Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data st...Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).展开更多
As one of the major volatile components in extraterrestrial materials,nitrogen(N_(2))isotopes serve not only as tracers for the formation and evolution of the solar system,but also play a critical role in assessing pl...As one of the major volatile components in extraterrestrial materials,nitrogen(N_(2))isotopes serve not only as tracers for the formation and evolution of the solar system,but also play a critical role in assessing planetary habitability and the search for extraterrestrial life.The integrated measurement of N_(2)and argon(Ar)isotopes by using noble gas mass spectrometry represents a state-of-the-art technique for such investigations.To support the growing demands of planetary science research in China,we have developed a high-efficiency,high-precision method for the integrated analysis of N_(2)and Ar isotopes.This was achieved by enhancing gas extraction and purification systems and integrating them with a static noble gas mass spectrometer.This method enables integrated N_(2)-Ar isotope measurements on submilligram samples,significantly improving sample utilization and reducing the impact of sample heterogeneity on volatile analysis.The system integrates CO_(2)laser heating,a modular two-stage Zr-Al getter pump,and a CuO furnace-based purification process,effectively reducing background levels(N_(2)blank as low as 0.35×10^(−6)cubic centimeters at standard temperature and pressure[ccSTP]).Analytical precision is ensured through calibration with atmospheric air and CO corrections.To validate the reliability of the method,we performed N_(2)-Ar isotope analyses on the Allende carbonaceous chondrite,one of the most extensively studied meteorites internationally.The measured N_(2)concentrations range from 19.2 to 29.8 ppm,withδ15N values between−44.8‰and−33.0‰.Concentrations of 40Ar,36Ar,and 38Ar are(12.5-21.1)×10^(−6)ccSTP/g,(90.9-150.3)×10^(−9)ccSTP/g,and(19.2-30.7)×10^(−9)ccSTP/g,respectively.These values correspond to cosmic-ray exposure ages of 4.5-5.7 Ma,consistent with previous reports.Step-heating experiments further reveal distinct release patterns of N and Ar isotopes,as well as their associations with specific mineral phases in the meteorite.In summary,the combined N_(2)-Ar isotopic system offers significant advantages for tracing volatile sources in extraterrestrial materials and will provide essential analytical support for upcoming Chinese planetary missions,such as Tianwen-2.展开更多
Five years＇ （2000-2004） continuous study has been carried out on small mammals such as rodents in seven different sample plots, at three different altitudes and in six different ecological environment types in the ...Five years＇ （2000-2004） continuous study has been carried out on small mammals such as rodents in seven different sample plots, at three different altitudes and in six different ecological environment types in the eastern part of the Wuling Mountains, south bank of the Three Gorges of Yangtze River in Hubei. A total of 29 297 rat clamps/times were placed and 2271 small mammals such as rodents were captured, and 26 small mammals were captured by other means. All the small mammals captured belonged to 8 families 19 genera and 24 species, of which rodentia accounted for 70.83% and insectivora 29.17%. Through analysis of the data, the results showed that： 1 ） although the species richness had a trend of increasing along different sample plots as altitude increased from south to north, quite a few species showed a wide habitat range in a vertical distribution （ 15 species were dispersed over three zones and two species over two zones） , indicating a strong adaptability of small mammals such as rOdents at lower altitudes in most areas and comparatively less vertical span of entire mountains; 2） whether in seven different sample plots or six different ecological types, Apodemus agrarius and Rattus norvegicus were dominant species below 1200m, and Anourosorex squamipes, Niviventer confucianus and Apodemus draco were dominant above altitudes of 1300m, however, in quantity they were short of identical regularity, meaning they did not increase as the altitude did, or decrease as the ecological areas changed; 3）the density in winter was obviously greater than that in spring, and the distribution showed an increasing trend along with altitude, but the density in different sample plots was short of identical regularity, showing changes in different seasons and altitude grades had an important impact on small mammals such as rodents; 4） in species diversity and evenness index, there were obvious changes between the seven different sample plots, probably caused by frequent human interference in this area. Comparatively speaking, there was less human interference at high altitudes where vegetation was rich and had a high diversity and evenness index, and the boundary effect and community stability were obvious. Most ecological types have been seriously interfered with due to excessive assart at low altitudes with singular vegetation and low diversity and evenness index and poor community stability, showing an ecosystem with poor anti-reversion. If human interference can be reduced in those communities at high altitudes with low diversity and evenness index, the biological diversity in the communities will gradually recover to similar levels of other ecological areas.展开更多
Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and earl...Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.展开更多
Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cyc...Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.展开更多
In order to study the dynamics of uneven-aged stands of interior Douglas-fir, Pseudotsuga menzesii var.glouca (Mirb.) Franco in future, six permanent sample plots wer set up on the Knife Creek Block of the Alex Fraser...In order to study the dynamics of uneven-aged stands of interior Douglas-fir, Pseudotsuga menzesii var.glouca (Mirb.) Franco in future, six permanent sample plots wer set up on the Knife Creek Block of the Alex Fraser Researh Forcst of University of British Columbia. The measurements and observations for all living trees within theboundaries of a plot wer madc, including DBH(diameter at breast height), TTH(total tree height), height to lowest livingbranch, crown diameter, tree vigor, angle of lean, distance of lean, direction of lean and tree location. Based on the data,some stand characteristics of the plots were analyzed simply and preliminarily. Results showed that most of the interiortrees on the plots are ranged 10-20 cm in distribution of DBH class, and 2-6 m in distribution of rm class. Trees withdifferent fors, however, are distributed unevenly. The relationship between total tree height and diameter at breast heightfollows a quadratic distribution, Y=a+bX+cX2.展开更多
The following qualitative conclusions of forest resources in Zigui can be drawn by the research on 73 plots and 5 vegetation plots:forest area is increasing; forest growing stock is increasing; the adjustment of fores...The following qualitative conclusions of forest resources in Zigui can be drawn by the research on 73 plots and 5 vegetation plots:forest area is increasing; forest growing stock is increasing; the adjustment of forest category structure is constantly improved; forest quality has been improving; stand structure is optimized continuously; biodiversity has initially appeared.展开更多
The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied...The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.展开更多
Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection...Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.展开更多
Marine gas hydrates are highly sensitive to temperature and pressure fluctuations,and deviations from in-situ conditions may cause irreversible changes in phase state,microstructure,and mechanical properties.However,c...Marine gas hydrates are highly sensitive to temperature and pressure fluctuations,and deviations from in-situ conditions may cause irreversible changes in phase state,microstructure,and mechanical properties.However,conventional samplers often fail to maintain sealing and thermal stability,resulting in low sampling success rates.To address these challenges,an in-situ temperature-and pressure-preserved sampler for marine applications has been developed.The experimental results indicate that the selfdeveloped magnetically controlled pressure-preserved controller reliably achieves autonomous triggering and self-sealing,provides an initial sealing force of 83 N,and is capable of maintaining pressures up to 40 MPa.Additionally,a custom-designed intelligent temperature control chip and high-precision sensors were integrated into the sampler.Through the design of an optimized heat transfer structure,a temperature-preserved system was developed,achieving no more than a 0.3℃ rise in temperature within 2 h.The performance evaluation and sampling operations of the sampler were conducted at the Haima Cold Seep in the South China Sea,resulting in the successful recovery of hydrate maintained under in-situ pressure of 13.8 MPa and a temperature of 6.5℃.This advancement enables the acquisition of high-fidelity hydrate samples,providing critical support for the safe exploitation and scientific analysis of marine gas hydrate resources.展开更多
Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fag...Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fagus orientalis Lipsky),a widespread species in the Hyrcanian region.Assessing the impacts of plot size on species diversity is fundamental for an ecosystem-based approach to forest management.This study determined the relation of species diversity and plot size by investigating species richness and abundance of both canopy and forest floor.Two hundred and fifty-six sample plots of 625 m^(2) each were layout in a grid pattern across 16 ha.Base plots(25 m×25 m)were integrated in different scales to investigate the effect of plot size on species diversity.The total included nine plots of 0.063,0.125,0.188,0.250,0.375,0.500,0.563,0.750 and 1 ha.Ten biodiversity indices were calculated.The results show that species richness in the different plot sizes was less than the actual value.The estimated value of the Simpson species diversity index was not significantly different from actual values for both canopy and forest floor diversity.The coefficient of variation of this index for the 1-ha sample plot showed the lowest amount across different plot sizes.Inverse Hill species diversity was insignificant difference across different plot sizes with an area greater than 0.500 ha.The modified Hill evenness index for the 1-ha sample size was a correct estimation of the 16-ha for both canopy and forest floor;however,the precision estimation was higher for the canopy layer.All plots greater than 0.250-ha provided an accurate estimation of the Camargo evenness index for forest floor species,but was inaccurate across different plot sizes for the canopy layer.The results indicate that the same plot size did not have the same effect across species diversity measurements.Our results show that correct estimation of species diversity measurements is related to the selection of appropriate indicators and plot size to increase the accuracy of the estimate so that the cost and time of biodiversity management may be reduced.展开更多
Three-dimensional printing(3DP)offers valuable insight into the characterization of natural rocks and the verification of theoretical models due to its high reproducibility and accurate replication of complex defects ...Three-dimensional printing(3DP)offers valuable insight into the characterization of natural rocks and the verification of theoretical models due to its high reproducibility and accurate replication of complex defects such as cracks and pores.In this study,3DP gypsum samples with different printing directions were subjected to a series of uniaxial compression tests with in situ micro-computed tomography(micro-CT)scanning to quantitatively investigate their mechanical anisotropic properties and damage evolution characteristics.Based on the two-dimensional(2D)CT images obtained at different scanning steps,a novel void ratio variable was derived using the mean value and variance of CT intensity.Additionally,a constitutive model was formulated incorporating the proposed damage variable,utilizing the void ratio variable.The crack evolution and crack morphology of 3DP gypsum samples were obtained and analyzed using the 3D models reconstructed from the CT images.The results indicate that 3DP gypsum samples exhibit mechanical anisotropic characteristics similar to those found in naturally sedimentary rocks.The mechanical anisotropy is attributed to the bedding planes formed between adjacent layers and pillar-like structures along the printing direction formed by CaSO_(4)·2H_(2)O crystals of needle-like morphology.The mean gray intensity of the voids has a positive linear relationship with the threshold value,while the CT variance and void ratio have concave and convex relationships,respectively.The constitutive model can effectively match the stress–strain curves obtained from uniaxial compression experiments.This study provides comprehensive explanations of the failure modes and anisotropic mechanisms of 3DP gypsum samples,which is important for characterizing and understanding the failure mechanism and microstructural evolution of 3DP rocks when modeling natural rock behavior.展开更多
Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining s...Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.展开更多
As an emerging microscopic detection tool,quantum microscopes based on the principle of quantum precision measurement have attracted widespread attention in recent years.Compared with the imaging of classical light,qu...As an emerging microscopic detection tool,quantum microscopes based on the principle of quantum precision measurement have attracted widespread attention in recent years.Compared with the imaging of classical light,quantum-enhanced imaging can achieve ultra-high resolution,ultra-sensitive detection,and anti-interference imaging.Here,we introduce a quantum-enhanced scanning microscope under illumination of an entangled NOON state in polarization.For the phase imager with NOON states,we propose a simple four-basis projection method to replace the four-step phase-shifting method.We have achieved the phase imaging of micrometer-sized birefringent samples and biological cell specimens,with sensitivity close to the Heisenberg limit.The visibility of transmittance-based imaging shows a great enhancement for NOON states.Besides,we also demonstrate that the scanning imaging with NOON states enables the spatial resolution enhancement of√N compared with classical measurement.Our imaging method may provide some reference for the practical application of quantum imaging and is expected to promote the development of microscopic detection.展开更多
An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extra...An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.展开更多
In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of...In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.展开更多
Medicinal and edible plants(MEPs)have attracted increasing interest worldwide due to their natural origin,reliable efficacy,and minimal side effects in recent years.However,the complex and fluctuating levels of inhere...Medicinal and edible plants(MEPs)have attracted increasing interest worldwide due to their natural origin,reliable efficacy,and minimal side effects in recent years.However,the complex and fluctuating levels of inherent chemical constituents and exogenous hazardous contaminants have triggered widespread concerns about their efficacy and safety.Developing analytical methods for both active components and exogenous contaminants concealed in these samples is central to the quality evaluation,in which sample preparation is crucial.This paper systematically reviewed the evolution of standard sample preparation methods,microextraction techniques based on novel solvents and nanomaterials,and innovative integrated techniques from 2019.Accordingly,their merits and weaknesses were discussed by showing fruitful applications in identifying and quantifying active components in these plants.Further,successful applications for analyzing exogenous contaminants were prominently showcased,highlighting the management of pesticides,heavy metals,mycotoxins,and polycyclic aromatic hydrocarbons(PAHs).Finally,forthcoming trends in sample preparation techniques were delineated to illuminate the development and implementation of more advanced sample preparation technologies.展开更多
Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage toward the realization of universal quantum computers.In the context of nonuniversal noi...Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage toward the realization of universal quantum computers.In the context of nonuniversal noisy intermediate quantum devices,photonic-based sampling machines solving the Gaussian boson sampling(GBS)problem currently play a central role in the experimental demonstration of quantum computational advantage.A relevant issue is the validation of the sampling process in the presence of experimental noise,such as photon losses,which could undermine the hardness of simulating the experiment.We test the capability of a validation protocol that exploits the connection between GBS and graph perfect match counting to perform such an assessment in a noisy scenario.In particular,we use as a test bench the recently developed machine Borealis,a large-scale sampling machine that has been made available online for external users,and address its operation in the presence of noise.The employed approach to validation is also shown to provide connections with the open question on the effective advantage of using noisy GBS devices for graph similarity and isomorphism problems and thus provides an effective method for certification of quantum hardware.展开更多
基金funding support from the National Science and Technology Council(NSTC),under Grant No.114-2410-H-011-026-MY3.
文摘Small datasets are often challenging due to their limited sample size.This research introduces a novel solution to these problems:average linkage virtual sample generation(ALVSG).ALVSG leverages the underlying data structure to create virtual samples,which can be used to augment the original dataset.The ALVSG process consists of two steps.First,an average-linkage clustering technique is applied to the dataset to create a dendrogram.The dendrogram represents the hierarchical structure of the dataset,with each merging operation regarded as a linkage.Next,the linkages are combined into an average-based dataset,which serves as a new representation of the dataset.The second step in the ALVSG process involves generating virtual samples using the average-based dataset.The research project generates a set of 100 virtual samples by uniformly distributing them within the provided boundary.These virtual samples are then added to the original dataset,creating a more extensive dataset with improved generalization performance.The efficacy of the ALVSG approach is validated through resampling experiments and t-tests conducted on two small real-world datasets.The experiments are conducted on three forecasting models:the support vector machine for regression(SVR),the deep learning model(DL),and XGBoost.The results show that the ALVSG approach outperforms the baseline methods in terms of mean square error(MSE),root mean square error(RMSE),and mean absolute error(MAE).
基金supported by the Bureau of Frontier Sciences and Basic Research,Chinese Academy of Sciences(Grant No.QYJ-2025-0103)the National Natural Science Foundation of China(Grant Nos.42441834,42241105,42441825,and 42203048)the Key Research Program of the Institute of Geology and Geophysics,Chinese Academy of Sciences(Grant No.IGGCAS-202401).
文摘As one of the major volatile components in extraterrestrial materials,nitrogen(N_(2))isotopes serve not only as tracers for the formation and evolution of the solar system,but also play a critical role in assessing planetary habitability and the search for extraterrestrial life.The integrated measurement of N_(2)and argon(Ar)isotopes by using noble gas mass spectrometry represents a state-of-the-art technique for such investigations.To support the growing demands of planetary science research in China,we have developed a high-efficiency,high-precision method for the integrated analysis of N_(2)and Ar isotopes.This was achieved by enhancing gas extraction and purification systems and integrating them with a static noble gas mass spectrometer.This method enables integrated N_(2)-Ar isotope measurements on submilligram samples,significantly improving sample utilization and reducing the impact of sample heterogeneity on volatile analysis.The system integrates CO_(2)laser heating,a modular two-stage Zr-Al getter pump,and a CuO furnace-based purification process,effectively reducing background levels(N_(2)blank as low as 0.35×10^(−6)cubic centimeters at standard temperature and pressure[ccSTP]).Analytical precision is ensured through calibration with atmospheric air and CO corrections.To validate the reliability of the method,we performed N_(2)-Ar isotope analyses on the Allende carbonaceous chondrite,one of the most extensively studied meteorites internationally.The measured N_(2)concentrations range from 19.2 to 29.8 ppm,withδ15N values between−44.8‰and−33.0‰.Concentrations of 40Ar,36Ar,and 38Ar are(12.5-21.1)×10^(−6)ccSTP/g,(90.9-150.3)×10^(−9)ccSTP/g,and(19.2-30.7)×10^(−9)ccSTP/g,respectively.These values correspond to cosmic-ray exposure ages of 4.5-5.7 Ma,consistent with previous reports.Step-heating experiments further reveal distinct release patterns of N and Ar isotopes,as well as their associations with specific mineral phases in the meteorite.In summary,the combined N_(2)-Ar isotopic system offers significant advantages for tracing volatile sources in extraterrestrial materials and will provide essential analytical support for upcoming Chinese planetary missions,such as Tianwen-2.
基金National Natural Science Foundation of China(30070679)the Natural Science Foundation of Hubei Province(2004ABA138)+1 种基金the Key Technology R&D Programme Foundation of Hubei Province(2002AA301C43)the Hubei Health Bureau Research Programme Foundation(NX200427)
文摘Five years＇ （2000-2004） continuous study has been carried out on small mammals such as rodents in seven different sample plots, at three different altitudes and in six different ecological environment types in the eastern part of the Wuling Mountains, south bank of the Three Gorges of Yangtze River in Hubei. A total of 29 297 rat clamps/times were placed and 2271 small mammals such as rodents were captured, and 26 small mammals were captured by other means. All the small mammals captured belonged to 8 families 19 genera and 24 species, of which rodentia accounted for 70.83% and insectivora 29.17%. Through analysis of the data, the results showed that： 1 ） although the species richness had a trend of increasing along different sample plots as altitude increased from south to north, quite a few species showed a wide habitat range in a vertical distribution （ 15 species were dispersed over three zones and two species over two zones） , indicating a strong adaptability of small mammals such as rOdents at lower altitudes in most areas and comparatively less vertical span of entire mountains; 2） whether in seven different sample plots or six different ecological types, Apodemus agrarius and Rattus norvegicus were dominant species below 1200m, and Anourosorex squamipes, Niviventer confucianus and Apodemus draco were dominant above altitudes of 1300m, however, in quantity they were short of identical regularity, meaning they did not increase as the altitude did, or decrease as the ecological areas changed; 3）the density in winter was obviously greater than that in spring, and the distribution showed an increasing trend along with altitude, but the density in different sample plots was short of identical regularity, showing changes in different seasons and altitude grades had an important impact on small mammals such as rodents; 4） in species diversity and evenness index, there were obvious changes between the seven different sample plots, probably caused by frequent human interference in this area. Comparatively speaking, there was less human interference at high altitudes where vegetation was rich and had a high diversity and evenness index, and the boundary effect and community stability were obvious. Most ecological types have been seriously interfered with due to excessive assart at low altitudes with singular vegetation and low diversity and evenness index and poor community stability, showing an ecosystem with poor anti-reversion. If human interference can be reduced in those communities at high altitudes with low diversity and evenness index, the biological diversity in the communities will gradually recover to similar levels of other ecological areas.
文摘Distribution transformers play a vital role in power distribution systems,and their reliable operation is crucial for grid stability.This study presents a simulation-based framework for active fault diagnosis and early warning of distribution transformers,integrating Sample Ensemble Learning(SEL)with a Self-Optimizing Support Vector Machine(SO-SVM).The SEL technique enhances data diversity and mitigates class imbalance,while SO-SVM adaptively tunes its hyperparameters to improve classification accuracy.A comprehensive transformer model was developed in MATLAB/Simulink to simulate diverse fault scenarios,including inter-turn winding faults,core saturation,and thermal aging.Feature vectors were extracted from voltage,current,and temperature measurements to train and validate the proposed hybrid model.Quantitative analysis shows that the SEL–SO-SVM framework achieves a classification accuracy of 97.8%,a precision of 96.5%,and an F1-score of 97.2%.Beyond classification,the model effectively identified incipient faults,providing an early warning lead time of up to 2.5 s before significant deviations in operational parameters.This predictive capability underscores its potential for preventing catastrophic transformer failures and enabling timely maintenance actions.The proposed approach demonstrates strong applicability for enhancing the reliability and operational safety of distribution transformers in simulated environments,offering a promising foundation for future real-time and field-level implementations.
基金supported by the State Forestry Administration of China under the national forestry commonwealth project grant#201404309the Expert Workstation of Academician Tang Shouzheng of Yunnan Province,the Yunnan provincial key project of Forestrythe Research Center of Kunming Forestry Information Engineering Technology
文摘Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.
文摘In order to study the dynamics of uneven-aged stands of interior Douglas-fir, Pseudotsuga menzesii var.glouca (Mirb.) Franco in future, six permanent sample plots wer set up on the Knife Creek Block of the Alex Fraser Researh Forcst of University of British Columbia. The measurements and observations for all living trees within theboundaries of a plot wer madc, including DBH(diameter at breast height), TTH(total tree height), height to lowest livingbranch, crown diameter, tree vigor, angle of lean, distance of lean, direction of lean and tree location. Based on the data,some stand characteristics of the plots were analyzed simply and preliminarily. Results showed that most of the interiortrees on the plots are ranged 10-20 cm in distribution of DBH class, and 2-6 m in distribution of rm class. Trees withdifferent fors, however, are distributed unevenly. The relationship between total tree height and diameter at breast heightfollows a quadratic distribution, Y=a+bX+cX2.
文摘The following qualitative conclusions of forest resources in Zigui can be drawn by the research on 73 plots and 5 vegetation plots:forest area is increasing; forest growing stock is increasing; the adjustment of forest category structure is constantly improved; forest quality has been improving; stand structure is optimized continuously; biodiversity has initially appeared.
文摘The research was carried out on the territory of the Karelian Isthmus of the Leningrad Region using Sentinel-2B images and data from a network of ground sample plots. The ground sample plots are located in the studied territory mainly in a regular manner, laid and surveyed according to the ICP-Forests methodology with some additions. The total area of the sample plots is a small part of the entire study area. One of the objectives of the study was to determine the possibility of using the k-NN (nearest neighbor method) to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery. The data of the ground-based sample plots were divided into 2 equal parts, one for the application of the k-NN method, the second for checking the results of the method application. The systematic error in determining the mean damage class of the tree stands on sample plots by the k-NN method turned out to be zero, the random error is equal to one point. These results offer a possibility to determine the state of the forest in the entire study area. The second objective of the study was to examine the possibility of using the short-wave vegetation index (SWVI) to assess the state of forests. As a result, a close statistically reliable dependence of the average score of the state of plantations and the value of the SWVI index was established, which makes it possible to use the established relationship to determine the state of forests throughout the studied territory. The joint use and statistical processing of remotely sensed data and ground-based test areas by the two studied methods make it possible to assess the state of forests throughout the large studied area within the image. The results obtained can be used to monitor the state of forests in large areas and design appropriate forestry protective measures.
文摘Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.
基金financially supported by Shenzhen Science and Technology Program(Nos.JSGG20220831105002005 and KJZD20231025152759002)Support from the National Natural Science Foundation of China(Nos.52374357 and 523B2101)funded by the Shared Voyages Project for Deep-sea and Abyss Scientific Research and Equipment Sea Trials of Hainan Deep-Sea Technology Innovation Center(No.DSTIC-GXHC-2022002)。
文摘Marine gas hydrates are highly sensitive to temperature and pressure fluctuations,and deviations from in-situ conditions may cause irreversible changes in phase state,microstructure,and mechanical properties.However,conventional samplers often fail to maintain sealing and thermal stability,resulting in low sampling success rates.To address these challenges,an in-situ temperature-and pressure-preserved sampler for marine applications has been developed.The experimental results indicate that the selfdeveloped magnetically controlled pressure-preserved controller reliably achieves autonomous triggering and self-sealing,provides an initial sealing force of 83 N,and is capable of maintaining pressures up to 40 MPa.Additionally,a custom-designed intelligent temperature control chip and high-precision sensors were integrated into the sampler.Through the design of an optimized heat transfer structure,a temperature-preserved system was developed,achieving no more than a 0.3℃ rise in temperature within 2 h.The performance evaluation and sampling operations of the sampler were conducted at the Haima Cold Seep in the South China Sea,resulting in the successful recovery of hydrate maintained under in-situ pressure of 13.8 MPa and a temperature of 6.5℃.This advancement enables the acquisition of high-fidelity hydrate samples,providing critical support for the safe exploitation and scientific analysis of marine gas hydrate resources.
基金funded by Gorgan University of Agricultural Sciences and Natural Resources(grant number 9318124503).
文摘Plant species diversity is one of the most widely used indicators in ecosystem management.The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests(Fagus orientalis Lipsky),a widespread species in the Hyrcanian region.Assessing the impacts of plot size on species diversity is fundamental for an ecosystem-based approach to forest management.This study determined the relation of species diversity and plot size by investigating species richness and abundance of both canopy and forest floor.Two hundred and fifty-six sample plots of 625 m^(2) each were layout in a grid pattern across 16 ha.Base plots(25 m×25 m)were integrated in different scales to investigate the effect of plot size on species diversity.The total included nine plots of 0.063,0.125,0.188,0.250,0.375,0.500,0.563,0.750 and 1 ha.Ten biodiversity indices were calculated.The results show that species richness in the different plot sizes was less than the actual value.The estimated value of the Simpson species diversity index was not significantly different from actual values for both canopy and forest floor diversity.The coefficient of variation of this index for the 1-ha sample plot showed the lowest amount across different plot sizes.Inverse Hill species diversity was insignificant difference across different plot sizes with an area greater than 0.500 ha.The modified Hill evenness index for the 1-ha sample size was a correct estimation of the 16-ha for both canopy and forest floor;however,the precision estimation was higher for the canopy layer.All plots greater than 0.250-ha provided an accurate estimation of the Camargo evenness index for forest floor species,but was inaccurate across different plot sizes for the canopy layer.The results indicate that the same plot size did not have the same effect across species diversity measurements.Our results show that correct estimation of species diversity measurements is related to the selection of appropriate indicators and plot size to increase the accuracy of the estimate so that the cost and time of biodiversity management may be reduced.
基金supported by grants from the Human Resources Development program(Grant No.20204010600250)the Training Program of CCUS for the Green Growth(Grant No.20214000000500)by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)funded by the Ministry of Trade,Industry,and Energy of the Korean Government(MOTIE).
文摘Three-dimensional printing(3DP)offers valuable insight into the characterization of natural rocks and the verification of theoretical models due to its high reproducibility and accurate replication of complex defects such as cracks and pores.In this study,3DP gypsum samples with different printing directions were subjected to a series of uniaxial compression tests with in situ micro-computed tomography(micro-CT)scanning to quantitatively investigate their mechanical anisotropic properties and damage evolution characteristics.Based on the two-dimensional(2D)CT images obtained at different scanning steps,a novel void ratio variable was derived using the mean value and variance of CT intensity.Additionally,a constitutive model was formulated incorporating the proposed damage variable,utilizing the void ratio variable.The crack evolution and crack morphology of 3DP gypsum samples were obtained and analyzed using the 3D models reconstructed from the CT images.The results indicate that 3DP gypsum samples exhibit mechanical anisotropic characteristics similar to those found in naturally sedimentary rocks.The mechanical anisotropy is attributed to the bedding planes formed between adjacent layers and pillar-like structures along the printing direction formed by CaSO_(4)·2H_(2)O crystals of needle-like morphology.The mean gray intensity of the voids has a positive linear relationship with the threshold value,while the CT variance and void ratio have concave and convex relationships,respectively.The constitutive model can effectively match the stress–strain curves obtained from uniaxial compression experiments.This study provides comprehensive explanations of the failure modes and anisotropic mechanisms of 3DP gypsum samples,which is important for characterizing and understanding the failure mechanism and microstructural evolution of 3DP rocks when modeling natural rock behavior.
基金the Key National Natural Science Foundation of China(No.U1864211)the National Natural Science Foundation of China(No.11772191)the Natural Science Foundation of Shanghai(No.21ZR1431500)。
文摘Industrial data mining usually deals with data from different sources.These heterogeneous datasets describe the same object in different views.However,samples from some of the datasets may be lost.Then the remaining samples do not correspond one-to-one correctly.Mismatched datasets caused by missing samples make the industrial data unavailable for further machine learning.In order to align the mismatched samples,this article presents a cooperative iteration matching method(CIMM)based on the modified dynamic time warping(DTW).The proposed method regards the sequentially accumulated industrial data as the time series.Mismatched samples are aligned by the DTW.In addition,dynamic constraints are applied to the warping distance of the DTW process to make the alignment more efficient.Then a series of models are trained with the cumulated samples iteratively.Several groups of numerical experiments on different missing patterns and missing locations are designed and analyzed to prove the effectiveness and the applicability of the proposed method.
基金supported by he National Natural Science Foundation of China(Grant Nos.12304359,12304398,12404382,12234009,12274215,and 12427808)the China Postdoctoral Science Foundation(Grant No.2023M731611)+4 种基金the Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2023ZB717)Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301400)Key R&D Program of Jiangsu Province(Grant No.BE2023002)Natural Science Foundation of Jiangsu Province(Grant Nos.BK20220759 and BK20233001)Program for Innovative Talents and Entrepreneurs in Jiangsu,and Key R&D Program of Guangdong Province(Grant No.2020B0303010001).
文摘As an emerging microscopic detection tool,quantum microscopes based on the principle of quantum precision measurement have attracted widespread attention in recent years.Compared with the imaging of classical light,quantum-enhanced imaging can achieve ultra-high resolution,ultra-sensitive detection,and anti-interference imaging.Here,we introduce a quantum-enhanced scanning microscope under illumination of an entangled NOON state in polarization.For the phase imager with NOON states,we propose a simple four-basis projection method to replace the four-step phase-shifting method.We have achieved the phase imaging of micrometer-sized birefringent samples and biological cell specimens,with sensitivity close to the Heisenberg limit.The visibility of transmittance-based imaging shows a great enhancement for NOON states.Besides,we also demonstrate that the scanning imaging with NOON states enables the spatial resolution enhancement of√N compared with classical measurement.Our imaging method may provide some reference for the practical application of quantum imaging and is expected to promote the development of microscopic detection.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272259 and 52005148).
文摘An intelligent diagnosis method based on self-adaptiveWasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction,which are commonly faced by rolling bearings and lead to low diagnostic accuracy.Initially,dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty(1D-2DWDCGAN)are constructed to augment the original dataset.A self-adaptive loss threshold control training strategy is introduced,and establishing a self-adaptive balancing mechanism for stable model training.Subsequently,a diagnostic model based on multidimensional feature fusion is designed,wherein complex features from various dimensions are extracted,merging the original signal waveform features,structured features,and time-frequency features into a deep composite feature representation that encompasses multiple dimensions and scales;thus,efficient and accurate small sample fault diagnosis is facilitated.Finally,an experiment between the bearing fault dataset of CaseWestern ReserveUniversity and the fault simulation experimental platformdataset of this research group shows that this method effectively supplements the dataset and remarkably improves the diagnostic accuracy.The diagnostic accuracy after data augmentation reached 99.94%and 99.87%in two different experimental environments,respectively.In addition,robustness analysis is conducted on the diagnostic accuracy of the proposed method under different noise backgrounds,verifying its good generalization performance.
文摘In the task of Facial Expression Recognition(FER),data uncertainty has been a critical factor affecting performance,typically arising from the ambiguity of facial expressions,low-quality images,and the subjectivity of annotators.Tracking the training history reveals that misclassified samples often exhibit high confidence and excessive uncertainty in the early stages of training.To address this issue,we propose an uncertainty-based robust sample selection strategy,which combines confidence error with RandAugment to improve image diversity,effectively reducing overfitting caused by uncertain samples during deep learning model training.To validate the effectiveness of the proposed method,extensive experiments were conducted on FER public benchmarks.The accuracy obtained were 89.08%on RAF-DB,63.12%on AffectNet,and 88.73%on FERPlus.
基金supported by the National Natural Science Foundation of China(Grant No.:81903794)Macao Science and Technology Development Fund(Grant Nos.:0031/2022/AGJ,0014/2022/ITP,005/2023/SKL and 001/2023/ALC)+2 种基金Guangdong Basic and Applied Basic Research Foundation(Grant No.:2024A1515030214)Guangdong-Macao Science and Technology Innovation Joint Research Special Fund(Grant No.:2023A0505020013)the Research Committee of the University of Macao(Grant Nos.:SRG2022-00035-ICMS,MYRG-CRG2022-00016-ICMS,MYRG2023-00205-ICMS,and MYRG2023-00234-ICMS-UMDF)。
文摘Medicinal and edible plants(MEPs)have attracted increasing interest worldwide due to their natural origin,reliable efficacy,and minimal side effects in recent years.However,the complex and fluctuating levels of inherent chemical constituents and exogenous hazardous contaminants have triggered widespread concerns about their efficacy and safety.Developing analytical methods for both active components and exogenous contaminants concealed in these samples is central to the quality evaluation,in which sample preparation is crucial.This paper systematically reviewed the evolution of standard sample preparation methods,microextraction techniques based on novel solvents and nanomaterials,and innovative integrated techniques from 2019.Accordingly,their merits and weaknesses were discussed by showing fruitful applications in identifying and quantifying active components in these plants.Further,successful applications for analyzing exogenous contaminants were prominently showcased,highlighting the management of pesticides,heavy metals,mycotoxins,and polycyclic aromatic hydrocarbons(PAHs).Finally,forthcoming trends in sample preparation techniques were delineated to illuminate the development and implementation of more advanced sample preparation technologies.
基金supported by the ERC Advanced Grant QU-BOSS(QUantum advantage via nonlinear BOSon Sampling,Grant No.884676)by ICSC-Centro Nazionale di Ricerca in High Performance Computing,Big Data,and Quantum Computing,funded by the European Union-NextGenerationEU.D.S.acknowledges Thales Alenia Space Italia for supporting the PhD fellowship.N.S.acknowledges funding from Sapienza Universitàdi Roma via Bando Ricerca 2020:Progetti di Ricerca Piccoli,Project No.RP120172B8A36B37.
文摘Quantum photonic processors are emerging as promising platforms to prove preliminary evidence of quantum computational advantage toward the realization of universal quantum computers.In the context of nonuniversal noisy intermediate quantum devices,photonic-based sampling machines solving the Gaussian boson sampling(GBS)problem currently play a central role in the experimental demonstration of quantum computational advantage.A relevant issue is the validation of the sampling process in the presence of experimental noise,such as photon losses,which could undermine the hardness of simulating the experiment.We test the capability of a validation protocol that exploits the connection between GBS and graph perfect match counting to perform such an assessment in a noisy scenario.In particular,we use as a test bench the recently developed machine Borealis,a large-scale sampling machine that has been made available online for external users,and address its operation in the presence of noise.The employed approach to validation is also shown to provide connections with the open question on the effective advantage of using noisy GBS devices for graph similarity and isomorphism problems and thus provides an effective method for certification of quantum hardware.
