With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair compar...With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.展开更多
Thermally activated delayed fluorescence(TADF)molecules have outstanding potential for applications in organic light-emitting diodes(OLEDs).Due to the lack of systematic studies on the correlation between molecular st...Thermally activated delayed fluorescence(TADF)molecules have outstanding potential for applications in organic light-emitting diodes(OLEDs).Due to the lack of systematic studies on the correlation between molecular structure and luminescence properties,TADF molecules are far from meeting the needs of practical applications in terms of variety and number.In this paper,three twisted TADF molecules are studied and their photophysical properties are theoretically predicted based on the thermal vibrational correlation function method combined with multiscale calculations.The results show that all the molecules exhibit fast reverse intersystem crossing(RISC)rates(kRISC),predicting their TADF luminescence properties.In addition,the binding of DHPAzSi as the donor unit with different acceptors can change the dihedral angle between the ground and excited states,and the planarity of the acceptors is positively correlated with the reorganization energy,a property that has a strong influence on the non-radiative process.Furthermore,a decrease in the energy of the molecular charge transfer state and an increase in the kRISC were observed in the films.This study not only provides a reliable explanation for the observed experimental results,but also offers valuable insights that can guide the design of future TADF molecules.展开更多
With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbin...With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.展开更多
Of Studies is an important one in The Essays of Francis Bacon, of which words are concise and refining, reasoning is profound and logic. Wang Zuoliang's translated version is so efficient and smooth with classical...Of Studies is an important one in The Essays of Francis Bacon, of which words are concise and refining, reasoning is profound and logic. Wang Zuoliang's translated version is so efficient and smooth with classical Chinese style that it perfectly reveals the original text, turning out to be the best version among all the translation of Of Studies. This thesis compares Of Studies and Wang Zuoliang's translated version to analyze their linguistic feature in terms of diction, sentence and figures of speech.展开更多
Ganoderma lucidum,one of the most well-known edible fungi,is believed to be very beneficial for longevity and vitality.A long usage history suggests that G.lucidum has various clinical therapeutic effects.And experime...Ganoderma lucidum,one of the most well-known edible fungi,is believed to be very beneficial for longevity and vitality.A long usage history suggests that G.lucidum has various clinical therapeutic effects.And experimental studies have confirmed that G.lucidum has multiple pharmacological effects,including antitumor,anti-microbial,anti-HIV protease,and antidiabetic activity and so on.With the deepening of research,more than 300 compounds have been isolated from G.lucidum.There is an increasing population of G.lucidum-based products,and its international development is expanding.Currently,G.lucidum has drawn much attention to its chemical composition,therapeutic effect,clinical value,and safety.This paper provides a comprehensive review of these aspects to enhance the global promotion of G.lucidum.展开更多
To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) s...To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.展开更多
Electricity-driven water splitting to produce hydrogen is one of the most efficient ways to alleviate energy crisis and environmental pollution problems,in which the anodic oxygen evolution reaction(OER)is the key hal...Electricity-driven water splitting to produce hydrogen is one of the most efficient ways to alleviate energy crisis and environmental pollution problems,in which the anodic oxygen evolution reaction(OER)is the key half-reaction of performance-limiting in water splitting.Given the complicated reaction process and surface reconstruction of the involved catalysts under actual working conditions,unraveling the real active sites,probing multiple reaction intermediates and clarifying catalytic pathways through in-situ characterization techniques and theoretical calculations are essential.In this review,we summarize the recent advancements in understanding the catalytic process,unlocking the water oxidation active phase and elucidating catalytic mechanism of water oxidation by various in-situ characterization techniques.Firstly,we introduce conventionally proposed traditional catalytic mechanisms and novel evolutionary mechanisms of OER,and highlight the significance of optimal catalytic pathways and intrinsic stability.Next,we provide a comprehensive overview of the fundamental working principles,different detection modes,applicable scenarios,and limitations associated with the in-situ characterization techniques.Further,we exemplified the in-situ studies and discussed phase transition detection,visualization of speciation evolution,electronic structure tracking,observation of reaction active intermediates,and monitoring of catalytic products,as well as establishing catalytic structure-activity relationships and catalytic mechanism.Finally,the key challenges and future perspectives for demystifying the water oxidation process are briefly proposed.展开更多
Background:Meta-analysis is a statistical method to synthesize evidence from a number of independent studies,including those from clinical studies with binary outcomes.In practice,when there are zero events in one or ...Background:Meta-analysis is a statistical method to synthesize evidence from a number of independent studies,including those from clinical studies with binary outcomes.In practice,when there are zero events in one or both groups,it may cause statistical problems in the subsequent analysis.Methods:In this paper,by considering the relative risk as the effect size,we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction,namely the generalized linear mixed models(GLMMs).To further advance the literature,we also introduce a new method of the continuity correction for estimating the relative risk.Results:From the simulation studies,the new method performs well in terms of mean squared error when there are few studies.In contrast,the generalized linear mixed model performs the best when the number of studies is large.In addition,by reanalyzing recent coronavirus disease 2019(COVID-19)data,it is evident that the double-zero-event studies impact the estimate of the mean effect size.Conclusions:We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis,or instead use the GLMM when the number of studies is large.The double-zero-event studies may be informative,and so we suggest not excluding them.展开更多
The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively...The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.展开更多
This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod ...This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.展开更多
Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a cr...Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.展开更多
Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance ...Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.展开更多
Recent studies have shown that the La-and Y-hydrides can exhibit significant superconducting properties under high pressures.In this paper,we investigate the stability,electronic and superconducting properties of LaYH...Recent studies have shown that the La-and Y-hydrides can exhibit significant superconducting properties under high pressures.In this paper,we investigate the stability,electronic and superconducting properties of LaYH_(x)(x=2,3,6 and 8)under 0-200 GPa.It is found that LaYH_(2) stabilizes in the C2/m phase at ambient pressure,and transforms to the Pmmn phase at 67 GPa.LaYH_(3) stabilizes in the C2/m phase at ambient pressure,and undergoes phase transitions of C2/m→P2_(1)/m→R3m at 12 GPa and 87 GPa,respectively.LaYH_(6) stabilizes in the P4_32_12 phase at ambient pressure,and undergoes phase transitions of P4_(3)2_(1)2→P4/mmm→Cmcm at 28 GPa and 79 GPa,respectively.LaYH_(8) stabilizes in the Imma phase at 60 GPa and transforms to the P4/mmm phase at 117 GPa.Calculations of the electronic band structures show that the P4/mmm-LaYH_(8) and all phases of LaYH_(2) and LaYH_(3) exhibit metallic character.For the metallic phases,we then study their superconducting properties.The calculated superconducting transition temperatures(T_c)are 0.47 K for C2/m-LaYH_(2) at 0 GPa,0 K for C2/m-LaYH_(3) at 0 GPa,and 55.51 K for P4/mmm-LaYH_(8) at 50 GPa.展开更多
Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease...Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods:In the present study,we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018.Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span,respectively.Results:A total of 768,129 hospitalizations for CKD was recorded during the study period.The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD,especially in sub-tropical cities.With a 1℃ increase in daily mean temperature,the cumulative relative risks(RR)over lag 0-7 d were 1.008[95% confidence interval(CI)1.003-1.012]for nationwide.The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%.Stronger associations were observed among younger patients and those with obstructive nephropathy.Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116,95%CI 1.069-1.166)above the effect of daily mean temperature.Conclusions:Short-term heat exposure may increase the risk of hospitalization for CKD.Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.展开更多
This paper presents an analogical study between electromagnetic and elastic wave fields,with a one-to-one correspondence principle established regarding the basic wave equations,the physical quantities and the differe...This paper presents an analogical study between electromagnetic and elastic wave fields,with a one-to-one correspondence principle established regarding the basic wave equations,the physical quantities and the differential operations.Using the electromagnetic-to-elastic substitution,the analogous relations of the conservation laws of energy and momentum are investigated between these two physical fields.Moreover,the energy-based and momentum-based reciprocity theorems for an elastic wave are also derived in the time-harmonic state,which describe the interaction between two elastic wave systems from the perspectives of energy and momentum,respectively.The theoretical results obtained in this analysis can not only improve our understanding of the similarities of these two linear systems,but also find potential applications in relevant fields such as medical imaging,non-destructive evaluation,acoustic microscopy,seismology and exploratory geophysics.展开更多
BACKGROUND:Previous studies have reported inconsistent results with positive,negative,and J-shaped associations between alcohol consumption and the hazard of aortic aneurysm and dissection(AAD).This study aimed to exa...BACKGROUND:Previous studies have reported inconsistent results with positive,negative,and J-shaped associations between alcohol consumption and the hazard of aortic aneurysm and dissection(AAD).This study aimed to examine the connections between weekly alcohol consumption and the subsequent risk of AAD.METHODS:The UK Biobank study is a population-based cohort study.Weekly alcohol consumption was assessed using self-reported questionnaires and the congenital risk of alcohol consumption was also evaluated using genetic risk score(GRS).Cox proportional hazards models were used to estimate hazard ratios(HRs)with 95% confidence intervals(CIs)for the associations between alcohol consumption and AAD.Several sensitivity analyses were performed to assess the robustness of the results.RESULTS:Among the 388,955 participants(mean age:57.1 years,47.4% male),2,895 incident AAD cases were documented during a median follow-up of 12.5 years.Compared with never-drinkers,moderate drinkers(adjusted HR:0.797,95%CI:0.646-0.984,P<0.05)and moderate-heavy drinkers(adjusted HR:0.794,95%CI:0.635-0.992,P<0.05)were significantly associated with a decreased risk of incident AAD.Interaction-based subgroup analysis revealed that the protective effect of moderate drinking was reflected mainly in participants younger than 65 years and women.CONCLUSION:Our findings support a protective effect of moderate alcohol consumption on AAD,but are limited to participants younger than 65 years and women.展开更多
基金supported by the National Natural Science Foundation of China (52075420)the National Key Research and Development Program of China (2020YFB1708400)。
文摘With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.
文摘Thermally activated delayed fluorescence(TADF)molecules have outstanding potential for applications in organic light-emitting diodes(OLEDs).Due to the lack of systematic studies on the correlation between molecular structure and luminescence properties,TADF molecules are far from meeting the needs of practical applications in terms of variety and number.In this paper,three twisted TADF molecules are studied and their photophysical properties are theoretically predicted based on the thermal vibrational correlation function method combined with multiscale calculations.The results show that all the molecules exhibit fast reverse intersystem crossing(RISC)rates(kRISC),predicting their TADF luminescence properties.In addition,the binding of DHPAzSi as the donor unit with different acceptors can change the dihedral angle between the ground and excited states,and the planarity of the acceptors is positively correlated with the reorganization energy,a property that has a strong influence on the non-radiative process.Furthermore,a decrease in the energy of the molecular charge transfer state and an increase in the kRISC were observed in the films.This study not only provides a reliable explanation for the observed experimental results,but also offers valuable insights that can guide the design of future TADF molecules.
基金Supported by the National Natural Science Foundation of China under Grant No.52131102.
文摘With the rapid advancement of machine learning technology and its growing adoption in research and engineering applications,an increasing number of studies have embraced data-driven approaches for modeling wind turbine wakes.These models leverage the ability to capture complex,high-dimensional characteristics of wind turbine wakes while offering significantly greater efficiency in the prediction process than physics-driven models.As a result,data-driven wind turbine wake models are regarded as powerful and effective tools for predicting wake behavior and turbine power output.This paper aims to provide a concise yet comprehensive review of existing studies on wind turbine wake modeling that employ data-driven approaches.It begins by defining and classifying machine learning methods to facilitate a clearer understanding of the reviewed literature.Subsequently,the related studies are categorized into four key areas:wind turbine power prediction,data-driven analytic wake models,wake field reconstruction,and the incorporation of explicit physical constraints.The accuracy of data-driven models is influenced by two primary factors:the quality of the training data and the performance of the model itself.Accordingly,both data accuracy and model structure are discussed in detail within the review.
文摘Of Studies is an important one in The Essays of Francis Bacon, of which words are concise and refining, reasoning is profound and logic. Wang Zuoliang's translated version is so efficient and smooth with classical Chinese style that it perfectly reveals the original text, turning out to be the best version among all the translation of Of Studies. This thesis compares Of Studies and Wang Zuoliang's translated version to analyze their linguistic feature in terms of diction, sentence and figures of speech.
基金supported by Macao Science and Technology Development Fund(001/2023/ALC and 0006/2020/AKP)the Research Fund of University of Macao(CPG2023-00028-ICMS)+1 种基金the Guangxi Science and Technology Major Project(GUIKEAA22096029)Macao Young Scholars Program(AM2022022)。
文摘Ganoderma lucidum,one of the most well-known edible fungi,is believed to be very beneficial for longevity and vitality.A long usage history suggests that G.lucidum has various clinical therapeutic effects.And experimental studies have confirmed that G.lucidum has multiple pharmacological effects,including antitumor,anti-microbial,anti-HIV protease,and antidiabetic activity and so on.With the deepening of research,more than 300 compounds have been isolated from G.lucidum.There is an increasing population of G.lucidum-based products,and its international development is expanding.Currently,G.lucidum has drawn much attention to its chemical composition,therapeutic effect,clinical value,and safety.This paper provides a comprehensive review of these aspects to enhance the global promotion of G.lucidum.
基金This work is funded by National Natural Science Foundation of China(Nos.42202292,42141011)the Program for Jilin University(JLU)Science and Technology Innovative Research Team(No.2019TD-35).The authors would also like to thank the reviewers and editors whose critical comments are very helpful in preparing this article.
文摘To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.
基金support from National Natural Science Foundation of China(Grant Nos.22125903,22209174)the National Key R&D Program of China(Grants 2022YFA1504100)+2 种基金Dalian Innovation Support Plan for High Level Talents(2019RT09)Dalian National Laboratory For Clean Energy(DNL),CAS,DNL Cooperation Fund,CAS(DNL202016,DNL202019)DICP(DICP I2020032).
文摘Electricity-driven water splitting to produce hydrogen is one of the most efficient ways to alleviate energy crisis and environmental pollution problems,in which the anodic oxygen evolution reaction(OER)is the key half-reaction of performance-limiting in water splitting.Given the complicated reaction process and surface reconstruction of the involved catalysts under actual working conditions,unraveling the real active sites,probing multiple reaction intermediates and clarifying catalytic pathways through in-situ characterization techniques and theoretical calculations are essential.In this review,we summarize the recent advancements in understanding the catalytic process,unlocking the water oxidation active phase and elucidating catalytic mechanism of water oxidation by various in-situ characterization techniques.Firstly,we introduce conventionally proposed traditional catalytic mechanisms and novel evolutionary mechanisms of OER,and highlight the significance of optimal catalytic pathways and intrinsic stability.Next,we provide a comprehensive overview of the fundamental working principles,different detection modes,applicable scenarios,and limitations associated with the in-situ characterization techniques.Further,we exemplified the in-situ studies and discussed phase transition detection,visualization of speciation evolution,electronic structure tracking,observation of reaction active intermediates,and monitoring of catalytic products,as well as establishing catalytic structure-activity relationships and catalytic mechanism.Finally,the key challenges and future perspectives for demystifying the water oxidation process are briefly proposed.
基金supported by grants awarded to Tie-Jun Tong from the General Research Fund(HKBU12303918)the National Natural Science Foundation of China(1207010822)the Initiation Grants for Faculty Niche Research Areas(RC-IG-FNRA/17-18/13,RC-FNRAIG/20-21/SCI/03)of Hong Kong Baptist University。
文摘Background:Meta-analysis is a statistical method to synthesize evidence from a number of independent studies,including those from clinical studies with binary outcomes.In practice,when there are zero events in one or both groups,it may cause statistical problems in the subsequent analysis.Methods:In this paper,by considering the relative risk as the effect size,we conduct a comparative study that consists of four continuity correction methods and another state-of-the-art method without the continuity correction,namely the generalized linear mixed models(GLMMs).To further advance the literature,we also introduce a new method of the continuity correction for estimating the relative risk.Results:From the simulation studies,the new method performs well in terms of mean squared error when there are few studies.In contrast,the generalized linear mixed model performs the best when the number of studies is large.In addition,by reanalyzing recent coronavirus disease 2019(COVID-19)data,it is evident that the double-zero-event studies impact the estimate of the mean effect size.Conclusions:We recommend the new method to handle the zero-event studies when there are few studies in a meta-analysis,or instead use the GLMM when the number of studies is large.The double-zero-event studies may be informative,and so we suggest not excluding them.
基金supported by the National Natural Science Foundation of China (Nos.52274048 and 52374017)Beijing Natural Science Foundation (No.3222037)the CNPC 14th five-year perspective fundamental research project (No.2021DJ2104)。
文摘The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272257,12102292,12032006)the special fund for Science and Technology Innovation Teams of Shanxi Province(Nos.202204051002006).
文摘This study employs a data-driven methodology that embeds the principle of dimensional invariance into an artificial neural network to automatically identify dominant dimensionless quantities in the penetration of rod projectiles into semi-infinite metal targets from experimental measurements.The derived mathematical expressions of dimensionless quantities are simplified by the examination of the exponent matrix and coupling relationships between feature variables.As a physics-based dimension reduction methodology,this way reduces high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless quantities in penetrating cases.Then the relative importance of various dimensionless feature variables on the penetration efficiencies for four impacting conditions is evaluated through feature selection engineering.The results indicate that the selected critical dimensionless feature variables by this synergistic method,without referring to the complex theoretical equations and aiding in the detailed knowledge of penetration mechanics,are in accordance with those reported in the reference.Lastly,the determined dimensionless quantities can be efficiently applied to conduct semi-empirical analysis for the specific penetrating case,and the reliability of regression functions is validated.
基金supported by the Chinese Scholarship Council(Nos.202208320055 and 202108320111)the support from the energy department of Aalborg University was acknowledged.
文摘Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.
基金supported by the National Natural Science Foundation of China (No.62173281,52377217,U23A20651)Sichuan Science and Technology Program (No.24NSFSC0024,23ZDYF0734,23NSFSC1436)+2 种基金Dazhou City School Cooperation Project (No.DZXQHZ006)Technopole Talent Summit Project (No.KJCRCFH08)Robert Gordon University。
文摘Lithium-ion batteries are the preferred green energy storage method and are equipped with intelligent battery management systems(BMSs)that efficiently manage the batteries.This not only ensures the safety performance of the batteries but also significantly improves their efficiency and reduces their damage rate.Throughout their whole life cycle,lithium-ion batteries undergo aging and performance degradation due to diverse external environments and irregular degradation of internal materials.This degradation is reflected in the state of health(SOH)assessment.Therefore,this review offers the first comprehensive analysis of battery SOH estimation strategies across the entire lifecycle over the past five years,highlighting common research focuses rooted in data-driven methods.It delves into various dimensions such as dataset integration and preprocessing,health feature parameter extraction,and the construction of SOH estimation models.These approaches unearth hidden insights within data,addressing the inherent tension between computational complexity and estimation accuracy.To enha nce support for in-vehicle implementation,cloud computing,and the echelon technologies of battery recycling,remanufacturing,and reuse,as well as to offer insights into these technologies,a segmented management approach will be introduced in the future.This will encompass source domain data processing,multi-feature factor reconfiguration,hybrid drive modeling,parameter correction mechanisms,and fulltime health management.Based on the best SOH estimation outcomes,health strategies tailored to different stages can be devised in the future,leading to the establishment of a comprehensive SOH assessment framework.This will mitigate cross-domain distribution disparities and facilitate adaptation to a broader array of dynamic operation protocols.This article reviews the current research landscape from four perspectives and discusses the challenges that lie ahead.Researchers and practitioners can gain a comprehensive understanding of battery SOH estimation methods,offering valuable insights for the development of advanced battery management systems and embedded application research.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.12364003,11804131,11704163,12375014,and 11875149)the Natural Science Foundation of Jiangxi Province of China (Grant Nos.20232BAB211022 and 20181BAB211007)。
文摘Recent studies have shown that the La-and Y-hydrides can exhibit significant superconducting properties under high pressures.In this paper,we investigate the stability,electronic and superconducting properties of LaYH_(x)(x=2,3,6 and 8)under 0-200 GPa.It is found that LaYH_(2) stabilizes in the C2/m phase at ambient pressure,and transforms to the Pmmn phase at 67 GPa.LaYH_(3) stabilizes in the C2/m phase at ambient pressure,and undergoes phase transitions of C2/m→P2_(1)/m→R3m at 12 GPa and 87 GPa,respectively.LaYH_(6) stabilizes in the P4_32_12 phase at ambient pressure,and undergoes phase transitions of P4_(3)2_(1)2→P4/mmm→Cmcm at 28 GPa and 79 GPa,respectively.LaYH_(8) stabilizes in the Imma phase at 60 GPa and transforms to the P4/mmm phase at 117 GPa.Calculations of the electronic band structures show that the P4/mmm-LaYH_(8) and all phases of LaYH_(2) and LaYH_(3) exhibit metallic character.For the metallic phases,we then study their superconducting properties.The calculated superconducting transition temperatures(T_c)are 0.47 K for C2/m-LaYH_(2) at 0 GPa,0 K for C2/m-LaYH_(3) at 0 GPa,and 55.51 K for P4/mmm-LaYH_(8) at 50 GPa.
基金This study was supported by the National Natural Science Foundation of China(82003529,72125009)the National Key Research and Development Program of the Ministry of Science and Technology of China(2019YFC2005000)+4 种基金the Chinese Scientific and Technical Innovation Project 2030(2018AAA0102100)the National High Level Hospital Clinical Research Funding(“Star of Outlook”Scientific Research Project of Peking University First Hospital,2022XW06)the CAMS Innovation Fund for Medical Sciences(2019-I2M-5-046)the Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)the PKU-Baidu Fund(2020BD004,2020BD005 and 2020BD032).
文摘Background:Climate change profoundly shapes the population health at the global scale.However,there was still insufficient and inconsistent evidence for the association between heat exposure and chronic kidney disease(CKD).Methods:In the present study,we studied the association of heat exposure with hospitalizations for cause-specific CKD using a national inpatient database in China during the study period of hot season from 2015 to 2018.Standard time-series regression models and random-effects Meta-analysis were developed to estimate the city-specific and national averaged associations at a 7 lag-day span,respectively.Results:A total of 768,129 hospitalizations for CKD was recorded during the study period.The results showed that higher temperature was associated with elevated risk of hospitalizations for CKD,especially in sub-tropical cities.With a 1℃ increase in daily mean temperature,the cumulative relative risks(RR)over lag 0-7 d were 1.008[95% confidence interval(CI)1.003-1.012]for nationwide.The attributable fraction of CKD hospitalizations due to high temperatures was 5.50%.Stronger associations were observed among younger patients and those with obstructive nephropathy.Our study also found that exposure to heatwaves was associated with added risk of hospitalizations for CKD compared to non-heatwave days(RR=1.116,95%CI 1.069-1.166)above the effect of daily mean temperature.Conclusions:Short-term heat exposure may increase the risk of hospitalization for CKD.Our findings provide insights into the health effects of climate change and suggest the necessity of guided protection strategies against the adverse effects of high temperatures.
基金funded by the National Natural Science Foundation of China(Grant No.12404507)the Natural Science Research of Jiangsu Higher Education Institutions of China(Grant No.24KJB140013)the Scientific Startup Foundation of Nanjing Normal University(Grant No.184080H201B49).
文摘This paper presents an analogical study between electromagnetic and elastic wave fields,with a one-to-one correspondence principle established regarding the basic wave equations,the physical quantities and the differential operations.Using the electromagnetic-to-elastic substitution,the analogous relations of the conservation laws of energy and momentum are investigated between these two physical fields.Moreover,the energy-based and momentum-based reciprocity theorems for an elastic wave are also derived in the time-harmonic state,which describe the interaction between two elastic wave systems from the perspectives of energy and momentum,respectively.The theoretical results obtained in this analysis can not only improve our understanding of the similarities of these two linear systems,but also find potential applications in relevant fields such as medical imaging,non-destructive evaluation,acoustic microscopy,seismology and exploratory geophysics.
基金supported by grants from the Ministry of Science and Technology of the People’s Republic of China,(No.2020AAA0109605 to XL)grants from the National Natural Science Foundation of China(No.82272246 and 82072225 to XL)+1 种基金Science and Technology Program of Guangzhou,China(No.202206010044 to XL)High-level Hospital Construction Project of Guangdong Provincial People’s Hospital(No.DFJHBF202104 to XL).
文摘BACKGROUND:Previous studies have reported inconsistent results with positive,negative,and J-shaped associations between alcohol consumption and the hazard of aortic aneurysm and dissection(AAD).This study aimed to examine the connections between weekly alcohol consumption and the subsequent risk of AAD.METHODS:The UK Biobank study is a population-based cohort study.Weekly alcohol consumption was assessed using self-reported questionnaires and the congenital risk of alcohol consumption was also evaluated using genetic risk score(GRS).Cox proportional hazards models were used to estimate hazard ratios(HRs)with 95% confidence intervals(CIs)for the associations between alcohol consumption and AAD.Several sensitivity analyses were performed to assess the robustness of the results.RESULTS:Among the 388,955 participants(mean age:57.1 years,47.4% male),2,895 incident AAD cases were documented during a median follow-up of 12.5 years.Compared with never-drinkers,moderate drinkers(adjusted HR:0.797,95%CI:0.646-0.984,P<0.05)and moderate-heavy drinkers(adjusted HR:0.794,95%CI:0.635-0.992,P<0.05)were significantly associated with a decreased risk of incident AAD.Interaction-based subgroup analysis revealed that the protective effect of moderate drinking was reflected mainly in participants younger than 65 years and women.CONCLUSION:Our findings support a protective effect of moderate alcohol consumption on AAD,but are limited to participants younger than 65 years and women.
