To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-r...To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.展开更多
Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,espe...Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.展开更多
Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,su...Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.展开更多
Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit signifi...Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.展开更多
Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experime...Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“I...This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“International Service Trade Statistics Manual”(MSITS 2002)established the international balance of payments statistics paradigm.The revised MSITS 2010 in 2010 introduced the expanded balance of payments service classification(EBOPS 2010),incorporating foreign affiliate service trade statistics(FATS),and constructing a comprehensive statistics system.The update of MSITS 2010 originated from changes in the global economic environment,technological progress leading to diversified forms of service trade,and the demands of international service trade negotiations.This standard has constructed a multi-level classification system.Since the release of MSITS 2010,many countries have implemented the new statistical framework,but some developing countries face challenges.International organizations and developed countries have provided corresponding support for service trade statistics standards.展开更多
Artificial intelligence(AI)is rapidly transforming healthcare and medical education.Strong statistical thinking skills are vital for evaluating and applying AI tools.However,traditional medical statistics education ha...Artificial intelligence(AI)is rapidly transforming healthcare and medical education.Strong statistical thinking skills are vital for evaluating and applying AI tools.However,traditional medical statistics education has not adapted to this demand.This paper first analyzes the connotation and importance of statistical thinking,points out the significant challenges currently faced by medical statistics education,and then proposes strategies such as innovative teaching methods combined with evidence-based medicine,utilizing AI platforms for supplemental teaching,multidisciplinary integration,and strengthening the understanding of the statistical foundations of AI to enhance the statistical thinking abilities of medical professionals.This study emphasizes the importance of cultivating medical statistical thinking in the era of AI to improve the quality of medical education and ensure the safety and effectiveness of future medical services.展开更多
This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were ide...This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.展开更多
Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex ...Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex web pages.Through ana-lyzing various statistical characteristics of HTML el-ements in web documents,this paper proposes,based on statistical features,an unsupervised web data ex-traction method—traversing the HTML DOM parse tree at first,calculating and generating the statistical matrix of the elements,and then locating data records by clustering method and heuristic rules that reveal in-herent links between the visual characteristics of the data recording areas and the statistical characteristics of the HTML nodes—which is both suitable for data records extraction of single-page and multi-pages,and it has strong generality and needs no training.The ex-periments show that the accuracy and efficiency of this method are equally better than the current data extrac-tion method.展开更多
Multi-angle statistical analysis of tropical cyclones(TCs)and their distant thermodynamic disturbances over Northwest Pacific from July to September during 2001-2020 was conducted.The results show that TCs could trigg...Multi-angle statistical analysis of tropical cyclones(TCs)and their distant thermodynamic disturbances over Northwest Pacific from July to September during 2001-2020 was conducted.The results show that TCs could trigger distant thermodynamic disturbances,which mainly caused an increase in air pressure and a rise in temperature in northern China.The distant thermodynamic disturbances triggered by TCs differed in spatial distribution and intensity in different months.In the same month,the spatial distribution of such disturbances triggered by high-intensity TCs was consistent with the overall pattern,and there was a significant increase in intensity and area.From the probability of TC activities and the significance test of variance of analysis under different levels of P-J index,it is found that TC activities could stimulate the increase of P-J teleconnection index.There was a significant positive correlation between them,which was accompanied by a step effect.展开更多
In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions...In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.展开更多
Extratropical transition(ET)is one of the last phases of tropical cyclones(TCs)and corresponds to the structural change from a tropical system to an extratropical system characterized by pronounced asymmetric distribu...Extratropical transition(ET)is one of the last phases of tropical cyclones(TCs)and corresponds to the structural change from a tropical system to an extratropical system characterized by pronounced asymmetric distributions of heavy rainfall and strong wind.This study analyzes the statistical characteristics of ET events involving TCs over the western North Pacific(WNP)during 1981–2022.The analysis employs the Cyclone Phase Space(CPS)method to evaluate the accuracy of the fifth-generation reanalysis from the European Centre for Medium-Range Weather Forecasts(ERA5)in identifying ET based on different TC center definitions.Results show that defining the TC center by the minimum sea level pressure yields the most accurate ET identification.Subsequently,the study investigates several characteristics of ET events in the WNP.It is found that TCs undergoing ET(ETTCs)primarily form in the region of 125°–155°E,10°–25°N,with ET typically initiating between 30°–40°N and completing between 35°–50°N.These ETTCs predominantly occur from April to December,with peak activity observed from August to October.Additionally,the average duration of the ET process is 18.5 h,with longer durations observed from August to October,displaying a roughly 6-year cycle.Spatially,ET events with longer durations tend to occur at lower latitudes.Correspondingly,TCs initiating their ET phase at lower latitudes are typically stronger and larger,and they also experience longer ET durations.展开更多
This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenari...This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.展开更多
Reconfigurable intelligent surface(RIS)is a promising candidate technology of the upcoming Sixth Generation(6G)communication system for its ability to provide unprecedented spectral and energy efficiency increment thr...Reconfigurable intelligent surface(RIS)is a promising candidate technology of the upcoming Sixth Generation(6G)communication system for its ability to provide unprecedented spectral and energy efficiency increment through passive beamforming.However,it is challenging to obtain instantaneous channel state information(I-CSI)for RIS,which obliges us to use statistical channel state information(S-CSI)to achieve passive beamforming.In this paper,RIS-aided multiple-input single-output(MISO)multi-user downlink communication system with correlated channels is investigated.Then,we formulate the problem of joint beamforming design at the AP and RIS to maximize the sum ergodic spectral efficiency(ESE)of all users to improve the network capacity.Since it is too hard to compute sum ESE,an ESE approximation is adopted to reformulate the problem into a more tractable form.Then,we present two joint beamforming algorithms,namely the singular value decomposition-gradient descent(SVD-GD)algorithm and the fractional programming-gradient descent(FP-GD)algorithm.Simulation results show the effectiveness of our proposed algorithms and validate that 2-bits quantizer is enough for RIS phase shifts implementation.展开更多
Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome th...Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.展开更多
To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The...To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The PID control strategy is combined with the difference in data variation to realize the dynamic adjustment of the data publishing intervals.The spatial-temporal correlations of the adjacent snapshots are utilized to design the grid clustering and adjustment algorithm,which facilitates saving the execution time of the publishing process.The budget distribution and budget absorption strategies are improved to form the sliding window-based differential privacy statistical publishing algorithm,which realizes continuous statistical publishing and privacy protection and improves the accuracy of published data.Experiments and analysis on large datasets of actual locations show that the privacy protection algorithm proposed in this paper is superior to other existing algorithms in terms of the accuracy of adaptive sampling time,the availability of published data,and the execution efficiency of data publishing methods.展开更多
We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via...We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via HEASOFT),we extracted light curves for each observational ID and for their aggregation.Countrate histograms were fitted using various statistical distributions;fit quality was assessed by chi-squared and the Bayesian Information Criterion.The first observational segment is best described by a Gaussian distribution(χ^(2)=68.4;BIC=7651.2),and the second by a Poisson distribution(χ^(2)=33.5;BIC=4413.3).When all segments are combined,the lognormal model provides the superior fit(χ^(2)=541.9;BIC=34365.5),indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process.These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics,the collective emission profile across multiple observations is better captured by a lognormal distribution,consistent with complex,compounded variability in GRB afterglows.展开更多
Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In ...Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In this work,we develop Atif-V2.0,an extended classical density functional theory(cDFT)framework that integrates the interfacial statistical associating fluid theory(iSAFT)to model multicomponent associating fluids composed of water-soluble polymers,alkanes,and water.Building on the original theoretical framework of Atif for modeling nanoconfined inhomogeneous fluids,Atif-V2.0 embeds explicit solvent and captures additional physical interactions-hydrogen bonding,which are critical in associating fluid systems.The other key feature of Atif-V2.0 is its ability to account for polymer topology.We demonstrate its capability by predicting the equilibrium structure and thermodynamic behavior of branched hydrolyzed polyacrylamide solutions near hard walls with various branching topologies,which provides a robust theoretical tool for the rational design of EOR polymers.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.52339001).
文摘To investigate the damage evolution caused by stress-driven and sub-critical crack propagation within the Beishan granite under multi-creep triaxial compressive conditions,the distributed optical fiber sensing and X-ray computed tomography were combined to obtain the strain distribution over the sample surface and internal fractures of the samples.The Gini and skewness(G-S)coefficients were used to quantify strain localization during tests,where the Gini coefficient reflects the degree of clustering of elements with high strain values,i.e.,strain localization/delocalization.The strain localization-induced asymmetry of data distribution is quantified by the skewness coefficient.A precursor to granite failure is defined by the rapid and simultaneous increase of the G-S coefficients,which are calculated from strain increment,giving an earlier warning of failure by about 8%peak stress than those from absolute strain values.Moreover,the process of damage accumulation due to stress-driven crack propagation in Beishan granite is different at various confining pressures as the stress exceeds the crack initiation stress.Concretely,strain localization is continuous until brittle failure at higher confining pressure,while both strain localization and delocalization occur at lower confining pressure.Despite the different stress conditions,a similar statistical characteristic of strain localization during the creep stage is observed.The Gini coefficient increases,and the skewness coefficient decreases slightly as the creep stress is below 95%peak stress.When the accelerated strain localization begins,the Gini and skewness coefficients increase rapidly and simultaneously.
基金funded by Vietnam National University Ho Chi Minh City(VNU-HCM)under grant number DS.C2025-28-06.
文摘Vaginal delivery is a fascinating physiological process,but also a high-risk process.Up to 85%–90%of vaginal deliveries lead to perineal trauma,with nearly 11%of severe perineal tearing.It is a common occurrence,especially for first-time mothers.Computational childbirth plays an essential role in the prediction and prevention of these traumas,but fast personalization of the pelvis and floor muscles is challenging due to their anatomical complexity.This study introduces a novel shape-prediction-based personalization of the pelvis and floor muscles for perineal tearing management and childbirth simulation.300 subjects were selected from public Computed Tomography(CT)databases.The pelvic bone nmjmeshes were generated using a coarse-to-fine non-rigid mesh alignment procedure.The floor muscle meshes were personalized using the bone mesh deformation information.A feature-to-pelvic structure reconstruction pipeline was proposed,incorporating various strategies.Ten-fold cross-validation helped determine the optimal reconstruction strategy,regression method,and feature sizes.The mesh-to-mesh distance metric was employed for evaluating.The statistical shape relation-based strategy,coupled with multi-output ridge regression,was the optimal approach for pelvic structure reconstruction.With a feature set ranging from 3 to 38,the mean errors were 2.672 to 1.613 mm,and 3.237 to 1.415 mm in muscle attachment regions.The best-and worst-case predictions had errors of 1.227±0.959 mm and 2.900±2.309 mm,respectively.This study provides a novel approach to achieving fast personalized childbirth modeling and simulation for perineal tearing management.
基金funded through India Meteorological Department,New Delhi,India under the Forecasting Agricultural output using Space,Agrometeorol ogy and Land based observations(FASAL)project and fund number:No.ASC/FASAL/KT-11/01/HQ-2010.
文摘Background Cotton is one of the most important commercial crops after food crops,especially in countries like India,where it’s grown extensively under rainfed conditions.Because of its usage in multiple industries,such as textile,medicine,and automobile industries,it has greater commercial importance.The crop’s performance is greatly influenced by prevailing weather dynamics.As climate changes,assessing how weather changes affect crop performance is essential.Among various techniques that are available,crop models are the most effective and widely used tools for predicting yields.Results This study compares statistical and machine learning models to assess their ability to predict cotton yield across major producing districts of Karnataka,India,utilizing a long-term dataset spanning from 1990 to 2023 that includes yield and weather factors.The artificial neural networks(ANNs)performed superiorly with acceptable yield deviations ranging within±10%during both vegetative stage(F1)and mid stage(F2)for cotton.The model evaluation metrics such as root mean square error(RMSE),normalized root mean square error(nRMSE),and modelling efficiency(EF)were also within the acceptance limits in most districts.Furthermore,the tested ANN model was used to assess the importance of the dominant weather factors influencing crop yield in each district.Specifically,the use of morning relative humidity as an individual parameter and its interaction with maximum and minimum tempera-ture had a major influence on cotton yield in most of the yield predicted districts.These differences highlighted the differential interactions of weather factors in each district for cotton yield formation,highlighting individual response of each weather factor under different soils and management conditions over the major cotton growing districts of Karnataka.Conclusions Compared with statistical models,machine learning models such as ANNs proved higher efficiency in forecasting the cotton yield due to their ability to consider the interactive effects of weather factors on yield forma-tion at different growth stages.This highlights the best suitability of ANNs for yield forecasting in rainfed conditions and for the study on relative impacts of weather factors on yield.Thus,the study aims to provide valuable insights to support stakeholders in planning effective crop management strategies and formulating relevant policies.
基金Supported by the Laoshan Laboratory(No.LSKJ 202202404)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB 42000000)+1 种基金the National Natural Science Foundation of China(NSFC)(No.42030410)the Startup Foundation for Introducing Talent of NUIST,and the Jiangsu Innovation Research Group(No.JSSCTD 202346)。
文摘Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation(ENSO)phenomenon in the tropical Pacific.Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO,underscoring the need for alternative modeling approaches.The Regional Ocean Modeling System(ROMS)is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models.However,its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored.For the first time,this study presents the development of a basin-scale hybrid coupled model(HCM)for the tropical Pacific,integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature(SST)and wind stress anomalies.The HCM is evaluated for its capability to simulate the annual mean,seasonal,and interannual variations of the oceanic state in the tropical Pacific.Results demonstrate that the model effectively reproduces the ENSO cycle,with a dominant oscillation period of approximately two years.The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.
基金National Key Research and Development Program of China (No.2021YFC3100800)the National Natural Science Foundation of China (Nos.42407235 and 42271026)+1 种基金the Project of Sanya Yazhou Bay Science and Technology City (No.SCKJ-JYRC-2023-54)supported by the Hefei advanced computing center
文摘Coral reef limestone(CRL)constitutes a distinctive marine carbonate formation with complex mechanical properties.This study investigates the multiscale damage and fracture mechanisms of CRL through integrated experimental testing,digital core technology,and theoretical modelling.Two CRL types with contrasting mesostructures were characterized across three scales.Macroscopically,CRL-I and CRL-II exhibited mean compressive strengths of 8.46 and 5.17 MPa,respectively.Mesoscopically,CRL-I featured small-scale highly interconnected pores,whilst CRL-II developed larger stratified pores with diminished connectivity.Microscopically,both CRL matrices demonstrated remarkable similarity in mineral composition and mechanical properties.A novel voxel average-based digital core scaling methodology was developed to facilitate numerical simulation of cross-scale damage processes,revealing network-progressive failure in CRL-I versus directional-brittle failure in CRL-II.Furthermore,a damage statistical constitutive model based on digital core technology and mesoscopic homogenisation theory established quantitative relationships between microelement strength distribution and macroscopic mechanical behavior.These findings illuminate the fundamental mechanisms through which mesoscopic structure governs the macroscopic mechanical properties of CRL.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
文摘Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
基金The interim results of the postdoctoral research project“Research on the Evolution Process of Shenzhen’s High-tech Industry Policies(1980-2022)”(Project Number:6023271023S)at the end of the postdoctoral period of Shenzhen Polytechnic University.Institute of Economic and Social Development(Phase III)Project:6025310002Q.
文摘This article focuses on the development of the international service trade statistics system.The 1994 General Agreement on Trade in Services(GATS)provided a institutional basis for service trade statistics.The 2002“International Service Trade Statistics Manual”(MSITS 2002)established the international balance of payments statistics paradigm.The revised MSITS 2010 in 2010 introduced the expanded balance of payments service classification(EBOPS 2010),incorporating foreign affiliate service trade statistics(FATS),and constructing a comprehensive statistics system.The update of MSITS 2010 originated from changes in the global economic environment,technological progress leading to diversified forms of service trade,and the demands of international service trade negotiations.This standard has constructed a multi-level classification system.Since the release of MSITS 2010,many countries have implemented the new statistical framework,but some developing countries face challenges.International organizations and developed countries have provided corresponding support for service trade statistics standards.
基金supported by the Doctoral Research Start-up Project of Wannan Medical College(WYRCQD2024019)the Quality Engineering Project of Anhui Provincial Department of Education-the“101 Project”(2023ylyjh046)the“Online and Offline Hybrid Course”Project(2020xsxxkc462).
文摘Artificial intelligence(AI)is rapidly transforming healthcare and medical education.Strong statistical thinking skills are vital for evaluating and applying AI tools.However,traditional medical statistics education has not adapted to this demand.This paper first analyzes the connotation and importance of statistical thinking,points out the significant challenges currently faced by medical statistics education,and then proposes strategies such as innovative teaching methods combined with evidence-based medicine,utilizing AI platforms for supplemental teaching,multidisciplinary integration,and strengthening the understanding of the statistical foundations of AI to enhance the statistical thinking abilities of medical professionals.This study emphasizes the importance of cultivating medical statistical thinking in the era of AI to improve the quality of medical education and ensure the safety and effectiveness of future medical services.
基金supported by the National Natural Science Foundation of China(Grants 42325404,42120104003,42204164,42474219 and U22A2006)the Chinese Meridian Project,the International Partnership Program of Chinese Academy of Sciences(Grant 183311KYSB20200003)+7 种基金Shandong Provincial Natural Science Foundation(Grants ZR2022QD077,ZR2022MD034)the Stable-Support Scientific Project of China Research Institute of Radiowave Propagation(Grant A132312191)the foundation of the National Key Laboratory of Electromagnetic Environment(Grant 6142403180204)the Chongqing Natural Science Foundation(Grants cstc2021ycjh-bgzxm0072,CSTB2023NSCQ-LZX0082)National Program on Key Basic Research Project(Grant 2022173-SD-1)The work in Norway is supported by the Research Council of Norway Grant 326039Work at UCLA has been supported by NSF grant AGS-2055192This research was supported by the International Space Science Institute(ISSI)in Bern and Beijing,through ISSI International Team project#511(Multi-Scale Magnetosphere-Ionosphere-Thermosphere Interaction).
文摘This paper is a statistical survey of Southern Hemisphere cold and hot polar cap patches,in relation to the interplanetary magnetic field(IMF)and ionospheric convection geometry.A total of 11,946 patch events were identified by Defense Meteorological Satellite Program(DMSP)F16 during the years 2011 to 2022.A temperature ratio of ion/electron temperature(T_(i)/T_(e))<0.68 is recommended to define a hot patch in the Southern Hemisphere,otherwise it is defined as a cold patch.The cold and hot patches have different dependencies on IMF clock angle,while their dependencies on IMF cone angle are similar.Both cold and hot patches appear most often on the duskside,and the distribution of cold patches gradually decreases from the dayside to the nightside,while hot patches have a higher occurrence rate near 14 and 21 magnetic local time(MLT).Moreover,we compared the key plasma characteristics of polar cap cold and hot patches in the Southern and Northern Hemispheres.The intensity of the duskside upward field-aligned current of patches in the Southern Hemisphere(SH)is stronger than that in the Northern Hemisphere(SH),which may be due to the discrepancy in conductivities between the two hemispheres,caused by the tilted dipole.In both hemispheres,the downward soft-electron energy flux of the dawnside patches is significantly greater than that of the duskside patches.
文摘Web data extraction has become a key technology for extracting valuable data from websites.At present,most extraction methods based on rule learning,visual pattern or tree matching have limited performance on complex web pages.Through ana-lyzing various statistical characteristics of HTML el-ements in web documents,this paper proposes,based on statistical features,an unsupervised web data ex-traction method—traversing the HTML DOM parse tree at first,calculating and generating the statistical matrix of the elements,and then locating data records by clustering method and heuristic rules that reveal in-herent links between the visual characteristics of the data recording areas and the statistical characteristics of the HTML nodes—which is both suitable for data records extraction of single-page and multi-pages,and it has strong generality and needs no training.The ex-periments show that the accuracy and efficiency of this method are equally better than the current data extrac-tion method.
基金the National Natural Science Foundation of China(42305011).
文摘Multi-angle statistical analysis of tropical cyclones(TCs)and their distant thermodynamic disturbances over Northwest Pacific from July to September during 2001-2020 was conducted.The results show that TCs could trigger distant thermodynamic disturbances,which mainly caused an increase in air pressure and a rise in temperature in northern China.The distant thermodynamic disturbances triggered by TCs differed in spatial distribution and intensity in different months.In the same month,the spatial distribution of such disturbances triggered by high-intensity TCs was consistent with the overall pattern,and there was a significant increase in intensity and area.From the probability of TC activities and the significance test of variance of analysis under different levels of P-J index,it is found that TC activities could stimulate the increase of P-J teleconnection index.There was a significant positive correlation between them,which was accompanied by a step effect.
基金supported by TUBITAK 112E568,114E092,and 115E915 projectsTNPGN-Active RINEX data set is available to the IONOLAB group for the TUBITAK 109E055 project。
文摘In this study,the gradients of Total Electron Content(TEC)for a midlatitude region are estimated and grouped with respect to the distance between neighboring stations,time periods within a day,and satellite directions.Annual medians of these gradients for quiet days are computed as templates.The metric distances(L2N)and Symmetric Kullback-Leibler Distances(SKLD)are obtained between the templates and the daily gradient series.The grouped histograms are fitted to the prospective Probability Density Functions(PDF).The method is applied to the Slant Total Electron Content(STEC)estimates from the Turkish National Permanent GPS Network(TNPGN-Active)for 2015.The highest gradients are observed in the east-west axis with a maximum of 25 mm/km during a geomagnetic storm.The maximum differences from the gradient templates occur for neighboring stations within100-130 km distance away from each other,during night hours,and for regions bordering the Black Sea and the Mediterranean in the northeast and southeast of Turkey.The empirical PDFs of the stationpair gradients are predominantly Weibull-distributed.The mean values of Weibull PDFs in all station groups are between 1.2 and 1.8 mm/km,with an increase during noon and afternoon hours.The standard deviations of the gradient PDFs generally increase during night hours.The algorithm will form a basis for quantifying the stochastic variations of the spatial rate of change of TEC trends in midlatitude regions,thus supplementing reliable and accurate regional monitoring of ionospheric variability.
基金Science and Technology Commission of Shanghai Municipality,China(23DZ1204703)。
文摘Extratropical transition(ET)is one of the last phases of tropical cyclones(TCs)and corresponds to the structural change from a tropical system to an extratropical system characterized by pronounced asymmetric distributions of heavy rainfall and strong wind.This study analyzes the statistical characteristics of ET events involving TCs over the western North Pacific(WNP)during 1981–2022.The analysis employs the Cyclone Phase Space(CPS)method to evaluate the accuracy of the fifth-generation reanalysis from the European Centre for Medium-Range Weather Forecasts(ERA5)in identifying ET based on different TC center definitions.Results show that defining the TC center by the minimum sea level pressure yields the most accurate ET identification.Subsequently,the study investigates several characteristics of ET events in the WNP.It is found that TCs undergoing ET(ETTCs)primarily form in the region of 125°–155°E,10°–25°N,with ET typically initiating between 30°–40°N and completing between 35°–50°N.These ETTCs predominantly occur from April to December,with peak activity observed from August to October.Additionally,the average duration of the ET process is 18.5 h,with longer durations observed from August to October,displaying a roughly 6-year cycle.Spatially,ET events with longer durations tend to occur at lower latitudes.Correspondingly,TCs initiating their ET phase at lower latitudes are typically stronger and larger,and they also experience longer ET durations.
文摘This study presents the results of a Monte Carlo simulation to compare the statistical power of Siegel-Tukey and Savage tests.The main purpose of the study is to evaluate the statistical power of both tests in scenarios involving Normal,Platykurtic and Skewed distributions over different sample sizes and standard deviation values.In the study,standard deviation ratios were set as 2,3,4,1/2,1/3 and 1/4 and power comparisons were made between small and large sample sizes.For equal sample sizes,small sample sizes of 5,8,10,12,16 and 20 and large sample sizes of 25,50,75 and 100 were used.For different sample sizes,the combinations of(4,16),(8,16),(10,20),(16,4),(16,8)and(20,10)small sample sizes and(10,30),(30,10),(50,75),(50,100),(75,50),(75,100),(100,50)and(100,75)large sample sizes were examined in detail.According to the findings,the power analysis under variance heterogeneity conditions shows that the Siegel-Tukey test has a higher statistical power than the other nonparametric Savage test at small and large sample sizes.In particular,the Siegel-Tukey test was reported to offer higher precision and power under variance heterogeneity,regardless of having equal or different sample sizes.
基金partially supported by the National Key Research and Development Project under Grant 2020YFB1806805Science and Technology on Communication Networks Laboratorysupported by China Scholarship Council.
文摘Reconfigurable intelligent surface(RIS)is a promising candidate technology of the upcoming Sixth Generation(6G)communication system for its ability to provide unprecedented spectral and energy efficiency increment through passive beamforming.However,it is challenging to obtain instantaneous channel state information(I-CSI)for RIS,which obliges us to use statistical channel state information(S-CSI)to achieve passive beamforming.In this paper,RIS-aided multiple-input single-output(MISO)multi-user downlink communication system with correlated channels is investigated.Then,we formulate the problem of joint beamforming design at the AP and RIS to maximize the sum ergodic spectral efficiency(ESE)of all users to improve the network capacity.Since it is too hard to compute sum ESE,an ESE approximation is adopted to reformulate the problem into a more tractable form.Then,we present two joint beamforming algorithms,namely the singular value decomposition-gradient descent(SVD-GD)algorithm and the fractional programming-gradient descent(FP-GD)algorithm.Simulation results show the effectiveness of our proposed algorithms and validate that 2-bits quantizer is enough for RIS phase shifts implementation.
基金partially funded by the National Natural Science Foundation of China(U2142205)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)+1 种基金the Special Fund for Forecasters of China Meteorological Administration(CMAYBY2020-094)the Graduate Student Research and Innovation Program of Central South University(2023ZZTS0347)。
文摘Traditional meteorological downscaling methods face limitations due to the complex distribution of meteorological variables,which can lead to unstable forecasting results,especially in extreme scenarios.To overcome this issue,we propose a convolutional graph neural network(CGNN)model,which we enhance with multilayer feature fusion and a squeeze-and-excitation block.Additionally,we introduce a spatially balanced mean squared error(SBMSE)loss function to address the imbalanced distribution and spatial variability of meteorological variables.The CGNN is capable of extracting essential spatial features and aggregating them from a global perspective,thereby improving the accuracy of prediction and enhancing the model's generalization ability.Based on the experimental results,CGNN has certain advantages in terms of bias distribution,exhibiting a smaller variance.When it comes to precipitation,both UNet and AE also demonstrate relatively small biases.As for temperature,AE and CNNdense perform outstandingly during the winter.The time correlation coefficients show an improvement of at least 10%at daily and monthly scales for both temperature and precipitation.Furthermore,the SBMSE loss function displays an advantage over existing loss functions in predicting the98th percentile and identifying areas where extreme events occur.However,the SBMSE tends to overestimate the distribution of extreme precipitation,which may be due to the theoretical assumptions about the posterior distribution of data that partially limit the effectiveness of the loss function.In future work,we will further optimize the SBMSE to improve prediction accuracy.
基金supported by National Nature Science Foundation of China(No.62361036)Nature Science Foundation of Gansu Province(No.22JR5RA279).
文摘To realize dynamic statistical publishing and protection of location-based data privacy,this paper proposes a differential privacy publishing algorithm based on adaptive sampling and grid clustering and adjustment.The PID control strategy is combined with the difference in data variation to realize the dynamic adjustment of the data publishing intervals.The spatial-temporal correlations of the adjacent snapshots are utilized to design the grid clustering and adjustment algorithm,which facilitates saving the execution time of the publishing process.The budget distribution and budget absorption strategies are improved to form the sliding window-based differential privacy statistical publishing algorithm,which realizes continuous statistical publishing and privacy protection and improves the accuracy of published data.Experiments and analysis on large datasets of actual locations show that the privacy protection algorithm proposed in this paper is superior to other existing algorithms in terms of the accuracy of adaptive sampling time,the availability of published data,and the execution efficiency of data publishing methods.
文摘We perform a time-resolved statistical study of GRB 221009A’s X-ray emission using Swift XRT Photon Counting and Windowed Timing data.After standard reduction(barycentric correction,pile-up,background subtraction via HEASOFT),we extracted light curves for each observational ID and for their aggregation.Countrate histograms were fitted using various statistical distributions;fit quality was assessed by chi-squared and the Bayesian Information Criterion.The first observational segment is best described by a Gaussian distribution(χ^(2)=68.4;BIC=7651.2),and the second by a Poisson distribution(χ^(2)=33.5;BIC=4413.3).When all segments are combined,the lognormal model provides the superior fit(χ^(2)=541.9;BIC=34365.5),indicating that the full data set’s count rates exhibit the skewness expected from a multiplicative process.These findings demonstrate that while individual time intervals conform to discrete or symmetric statistics,the collective emission profile across multiple observations is better captured by a lognormal distribution,consistent with complex,compounded variability in GRB afterglows.
基金financially supported by the Key Technologies R&D Program of China National Offshore Oil Corporation(No.KJGG2021-0504)。
文摘Understanding the thermodynamic behavior of complex fluids in confined environments is critical for various industrial and natural processes including but not limited to polymer flooding enhanced oil recovery(EOR).In this work,we develop Atif-V2.0,an extended classical density functional theory(cDFT)framework that integrates the interfacial statistical associating fluid theory(iSAFT)to model multicomponent associating fluids composed of water-soluble polymers,alkanes,and water.Building on the original theoretical framework of Atif for modeling nanoconfined inhomogeneous fluids,Atif-V2.0 embeds explicit solvent and captures additional physical interactions-hydrogen bonding,which are critical in associating fluid systems.The other key feature of Atif-V2.0 is its ability to account for polymer topology.We demonstrate its capability by predicting the equilibrium structure and thermodynamic behavior of branched hydrolyzed polyacrylamide solutions near hard walls with various branching topologies,which provides a robust theoretical tool for the rational design of EOR polymers.
