Comparison of nitrate-removal efficiency and bacterial properties using PCL and PHBV polymers as a carbon source to treat aquaculture water

Nitrate(NO_(3)^(−))accumulation in recirculating aquaculture systems(RASs)with high stocking densities presents a problem for reared animals and the environment.The use of a biodegradable polymer as organic carbon for heterotrophic denitrification exhibits good performance for NO_(3)^(−)removal from wastewater.A comparison of NO_(3)^(−)–N removal efficiency and bacterial properties using polycaprolactone(PCL)and poly(3-hydroxybutyrateco-3-hydroxyvalerate)(PHBV)as carbon sources to treat aquaculture water was conducted for a 102-day period.The results indicated that the NO_(3)^(−)–N removal rates of 0.27±0.07 and 0.19±0.05 g/L per day,respectively,could be achieved with influent concentrations ranging from 81.1 to 132.75 mg/L and a flow rate of 1 L/h.The removal of NO_(3)^(−)–N versus consumed PCL(1:1 w/w)was significantly higher than that versus consumed PHBV(0.3:1 w/w)(P<0.05).The concentrations of effluent nitrite-nitrogen and total ammonium nitrogen were maintained at an acceptable level.The bacterial community structures between the two types of reactors varied significantly.Acidovorax and Denitratisoma were the top two genera of the bacterial community in the biofilm in the PCL beads with a dominance of 26.83%and 6.67%,respectively.In the PHBV beads,Acidovorax at 17.95%and Bdellovibrio at 6.37%were the top two genera.The PCL-denitrification reactor developed in this study showed better potential than the PHBV-denitrification reactor in removing NO_(3)^(−)from aquaculture water.

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data.However,data interlinking,which is the most valuable contribution of Linked Data,remains incomplete and inaccurate.This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain.According to the characteristics and roles of geospatial data in data discovery,eight elementary data characteristics are adopted as data interlinking types.These elementary characteristics are further combined to form compound and overall data interlinking types.Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively.Therefore,geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value.The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data.The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network(NSTI-GEO)and data-links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.

Geospatial data ontology:the semantic foundation of geospatial data integration and sharing

Effective integration and wide sharing of geospatial data is an important and basic premise to facilitate the research and applications of geographic information science.However,the semantic heterogeneity of geospatial data is a major problem that significantly hinders geospatial data integration and sharing.Ontologies are regarded as a promising way to solve semantic problems by providing a formalized representation of geographic entities and relationships between them in a manner understandable to machines.Thus,many efforts have been made to explore ontology-based geospatial data integration and sharing.However,there is a lack of a specialized ontology that would provide a unified description for geospatial data.In this paper,with a focus on the characteristics of geospatial data,we propose a unified framework for geospatial data ontology,denoted GeoDataOnt,to establish a semantic foundation for geospatial data integration and sharing.First,we provide a characteristics hierarchy of geospatial data.Next,we analyze the semantic problems for each characteristic of geospatial data.Subsequently,we propose the general framework of GeoDataOnt,targeting these problems according to the characteristics of geospatial data.GeoDataOnt is then divided into multiple modules,and we show a detailed design and implementation for each module.Key limitations and challenges of GeoDataOnt are identified,and broad applications of GeoDataOnt are discussed.

A unified framework of temporal information expression in geosciences knowledge system

Time is an essential reference system for recording objects,events,and processes in the field of geosciences.There are currently various time references,such as solar calendar,geological time,and regional calendar,to represent the knowledge in different domains and regions,which subsequently entails a time conversion process required to interpret temporal information under different time references.However,the current time conversion method is limited by the application scope of existing time ontologies(e.g.,“Jurassic”is a period in geological ontology,but a point value in calendar ontology)and the reliance on experience in conversion processes.These issues restrict accurate and efficient calculation of temporal information across different time references.To address these issues,this paper proposes a Unified Time Framework(UTF)in the geosciences knowledge system.According to a systematic time element parsing from massive time references,the proposed UTF designs an independent time root node to get rid of irrelevant nodes when accessing different time types and to adapt to the time expression of different geoscience disciplines.Furthermore,this UTF carries out several designs:to ensure the accuracy of time expressions by designing quantitative relationship definitions;to enable time calculations across different time elements by designing unified time nodes and structures,and to link to the required external ontologies by designing adequate interfaces.By comparing the time conversion methods,the experiment proves the UTF greatly supports accurate and efficient calculation of temporal information across different time references in SPARQL queries.Moreover,it shows a higher and more stable performance of temporal information queries than the time conversion method.With the advent of the Big Data era in the geosciences,the UTF can be used more widely to discover new geosciences knowledge across different time references.