Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline d...Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline development. In most existing studies, remote sensing images are used to quantify the morphological characteristics of lakes. However, the extraction accuracy of lake water is greatly affected by cloud cover and vegetation cover, and the inversion accuracy of lake elevation data is poor, which cannot accurately describe the response relationship of lake landscape morphology with water level change. Therefore, this paper takes Tonle Sap Lake as the research object, which is the largest natural freshwater lake in Southeast Asia. DEM is constructed based on high-resolution measured topographic data, and morphological indicators such as lake area, lake shoreline length, perimeter area ratio, longest axis length, maximum width, shoreline development index, lake shape complexity, compactness ratio and form ratio are adopted to researching the evolution law of high water overflows and low water outbursts quantitatively, and clarifying the variation characteristics of landscape morphology with water level gradient in Tonle Sap Lake. The research results have important theoretical significance for the scientific utilization of Tonle Sap Lake water resources and the protection of the lake ecosystem.展开更多
BACKGROUND With the increasingly extensive application of artificial intelligence(AI)in medical systems,the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.AIM To inve...BACKGROUND With the increasingly extensive application of artificial intelligence(AI)in medical systems,the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.AIM To investigate the accuracy of AI diagnostic software(Shukun)in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion.METHODS From November 2021 to March 2022,consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy(MT)post-Shukun AI penumbra assessment were included.Computed tomography angiography(CTA)and perfusion exams were analyzed by AI,reviewed by senior neurointerventional experts.In the case of divergences among the three experts,discussions were held to reach a final conclusion.When the results of AI were inconsistent with the neurointerventional experts’diagnosis,the diagnosis by AI was considered inaccurate.RESULTS A total of 22 patients were included in the study.The vascular recanalization rate was 90.9%,and 63.6%of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up.The computed tomography(CT)perfusion diagnosis by Shukun(AI)was confirmed to be invalid in 3 patients(inaccuracy rate:13.6%).CONCLUSION AI(Shukun)has limits in assessing ischemic penumbra.Integrating clinical and imaging data(CT,CTA,and even magnetic resonance imaging)is crucial for MT decision-making.展开更多
Objective:To explore the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy based on network pharmacology.Method:Appling TCMSP database to get the activity of traditional Chinese medicine(TCM)in chemica...Objective:To explore the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy based on network pharmacology.Method:Appling TCMSP database to get the activity of traditional Chinese medicine(TCM)in chemical composition of Zhenwu Tang,and get the target,using of Genecards,OMIM,TTD databases to get the disease related target for diabetic nephropathy,providing database using cytoscape3.7.2 construction of TCM-chemical-disease target network diagram,using the String database to get the corresponding target constructing PPI network,DAVID database is used to obtain the corresponding target GO enrichment analysis and KEGG pathway analysis.Results:Get Zhenwu Tang in active ingredients mainly for beta sitosterol,kaempferol,Stigmasterol,hederagenin,3 beta acetoxyatractylone,core targets including TNF,AKT1 and IL6,involved in biological process(BP)including drug reaction,estradiol,lipopolysaccharide,etc.,It is involved in membrane raft,cave-like depression of cell membrane,plasma membrane,extracellular space and other cellular components(CC),and participates in molecular functions(MF)such as enzyme binding,protein homologous dimerization,heme binding,REDOX enzyme activity,etc.,mainly involving Hepatitis B,TNF signaling pathway,pathway in cancer,etc.Conclusion:This study explored the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy with the method of network pharmacology,providing a theoretical basis for further study of the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy.展开更多
The performance of the metal halide perovskite solar cells(PSCs)highly relies on the experimental parameters,including the fabrication processes and the compositions of the perovskites;tremendous experimental work has...The performance of the metal halide perovskite solar cells(PSCs)highly relies on the experimental parameters,including the fabrication processes and the compositions of the perovskites;tremendous experimental work has been done to optimize these factors.However,predicting the device performance of the PSCs from the fabrication parameters before experiments is still challenging.Herein,we bridge this gap by machine learning(ML)based on a dataset including 1072 devices from peer-reviewed publications.The optimized ML model accurately predicts the PCE from the experimental parameters with a root mean square error of 1.28%and a Pearson coefficientr of 0.768.Moreover,the factors governing the device performance are ranked by shapley additive explanations(SHAP),among which,A-site cation is crucial to getting highly efficient PSCs.Experiments and density functional theory calculations are employed to validate and help explain the predicting results by the ML model.Our work reveals the feasibility of ML in predicting the device performance from the experimental parameters before experiments,which enables the reverse experimental design toward highly efficient PSCs.展开更多
CO_(2) electrolysis with solid oxide electrolytic cells(SOECs)using intermittently available renewable energy has potential applications for carbon neutrality and energy storage.In this study,a pulsed current strategy...CO_(2) electrolysis with solid oxide electrolytic cells(SOECs)using intermittently available renewable energy has potential applications for carbon neutrality and energy storage.In this study,a pulsed current strategy is used to replicate intermittent energy availability,and the stability and conversion rate of the cyclic operation by a large-scale flat-tube SOEC are studied.One hundred cycles under pulsed current ranging from -100 to -300 mA/cm^(2) with a total operating time of about 800 h were carried out.The results show that after 100 cycles,the cell voltage attenuates by 0.041%/cycle in the high current stage of−300 mA/cm^(2),indicating that the lifetime of the cell can reach up to about 500 cycles.The total CO_(2) conversion rate reached 52%,which is close to the theoretical value of 54.3% at -300 mA/cm^(2),and the calculated efficiency approached 98.2%,assuming heat recycling.This study illustrates the significant advantages of SOEC in efficient electrochemical energy conversion,carbon emission mitigation,and seasonal energy storage.展开更多
Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. He...Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. Here, on the basis of the previous studies, we employ machine learning to map the relationship between the interface material and the device performance, leading to intelligently screening interface materials towards minimizing voltage losses in p-i-n type PSCs. To enhance the explainability of the machine learning models, molecular descriptors are used to represent the materials. Furthermore,experimental analysis with different characterization methods and device simulation based on the drift-diffusion physical model are conducted to get physical insights and validate the machine learning models. Accordingly, 3-thiophene ethylamine hydrochloride(Th EACl) is screened as an example, which enables remarkable improvements in VOCand PCE of the PSCs. Our work reveals the critical role of datadriven analysis in the high throughput screening of interface materials, which will significantly accelerate the exploration of new materials for high-efficiency PSCs.展开更多
文摘Lake is an important part of the natural ecosystem, and its morphological characteristics reflect the capacity of lake regulation and storage, the strength of material migration, and the characteristics of shoreline development. In most existing studies, remote sensing images are used to quantify the morphological characteristics of lakes. However, the extraction accuracy of lake water is greatly affected by cloud cover and vegetation cover, and the inversion accuracy of lake elevation data is poor, which cannot accurately describe the response relationship of lake landscape morphology with water level change. Therefore, this paper takes Tonle Sap Lake as the research object, which is the largest natural freshwater lake in Southeast Asia. DEM is constructed based on high-resolution measured topographic data, and morphological indicators such as lake area, lake shoreline length, perimeter area ratio, longest axis length, maximum width, shoreline development index, lake shape complexity, compactness ratio and form ratio are adopted to researching the evolution law of high water overflows and low water outbursts quantitatively, and clarifying the variation characteristics of landscape morphology with water level gradient in Tonle Sap Lake. The research results have important theoretical significance for the scientific utilization of Tonle Sap Lake water resources and the protection of the lake ecosystem.
文摘BACKGROUND With the increasingly extensive application of artificial intelligence(AI)in medical systems,the accuracy of AI in medical diagnosis in the real world deserves attention and objective evaluation.AIM To investigate the accuracy of AI diagnostic software(Shukun)in assessing ischemic penumbra/core infarction in acute ischemic stroke patients due to large vessel occlusion.METHODS From November 2021 to March 2022,consecutive acute stroke patients with large vessel occlusion who underwent mechanical thrombectomy(MT)post-Shukun AI penumbra assessment were included.Computed tomography angiography(CTA)and perfusion exams were analyzed by AI,reviewed by senior neurointerventional experts.In the case of divergences among the three experts,discussions were held to reach a final conclusion.When the results of AI were inconsistent with the neurointerventional experts’diagnosis,the diagnosis by AI was considered inaccurate.RESULTS A total of 22 patients were included in the study.The vascular recanalization rate was 90.9%,and 63.6%of patients had modified Rankin scale scores of 0-2 at the 3-month follow-up.The computed tomography(CT)perfusion diagnosis by Shukun(AI)was confirmed to be invalid in 3 patients(inaccuracy rate:13.6%).CONCLUSION AI(Shukun)has limits in assessing ischemic penumbra.Integrating clinical and imaging data(CT,CTA,and even magnetic resonance imaging)is crucial for MT decision-making.
基金TCM Science and Technology Development Project of Shandong Province(No.2019-0043)。
文摘Objective:To explore the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy based on network pharmacology.Method:Appling TCMSP database to get the activity of traditional Chinese medicine(TCM)in chemical composition of Zhenwu Tang,and get the target,using of Genecards,OMIM,TTD databases to get the disease related target for diabetic nephropathy,providing database using cytoscape3.7.2 construction of TCM-chemical-disease target network diagram,using the String database to get the corresponding target constructing PPI network,DAVID database is used to obtain the corresponding target GO enrichment analysis and KEGG pathway analysis.Results:Get Zhenwu Tang in active ingredients mainly for beta sitosterol,kaempferol,Stigmasterol,hederagenin,3 beta acetoxyatractylone,core targets including TNF,AKT1 and IL6,involved in biological process(BP)including drug reaction,estradiol,lipopolysaccharide,etc.,It is involved in membrane raft,cave-like depression of cell membrane,plasma membrane,extracellular space and other cellular components(CC),and participates in molecular functions(MF)such as enzyme binding,protein homologous dimerization,heme binding,REDOX enzyme activity,etc.,mainly involving Hepatitis B,TNF signaling pathway,pathway in cancer,etc.Conclusion:This study explored the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy with the method of network pharmacology,providing a theoretical basis for further study of the mechanism of Zhenwu Tang in the treatment of diabetic nephropathy.
基金the National Natural Science Foundation of China(Grant No.62075006)the National Key Research and Development Program of China(Grant No.2021YFB3600403)the Natural Science Talents Foundation(Grant No.KSRC22001532)。
文摘The performance of the metal halide perovskite solar cells(PSCs)highly relies on the experimental parameters,including the fabrication processes and the compositions of the perovskites;tremendous experimental work has been done to optimize these factors.However,predicting the device performance of the PSCs from the fabrication parameters before experiments is still challenging.Herein,we bridge this gap by machine learning(ML)based on a dataset including 1072 devices from peer-reviewed publications.The optimized ML model accurately predicts the PCE from the experimental parameters with a root mean square error of 1.28%and a Pearson coefficientr of 0.768.Moreover,the factors governing the device performance are ranked by shapley additive explanations(SHAP),among which,A-site cation is crucial to getting highly efficient PSCs.Experiments and density functional theory calculations are employed to validate and help explain the predicting results by the ML model.Our work reveals the feasibility of ML in predicting the device performance from the experimental parameters before experiments,which enables the reverse experimental design toward highly efficient PSCs.
基金National Key Research&Development Project,Grant/Award Number:2017YFE0129300Ningbo Science and Technology Innovation 2025 Major Project,Grant/Award Numbers:2019B10046,2020Z107+2 种基金Zhejiang Provincial Key R&D Program,Grant/Award Number:2021C01101National Natural Science Foundation of China,Grant/Award Numbers:U20A20251,11932005The from 0 to 1 Innovative Program of CAS,Grant/Award Number:ZDBS-LY-JSC021。
文摘CO_(2) electrolysis with solid oxide electrolytic cells(SOECs)using intermittently available renewable energy has potential applications for carbon neutrality and energy storage.In this study,a pulsed current strategy is used to replicate intermittent energy availability,and the stability and conversion rate of the cyclic operation by a large-scale flat-tube SOEC are studied.One hundred cycles under pulsed current ranging from -100 to -300 mA/cm^(2) with a total operating time of about 800 h were carried out.The results show that after 100 cycles,the cell voltage attenuates by 0.041%/cycle in the high current stage of−300 mA/cm^(2),indicating that the lifetime of the cell can reach up to about 500 cycles.The total CO_(2) conversion rate reached 52%,which is close to the theoretical value of 54.3% at -300 mA/cm^(2),and the calculated efficiency approached 98.2%,assuming heat recycling.This study illustrates the significant advantages of SOEC in efficient electrochemical energy conversion,carbon emission mitigation,and seasonal energy storage.
基金supported by the National Natural Science Foundation of China (62075006)the National Key R&D Program of China (2018YFB1500200)。
文摘Interface engineering is proved to be the most important strategy to push the device performance of the perovskite solar cell(PSC) to its limit, and numerous works have been conducted to screen efficient materials. Here, on the basis of the previous studies, we employ machine learning to map the relationship between the interface material and the device performance, leading to intelligently screening interface materials towards minimizing voltage losses in p-i-n type PSCs. To enhance the explainability of the machine learning models, molecular descriptors are used to represent the materials. Furthermore,experimental analysis with different characterization methods and device simulation based on the drift-diffusion physical model are conducted to get physical insights and validate the machine learning models. Accordingly, 3-thiophene ethylamine hydrochloride(Th EACl) is screened as an example, which enables remarkable improvements in VOCand PCE of the PSCs. Our work reveals the critical role of datadriven analysis in the high throughput screening of interface materials, which will significantly accelerate the exploration of new materials for high-efficiency PSCs.
