The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques we...The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.展开更多
Objective:The objective of this study was to investigate the alterations and potential implications of the Osteoprotegerin(OPG)/Receptor Activator of Nuclear Factor-kappa B Ligand(RANKL)/Receptor Activator of Nuclear ...Objective:The objective of this study was to investigate the alterations and potential implications of the Osteoprotegerin(OPG)/Receptor Activator of Nuclear Factor-kappa B Ligand(RANKL)/Receptor Activator of Nuclear Factor-kappa B(RANK)signaling pathway factors in a murine model of sepsis-associated acute kidney injury(SA-AKI).This research aimed to offer novel insights into the mechanistic exploration of SA-AKI.Methods:The SA-AKI model group(CLP group)was established through cecal ligation and puncture surgery(CLP),while the control group consisted of sham-operated animals(Sham group)subjected only to laparotomy without cecal ligation and puncture.Blood samples were collected 24 h post-surgery,and murine kidney tissues were harvested upon euthanasia.Serum levels of Serum Creatinine(Scr)and Blood Urea Nitrogen(BUN)were quantified using assay kits.Furthermore,serum levels of interleukin-6(IL-6),tumor necrosis factor-alpha(TNF-α),and interleukin-1 beta(IL-1β)were assessed through enzyme-linked immunosorbent assay(ELISA).Renal tissue pathological alterations were examined employing hematoxylin-eosin staining(HE),and the mRNA and protein levels of OPG,RANKL,and RANK in murine kidney tissues were determined via reverse transcription-quantitative polymerase chain reaction(RT-qPCR)and Western blotting.Results:Comparative analysis revealed that,in comparison to the Sham group,the CLP group demonstrated a significant elevation in the levels of Scr,BUN,IL-6,TNF-α,and IL-1β,with statistically significant disparities(all P<0.05).Histopathological examination of the CLP group's kidneys unveiled glomerular congestion,edema,partial ischemic wrinkling,enlargement of interstitial spaces,the presence of necrotic epithelial cells in select renal tubules,tubular luminal dilation,varying degrees of interstitial edema,and infiltration by a limited number of inflammatory cells.In parallel,relative to the Sham group,the CLP group exhibited substantial upregulation in mRNA expression of OPG and RANK in renal tissues,while RANKL mRNA expression experienced marked downregulation,with statistically significant distinctions(all P<0.05).Moreover,in comparison with the Sham group,the CLP group demonstrated an elevation in protein expression of OPG and RANK in kidney tissues,whereas RANKL protein expression displayed significant downregulation,with statistically significant differences(all P<0.05).Conclusion:In a murine sepsis model,augmented expression of OPG and RANK,coupled with diminished RANKL expression,suggests the potential involvement of the OPG/RANKL/RANK signaling pathway in the pathophysiological progression of SA-AKI.展开更多
Recent rapid advancements in communication technology have brought forth the era of Web 3.0,representing a substantial transformation in the Internet landscape.This shift has led to the emergence of various decentrali...Recent rapid advancements in communication technology have brought forth the era of Web 3.0,representing a substantial transformation in the Internet landscape.This shift has led to the emergence of various decentralized metaverse applications that leverage blockchain as their underlying technology to enable users to exchange value directly from point to point.However,blockchains are blind to the real world,and smart contracts cannot directly access data from the external world.To address this limitation,the technology of oracles has been introduced to provide real-world data for smart contracts and other blockchain applications.In this paper,we focus on mitigating the risks associated with oracles providing corrupt or incorrect data.We propose a novel Web 3.0 architecture for the Metaverse based on the multiidentifier network(MIN),and its decentralized blockchain oracle model called Meta Oracle.The experimental results show that the proposed scheme can achieve minor time investment in return for significantly more reliable data and increased throughput.展开更多
In order to expand the advantages of strong durability and high compressive strength of calcium silicate hydrates(C-S-H),at the same time to make up for the poor early mechanical strength of magnesium silicate hydrate...In order to expand the advantages of strong durability and high compressive strength of calcium silicate hydrates(C-S-H),at the same time to make up for the poor early mechanical strength of magnesium silicate hydrates (M-S-H),we present the features and advantages of C-S-H and M-S-H and a comprehensive review of the progress on CaO-MgO-SiO_(2)-H_(2)O.Moreover,we systematically describe natural calcium and magnesium silicate minerals and thermodynamic properties of CaO-MgO-SiO_(2)-H_(2)O.The effect of magnesium on C-S-H and calcium on M-S-H is summarized deeply;the formation and structural feature of CaO-MgO-SiO_(2)-H_(2)O is also explained in detail.Finally,the development of calcium and magnesium silicate hydrates in the future is pointed out,and the further research is discussed and estimated.展开更多
基金supported by the Second Tibetan Plateau Scientific Expedition and Research Program(Grant no.2019QZKK0904)Natural Science Foundation of Hebei Province(Grant no.D2022403032)S&T Program of Hebei(Grant no.E2021403001).
文摘The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.
基金Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01C604)。
文摘Objective:The objective of this study was to investigate the alterations and potential implications of the Osteoprotegerin(OPG)/Receptor Activator of Nuclear Factor-kappa B Ligand(RANKL)/Receptor Activator of Nuclear Factor-kappa B(RANK)signaling pathway factors in a murine model of sepsis-associated acute kidney injury(SA-AKI).This research aimed to offer novel insights into the mechanistic exploration of SA-AKI.Methods:The SA-AKI model group(CLP group)was established through cecal ligation and puncture surgery(CLP),while the control group consisted of sham-operated animals(Sham group)subjected only to laparotomy without cecal ligation and puncture.Blood samples were collected 24 h post-surgery,and murine kidney tissues were harvested upon euthanasia.Serum levels of Serum Creatinine(Scr)and Blood Urea Nitrogen(BUN)were quantified using assay kits.Furthermore,serum levels of interleukin-6(IL-6),tumor necrosis factor-alpha(TNF-α),and interleukin-1 beta(IL-1β)were assessed through enzyme-linked immunosorbent assay(ELISA).Renal tissue pathological alterations were examined employing hematoxylin-eosin staining(HE),and the mRNA and protein levels of OPG,RANKL,and RANK in murine kidney tissues were determined via reverse transcription-quantitative polymerase chain reaction(RT-qPCR)and Western blotting.Results:Comparative analysis revealed that,in comparison to the Sham group,the CLP group demonstrated a significant elevation in the levels of Scr,BUN,IL-6,TNF-α,and IL-1β,with statistically significant disparities(all P<0.05).Histopathological examination of the CLP group's kidneys unveiled glomerular congestion,edema,partial ischemic wrinkling,enlargement of interstitial spaces,the presence of necrotic epithelial cells in select renal tubules,tubular luminal dilation,varying degrees of interstitial edema,and infiltration by a limited number of inflammatory cells.In parallel,relative to the Sham group,the CLP group exhibited substantial upregulation in mRNA expression of OPG and RANK in renal tissues,while RANKL mRNA expression experienced marked downregulation,with statistically significant distinctions(all P<0.05).Moreover,in comparison with the Sham group,the CLP group demonstrated an elevation in protein expression of OPG and RANK in kidney tissues,whereas RANKL protein expression displayed significant downregulation,with statistically significant differences(all P<0.05).Conclusion:In a murine sepsis model,augmented expression of OPG and RANK,coupled with diminished RANKL expression,suggests the potential involvement of the OPG/RANKL/RANK signaling pathway in the pathophysiological progression of SA-AKI.
基金supported by Shenzhen Fundamental Research Programs under Grant Nos.JCYJ20220531093206015,GXWD20201231165807007-20200807164903001,JCYJ20210324122013036,and JCYJ20190808155607340Guang Dong Prov.R&D Key Programs under Grant Nos.2019B010137001 and 2018B010124001+4 种基金Basic Research Enhancement Program of China under Grant No.2021-JCJQ-JJ-0483National Keystone R&D Program of China under Grant No.2017YFB0803204Natural Science Foundation of China under Grant No.61671001ZTE Industry-University-Institute Fund Project under Grant No.2019ZTE03-01HuaWei Funding under Grant No.TC20201222002。
文摘Recent rapid advancements in communication technology have brought forth the era of Web 3.0,representing a substantial transformation in the Internet landscape.This shift has led to the emergence of various decentralized metaverse applications that leverage blockchain as their underlying technology to enable users to exchange value directly from point to point.However,blockchains are blind to the real world,and smart contracts cannot directly access data from the external world.To address this limitation,the technology of oracles has been introduced to provide real-world data for smart contracts and other blockchain applications.In this paper,we focus on mitigating the risks associated with oracles providing corrupt or incorrect data.We propose a novel Web 3.0 architecture for the Metaverse based on the multiidentifier network(MIN),and its decentralized blockchain oracle model called Meta Oracle.The experimental results show that the proposed scheme can achieve minor time investment in return for significantly more reliable data and increased throughput.
基金Funded by Natural Science Basic Research Plan in Shaanxi Province of China (Nos.2021JQ-500, 2021GY-203, 2023-JCYB-096)Shaanxi Provincial Education Department of Key Scientific Research Plan (No.20JS079)Shaanxi Provincial Education Department of Normal Scientific Research Plan (No.20JK0727)。
文摘In order to expand the advantages of strong durability and high compressive strength of calcium silicate hydrates(C-S-H),at the same time to make up for the poor early mechanical strength of magnesium silicate hydrates (M-S-H),we present the features and advantages of C-S-H and M-S-H and a comprehensive review of the progress on CaO-MgO-SiO_(2)-H_(2)O.Moreover,we systematically describe natural calcium and magnesium silicate minerals and thermodynamic properties of CaO-MgO-SiO_(2)-H_(2)O.The effect of magnesium on C-S-H and calcium on M-S-H is summarized deeply;the formation and structural feature of CaO-MgO-SiO_(2)-H_(2)O is also explained in detail.Finally,the development of calcium and magnesium silicate hydrates in the future is pointed out,and the further research is discussed and estimated.
