A simple and efficient dithizone-functionalized solid-phase extraction(SPE)procedure,online coupled with high-performance liquid chromatography(HPLC)-inductively coupled plasma mass spectrometry,was developed for the ...A simple and efficient dithizone-functionalized solid-phase extraction(SPE)procedure,online coupled with high-performance liquid chromatography(HPLC)-inductively coupled plasma mass spectrometry,was developed for the first time for enrichment and determination of ultra-trace mercury(Hg)species(inorganic divalent Hg(Hg(Ⅱ)),methylmercury(CH_(3)Hg(Ⅱ))and ethylmercury(C_(2)H_(5)Hg(Ⅱ))in cereals and environmental samples.In the proposed method,functionalization of the commercial C_(18) column with dithizone,enrichment,and elution of the above Hg species can be completed online with the developed SPE device.A simple solution of 2-mercaptoethanol(1%(V/V))could be used as an eluent for both the SPE and HPLC separation of Hg species,significantly simplifying the method and instrumen-tation.The online SPE method was optimized by varying dithizone dose,2-mercaptoethanol concentration,and sample volume.In addition,the effect of pH,coexisting interfering ions,and salt effect on the enrichment was also discussed.Under the optimized conditions,the detection limits of Hg species for 5 mL water sample were 0.15 ng/L for Hg(Ⅱ),0.07 ng/L for CH_(3)Hg(Ⅱ),and 0.04 ng/L for C_(2)H_(5)Hg(Ⅱ)with recoveries in the range of 85%-100%.The developed dithizone-functionalized C_(18) SPE column can be reused after a single function-alization,which significantly simplifies the enrichment step.Moreover,the stability of Hg species enriched on the SPE column demonstrated its suitability for field sampling of Hg species for later laboratory analysis.This environment-friendly method offers a robust tool to detect ultra-trace Hg species in cereals and environmental samples.展开更多
The authors regret<the wrong file for Appendix A and the mistake in Figure 3;the correct Appendix has been updated as the attachment.>The authors would like to apologize for any inconvenience caused.
Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make...Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.展开更多
In recent years,artificial intelligence(AI)has achieved tremendous development,akin to a significant leap,similar to progressing from 1 to 100.However,a significant gap still exists between current machine intelligenc...In recent years,artificial intelligence(AI)has achieved tremendous development,akin to a significant leap,similar to progressing from 1 to 100.However,a significant gap still exists between current machine intelligence and human wisdom:machine intelligence is constrained to post hoc inference based on existing data,lacking the ability for genuine exploratory innovation and possessing no prospective reasoning inherent to human wisdom.Drawing inspiration from human wisdom,this article presents conjectures for overcoming the four dilemmas faced by machine intelligence:neglect of silicon-based cognition,lack of artistry,pitfall of perfectionism,and obsession with uniformity.These conjectures aim to propel machine intelligence toward machine wisdom,achieving a great leap from 1 to i.展开更多
Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty,unreliable predictions,and poor decision-making.To address this problem,we...Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty,unreliable predictions,and poor decision-making.To address this problem,we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models.The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs.As an example,by modeling coronavirus disease 2019 mitigation,we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data.Our work suggests that a nation’s intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments.Our solution has been validated for epidemic control,and it can be generalized to other urban issues as well.展开更多
基金This work was supported by the National Key Research and Development Project(No.2020YFA0907400)the National Natural Science Foundation of China(Nos.21777178,21976193)+1 种基金Y.Yin acknowledges the supports from the National Young Top-Notch Talents(No.W03070030)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.Y202011).
文摘A simple and efficient dithizone-functionalized solid-phase extraction(SPE)procedure,online coupled with high-performance liquid chromatography(HPLC)-inductively coupled plasma mass spectrometry,was developed for the first time for enrichment and determination of ultra-trace mercury(Hg)species(inorganic divalent Hg(Hg(Ⅱ)),methylmercury(CH_(3)Hg(Ⅱ))and ethylmercury(C_(2)H_(5)Hg(Ⅱ))in cereals and environmental samples.In the proposed method,functionalization of the commercial C_(18) column with dithizone,enrichment,and elution of the above Hg species can be completed online with the developed SPE device.A simple solution of 2-mercaptoethanol(1%(V/V))could be used as an eluent for both the SPE and HPLC separation of Hg species,significantly simplifying the method and instrumen-tation.The online SPE method was optimized by varying dithizone dose,2-mercaptoethanol concentration,and sample volume.In addition,the effect of pH,coexisting interfering ions,and salt effect on the enrichment was also discussed.Under the optimized conditions,the detection limits of Hg species for 5 mL water sample were 0.15 ng/L for Hg(Ⅱ),0.07 ng/L for CH_(3)Hg(Ⅱ),and 0.04 ng/L for C_(2)H_(5)Hg(Ⅱ)with recoveries in the range of 85%-100%.The developed dithizone-functionalized C_(18) SPE column can be reused after a single function-alization,which significantly simplifies the enrichment step.Moreover,the stability of Hg species enriched on the SPE column demonstrated its suitability for field sampling of Hg species for later laboratory analysis.This environment-friendly method offers a robust tool to detect ultra-trace Hg species in cereals and environmental samples.
文摘The authors regret<the wrong file for Appendix A and the mistake in Figure 3;the correct Appendix has been updated as the attachment.>The authors would like to apologize for any inconvenience caused.
基金supported by the National Natural Science Foundation of China under grant nos.62372470,72225011,62402414,U23B2059,62173034,32222070,62402017,72421002,62206303,62476264,62406312,62102266,52173241,and U23A20468the National Key Research and Development Program of China(2023YFD1900604)+8 种基金the Strategic Priority Research Program of the Chinese Academy of Science(XDB0680301)the Youth Innovation Promotion Association CAS(2023112)the National High Level Hospital Clinical Research funding(2022-PUMCH-A-014),the Beijing Natural Science Foundation(4244098)the Science and Technology Innovation Program of Hunan Province(2023RC3009)the Key Research and Development Program of Yunnan Province(202202AE090034)the MNR Key Laboratory for Geo-Environmental Monitoring of Greater Bay Area(GEMLab-2023001)the Science and Technology Innovation Key R&D Program of Chongqing(CSTB2024TIAD-STX0024)the China National Postdoctoral Program for Innovative Talents(BX20240385)the River Talent Recruitment Program of Guangdong Province(2019ZT08X603).
文摘Intelligent decision-making(IDM)is a cornerstone of artificial intelligence(AI)designed to automate or augment decision processes.Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps,such as AI agents and high-level reinforcement learning.Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision,language,and sensory data—into a cohesive decision-making process.Foundation models(FMs)have become pivotal in science and industry,transforming decision-making and research capabilities.Their large-scale,multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare,life sciences,and education.This survey examines IDM’s evolution,advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains,highlighting the challenges and opportunities in building efficient,adaptive,and ethical decision systems.
基金supported by the National Natural Science Foundation of China(NSFC72421002,72025405,and 62206303)+2 种基金the Science and Technology Innovation Program of Hunan Province(2023RC3009)the Science and Technology Project for Young andMiddle-aged Talents of Hunan(2023TJ-Z03)the Scientific Project ofNUDT(23-ZZCX-JDZ-28 and JS24-05).
文摘In recent years,artificial intelligence(AI)has achieved tremendous development,akin to a significant leap,similar to progressing from 1 to 100.However,a significant gap still exists between current machine intelligence and human wisdom:machine intelligence is constrained to post hoc inference based on existing data,lacking the ability for genuine exploratory innovation and possessing no prospective reasoning inherent to human wisdom.Drawing inspiration from human wisdom,this article presents conjectures for overcoming the four dilemmas faced by machine intelligence:neglect of silicon-based cognition,lack of artistry,pitfall of perfectionism,and obsession with uniformity.These conjectures aim to propel machine intelligence toward machine wisdom,achieving a great leap from 1 to i.
基金supported by the National Natural Science Foundation of China(62173337,21808181,and 72071207)supported by the National Natural Science Foundation of China(71790615,72025405,91846301,72088101)+2 种基金the Hunan Science and Technology Plan Project(2020TP1013 and 2020JJ4673)the Shenzhen Basic Research Project for Development of Science and Technology(JCYJ20200109141218676 and 202008291726500001)the Innovation Team Project of Colleges in Guangdong Province(2020KCXTD040).
文摘Strategy evaluation and optimization in response to troubling urban issues has become a challenging issue due to increasing social uncertainty,unreliable predictions,and poor decision-making.To address this problem,we propose a universal computational experiment framework with a fine-grained artificial society that is integrated with data-based models.The purpose of the framework is to evaluate the consequences of various combinations of strategies geared towards reaching a Pareto optimum with regards to efficacy versus costs.As an example,by modeling coronavirus disease 2019 mitigation,we show that Pareto frontier nations could achieve better economic growth and more effective epidemic control through the analysis of real-world data.Our work suggests that a nation’s intervention strategy could be optimized based on the measures adopted by Pareto frontier nations through large-scale computational experiments.Our solution has been validated for epidemic control,and it can be generalized to other urban issues as well.
