study reveals that resource abundance acts as a blessing rather than a curse in terms of energy poverty,implying that countries rich in natural resources may have better opportunities to combat energy deprivation.Finally,we emphasize the vital role of financial markets in addressing energy poverty on a global scale,suggesting that robust financial systems can facilitate investments and innovations aimed at improving energy access for vulnerable populations.The results from the robustness analysis support the empirical results obtained from the main estimation.The empirical findings of the present study advance important comprehensions for policymakers to adopt energy policies that address the complex challenges of energy poverty and promote inclusive energy access.展开更多
Dispatched by the Chinese government,a multidisciplinary team of 30 researchers collaborated with a team from Myanmar to conduct a 14-day on-site investigation.The work encompassed seismic intensity assessments,field ...Dispatched by the Chinese government,a multidisciplinary team of 30 researchers collaborated with a team from Myanmar to conduct a 14-day on-site investigation.The work encompassed seismic intensity assessments,field surveys,and loss evaluations.The paper focuses on the intensity distribution and structural damage characteristics of the 2025 M7.9 Myanmar earthquake,yielding the following key findings.(1)The seismogenic fault rupture propagated in a nearly N-S direction,with a surface rupture length of approximately 450 km.The seismic impact zone exhibited an elongated N-S distribution and a shorter E-W span,distributed like a belt around the seismogenic fault.(2)Within the seismic impact zones,existing buildings comprised five primary structural types,with timber(bamboo)structures constituting the largest proportion(≈80%in rural areas,≈50%in urban areas).The relatively low disaster losses and casualties were primarily attributable to the good seismic performance and low damage ratio of timber(bamboo)structures across varying intensity zones.(3)An anomalous zone of intensityⅨwas located at the boundary between intensityⅥandⅦregions in Nay Pyi Taw.Here,ridge topography combined with soft soil layers significantly amplified ground motion,exacerbating structural damage.(4)Directional effects of ground motion were observed,with the structural damage phenomena and peak ground acceleration(PGA)values in the N-S direction exceeding those in the E-W direction.This validates that the maximum PGA distribution of strike-slip fault earthquakes aligns with the fault strike.The research is expected to provide technical support for post-disaster reconstruction planning,site selection,and disaster mitigation strategies in Myanmar.展开更多
The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismi...The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.展开更多
One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are ...One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are important parameters for measuring ground motion intensity and assessing earthquake damage.Due to the limited available information in EEW,CAV,I_(A),and SI cannot be accurately predicted using traditional EEW methods.In this paper,we propose an end-to-end deep learning-based Ground motion Intensity prediction Network(ENGINet)for on-site EEW.The aim of the ENGINet is to predict CAV,I_(A),and SI rapidly and reliably.ENGINet is based on a convolutional neural network and recurrent neural network.The inputs of the network are three-component acceleration records,three-component velocity records,and three-component displacement records obtained by a single station.The results from the test dataset show that at 3 s after the P-wave arrival,compared with the baseline models and other traditional methods,ENGINet has better performance in predicting CAV,I_(A),and SI.Our results indicate that ENGINet can quickly and accurately predict CAV,I_(A),and SI to some extent and has good potential in EEW efforts.展开更多
Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progr...Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.展开更多
Ultra-intense electromagnetic fields exceeding 10^(23)W∕cm^(2)are enabling breakthroughs in compact laser-driven particle accelerators and revealing new quantum electrodynamics(QED)phenomena.However,conventional lase...Ultra-intense electromagnetic fields exceeding 10^(23)W∕cm^(2)are enabling breakthroughs in compact laser-driven particle accelerators and revealing new quantum electrodynamics(QED)phenomena.However,conventional laser-focusing methods face considerable engineering challenges and require substantial costs.Focusing schemes utilizing plasma optics can produce sub-micrometer focus spots beyond the diffraction limit and substantially enhance the peak intensity;however,owing to significant energy dissipation,they may fail to simultaneously increase the laser fluence.To address these challenges,we propose a focusing scheme employing a near-critical-density hollow plasma fiber(HPF)that utilizes graded refractive index dynamics to boost both laser peak intensity and fluence at the same time.Three-dimensional particle-in-cell simulations demonstrate the HPF’s capability to focus a 4.5-μm-diameter Gaussian beam to a sub-diffraction-limited 0.6-μm-diameter spot.The peak intensity and laser fluence can be enhanced by factors of 22 and 10,respectively,marking a substantial improvement over existing plasma-based focusing schemes.Furthermore,the proposed scheme exhibits wide-range parameter adaptation and high robustness,making it suitable for direct implementation in PW-class ultra-intense laser experiments.展开更多
High-Intensity Interval Training(HIIT)has gained prominence as a time-efficient and effective exercise modality to improve cardiovascular(CV)fitness,metabolic health,and physical performance.Therefore,our aim was to s...High-Intensity Interval Training(HIIT)has gained prominence as a time-efficient and effective exercise modality to improve cardiovascular(CV)fitness,metabolic health,and physical performance.Therefore,our aim was to synthesize current clinical research on the effects of HIIT on the Autonomic Nervous System.We conducted the search for studies in the Directory of Open Access Journals,Embase,Virtual Health Library,Pubmed,and Scielo databases,in January of 2024.We included a total of 20 studies in our review.This literature review highlights the potential of HIIT to modulate the Autonomic Nervous System,enhancing CV function and overall health.Despite the promising findings,the interpretation of the results is tempered by the variability in study designs,populations,and methodologies.Future research should address these limitations,aiming for a more nuanced understanding of the relationship between HIIT and Autonomic Nervous System function.The review indicates that standardized protocols need to consider individual characteristics and baseline autonomic states for clinical application.As the body of evidence grows,HIIT may emerge as a cornerstone of exercise prescriptions aimed at optimizing autonomic function and promoting CV health.展开更多
We investigated the effects of high-intensity intermittent cross-training(HIICT)on maximal oxygen uptake(VO_(2)max).The HIICT consisted of alternating intermittent 20-s treadmill running(1^(st),3^(rd),5^(th),and 7^(th...We investigated the effects of high-intensity intermittent cross-training(HIICT)on maximal oxygen uptake(VO_(2)max).The HIICT consisted of alternating intermittent 20-s treadmill running(1^(st),3^(rd),5^(th),and 7^(th) bouts)and 20-s bicycle exercise(2^(nd),4^(th),and 6^(th) bouts)with a 10-s rest period.Each intensity for running and bicycling of the HIICT corresponded to an oxygen demand of~160% and~170%of the VO_(2)max,respectively.Fifteen healthy young males(aged[24±1]yrs)were randomly assigned to training(TG,n?8)and non-training control(CG,n?7)groups.The TG completed this HIICT daily 4 days/week for 6 weeks.Significant group×time interactions were observed for both the running and bicycling VO_(2)max(p<0.001 each).After the training,the VO_(2)max for both running([57.4±4.8]mL·kg^(-1)·min^(-1))and bicycling([50.6±3.7]mL·kg^(-1)·min^(-1))in the TG were significantly higher than those for running([50.1±3.1]mL·kg^(-1)·min^(-1))and bicycling([43.7±3.6]mL·kg^(-1)·min^(-1))in the CG,respectively(p<0.01 each).Post-hoc tests revealed a significant increase in VO_(2)max for running and bicycling in the TG after the HIICT(p<0.001 each)but no significant difference in the CG.These results demonstrated that the newly developed HIICT increases the VO_(2)max for both running and bicycling.展开更多
Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are pr...Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are primarily identified through teachers’observations and postclass questionnaires or interviews.The expression intensity of students,which is extremely important for fine-grained emotion analysis,is not considered.Hence,a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented.This method aims to recognize the positive emotion levels of a student in an end-to-end framework.First,the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity.Then,a deep-asymmetric convolutional neural network learns the expression model through dual neural networks,to enhance the stability of the network model and avoid the extreme attention region learned.Further,dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information.Thus,diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives.Finally,comparative experiments for the convergence speed,model-training parameters,confusion matrix,and classification probability are performed.The proposed method was applied to a real classroom scene to analyze the emotions of students.Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.展开更多
In the implementation of quantum key distribution,Security certification is a prerequisite for social deployment.Trans-mitters in decoy-BB84 systems typically employ gain-switched semiconductor lasers(GSSLs)to generat...In the implementation of quantum key distribution,Security certification is a prerequisite for social deployment.Trans-mitters in decoy-BB84 systems typically employ gain-switched semiconductor lasers(GSSLs)to generate optical pulses for encod-ing quantum information.However,the working state of the laser may violate the assumption of pulse independence.Here,we explored the dependence of intensity fluctuation and high-order correlation distribution of optical pulses on driving cur-rents at 2.5 GHz.We found the intensity correlation distribution had a significant dependence on the driving currents,which would affect the final key rate.By utilizing rate equations in our simulation,we confirmed the fluctuation and correlation origi-nated from the instability of gain-switched laser driven at a GHz-repetitive frequency.Finally,we evaluated the impact of inten-sity fluctuation on the secure key rate.This work will provide valuable insights for assessing whether the transmitter is operat-ing at optimal state in practice.展开更多
Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise resul...Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise results.This work proposes the ViT-TC model based on the Vision Transformer(ViT)architecture.Satellite images of TCs,including infrared(IR),water vapor(WV),and passive microwave(PMW),are used as inputs for intensity estimation.Experiments indicate that combining IR,WV,and PMW as inputs yields more accurate estimations than other channel combinations.The ensemble mean technique is applied to enhance the model's estimations,reducing the root-mean-square error to 9.32 kt(knots,1 kt≈0.51 m s^(-1))and the mean absolute error to 6.49 kt,which outperforms traditional methods and is comparable to existing deep learning models.The model assigns high attention weights to areas with high PMW,indicating that PMW magnitude is essential information for the model's estimation.The model also allocates significance to the cloud-cover region,suggesting that the model utilizes the whole TC cloud structure and TC eye to determine TC intensity.展开更多
Reference-frame-independent quantum key distribution(RFI-QKD)can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process.However,due to imperfections of optica...Reference-frame-independent quantum key distribution(RFI-QKD)can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process.However,due to imperfections of optical devices,there will inevitably exist intensity fluctuations in the source side of the QKD system,which will affect the final secure key rate.To reduce the influence of intensity fluctuations,an improved 3-intensity RFI-QKD scheme is proposed in this paper.After considering statistical fluctuations and implementing global parameter optimization,we conduct corresponding simulation analysis.The results show that our present work can present both higher key rate and a farther transmission distance than the standard method.展开更多
Background There is a lack of research examining the interplay between objectively measured physical activity volume and intensity with life expectancy.The purpose of the study was to investigate the interplay between...Background There is a lack of research examining the interplay between objectively measured physical activity volume and intensity with life expectancy.The purpose of the study was to investigate the interplay between objectively measured PA volume and intensity profiles with modeled life expectancy in women and men within the UK Biobank cohort study and interpret findings in relation to brisk walking.Methods Individuals from UK Biobank with wrist-worn accelerometer data were included.The average acceleration and intensity gradient were extracted to describe the physical activity volume and intensity profile.Mortality data were obtained from national registries.Adjusted life expectancies were estimated using parametric flexible survival models.Results 40,953(57.1%)women(median age=61.9 years)and 30,820(42.9%)men(63.1 years)were included.Over a median follow-up of 6.9 years,there were 1719(2.4%)deaths(733 in women;986 in men).At 60 years,life expectancy was progressively longer for higher physical activity volume and intensity profiles,reaching 95.6 years in women and 94.5 years in men at the 90th centile for both volume and intensity,corresponding to 3.4 additional years(95%confidence interval(95%CI):2.4-4.4)in women and 4.6 additional years(95%CI:3.6-5.6)in men compared to those at the 10th centiles.An additional 10-min or 30-min daily brisk walk was associated with 0.9(95%CI:0.5-1.3)and 1.4 years(95%CI:0.9-1.9)longer life expectancy,respectively,in inactive women;and 1.4 years(95%CI:1.0-1.8)and 2.5(95%CI:1.9-3.1)in inactive men.Conclusion Higher physical activity volumes were associated with longer life expectancy,with a higher physical activity intensity profile further adding to a longer life.Adding as little as a 10-min brisk walk to daily activity patterns may result in a meaningful benefit to life expectancy.展开更多
Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribut...Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.展开更多
Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challe...Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challenges.In this work,we demonstrate for the firs time that the coherent radiation farfiel patterns from laser–foil interactions can serve as an in situ,real-time,and easy-to-implement diagnostic for an ultraintense laser focus.The laser-driven electron sheets,curved by the spatially varying laser fiel and leaving the targets at nearly the speed of light,produce doughnut-shaped patterns depending on the shapes of the focal spot and the absolute laser intensities.Assisted by particle-in-cell simulations,we can achieve measurements of the intensity and the focal spot,and provide immediate feedback to optimize the focal spots for extremely high intensity.展开更多
Ultraintense laser–plasma experiments generate a variety of high-energy radiations,including nonlinear inverse Compton scattered(NCS)X-rays,which are expected to be a key experimental observable as we transition into...Ultraintense laser–plasma experiments generate a variety of high-energy radiations,including nonlinear inverse Compton scattered(NCS)X-rays,which are expected to be a key experimental observable as we transition into the quantum electrodynamic plasma regime.However,there is also a high bremsstrahlung X-ray background that reduces our ability to observe NCS X-rays.Previous numerical studies comparing NCS and bremsstrahlung emissions fail to capture the full temporal emission of both processes.We present for the first time two-dimensional particle-in-cell(PIC)and three-dimensional hybrid-PIC EPOCH simulations that capture up to 150 ps of the laser–plasma interaction and directly compare the NCS and bremsstrahlung emissions for a plastic target for intensities of 10^(20)-10^(23)W/cm^(2).We present angular distribution plots where the NCS emission is seen to dominate at intensities greater than 5×10^(21)W/cm^(2)and the target design is seen to successfully divert the bremsstrahlung signal away from the NCS lobe regions,making the experimental observation of nonlinear inverse Compton scattering at lower intensities more likely.展开更多
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
文摘Rowlands et al.1present an analysis of accelerometer data from the UK Biobank cohort,examining variations in the duration,intensity,and accumulation of moderate-intensity physical activity(MPA)and vigorous-intensity physical activity(VPA)sufficient to reduce the risk of all-cause mortality.In this study,the authors questioned if shorter durations(i.e.,1,2,3,4,5,10,15,and 20 min/day)of MPA and VPA performed continuously or accumulated throughout the day would equally reduce the risks of all-cause mortality as longer duration MPA and VPA recommended in the physical activity(PA)guidelines.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金funded by the Deanship of Scientific Research and Libraries at Princess Nourah bint Abdulrahman University,through the“Nafea”Program,Grant No.(NP-45-082).
文摘Sustainable energy systems will entail a change in the carbon intensity projections,which should be carried out in a proper manner to facilitate the smooth running of the grid and reduce greenhouse emissions.The present article outlines the TransCarbonNet,a novel hybrid deep learning framework with self-attention characteristics added to the bidirectional Long Short-Term Memory(Bi-LSTM)network to forecast the carbon intensity of the grid several days.The proposed temporal fusion model not only learns the local temporal interactions but also the long-term patterns of the carbon emission data;hence,it is able to give suitable forecasts over a period of seven days.TransCarbonNet takes advantage of a multi-head self-attention element to identify significant temporal connections,which means the Bi-LSTM element calculates sequential dependencies in both directions.Massive tests on two actual data sets indicate much improved results in comparison with the existing results,with mean relative errors of 15.3 percent and 12.7 percent,respectively.The framework has given explicable weights of attention that reveal critical periods that influence carbon intensity alterations,and informed decisions on the management of carbon sustainability.The effectiveness of the proposed solution has been validated in numerous cases of operations,and TransCarbonNet is established to be an effective tool when it comes to carbon-friendly optimization of the grid.
文摘Energy poverty in developing countries is a critical issue characterized by the lack of access to modern energy services,such as electricity and clean cooking facilities,as marked in SDG 7.This study explores the correlations between energy poverty,energy intensity,resource abundance,and income inequality,as these factors have been theorized to play important roles in influencing energy poverty in developing countries.By observing that the dataset is heterogeneous across the countries and over the time frame,we use the Method of Moments Quantile Regression(MMQR)to analyze our developing countries’data from 2000 to 2019.Our findings indicate that energy intensity is a significant factor influencing energy poverty,suggesting that higher energy consumption relative to the sample countries can exacerbate this issue.Additionally,we observe that income inequality within the sample countries is a critical determinant of energy poverty levels,highlighting the dynamics between economic disparity and access to energy resources.Interestingly,our study reveals that resource abundance acts as a blessing rather than a curse in terms of energy poverty,implying that countries rich in natural resources may have better opportunities to combat energy deprivation.Finally,we emphasize the vital role of financial markets in addressing energy poverty on a global scale,suggesting that robust financial systems can facilitate investments and innovations aimed at improving energy access for vulnerable populations.The results from the robustness analysis support the empirical results obtained from the main estimation.The empirical findings of the present study advance important comprehensions for policymakers to adopt energy policies that address the complex challenges of energy poverty and promote inclusive energy access.
基金National Natural Science Foundation of China under Grant No.U2239252National Natural Science Foundation of China under Grant No.52279128Natural Science Foundation of Heilongjiang Province of China under Grant No.YQ2022E013。
文摘Dispatched by the Chinese government,a multidisciplinary team of 30 researchers collaborated with a team from Myanmar to conduct a 14-day on-site investigation.The work encompassed seismic intensity assessments,field surveys,and loss evaluations.The paper focuses on the intensity distribution and structural damage characteristics of the 2025 M7.9 Myanmar earthquake,yielding the following key findings.(1)The seismogenic fault rupture propagated in a nearly N-S direction,with a surface rupture length of approximately 450 km.The seismic impact zone exhibited an elongated N-S distribution and a shorter E-W span,distributed like a belt around the seismogenic fault.(2)Within the seismic impact zones,existing buildings comprised five primary structural types,with timber(bamboo)structures constituting the largest proportion(≈80%in rural areas,≈50%in urban areas).The relatively low disaster losses and casualties were primarily attributable to the good seismic performance and low damage ratio of timber(bamboo)structures across varying intensity zones.(3)An anomalous zone of intensityⅨwas located at the boundary between intensityⅥandⅦregions in Nay Pyi Taw.Here,ridge topography combined with soft soil layers significantly amplified ground motion,exacerbating structural damage.(4)Directional effects of ground motion were observed,with the structural damage phenomena and peak ground acceleration(PGA)values in the N-S direction exceeding those in the E-W direction.This validates that the maximum PGA distribution of strike-slip fault earthquakes aligns with the fault strike.The research is expected to provide technical support for post-disaster reconstruction planning,site selection,and disaster mitigation strategies in Myanmar.
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2023C01National Natural Science Foundation of China under Grant No.52478570Distinguished Young Scholars Program of the Natural Science Foundation of Heilongjiang Province,China under Grant No.JQ2024E002。
文摘The 2025 M_(w)7.7 Myanmar earthquake highlighted the challenge of near-fault seismic intensity field reconstruction due to sparse seismic networks.To address this limitation,a framework was proposed integrating seismic wave simulation with a data-constrained finite-fault rupture model.The constraint is implemented by identifying the optimal ground motion models(GMMs)through a scoring system that selects the best-fit GMMs to mid-and far-field China Earthquake Networks Center(CENC)seismic network data;and applying the optimal GMMs to refine the rupture model parameters for near-fault intensity field simulation.The simulated near-fault seismic intensity field reproduces seismic intensities collected from Myanmar’s sparse seismic network and concentrated in≥Ⅷintensity zones within 50 km of the projected fault plane;and identifies abnormal intensity regions exhibiting≥Ⅹintensity along the Meiktila-Naypyidaw corridor and near Shwebo that are attributed to soft soil amplification effects and near-fault directivity.This framework can also be applied to post-earthquake assessments in other similar regions.
基金Scientific Research Fund of Institute of Engineering Mechanics,China Earthquake Administration under Grant No.2024B08。
文摘One of the primary tasks of earthquake early warning(EEW)systems is to predict potential earthquake damage rapidly and accurately.Cumulative absolute velocity(CAV),Arias intensity(I_(A)),and spectrum intensity(SI)are important parameters for measuring ground motion intensity and assessing earthquake damage.Due to the limited available information in EEW,CAV,I_(A),and SI cannot be accurately predicted using traditional EEW methods.In this paper,we propose an end-to-end deep learning-based Ground motion Intensity prediction Network(ENGINet)for on-site EEW.The aim of the ENGINet is to predict CAV,I_(A),and SI rapidly and reliably.ENGINet is based on a convolutional neural network and recurrent neural network.The inputs of the network are three-component acceleration records,three-component velocity records,and three-component displacement records obtained by a single station.The results from the test dataset show that at 3 s after the P-wave arrival,compared with the baseline models and other traditional methods,ENGINet has better performance in predicting CAV,I_(A),and SI.Our results indicate that ENGINet can quickly and accurately predict CAV,I_(A),and SI to some extent and has good potential in EEW efforts.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42075138 and 42375147)the Program on Key Basic Research Project of Jiangsu(Grant No.BE2023829)。
文摘Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.
基金supported by the National Grand Instrument Project(Grant No.2019YFF01014402)the National Key Research and Development Program of China(Grant No.2024YFF0726304)+2 种基金the Guangdong High Level Innovation Research Institute(Grant No.2021B0909050006)the National Natural Science Foundation of China(Grant No.12205008)W.Ma acknowledges support from the National Science Fund for Distinguished Young Scholars(Grant No.12225501)。
文摘Ultra-intense electromagnetic fields exceeding 10^(23)W∕cm^(2)are enabling breakthroughs in compact laser-driven particle accelerators and revealing new quantum electrodynamics(QED)phenomena.However,conventional laser-focusing methods face considerable engineering challenges and require substantial costs.Focusing schemes utilizing plasma optics can produce sub-micrometer focus spots beyond the diffraction limit and substantially enhance the peak intensity;however,owing to significant energy dissipation,they may fail to simultaneously increase the laser fluence.To address these challenges,we propose a focusing scheme employing a near-critical-density hollow plasma fiber(HPF)that utilizes graded refractive index dynamics to boost both laser peak intensity and fluence at the same time.Three-dimensional particle-in-cell simulations demonstrate the HPF’s capability to focus a 4.5-μm-diameter Gaussian beam to a sub-diffraction-limited 0.6-μm-diameter spot.The peak intensity and laser fluence can be enhanced by factors of 22 and 10,respectively,marking a substantial improvement over existing plasma-based focusing schemes.Furthermore,the proposed scheme exhibits wide-range parameter adaptation and high robustness,making it suitable for direct implementation in PW-class ultra-intense laser experiments.
文摘High-Intensity Interval Training(HIIT)has gained prominence as a time-efficient and effective exercise modality to improve cardiovascular(CV)fitness,metabolic health,and physical performance.Therefore,our aim was to synthesize current clinical research on the effects of HIIT on the Autonomic Nervous System.We conducted the search for studies in the Directory of Open Access Journals,Embase,Virtual Health Library,Pubmed,and Scielo databases,in January of 2024.We included a total of 20 studies in our review.This literature review highlights the potential of HIIT to modulate the Autonomic Nervous System,enhancing CV function and overall health.Despite the promising findings,the interpretation of the results is tempered by the variability in study designs,populations,and methodologies.Future research should address these limitations,aiming for a more nuanced understanding of the relationship between HIIT and Autonomic Nervous System function.The review indicates that standardized protocols need to consider individual characteristics and baseline autonomic states for clinical application.As the body of evidence grows,HIIT may emerge as a cornerstone of exercise prescriptions aimed at optimizing autonomic function and promoting CV health.
基金supported in part by a Grant-in-Aid for Scientific Research(C)(no.24K14417)from the Japan Society for the Promotion of Sciences(JSPS)KAKENHI.
文摘We investigated the effects of high-intensity intermittent cross-training(HIICT)on maximal oxygen uptake(VO_(2)max).The HIICT consisted of alternating intermittent 20-s treadmill running(1^(st),3^(rd),5^(th),and 7^(th) bouts)and 20-s bicycle exercise(2^(nd),4^(th),and 6^(th) bouts)with a 10-s rest period.Each intensity for running and bicycling of the HIICT corresponded to an oxygen demand of~160% and~170%of the VO_(2)max,respectively.Fifteen healthy young males(aged[24±1]yrs)were randomly assigned to training(TG,n?8)and non-training control(CG,n?7)groups.The TG completed this HIICT daily 4 days/week for 6 weeks.Significant group×time interactions were observed for both the running and bicycling VO_(2)max(p<0.001 each).After the training,the VO_(2)max for both running([57.4±4.8]mL·kg^(-1)·min^(-1))and bicycling([50.6±3.7]mL·kg^(-1)·min^(-1))in the TG were significantly higher than those for running([50.1±3.1]mL·kg^(-1)·min^(-1))and bicycling([43.7±3.6]mL·kg^(-1)·min^(-1))in the CG,respectively(p<0.01 each).Post-hoc tests revealed a significant increase in VO_(2)max for running and bicycling in the TG after the HIICT(p<0.001 each)but no significant difference in the CG.These results demonstrated that the newly developed HIICT increases the VO_(2)max for both running and bicycling.
基金supported in part by the National Natural Science Foundation of China(62067003,61967010,62262030)Key Project of Science and Technology Research of Education Department of Jiangxi Province(GJJ210309)Jiangxi Provincial Natural Science Foundation Youth Foundation(2024BAB20047).
文摘Positive emotional experiences can improve learning efficiency and cognitive ability,stimulate students’interest in learning,and improve teacher-student relationships.However,positive emotions in the classroom are primarily identified through teachers’observations and postclass questionnaires or interviews.The expression intensity of students,which is extremely important for fine-grained emotion analysis,is not considered.Hence,a novel method based on smile intensity estimation using sequence-relative key-frame labeling is presented.This method aims to recognize the positive emotion levels of a student in an end-to-end framework.First,the intensity label is generated robustly for each frame in the expression sequence based on the relative key frames to address the lack of annotations for smile intensity.Then,a deep-asymmetric convolutional neural network learns the expression model through dual neural networks,to enhance the stability of the network model and avoid the extreme attention region learned.Further,dual neural networks and the dual attention mechanism are integrated using the intensity label based on the relative key frames as the supervised information.Thus,diverse features are effectively extracted and subtle appearance differences between different smiles are perceived based on different perspectives.Finally,comparative experiments for the convergence speed,model-training parameters,confusion matrix,and classification probability are performed.The proposed method was applied to a real classroom scene to analyze the emotions of students.Numerous experiments validated that the proposed method is promising for analyzing the differences in the positive emotion of students while learning in a classroom.
基金support from the National Natural Science Foundation of China(62250710162).
文摘In the implementation of quantum key distribution,Security certification is a prerequisite for social deployment.Trans-mitters in decoy-BB84 systems typically employ gain-switched semiconductor lasers(GSSLs)to generate optical pulses for encod-ing quantum information.However,the working state of the laser may violate the assumption of pulse independence.Here,we explored the dependence of intensity fluctuation and high-order correlation distribution of optical pulses on driving cur-rents at 2.5 GHz.We found the intensity correlation distribution had a significant dependence on the driving currents,which would affect the final key rate.By utilizing rate equations in our simulation,we confirmed the fluctuation and correlation origi-nated from the instability of gain-switched laser driven at a GHz-repetitive frequency.Finally,we evaluated the impact of inten-sity fluctuation on the secure key rate.This work will provide valuable insights for assessing whether the transmitter is operat-ing at optimal state in practice.
基金Research funding for this project was provided by the National Natural Science Foundation of China(Grant Nos.42192563 and 42120104001)the Hong Kong RGC General Research Fund(Grant No.11300920)+1 种基金Anhui Provincial Natural Science Foundation(Grant Nos.2208085UQ12,2308085US01)Anhui&Huaihe River Institute of Hydraulic Research(Grant Nos.KJGG202201,KY202306)。
文摘Tropical cyclone(TC)intensity estimation is a fundamental aspect of TC monitoring and forecasting.Deep learning models have recently been employed to estimate TC intensity from satellite images and yield precise results.This work proposes the ViT-TC model based on the Vision Transformer(ViT)architecture.Satellite images of TCs,including infrared(IR),water vapor(WV),and passive microwave(PMW),are used as inputs for intensity estimation.Experiments indicate that combining IR,WV,and PMW as inputs yields more accurate estimations than other channel combinations.The ensemble mean technique is applied to enhance the model's estimations,reducing the root-mean-square error to 9.32 kt(knots,1 kt≈0.51 m s^(-1))and the mean absolute error to 6.49 kt,which outperforms traditional methods and is comparable to existing deep learning models.The model assigns high attention weights to areas with high PMW,indicating that PMW magnitude is essential information for the model's estimation.The model also allocates significance to the cloud-cover region,suggesting that the model utilizes the whole TC cloud structure and TC eye to determine TC intensity.
基金financial support from the Industrial Prospect and Key Core Technology Projects of Jiangsu Provincial Key R&D Program(Grant No.BE2022071)the Natural Science Foundation of Jiangsu Province(Grant No.BK20192001)+1 种基金the National Natural Science Foundation of China(Grant No.12074194)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(Grant No.KYCX220954)。
文摘Reference-frame-independent quantum key distribution(RFI-QKD)can avoid real-time calibration operation of reference frames and improve the efficiency of the communication process.However,due to imperfections of optical devices,there will inevitably exist intensity fluctuations in the source side of the QKD system,which will affect the final secure key rate.To reduce the influence of intensity fluctuations,an improved 3-intensity RFI-QKD scheme is proposed in this paper.After considering statistical fluctuations and implementing global parameter optimization,we conduct corresponding simulation analysis.The results show that our present work can present both higher key rate and a farther transmission distance than the standard method.
基金funded by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (BRC)the Applied Research Collaborations East Midlands (ARC-EM)supported by a UKRI project grant (MR/T031816/1)。
文摘Background There is a lack of research examining the interplay between objectively measured physical activity volume and intensity with life expectancy.The purpose of the study was to investigate the interplay between objectively measured PA volume and intensity profiles with modeled life expectancy in women and men within the UK Biobank cohort study and interpret findings in relation to brisk walking.Methods Individuals from UK Biobank with wrist-worn accelerometer data were included.The average acceleration and intensity gradient were extracted to describe the physical activity volume and intensity profile.Mortality data were obtained from national registries.Adjusted life expectancies were estimated using parametric flexible survival models.Results 40,953(57.1%)women(median age=61.9 years)and 30,820(42.9%)men(63.1 years)were included.Over a median follow-up of 6.9 years,there were 1719(2.4%)deaths(733 in women;986 in men).At 60 years,life expectancy was progressively longer for higher physical activity volume and intensity profiles,reaching 95.6 years in women and 94.5 years in men at the 90th centile for both volume and intensity,corresponding to 3.4 additional years(95%confidence interval(95%CI):2.4-4.4)in women and 4.6 additional years(95%CI:3.6-5.6)in men compared to those at the 10th centiles.An additional 10-min or 30-min daily brisk walk was associated with 0.9(95%CI:0.5-1.3)and 1.4 years(95%CI:0.9-1.9)longer life expectancy,respectively,in inactive women;and 1.4 years(95%CI:1.0-1.8)and 2.5(95%CI:1.9-3.1)in inactive men.Conclusion Higher physical activity volumes were associated with longer life expectancy,with a higher physical activity intensity profile further adding to a longer life.Adding as little as a 10-min brisk walk to daily activity patterns may result in a meaningful benefit to life expectancy.
基金funded by the Research and Development Fund Project of China Academy of Railway Science Group Co.,Ltd.,(No:2023YJ259)the Science and Technology Research and Development Program Project of China State Railway Group Co.,Ltd.(No:J2024G008).
文摘Purpose–This research aims to monitor seismic intensity along railway lines,study methods for calculating the extent of earthquake impact on railways and address practical challenges in estimating intensity distribution along railway routes,thereby achieving graded post-earthquake response measures.Design/methodology/approach–The seismic intensity monitoring system for railways adopts a two-level architecture,namely the seismic intensity monitoring equipment and the seismic intensity rapid reporting information center processing platform.The platform obtains measured instrumental intensity through the seismic intensity monitoring equipment deployed along railways and combines it with the National Seismic Network Earthquake Catalog to generate real-time railway seismic intensity distribution maps using the Kriging interpolation algorithm.A calculation method for railway seismic impact intervals is designed to calculate the mileage intervals where the intensity area corresponding to each contour line in the seismic intensity distribution map intersects with the railway line.Findings–The system was deployed for practical earthquake monitoring demonstration applications on the Nanjiang Railway Line in Xinjiang.During the operational period,the seismic intensity monitoring equipment calculated and uploaded instrumental intensity values to the seismic intensity rapid reporting information center processing platform a total of nine times.Among these,earthquakes triggering the Kriging interpolation algorithm occurred twice.The system operated stably throughout the application period and successfully visualized relevant seismic impact data,such as earthquake intensity distribution maps and affected railway mileage sections.These results validate the system’s practicality and effectiveness.Originality/value–The seismic intensity monitoring for the railway system designed in this study can integrate the measured instrumental intensity data along railways and the earthquake catalog of the National Seismic Network.It uses the Kriging interpolation method to calculate the intensity distribution and determine the seismic impact scope,thereby addressing the issue that the seismic intensity distribution calculated by traditional attenuation formulas deviates from reality.The system can provide clear graded interval recommendations for post-earthquake disposal,effectively improve the efficiency of post-earthquake recovery and inspection and offer a decision-making basis for restoring railway operations quickly.
基金supported by the Guangdong High Level Innovation Research Institute(Grant No.2021B0909050006)the National Grand Instrument Project(Grant No.2019YFF01014402)+1 种基金the National Natural Science Foundation of China(Grant No.12205008)support from the National Science Fund for Distinguished Young Scholars(Grant No.12225501)。
文摘Experimental validation of laser intensity is particularly important for the study of fundamental physics at extremely high intensities.However,reliable diagnosis of the focal spot and peak intensity faces huge challenges.In this work,we demonstrate for the firs time that the coherent radiation farfiel patterns from laser–foil interactions can serve as an in situ,real-time,and easy-to-implement diagnostic for an ultraintense laser focus.The laser-driven electron sheets,curved by the spatially varying laser fiel and leaving the targets at nearly the speed of light,produce doughnut-shaped patterns depending on the shapes of the focal spot and the absolute laser intensities.Assisted by particle-in-cell simulations,we can achieve measurements of the intensity and the focal spot,and provide immediate feedback to optimize the focal spots for extremely high intensity.
基金the Science and Technology Facilities Council and the Engineering and Physical Sciences Research Council for their funding towards this project,and the continued support of research into QED laser–plasma physicsfunding fromthe Engineering and Physical Sciences Research Council[EP/S022430/1]。
文摘Ultraintense laser–plasma experiments generate a variety of high-energy radiations,including nonlinear inverse Compton scattered(NCS)X-rays,which are expected to be a key experimental observable as we transition into the quantum electrodynamic plasma regime.However,there is also a high bremsstrahlung X-ray background that reduces our ability to observe NCS X-rays.Previous numerical studies comparing NCS and bremsstrahlung emissions fail to capture the full temporal emission of both processes.We present for the first time two-dimensional particle-in-cell(PIC)and three-dimensional hybrid-PIC EPOCH simulations that capture up to 150 ps of the laser–plasma interaction and directly compare the NCS and bremsstrahlung emissions for a plastic target for intensities of 10^(20)-10^(23)W/cm^(2).We present angular distribution plots where the NCS emission is seen to dominate at intensities greater than 5×10^(21)W/cm^(2)and the target design is seen to successfully divert the bremsstrahlung signal away from the NCS lobe regions,making the experimental observation of nonlinear inverse Compton scattering at lower intensities more likely.
