As the largest jujube cultivating areas of the world,the market price and quality of Chinese jujube are obviously affected by its producing regions in China.Therefore,traceability of Chinese jujube has become an impor...As the largest jujube cultivating areas of the world,the market price and quality of Chinese jujube are obviously affected by its producing regions in China.Therefore,traceability of Chinese jujube has become an important issue.To search the suitable geographical labels of Chinese jujube,26 major cultivars from five geographical regions of China were investigated by evaluating their bioactive compounds and antioxidant activity profiling.Multivariate statistical techniques(e,g.,PCA,PLS,VIP value)applied to the above-obtained data were further used to identify geographical origin in different areas of Chinese jujube.The results indicated that the highest median contents of total phenols(TP)and flavonoids(TF)of Chinese jujube were originated from Ningxia and Henan provinces,both of which also exhibited the stronger bioactivity.p-Hydroxybenzoic acid and rutin exhibited the highest contents among the all detected individual phenolic compounds.The highest median contents of cyclic adenosine monophosphate(cAMP),cyclic guanosine monophosphate(cGMP)and polysaccharides were all found in Chinese jujube samples of Xinjiang province.In particular,p-hydroxybenzoic acid,rutin,cAMP,cGMP and polysaccharides can be regarded as the potential biomarkers in distinguishing the geographical origin of Chinese jujube.Additionally,the high altitude and latitude might be benefit for the accumulation of cAMP,cGMP and polysaccharides(primary metabolites),and high longitude and annual precipitation maybe improve the contents of TP and TF(secondary metabolites).These information could provide an effective methods in distinguishing the geographical features of Chinese jujube,and also be benefit for the products development.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage...Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.展开更多
Chengde Mountain Resort,Hebei Province.Built during the Qing Dynasty(1616-1911),the resort is one of the largest existing imperial gardens and temple complexes in the world.Following the design concept of"in harm...Chengde Mountain Resort,Hebei Province.Built during the Qing Dynasty(1616-1911),the resort is one of the largest existing imperial gardens and temple complexes in the world.Following the design concept of"in harmony with nature and the surrounding terrain,"the site was selected and laid out with the idea of reflecting China's geographical features.展开更多
General Features The region under discussion borders ihe gorge of the Wu River on the north and the head-water area of the Maotiao River on the south, reaching the Szechuan-Kweichow Highway on the east and the Chiengc...General Features The region under discussion borders ihe gorge of the Wu River on the north and the head-water area of the Maotiao River on the south, reaching the Szechuan-Kweichow Highway on the east and the Chiengchen-Pichieh Highway on the west.It consists of four districts namely, Kweiyang, Chiengchen, Sinwen and Pingpa.展开更多
Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetl...Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.展开更多
Carbon (C) quality of non-leaf litter is closely related to decomposition rate and plays a vital role in terrestrial ecosystem C sequestration.However,to date,the global patterns and influencing factors of non-leaf li...Carbon (C) quality of non-leaf litter is closely related to decomposition rate and plays a vital role in terrestrial ecosystem C sequestration.However,to date,the global patterns and influencing factors of non-leaf litter C quality remain unclear.Here,using meta-analysis method,we quantified the characteristics and driving factors of the initial C quality of non-leaf litter (bark,branch,flower,fruit,root,stem,and wood) with 996 observations collected from 279 independent publications,including the concentrations of total C,lignin,cellulose,and hemicellulose.Results showed that (1) only total C and cellulose concentrations significantly varied among different types of non-leaf litter;(2) C quality is higher (i.e.,lower concentration) in bark,branch,root,stem and wood litter from angiosperms than gymnosperms,from herbaceous than woody plants,from broadleaved than coniferous trees,and from arbuscular mycorrhizal (AM) than ectomycorrhizal (ECM) plants (except for hemicellulose concentration);and (3) the impacts of different geographic features on C quality of non-leaf litter differed among different litter types,while soil properties generally exhibited strong impacts.Overall,our results clearly show the global patterns of C quality and associated influencing factors for different types of non-leaf litter,which would be helpful for a better understanding of role of non-leaf litter in terrestrial ecosystem C cycling and for the improvement of C cycling models.展开更多
基金This work was supported by the Key Development Projects in Shaanxi Province(2018ZDXM-NY-082)Key projects to promote scientific and technological achievements by Northwest A&F University(XTG 2018-35).
文摘As the largest jujube cultivating areas of the world,the market price and quality of Chinese jujube are obviously affected by its producing regions in China.Therefore,traceability of Chinese jujube has become an important issue.To search the suitable geographical labels of Chinese jujube,26 major cultivars from five geographical regions of China were investigated by evaluating their bioactive compounds and antioxidant activity profiling.Multivariate statistical techniques(e,g.,PCA,PLS,VIP value)applied to the above-obtained data were further used to identify geographical origin in different areas of Chinese jujube.The results indicated that the highest median contents of total phenols(TP)and flavonoids(TF)of Chinese jujube were originated from Ningxia and Henan provinces,both of which also exhibited the stronger bioactivity.p-Hydroxybenzoic acid and rutin exhibited the highest contents among the all detected individual phenolic compounds.The highest median contents of cyclic adenosine monophosphate(cAMP),cyclic guanosine monophosphate(cGMP)and polysaccharides were all found in Chinese jujube samples of Xinjiang province.In particular,p-hydroxybenzoic acid,rutin,cAMP,cGMP and polysaccharides can be regarded as the potential biomarkers in distinguishing the geographical origin of Chinese jujube.Additionally,the high altitude and latitude might be benefit for the accumulation of cAMP,cGMP and polysaccharides(primary metabolites),and high longitude and annual precipitation maybe improve the contents of TP and TF(secondary metabolites).These information could provide an effective methods in distinguishing the geographical features of Chinese jujube,and also be benefit for the products development.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
文摘Twitter is a well-known microblogging platform for rapid diffusion of views,ideas,and information.During disasters,it has widely been used to communicate evacuation plans,distribute calls for help,and assist in damage assessment.The reliability of such information is very important for decision-making in a crisis situation,but also difficult to assess.There is little research so far on the transferability of quality assessment methods from one geographic region to another.The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters.We examine tweeting activity during two earthquakes in Italy and Myanmar.We compare the granularity of geographic references used,user profile characteristics that are related to credibility,and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.Our results show similar geographic granularity for Myanmar and Italy earthquake events,but the Myanmar earthquake event has less information from locations nearby when compared to Italy.Additionally,there are significant and complex differences in user and usage characteristics,but a high performance for the Naive Bayes classifier even when applied to data from a different geographic region.This research provides a basis for further research in credibility assessment of users reporting about disasters.
文摘Chengde Mountain Resort,Hebei Province.Built during the Qing Dynasty(1616-1911),the resort is one of the largest existing imperial gardens and temple complexes in the world.Following the design concept of"in harmony with nature and the surrounding terrain,"the site was selected and laid out with the idea of reflecting China's geographical features.
文摘General Features The region under discussion borders ihe gorge of the Wu River on the north and the head-water area of the Maotiao River on the south, reaching the Szechuan-Kweichow Highway on the east and the Chiengchen-Pichieh Highway on the west.It consists of four districts namely, Kweiyang, Chiengchen, Sinwen and Pingpa.
基金supported by the Natural Science Foundation of Jilin Province,China[YDZJ202301ZYTS218]the National Natural Science Foundation of China[42301430,42222103,42171379,U2243230,and 42101379]+1 种基金the Youth Innovation Promotion Association of the Chinese Academy of Sciences[2017277 and 2021227]the Professional Association of the Alliance of International Science Organizations[ANSO-PA-2020-14].
文摘Climate change and human activities have reduced the area and degraded the functions and services of wetlands in China.To protect and restore wetlands,it is urgent to predict the spatial distribution of potential wetlands.In this study,the distribution of potential wetlands in China was simulated by integrating the advantages of Google Earth Engine with geographic big data and machine learning algorithms.Based on a potential wetland database with 46,000 samples and an indicator system of 30 hydrologic,soil,vegetation,and topographic factors,a simulation model was constructed by machine learning algorithms.The accuracy of the random forest model for simulating the distribution of potential wetlands in China was good,with an area under the receiver operating characteristic curve value of 0.851.The area of potential wetlands was 332,702 km^(2),with 39.0%of potential wetlands in Northeast China.Geographic features were notable,and potential wetlands were mainly concentrated in areas with 400-600 mm precipitation,semi-hydric and hydric soils,meadow and marsh vegetation,altitude less than 700 m,and slope less than 3°.The results provide an important reference for wetland remote sensing mapping and a scientific basis for wetland management in China.
基金supported by the National Natural Science Foundation of China (32201342)the State Key Laboratory of Subtropical Silviculture (SKLSS-KF2024-02)the Natural Science Foundation of Fujian Province (2022J01642)。
文摘Carbon (C) quality of non-leaf litter is closely related to decomposition rate and plays a vital role in terrestrial ecosystem C sequestration.However,to date,the global patterns and influencing factors of non-leaf litter C quality remain unclear.Here,using meta-analysis method,we quantified the characteristics and driving factors of the initial C quality of non-leaf litter (bark,branch,flower,fruit,root,stem,and wood) with 996 observations collected from 279 independent publications,including the concentrations of total C,lignin,cellulose,and hemicellulose.Results showed that (1) only total C and cellulose concentrations significantly varied among different types of non-leaf litter;(2) C quality is higher (i.e.,lower concentration) in bark,branch,root,stem and wood litter from angiosperms than gymnosperms,from herbaceous than woody plants,from broadleaved than coniferous trees,and from arbuscular mycorrhizal (AM) than ectomycorrhizal (ECM) plants (except for hemicellulose concentration);and (3) the impacts of different geographic features on C quality of non-leaf litter differed among different litter types,while soil properties generally exhibited strong impacts.Overall,our results clearly show the global patterns of C quality and associated influencing factors for different types of non-leaf litter,which would be helpful for a better understanding of role of non-leaf litter in terrestrial ecosystem C cycling and for the improvement of C cycling models.
