In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fi...In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation.A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs.We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations;these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified:(i) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality;(ii) quartz-cemented facies,which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality;(iii) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality;(iv) carbonate-cemented facies and(v) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains;thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions(conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.展开更多
Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the pro...Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.展开更多
The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF str...The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical applications.However, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, ex...Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical applications.However, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 donor.Accordingly, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.展开更多
In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated ...In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated spectrum is established,and a convolutional neural network consisting of 51 layers and more than 105 parameters is constructed to directly predict the entire continuum from the extracted global spectrum features.For testing,an in-house NaI-type whole-body counter is used,and 106 training spectrum samples(20%of which are reserved for testing)are generated using Monte Carlo simulations.In addition,the existing fitting,step-type,and peak erosion methods are selected for comparison.The proposed method exhibits excellent performance,as evidenced by its activity error distribution and the smallest mean activity error of 1.5%among the evaluated methods.Additionally,a validation experiment is performed using a whole-body counter to analyze a human physical phantom containing four radionuclides.The largest activity error of the proposed method is−5.1%,which is considerably smaller than those of the comparative methods,confirming the test results.The multiscale feature extraction and nonlinear relation modeling in the proposed method establish a novel approach for accurate and convenient continuum estimation in a low-resolution gamma-ray spectrum.Thus,the proposed method is promising for accurate quantitative radioactivity analysis in practical applications.展开更多
Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction(HER),which faces challenges of slow kinetics and high overpotential.Efficient electrocatalysts,particularly single-at...Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction(HER),which faces challenges of slow kinetics and high overpotential.Efficient electrocatalysts,particularly single-atom catalysts (SACs) on two-dimensional (2D) materials,are essential.This study presents a few-shot machine learning (ML) assisted high-throughput screening of 2D septuple-atomic-layer Ga_(2)CoS_(4-x)supported SACs to predict HER catalytic activity.Initially,density functional theory (DFT)calculations showed that 2D Ga_(2)CoS4is inactive for HER.However,defective Ga_(2)CoS_(4-x)(x=0–0.25)monolayers exhibit excellent HER activity due to surface sulfur vacancies (SVs),with predicted overpotentials (0–60 mV) comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER sites.We demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D Ga_(2)CoS_(4-x).Using DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was developed.An intrinsic descriptor linking SAC atomic properties to HER overpotential was identified.This study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.展开更多
As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of u...As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.展开更多
Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air tr...Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.展开更多
Background:Studies have revealed the protective effect of DL-3-n-butylphthalide(NBP)against diseases associated with ischemic hypoxia.However,the role of NBP in animals with hypobaric hypoxia has not been elucidated.T...Background:Studies have revealed the protective effect of DL-3-n-butylphthalide(NBP)against diseases associated with ischemic hypoxia.However,the role of NBP in animals with hypobaric hypoxia has not been elucidated.This study investigated the effects of NBP on rodents with acute and chronic hypobaric hypoxia.Methods:Sprague-Dwaley rats and Kunming mice administered with NBP(0,60,120,and 240 mg/kg for rats and 0,90,180,and 360 mg/kg for mice)were placed in a hypobaric hypoxia chamber at 10,000 m and the survival percentages at 30 min were determined.Then,the time and distance to exhaustion of drug-treated rodents were evaluated during treadmill running and motor-driven wheel-track treadmill experiments,conducted at 5800 m for 3 days or 20 days,to evaluate changes in physical functions.The frequency of active escapes and duration of active escapes were also determined for rats in a shuttle-box experiment,conducted at 5800 m for 6 days or 27 days,to evaluate changes in learning and memory function.ATP levels were measured in the gastrocnemius muscle and malonaldehyde(MDA),superoxide dismutase(SOD),hydrogen peroxide(H_(2)O_(2)),glutathione peroxidase(GSH-Px),and lactate were detected in sera of rats,and routine blood tests were also performed.Results:Survival analysis at 10,000 m indicated NBP could improve hypoxia tolerance ability.The time and distance to exhaustion for mice(NBP,90 mg/kg)and time to exhaustion for rats(NBP,120 and 240 mg/kg)significantly increased under conditions of acute hypoxia compared with control group.NBP treatment also significantly increased the time to exhaustion for rats when exposed to chronic hypoxia.Moreover,240 mg/kg NBP significantly increased the frequency of active escapes under conditions of acute hypoxia.Furthermore,the levels of MDA and H_(2)O_(2) decreased but those of SOD and GSH-Px in the sera of rats increased under conditions of acute and chronic hypoxia.Additionally,ATP levels in the gastrocnemius muscle significantly increased,while lactate levels in sera significantly decreased.Conclusion:NBP improved physical and learning and memory functions in rodents exposed to acute or chronic hypobaric hypoxia by increasing their anti-oxidative capacity and energy supply.展开更多
With the implementation of the New Standards for English Curriculums and the reformation of the educational system,many teachers have begun to focus on students’learning process and experience.However,the phenomenon ...With the implementation of the New Standards for English Curriculums and the reformation of the educational system,many teachers have begun to focus on students’learning process and experience.However,the phenomenon of students’passive learning also exists.Facing such situation,it is imperative to change students’learning ways.Experiential learning,as a new way of learning has been widely recognized as an effective approach in learning.Therefore,in order to change students’learning ways,improve their comprehensive English level and learning interest,it is important for teachers to apply the experiential learn ing in English teaching,thus,improving the current situation of junior middle school English teaching.展开更多
When students enter high school, they have a vague sense of adult psychologically, they have the desire to be independent and creative, and their confidence in their own ability is rising day by day. However, in forei...When students enter high school, they have a vague sense of adult psychologically, they have the desire to be independent and creative, and their confidence in their own ability is rising day by day. However, in foreign language learning, they still lack the ability of practice and self-study in foreign language learning. In particular, English teaching is limited to the traditional classroom teaching methods, it is difficult to arouse the enthusiasm of the students in higher vocational education. Using the advantages of App to apply it to English teaching can make the teaching mode interactive and fresh. At the same time, it has a convenient feedback mechanism, teachers can guide students to discuss and study outside the classroom, adjust the teaching arrangement in time, and achieve the real "student-centered".展开更多
基金financially supported by the National Natural Science Foundation of China (No. 42272156)research on efficient exploration and development technology for tight stone gas of China United Coalbed Methane Corporation (No. ZZGSECCYWG 2021-322)。
文摘In this study, an integrated approach for diagenetic facies classification, reservoir quality analysis and quantitative wireline log prediction of tight gas sandstones(TGSs) is introduced utilizing a combination of fit-for-purpose complementary testing and machine learning techniques. The integrated approach is specialized for the middle Permian Shihezi Formation TGSs in the northeastern Ordos Basin, where operators often face significant drilling uncertainty and increased exploration risks due to low porosities and micro-Darcy range permeabilities. In this study, detrital compositions and diagenetic minerals and their pore type assemblages were analyzed using optical light microscopy, cathodoluminescence, standard scanning electron microscopy, and X-ray diffraction. Different types of diagenetic facies were delineated on this basis to capture the characteristic rock properties of the TGSs in the target formation.A combination of He porosity and permeability measurements, mercury intrusion capillary pressure and nuclear magnetic resonance data was used to analyze the mechanism of heterogeneous TGS reservoirs.We found that the type, size and proportion of pores considerably varied between diagenetic facies due to differences in the initial depositional attributes and subsequent diagenetic alterations;these differences affected the size, distribution and connectivity of the pore network and varied the reservoir quality. Five types of diagenetic facies were classified:(i) grain-coating facies, which have minimal ductile grains, chlorite coatings that inhibit quartz overgrowths, large intergranular pores that dominate the pore network, the best pore structure and the greatest reservoir quality;(ii) quartz-cemented facies,which exhibit strong quartz overgrowths, intergranular porosity and a pore size decrease, resulting in the deterioration of the pore structure and reservoir quality;(iii) mixed-cemented facies, in which the cementation of various authigenic minerals increases the micropores, resulting in a poor pore structure and reservoir quality;(iv) carbonate-cemented facies and(v) tightly compacted facies, in which the intergranular pores are filled with carbonate cement and ductile grains;thus, the pore network mainly consists of micropores with small pore throat sizes, and the pore structure and reservoir quality are the worst. The grain-coating facies with the best reservoir properties are more likely to have high gas productivity and are the primary targets for exploration and development. The diagenetic facies were then translated into wireline log expressions(conventional and NMR logging). Finally, a wireline log quantitative prediction model of TGSs using convolutional neural network machine learning algorithms was established to successfully classify the different diagenetic facies.
基金supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155)the Taishan Scholars Project Special Funds(tsqn202312035)the open research foundation of State Key Laboratory of Integrated Chips and Systems.
文摘Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.
基金financial support from the National Key Research and Development Program of China(2021YFB 3501501)the National Natural Science Foundation of China(No.22225803,22038001,22108007 and 22278011)+1 种基金Beijing Natural Science Foundation(No.Z230023)Beijing Science and Technology Commission(No.Z211100004321001).
文摘The high porosity and tunable chemical functionality of metal-organic frameworks(MOFs)make it a promising catalyst design platform.High-throughput screening of catalytic performance is feasible since the large MOF structure database is available.In this study,we report a machine learning model for high-throughput screening of MOF catalysts for the CO_(2) cycloaddition reaction.The descriptors for model training were judiciously chosen according to the reaction mechanism,which leads to high accuracy up to 97%for the 75%quantile of the training set as the classification criterion.The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding.12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100℃ and 1 bar within one day using the model,and 239 potentially efficient catalysts were discovered.Among them,MOF-76(Y)achieved the top performance experimentally among reported MOFs,in good agreement with the prediction.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金National Research Foundation of Korea (NRF) grant (No. 2016R1A3B 1908249) funded by the Korean government。
文摘Organic solar cells(OSCs) hold great potential as a photovoltaic technology for practical applications.However, the traditional experimental trial-and-error method for designing and engineering OSCs can be complex, expensive, and time-consuming. Machine learning(ML) techniques enable the proficient extraction of information from datasets, allowing the development of realistic models that are capable of predicting the efficacy of materials with commendable accuracy. The PM6 donor has great potential for high-performance OSCs. However, it is crucial for the rational design of a ternary blend to accurately forecast the power conversion efficiency(PCE) of ternary OSCs(TOSCs) based on a PM6 donor.Accordingly, we collected the device parameters of PM6-based TOSCs and evaluated the feature importance of their molecule descriptors to develop predictive models. In this study, we used five different ML algorithms for analysis and prediction. For the analysis, the classification and regression tree provided different rules, heuristics, and patterns from the heterogeneous dataset. The random forest algorithm outperforms other prediction ML algorithms in predicting the output performance of PM6-based TOSCs. Finally, we validated the ML outcomes by fabricating PM6-based TOSCs. Our study presents a rapid strategy for assessing a high PCE while elucidating the substantial influence of diverse descriptors.
基金supported by the National Natural Science Foundation of China(No.12005198).
文摘In this study,an end-to-end deep learning method is proposed to improve the accuracy of continuum estimation in low-resolution gamma-ray spectra.A novel process for generating the theoretical continuum of a simulated spectrum is established,and a convolutional neural network consisting of 51 layers and more than 105 parameters is constructed to directly predict the entire continuum from the extracted global spectrum features.For testing,an in-house NaI-type whole-body counter is used,and 106 training spectrum samples(20%of which are reserved for testing)are generated using Monte Carlo simulations.In addition,the existing fitting,step-type,and peak erosion methods are selected for comparison.The proposed method exhibits excellent performance,as evidenced by its activity error distribution and the smallest mean activity error of 1.5%among the evaluated methods.Additionally,a validation experiment is performed using a whole-body counter to analyze a human physical phantom containing four radionuclides.The largest activity error of the proposed method is−5.1%,which is considerably smaller than those of the comparative methods,confirming the test results.The multiscale feature extraction and nonlinear relation modeling in the proposed method establish a novel approach for accurate and convenient continuum estimation in a low-resolution gamma-ray spectrum.Thus,the proposed method is promising for accurate quantitative radioactivity analysis in practical applications.
文摘Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction(HER),which faces challenges of slow kinetics and high overpotential.Efficient electrocatalysts,particularly single-atom catalysts (SACs) on two-dimensional (2D) materials,are essential.This study presents a few-shot machine learning (ML) assisted high-throughput screening of 2D septuple-atomic-layer Ga_(2)CoS_(4-x)supported SACs to predict HER catalytic activity.Initially,density functional theory (DFT)calculations showed that 2D Ga_(2)CoS4is inactive for HER.However,defective Ga_(2)CoS_(4-x)(x=0–0.25)monolayers exhibit excellent HER activity due to surface sulfur vacancies (SVs),with predicted overpotentials (0–60 mV) comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER sites.We demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D Ga_(2)CoS_(4-x).Using DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was developed.An intrinsic descriptor linking SAC atomic properties to HER overpotential was identified.This study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.
基金the National Natural Science Foundation of China(Grant Nos.61905115,62105151,62175109,U21B2033)Leading Technology of Jiangsu Basic Research Plan(Grant No.BK20192003)+2 种基金Youth Foundation of Jiangsu Province(Grant Nos.BK20190445,BK20210338)Fundamental Research Funds for the Central Universities(Grant No.30920032101)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(Grant No.JSGP202105)to provide fund for conducting experiments。
文摘As the representative of flexibility in optical imaging media,in recent years,fiber bundles have emerged as a promising architecture in the development of compact visual systems.Dedicated to tackling the problems of universal honeycomb artifacts and low signal-to-noise ratio(SNR)imaging in fiber bundles,the iterative super-resolution reconstruction network based on a physical model is proposed.Under the constraint of solving the two subproblems of data fidelity and prior regularization term alternately,the network can efficiently“regenerate”the lost spatial resolution with deep learning.By building and calibrating a dual-path imaging system,the real-world dataset where paired low-resolution(LR)-high-resolution(HR)images on the same scene can be generated simultaneously.Numerical results on both the United States Air Force(USAF)resolution target and complex target objects demonstrate that the algorithm can restore high-contrast images without pixilated noise.On the basis of super-resolution reconstruction,compound eye image composition based on fiber bundle is also embedded in this paper for the actual imaging requirements.The proposed work is the first to apply a physical model-based deep learning network to fiber bundle imaging in the infrared band,effectively promoting the engineering application of thermal radiation detection.
基金This work was supported by the Na⁃tional Natural Science Foundation of China(No.61903187)Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20190732)。
文摘Air traffic complexity is a critical indicator for air traffic operation,and plays an important role in air traffic management(ATM),such as airspace reconfiguration,air traffic flow management and allocation of air traffic controllers(ATCos).Recently,many machine learning techniques have been used to evaluate air traffic complexity by constructing a mapping from complexity related factors to air traffic complexity labels.However,the low quality of complexity labels,which is named as label noise,has often been neglected and caused unsatisfactory performance in air traffic complexity evaluation.This paper aims at label noise in air traffic complexity samples,and proposes a confident learning and XGBoost-based approach to evaluate air traffic complexity under label noise.The confident learning process is applied to filter out noisy samples with various label probability distributions,and XGBoost is used to train a robust and high-performance air traffic complexity evaluation model on the different label noise filtered ratio datasets.Experiments are carried out on a real dataset from the Guangzhou airspace sector in China,and the results prove that the appropriate label noise removal strategy and XGBoost algorithm can effectively mitigate the label noise problem and achieve better performance in air traffic complexity evaluation.
基金supported by grants from the National Science and Technology Major Project(2014ZX09J14102-05B and 2018ZX09J18109)。
文摘Background:Studies have revealed the protective effect of DL-3-n-butylphthalide(NBP)against diseases associated with ischemic hypoxia.However,the role of NBP in animals with hypobaric hypoxia has not been elucidated.This study investigated the effects of NBP on rodents with acute and chronic hypobaric hypoxia.Methods:Sprague-Dwaley rats and Kunming mice administered with NBP(0,60,120,and 240 mg/kg for rats and 0,90,180,and 360 mg/kg for mice)were placed in a hypobaric hypoxia chamber at 10,000 m and the survival percentages at 30 min were determined.Then,the time and distance to exhaustion of drug-treated rodents were evaluated during treadmill running and motor-driven wheel-track treadmill experiments,conducted at 5800 m for 3 days or 20 days,to evaluate changes in physical functions.The frequency of active escapes and duration of active escapes were also determined for rats in a shuttle-box experiment,conducted at 5800 m for 6 days or 27 days,to evaluate changes in learning and memory function.ATP levels were measured in the gastrocnemius muscle and malonaldehyde(MDA),superoxide dismutase(SOD),hydrogen peroxide(H_(2)O_(2)),glutathione peroxidase(GSH-Px),and lactate were detected in sera of rats,and routine blood tests were also performed.Results:Survival analysis at 10,000 m indicated NBP could improve hypoxia tolerance ability.The time and distance to exhaustion for mice(NBP,90 mg/kg)and time to exhaustion for rats(NBP,120 and 240 mg/kg)significantly increased under conditions of acute hypoxia compared with control group.NBP treatment also significantly increased the time to exhaustion for rats when exposed to chronic hypoxia.Moreover,240 mg/kg NBP significantly increased the frequency of active escapes under conditions of acute hypoxia.Furthermore,the levels of MDA and H_(2)O_(2) decreased but those of SOD and GSH-Px in the sera of rats increased under conditions of acute and chronic hypoxia.Additionally,ATP levels in the gastrocnemius muscle significantly increased,while lactate levels in sera significantly decreased.Conclusion:NBP improved physical and learning and memory functions in rodents exposed to acute or chronic hypobaric hypoxia by increasing their anti-oxidative capacity and energy supply.
文摘With the implementation of the New Standards for English Curriculums and the reformation of the educational system,many teachers have begun to focus on students’learning process and experience.However,the phenomenon of students’passive learning also exists.Facing such situation,it is imperative to change students’learning ways.Experiential learning,as a new way of learning has been widely recognized as an effective approach in learning.Therefore,in order to change students’learning ways,improve their comprehensive English level and learning interest,it is important for teachers to apply the experiential learn ing in English teaching,thus,improving the current situation of junior middle school English teaching.
文摘When students enter high school, they have a vague sense of adult psychologically, they have the desire to be independent and creative, and their confidence in their own ability is rising day by day. However, in foreign language learning, they still lack the ability of practice and self-study in foreign language learning. In particular, English teaching is limited to the traditional classroom teaching methods, it is difficult to arouse the enthusiasm of the students in higher vocational education. Using the advantages of App to apply it to English teaching can make the teaching mode interactive and fresh. At the same time, it has a convenient feedback mechanism, teachers can guide students to discuss and study outside the classroom, adjust the teaching arrangement in time, and achieve the real "student-centered".
