It is quite important to ensure the safety and sustainable development of nuclear energy for the treatment of radioactive wastewater. To treat radioactive wastewater efficiently and rapidly, two multi-amine β-cyclode...It is quite important to ensure the safety and sustainable development of nuclear energy for the treatment of radioactive wastewater. To treat radioactive wastewater efficiently and rapidly, two multi-amine β-cyclodextrin polymers(diethylenetriamine β-cyclodextrin polymer(DETA-TFCDP) and triethylenetetramine β-cyclodextrin polymer(TETA-TFCDP)) were prepared and applied to capture uranium. Results exhibited that DETA-TFCDP and TETA-TFCDP displayed the advantages of high adsorption amounts(612.2and 628.2 mg·g-1, respectively) and rapid adsorption rates, which can reach(88 ± 1)% of their equilibrium adsorption amounts in 10 min. Moreover, the adsorbent processes of DETA-TFCDP and TETATFCDP on uranium(Ⅵ) followed the Langmuir model and pseudo-second-order model, stating they were mainly chemisorption and self-endothermic. Besides, TETA-TFCDP also showed excellent selectivity in the presence of seven competing cations and could be effectively reused five times via Na2CO3as the desorption reagent. Meanwhile, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy illustrated that the enriched multi-amine groups and oxygen-containing functional groups on the surface of TETA-TFCDP were the main active sites for capturing uranium(Ⅵ). Hence, multi-amine β-cyclodextrin polymers are a highly efficient, rapid, and promising adsorbent for capturing uranium(Ⅵ)from radioactive wastewater.展开更多
Rock and geotechnical engineering investigations involve drilling holes in ground with or without retrieving soil and rock samples to construct the subsurface ground profile.On the basis of an actual soil nailing dril...Rock and geotechnical engineering investigations involve drilling holes in ground with or without retrieving soil and rock samples to construct the subsurface ground profile.On the basis of an actual soil nailing drilling for a slope stability project in Hong Kong,this paper further develops the drilling process monitoring(DPM)method for digitally profiling the subsurface geomaterials of weathered granitic rocks using a compressed airflow driven percussive-rotary drilling machine with down-the-hole(DTH)hammer.Seven transducers are installed on the drilling machine and record the chuck displacement,DTH rotational speed,and five pressures from five compressed airflows in real-time series.The mechanism and operations of the drilling machine are elaborated in detail,which is essential for understanding and evaluating the drilling data.A MATLAB program is developed to automatically filter the recorded drilling data in time series and classify them into different drilling processes in sub-time series.These processes include penetration,push-in with or without rod,pull-back with or without rod,rod-tightening and rod-untightening.The drilling data are further reconstructed to plot the curve of drill-bit depth versus the net drilling time along each of the six drillholes.Each curve is found to contain multiple linear segments with a constant penetration rate,which implies a zone of homogenous geomaterial with different weathering grades.The effect from fluctuation of the applied pressures is evaluated quantitatively.Detailed analyses are presented for accurately assess and verify the underground profiling and strength in weathered granitic rock,which provided the basis of using DPM method to confidently assess drilling measurements to interpret the subsurface profile in real time.展开更多
The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classificatio...The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.展开更多
Intercalation provides to the host materials a means for controlled variation of many physical/chemical properties and dominates the reactions in metal‐ion batteries.Of particular interest is the graphite intercalati...Intercalation provides to the host materials a means for controlled variation of many physical/chemical properties and dominates the reactions in metal‐ion batteries.Of particular interest is the graphite intercalation compounds with intriguing staging structures,which however are still unclear,especially in their nanostructure and dynamic transition mechanism.Herein,the nature of the staging structure and evolution of the lithium(Li)‐intercalated graphite was revealed by cryogenic‐transmission electron microscopy and other methods at the nanoscale.The intercalated Li‐ions distribute unevenly,generating local stress and dislocations in the graphitic structure.Each staging compound is found macroscopically ordered but microscopically inhomogeneous,exhibiting a localized‐domains structural model.Our findings uncover the correlation between the long‐range ordered structure and short‐range domains,refresh the insights on the staging structure and transition of Li‐intercalated/deintercalated graphite,and provide effective ways to enhance the reaction kinetic in rechargeable batteries by defect engineering.展开更多
In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,i...In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.展开更多
Firstly, in the general normed linear space, the concepts of generalized isosceles orthogonal group, generalized Birkhoff orthogonal group, generalized Roberts orthogonal group, strong Birkhoff orthogonal group and ge...Firstly, in the general normed linear space, the concepts of generalized isosceles orthogonal group, generalized Birkhoff orthogonal group, generalized Roberts orthogonal group, strong Birkhoff orthogonal group and generalized orthogonal basis are introduced. Secondly, the conclusion that any two nonzero generalized orthogonal groups must be linearly independent group is proven. And the existence of nonzero generalized orthogonal group and its linear correlation are discussed preliminarily, as well as some related properties of nonempty generalized orthogonal group in specific normed linear space namely the <em>l<sub>p</sub></em> space.展开更多
The Sichuan-Xizang Railway is a global challenge,surpassing other known railway projects in terms of geological and topographical complexity.This paper presents an approach for rapidly profiling rock mass quality unde...The Sichuan-Xizang Railway is a global challenge,surpassing other known railway projects in terms of geological and topographical complexity.This paper presents an approach for rapidly profiling rock mass quality underneath tunnel face for the ongoing construction of the Sichuan-Xizang Railway.It adopts the time-series method and carries out the quantitative analysis of the rock mass quality using the depth-series measurement-while-drilling(MWD)data associated with drilling of blastholes.A tunnel face with 15 blastholes is examined for illustration.The results include identification of the boundary of homogeneous geomaterial by plotting the blasthole depth against the net drilling time,as well as quantification of rock mass quality through the recalculation of the new specific energy.The new specific energy profile is compared and highly consistent with laboratory test,manual logging and tunnel seismic prediction results.This consistency can enhance the blasthole pattern design and facilitate the dynamic determination of charge placement and amount.This paper highlights the importance of digital monitoring during blasthole drilling for rapidly profiling rock mass quality underneath and ahead of tunnel face.It upgrades the MWD technique for rapid profiling rock mass quality in drilling and blasting tunnels.展开更多
With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to rele...With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.展开更多
Patients with chronic obstructive pulmonary disease(COPD)who exhibit elevated blood eosinophil levels often experience worsened lung function and more severe emphysema.This implies the potential involvement of eosinop...Patients with chronic obstructive pulmonary disease(COPD)who exhibit elevated blood eosinophil levels often experience worsened lung function and more severe emphysema.This implies the potential involvement of eosinophils in the development of emphysema.However,the precise mechanisms underlying the development of eosinophil-mediated emphysema remain unclear.In this study,we employed single-cell RNA sequencing to identify eosinophil subgroups in mouse models of asthma and emphysema,followed by functional analyses of these subgroups.Assessment of accumulated eosinophils unveiled distinct transcriptomes in the lungs of mice with elastase-induced emphysema and ovalbumin-induced asthma.Depletion of eosinophils through the use of anti-interleukin-5 antibodies ameliorated elastase-induced emphysema.A particularly noteworthy discovery is that eosinophil-derived cathepsin L contributed to the degradation of the extracellular matrix,thereby leading to emphysema in pulmonary tissue Inhibition of cathepsin L resulted in a reduction of elastase-induced emphysema in a mouse model.Importantly,eosinophil levels correlated positively with serum cathepsin L levels,which were higher in emphysema patients than those without emphysema.Expression of cathepsin L in eosinophils demonstrated a direct association with lung emphysema in COPD patients.Collectively,these findings underscore the significant role of eosinophil-derived cathepsin L in extracellular matrix degradation and remodeling,and its relevance to emphysema in coPD patients.Consequently,targeting eosinophil-derived cathepsin L could potentially offer a therapeutic avenue for emphysema patients.Further investigations are warranted to explore therapeutic strategies targeting cathepsin L in emphysema patients.展开更多
ABSTRACT The concept of healthy life expectancy(HLE)integrates the ideas of life expectancy and health status,providing a valuable metric to evaluate both the length and quality of life.This paper seeks to aid policym...ABSTRACT The concept of healthy life expectancy(HLE)integrates the ideas of life expectancy and health status,providing a valuable metric to evaluate both the length and quality of life.This paper seeks to aid policymakers in creating an inclusive HLE indicator system through a systematic review of methodologies for defining and measuring HLE,along with relevant published studies’descriptions.Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement.展开更多
Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migratio...Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migration performance estimation.The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures,and the computational cost is always high because of the task-intensive features.The last one relates to the kinetic problems,for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods.In this work,two predictive models,ionic migration activation energy model and structural optimization model,are developed based on machine learning(ML)techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures,respectively.By training 3136 energy barrier data calculated by bond valence(BV)method,an ionic migration activation energy model(Ea model)with mean absolute error(MAE)of 0.26 eV on testing data set is obtained.We apply this model and filter LiBiOS as a promising fast Li^(+)conductor from 49 Licontaining hetero-anionic compounds.Although the model-predicted result shows relatively low energy barrier,further analysis indicates that the high carrier formation energy restricts the ionic transportability.Therefore,we substitute fractional Li^(+)with Mg^(2+)in LiBiOS to relieve the large difficulty of forming carriers in the structure.In order to fast explore the optimal doping scheme,we develop the structural optimization model(E-f model)containing the ML-based energy and force prediction to accelerate the structural optimization under various LieMg ratio and doping configurations.Decent doping scheme Li_(1-2x)Mg_(x)BiOS(x=0.1875)shows much better Li^(+)migration performance compared with LiBiOS without substitution.This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.展开更多
基金National Natural Science Foundation of China(21603064,52102214)Natural Science Foundation of Jiangxi Province(20202BABL203026,20212BAB203001,20202BABL214016)College Student Innovation and Enterprise Programme of Jiangxi Province(S202310405010)provided funding for this study.
文摘It is quite important to ensure the safety and sustainable development of nuclear energy for the treatment of radioactive wastewater. To treat radioactive wastewater efficiently and rapidly, two multi-amine β-cyclodextrin polymers(diethylenetriamine β-cyclodextrin polymer(DETA-TFCDP) and triethylenetetramine β-cyclodextrin polymer(TETA-TFCDP)) were prepared and applied to capture uranium. Results exhibited that DETA-TFCDP and TETA-TFCDP displayed the advantages of high adsorption amounts(612.2and 628.2 mg·g-1, respectively) and rapid adsorption rates, which can reach(88 ± 1)% of their equilibrium adsorption amounts in 10 min. Moreover, the adsorbent processes of DETA-TFCDP and TETATFCDP on uranium(Ⅵ) followed the Langmuir model and pseudo-second-order model, stating they were mainly chemisorption and self-endothermic. Besides, TETA-TFCDP also showed excellent selectivity in the presence of seven competing cations and could be effectively reused five times via Na2CO3as the desorption reagent. Meanwhile, X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy illustrated that the enriched multi-amine groups and oxygen-containing functional groups on the surface of TETA-TFCDP were the main active sites for capturing uranium(Ⅵ). Hence, multi-amine β-cyclodextrin polymers are a highly efficient, rapid, and promising adsorbent for capturing uranium(Ⅵ)from radioactive wastewater.
基金supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region,China(Project Nos.HKU 7137/03E and R7005/01E)。
文摘Rock and geotechnical engineering investigations involve drilling holes in ground with or without retrieving soil and rock samples to construct the subsurface ground profile.On the basis of an actual soil nailing drilling for a slope stability project in Hong Kong,this paper further develops the drilling process monitoring(DPM)method for digitally profiling the subsurface geomaterials of weathered granitic rocks using a compressed airflow driven percussive-rotary drilling machine with down-the-hole(DTH)hammer.Seven transducers are installed on the drilling machine and record the chuck displacement,DTH rotational speed,and five pressures from five compressed airflows in real-time series.The mechanism and operations of the drilling machine are elaborated in detail,which is essential for understanding and evaluating the drilling data.A MATLAB program is developed to automatically filter the recorded drilling data in time series and classify them into different drilling processes in sub-time series.These processes include penetration,push-in with or without rod,pull-back with or without rod,rod-tightening and rod-untightening.The drilling data are further reconstructed to plot the curve of drill-bit depth versus the net drilling time along each of the six drillholes.Each curve is found to contain multiple linear segments with a constant penetration rate,which implies a zone of homogenous geomaterial with different weathering grades.The effect from fluctuation of the applied pressures is evaluated quantitatively.Detailed analyses are presented for accurately assess and verify the underground profiling and strength in weathered granitic rock,which provided the basis of using DPM method to confidently assess drilling measurements to interpret the subsurface profile in real time.
基金funded by the Informatization Plan of Chinese Academy of Sciences(Grant No.CASWX2021SF-0102)the National Key R&D Program of China(Grant Nos.2022YFA1603903,2022YFA1403800,and 2021YFA0718700)+1 种基金the National Natural Science Foundation of China(Grant Nos.11925408,11921004,and 12188101)the Chinese Academy of Sciences(Grant No.XDB33000000)。
文摘The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.
基金support from the National Natural Science Foundation of China(NSFC nos.52172257,22005334,21773301 and 52022106)the Natural Science Foundation of Beijing(grant no.Z200013).
文摘Intercalation provides to the host materials a means for controlled variation of many physical/chemical properties and dominates the reactions in metal‐ion batteries.Of particular interest is the graphite intercalation compounds with intriguing staging structures,which however are still unclear,especially in their nanostructure and dynamic transition mechanism.Herein,the nature of the staging structure and evolution of the lithium(Li)‐intercalated graphite was revealed by cryogenic‐transmission electron microscopy and other methods at the nanoscale.The intercalated Li‐ions distribute unevenly,generating local stress and dislocations in the graphitic structure.Each staging compound is found macroscopically ordered but microscopically inhomogeneous,exhibiting a localized‐domains structural model.Our findings uncover the correlation between the long‐range ordered structure and short‐range domains,refresh the insights on the staging structure and transition of Li‐intercalated/deintercalated graphite,and provide effective ways to enhance the reaction kinetic in rechargeable batteries by defect engineering.
基金This work was supported in part by National Natural Science Foundation of China under Grant 62076199in part by the Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety under Grant BTBD-2020KF08+2 种基金Beijing Technology and Business University,in part by the China Postdoctoral Science Foundation under Grant 2019M653784in part by Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences under Grant LSIT201801Din part by the Key R&D Project of Shaan’xi Province under Grant 2021GY-027。
文摘In this paper,we propose a neoteric and high-efficiency single image dehazing algorithm via contrast enhancement which is called STRASS(Spatio-Temporal Retinex-Inspired by an Averaging of Stochastic Samples)dehazing,it is realized by constructing an efficient high-pass filter to process haze images and taking the influence of human vision system into account in image dehazing principles.The novel high-pass filter works by getting each pixel using RSR and computes the average of the samples.Then the low-pass filter resulting from the minimum envelope in STRESS framework has been replaced by the average of the samples.The final dehazed image is yielded after iterations of the high-pass filter.STRASS can be run directly without any machine learning.Extensive experimental results on datasets prove that STRASS surpass the state-of-the-arts.Image dehazing can be applied in the field of printing and packaging,our method is of great significance for image pre-processing before printing.
文摘Firstly, in the general normed linear space, the concepts of generalized isosceles orthogonal group, generalized Birkhoff orthogonal group, generalized Roberts orthogonal group, strong Birkhoff orthogonal group and generalized orthogonal basis are introduced. Secondly, the conclusion that any two nonzero generalized orthogonal groups must be linearly independent group is proven. And the existence of nonzero generalized orthogonal group and its linear correlation are discussed preliminarily, as well as some related properties of nonempty generalized orthogonal group in specific normed linear space namely the <em>l<sub>p</sub></em> space.
基金partially supported by grants from the Research Grant Council of the Hong Kong,China(Project Nos.HKU 17207518 and R5037-18)。
文摘The Sichuan-Xizang Railway is a global challenge,surpassing other known railway projects in terms of geological and topographical complexity.This paper presents an approach for rapidly profiling rock mass quality underneath tunnel face for the ongoing construction of the Sichuan-Xizang Railway.It adopts the time-series method and carries out the quantitative analysis of the rock mass quality using the depth-series measurement-while-drilling(MWD)data associated with drilling of blastholes.A tunnel face with 15 blastholes is examined for illustration.The results include identification of the boundary of homogeneous geomaterial by plotting the blasthole depth against the net drilling time,as well as quantification of rock mass quality through the recalculation of the new specific energy.The new specific energy profile is compared and highly consistent with laboratory test,manual logging and tunnel seismic prediction results.This consistency can enhance the blasthole pattern design and facilitate the dynamic determination of charge placement and amount.This paper highlights the importance of digital monitoring during blasthole drilling for rapidly profiling rock mass quality underneath and ahead of tunnel face.It upgrades the MWD technique for rapid profiling rock mass quality in drilling and blasting tunnels.
基金supported by the Key Science and Technology Project of Henan Province(201300210400)National Key Research and Development Project(2018YFB1800304)+1 种基金National Natural Science Foundation of China(61762058),Fundamental Research Funds for the Central Universities(xzy012020112)Natural Science Foundation of Gansu Province(21JR7RA282).
文摘With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive data.Considering the limitation of calculation power,edge computing is introduced to release unnecessary data transmission.In edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically.ECDs involved in one task are required to cooperate to process the task data.The privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and tamperresistance.In crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced.As mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality assessment.Therefore,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this paper.DPoR(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in BFEC.Iteratively,the final result is calculated without revealing the privacy of participants.Experiments on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.
基金the National Natural Science Foundation of China(81970036)the Natural Science Foundation of Beijing(7202130)the CAMS Innovation Fund for Medical Sciences(2021-12M-1-049 and 2022-12M-C&T-B-107).
文摘Patients with chronic obstructive pulmonary disease(COPD)who exhibit elevated blood eosinophil levels often experience worsened lung function and more severe emphysema.This implies the potential involvement of eosinophils in the development of emphysema.However,the precise mechanisms underlying the development of eosinophil-mediated emphysema remain unclear.In this study,we employed single-cell RNA sequencing to identify eosinophil subgroups in mouse models of asthma and emphysema,followed by functional analyses of these subgroups.Assessment of accumulated eosinophils unveiled distinct transcriptomes in the lungs of mice with elastase-induced emphysema and ovalbumin-induced asthma.Depletion of eosinophils through the use of anti-interleukin-5 antibodies ameliorated elastase-induced emphysema.A particularly noteworthy discovery is that eosinophil-derived cathepsin L contributed to the degradation of the extracellular matrix,thereby leading to emphysema in pulmonary tissue Inhibition of cathepsin L resulted in a reduction of elastase-induced emphysema in a mouse model.Importantly,eosinophil levels correlated positively with serum cathepsin L levels,which were higher in emphysema patients than those without emphysema.Expression of cathepsin L in eosinophils demonstrated a direct association with lung emphysema in COPD patients.Collectively,these findings underscore the significant role of eosinophil-derived cathepsin L in extracellular matrix degradation and remodeling,and its relevance to emphysema in coPD patients.Consequently,targeting eosinophil-derived cathepsin L could potentially offer a therapeutic avenue for emphysema patients.Further investigations are warranted to explore therapeutic strategies targeting cathepsin L in emphysema patients.
基金Supported by the National Key Research and Development Program of China(2022YFC 2602301).
文摘ABSTRACT The concept of healthy life expectancy(HLE)integrates the ideas of life expectancy and health status,providing a valuable metric to evaluate both the length and quality of life.This paper seeks to aid policymakers in creating an inclusive HLE indicator system through a systematic review of methodologies for defining and measuring HLE,along with relevant published studies’descriptions.Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews statement.
基金We acknowledge the National Natural Science Foundation of China(grant number 52022106)SAMSUNG Research China for financial support and idea explorationwell as Tianjin Supercomputer Center for providing computing resources.
文摘Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migration performance estimation.The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures,and the computational cost is always high because of the task-intensive features.The last one relates to the kinetic problems,for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods.In this work,two predictive models,ionic migration activation energy model and structural optimization model,are developed based on machine learning(ML)techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures,respectively.By training 3136 energy barrier data calculated by bond valence(BV)method,an ionic migration activation energy model(Ea model)with mean absolute error(MAE)of 0.26 eV on testing data set is obtained.We apply this model and filter LiBiOS as a promising fast Li^(+)conductor from 49 Licontaining hetero-anionic compounds.Although the model-predicted result shows relatively low energy barrier,further analysis indicates that the high carrier formation energy restricts the ionic transportability.Therefore,we substitute fractional Li^(+)with Mg^(2+)in LiBiOS to relieve the large difficulty of forming carriers in the structure.In order to fast explore the optimal doping scheme,we develop the structural optimization model(E-f model)containing the ML-based energy and force prediction to accelerate the structural optimization under various LieMg ratio and doping configurations.Decent doping scheme Li_(1-2x)Mg_(x)BiOS(x=0.1875)shows much better Li^(+)migration performance compared with LiBiOS without substitution.This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.
