[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"_public_publisher_byId_e43da59d-3576-44ff-803e-e57d67f6b29b":3,"_public_publication_all{\"sortAscending\":false,\"sortField\":\"updateTime\",\"page\":0,\"size\":10,\"facet\":true,\"searchKey\":\"publisherId:e43da59d-3576-44ff-803e-e57d67f6b29b,\"}":75},{"code":4,"data":5,"meta":22},"SUCCESS",{"id":6,"createTime":7,"updateTime":8,"relativeEntities":9,"slug":10,"properties":11,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":24,"manageAffiliations":25,"indexDatabases":26,"url":22,"thumbnailPath":22,"statistic":42,"gsStatistic":22,"type":74,"analyzePriority":22},"e43da59d-3576-44ff-803e-e57d67f6b29b","2024-04-05T13:06:32.047+00:00","2025-11-21T10:01:33.692+00:00",[],"Arabian-Journal-of-Geosciences",{"eissn":12,"issn":14,"title":16,"url":18},{"VOID":13},"18667511",{"VOID":15},"18667538",{"EN":17},"Arabian Journal of Geosciences",{"VOID":19},"https:\u002F\u002Flink.springer.com\u002Fjournal\u002F12517","PUBLISHER","PENDING",null,0,[],[],[27],{"id":28,"indexDatabase":29,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},"ee96e91c-ae21-4a66-9262-ee9d068150b3",{"id":30,"createTime":22,"updateTime":22,"relativeEntities":31,"label":32,"description":34,"key":36,"publicationTags":37,"standard":22},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9",[],{"EN":33,"VI":33},"Scopus - Elsevier",{"EN":33,"VI":35},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[38],"SCOPUS","https:\u002F\u002Fwww.scopus.com\u002Fsourceid\u002F17400154823","2008-2021","NONE",{"impactFactor":23,"impactFactorByYear":43,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":48,"totalCitation":65,"totalCitationByYear":66,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":70,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},0.01,2,1,6363,{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},10,46,68,129,166,220,284,180,371,295,413,453,693,1638,966,370,61,{"2018":67,"2019":46,"2020":68,"2021":69},34,17,9,{"2018":71,"2019":23,"2020":72,"2021":44},0.08,0.02,4,"JOURNAL",{"meta":76,"data":78},{"total":77},"7886",[79,176,253,400,561,645,730,889,984,1068],{"id":80,"createTime":81,"updateTime":82,"relativeEntities":83,"slug":84,"properties":85,"entityType":95,"verifyStatus":96,"verifyTime":97,"verifyNote":98,"languages":22,"translateLanguages":99,"viewCount":23,"primaryUrl":101,"fullTextUrl":22,"authors":102,"publicationType":145,"publisherRelationship":146,"citationCount":22,"citationInfo":22,"publishDate":172,"publishYear":173,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":174,"openAccess":22,"references":22,"isForceReanalyzing":175},"320bb0e6-2397-4cd4-840e-eb0115e1c4b3","2024-01-27T05:02:20.238+00:00","2026-09-08T09:16:21.937+00:00",[],"The-development-efficiency-of-China-s-innovative-industrial-clusters-based-on-the-DEA-Malmquist-model",{"abstract":86,"title":88,"references":91,"doi":93},{"EN":87},"Innovative industrial clusters are regarded as regional innovation centers and have become an important support for the national innovation strategy in China. However, in the actual development process of innovative industrial clusters, there are problems of unbalanced development and low development efficiency. This study selected the data envelopment analysis (DEA)–Malmquist model to analyze the development efficiency, spatiotemporal evolution characteristics, and spatial improvement of China’s innovation industrial clusters. This study is different from previous studies that used a single industrial cluster as a decision-making unit. Instead, it uses provinces as a decision-making unit. In calculation and analysis, provinces are used as decision-making units instead of single industrial clusters as decision-making units in previous studies. The results showed the following: (1) The average development efficiency of the innovative industrial clusters was 0.652, which was relatively low and had great potential for effective development. Meanwhile, the average value of the Malmquist index was 0.932, which declined at an average annual rate of 7.5% during the study period, mainly due to the slowdown in technological progress and to the decline in pure technical efficiency. (2) There were no significant differences in the development efficiency, Malmquist index, or decomposition indices of China’s innovation industry among the four regions investigated, indicating that regional advantages have not been significantly exerted. (3) Through further analysis of the potential for improvement, a quantitative improvement of the space of the input–output factors of China’s 19 DEA-inefficient provinces was obtained.",{"EN":89,"VI":90},"The development efficiency of China’s innovative industrial clusters-based on the DEA-Malmquist model","Hiệu quả phát triển của các cụm công nghiệp đổi mới sáng tạo ở Trung Quốc dựa trên mô hình DEA-Malmquist",{"VOID":92},"Boschma RA (1999) The rise of clusters of innovative industries in Belgium during the industrial epoch. Res Policy 28(8):853–871. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0048-7333(99)00026-8\nCaves DW, Christensen LR, Diewert W, Erwin (1982A) Multilateral comparisons of output, input, and productivity using superlative index numbers. Economic Journal 92(365):73–86\nCharnes A, Cooper WW, Rhodes E (1979) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0377-2217(78)90138-8\nChen CJ, Wu HL, Lin BW (2006) Evaluating the development of high-tech industries: Taiwan’s science park. Technological Forecasting & Social Change 73(4):452–465. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.techfore.2005.04.003\nChen H, Lin H, Zou W (2020) Research on the regional differences and influencing factors of the innovation efficiency of China’s high-tech industries: based on a shared inputs two-stage network DEA. Sustainability 12(8):3284. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu12083284\nChen S, Wang J, Tan L (2019) Research on development efficiency of innovative industrial clusters in China based on three-stage DEA Model. Inquiry into Economic Issues (9):148–157\nCheng LH, Li X (2015) Data Sources: China Torch Statistical Yearbook. https:\u002F\u002Fdata.cnki.net\u002Ftrade\u002FYearbook\u002FSingle\u002FN2016010127?z=Z018\nCook WD, Seiford LM (2009) Data envelopment analysis (DEA)—thirty years on. Eur J Oper Res 192(1):1–17. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ejor.2008.01.032\nDaniela MN, Ruxandra B, Valentin HC (2020) Challenges and opportunities for creative-innovative clusters partnerships. Proceedings of the International Conference on Business Excellence 14(1): 1057–1070. https:\u002F\u002Fdoi.org\u002F10.2478\u002Fpicbe-2020-0100\nDelgado M, Porter ME, Stern S (2014) Clusters, convergence, and economic performance. Res Policy 43(10):1785–1799. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.respol.2014.05.007\nDoyle J, Green R (1994) Efficiency and cross-efficiency in DEA: derivations, meanings and uses. J Oper Res Soc 45(5):567–578. https:\u002F\u002Fdoi.org\u002F10.2307\u002F2584392\nEgilmez G, Mcavoy D (2013) Benchmarking road safety of U.S. states: a DEA-based Malmquist productivity index approach. Accid Anal Prev 53(4):55–64. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.aap.2012.12.038\nFäre R, Grosskopf S (1994) Measuring productivity: a comment. Int J Oper Prod Manag 14(9):83–88. https:\u002F\u002Fdoi.org\u002F10.1108\u002F01443579410066802\nFäre R, Grosskopf S, Norris M (1994) Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Association 84(1):66–83\nForsund FR (2018) Economic interpretations of DEA. Socio Econ Plan Sci 61(3):9–15. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.seps.2017.03.004\nFurman JL, Porter ME, Stern S (2002) The determinants of national innovative capacity. Research Policy 31. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0048-7333(01)00152-4\nHu C, Liu F, Hu C (2017) A hybrid fuzzy DEA\u002FAHP methodology for ranking units in a fuzzy environment. Symmetry 9(11):273. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsym9110273\nJamaluddin MY, David H (1997) The efficiency of the National Electricity Board in Malaysia: an intercountry comparison using DEA. Energy Econ 19:255–269. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0140-9883(96)01018-3\nKrugman PR (1991) Geography and trade. MIT Press, Massachusetts\nKuesten C (2012) Knowledge matters: technology, innovation and entrepreneurship in innovation networks and knowledge clusters. J Prod Innov Manag 29(2):332–334. https:\u002F\u002Fdoi.org\u002F10.1111\u002Fj.1540-5885.2011.00899.x\nKuo CC, Shyu JZ, Ding K (2019) Industrial revitalization via industry 4.0—a comparative policy analysis among China, Germany and the USA. Global Transitions 1:3–14. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.glt.2018.12.001\nKuo KC, Lu WM, Dinh TN (2020) The effect of special economic zones on governance performance and their spillover effects in Chinese provinces. Manag Decis Econ 41(3):446–460. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fmde.3112\nLi J, Zhang J, Gong L, Miao P (2015) Research on the total factor productivity and decomposition of Chinese coastal marine economy: based on DEA-Malmquist index. J Coast Res 73:283–289. https:\u002F\u002Fdoi.org\u002F10.2112\u002FSI73-050.1\nLi L, Liu B, Liu W (2017a) Efficiency evaluation of the regional high-tech industry in China: a new framework based on meta-frontier dynamic DEA analysis. Socio Econ Plan Sci 60(12):24–33. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.seps.2017.02.001\nLi Y, Wang Y, Cui Q (2017b) An evaluation of R&D efficiencies of industrial clusters through the three-stage benevolent DEA. Science Research Management V38(007):54–61. https:\u002F\u002Fdoi.org\u002F10.19571\u002Fj.cnki.1000-2995.2017.07.007\nLi J, Webster D, Cai J, Muller L (2019) Innovation clusters revisited: on dimensions of agglomeration, institution, and built-environment. Sustainability 11. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu11123338\nLiu W (2015) Measurement on innovation efficiency of hi-tech industries in China-based on three-stage DEA model. Mathematical Statistics and Management 34(1):17–28. https:\u002F\u002Fdoi.org\u002F10.13860\u002Fj.cnki.sltj.20150122-013\nLiu JS, Lu LYY, Lu WM, Lin BJY (2013) A survey of DEA applications. Omega 41(5):893–902. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.omega.2012.11.004\nMarkusen A (1996) Sticky places in slippery space: a typology of industrial districts. Econ Geogr 72(3):293–313. https:\u002F\u002Fdoi.org\u002F10.2307\u002F144402\nMarshall A (1920) Principles of economics (8th EDN). Macmillan, London\nMaskell P, Malmberg A (1995) Localized learning and industrial competitiveness. Camb J Econ 23(2):167–185. https:\u002F\u002Fdoi.org\u002F10.1093\u002Fcje\u002F23.2.167\nMccormick D (1999) African enterprise clusters and industrialization: theory and reality. World Dev 27(9):1531–1551. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0305-750X(99)00074-1\nMoutinho V, Madaleno M, Robaina M (2017) The economic and environmental efficiency assessment in EU cross-country: evidence from DEA and quantile regression approach. Ecol Indic 78(7):85–97. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.ecolind.2017.02.04\nMytelka L, Farinelli F (2000) Local Clusters, Innovation Systems and Sustained Competitiveness, UNU-INTECH Discussion Paper Series 2000-05, United Nations University - INTECH\nNeamtu DM, Bejinaru R, Hapenciuc CV (2020) Challenges and opportunities for creative-innovative clusters partnerships. Proceedings of the Inernational Conference on Business Excellence 14(1):‏1057–1070. https:\u002F\u002Fdoi.org\u002F10.2478\u002Fpicbe-2020-0100\nOrganization for Economic Co-operation and Development (2004) Innovation clusters: the driving force of the National Innovation System. Science and Technology Documentation Press\nPark E, Yoo K, Kwon SJ (2016) Effects of innovation cluster and type of core technology on firms’ economic performance. Journal of Engineering Research 4(2):17. https:\u002F\u002Fdoi.org\u002F10.7603\u002Fs40632-016-0017-z\nPorter M (1990) The competitive advantage of nation. Free Press, New York\nRoelandt T, Hertog P (1999) Boosting innovation: the cluster approach. OECD Publishing, Paris\nRui B, Peter S (1998) Do firms in clusters innovate more? Res Policy 27(5):525–540. https:\u002F\u002Fdoi.org\u002F10.1016\u002FS0048-7333(98)00065-1\nSaxenian AL (1994) Regional advantage: culture and competition in Silicon Valley and Route 128. Harvard University Press\nShah AA, Wu D, Korotkov V, Jabeen G (2019) Do commercial banks benefited from the belt and road initiative? a three-stage DEA-TOBIT-NN analysis. IEEE Access:1-1. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2019.2897137\nShen X, Li C (2014) The influence factors and dynamic mechanisms of innovative industrial clusters development. Science and Technology Management Research 34(14):144–148. https:\u002F\u002Fdoi.org\u002F10.3969\u002Fj.issn.1000-7695.2014.14.030\nShenkoya T, Kim E (2019) A Case Study of the Daedeok Innopolis Innovation Cluster and Its Implications for Nigeria. World Technopolis Review 8(2):104–119\nStern PS (2002) The determinants of national innovative capacity. Res Policy 31(6):899–933. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fs0048-7333(01)00152-4\nStorper M (2016) The resurgence of regional economies, ten years later: the region as a nexus of untraded interdependencies. European Urban & Regional Studies 2(3):191–221. https:\u002F\u002Fdoi.org\u002F10.1177\u002F096977649500200301\nSuvorova LA, Zaushitsyna LL, Radionov AA, Shmidt AV, Bayev IA, Khudyakova TA, Keller AV, Kolbachev YB, Babkin AV, Savaley VV (2017) The formation of the development model of the innovative industrial cluster and methods for evaluating its economic effectiveness. Shs Web of Conferences 35. https:\u002F\u002Fdoi.org\u002F10.1051\u002Fshsconf\u002F20173501089\nTang Y, Zhou X, Zhang X (2015) Innovation efficiency of Guangdong Innovative Industrial Clusters based on DEA-SBM and Malmquist. Industrial Engineering Journal 18(002):100–107. https:\u002F\u002Fdoi.org\u002F10.3969\u002Fj.issn.1007-7375.2015.02.015\nTone K, Tsutsui M (2010) Dynamic DEA: A slacks-based measure approach. Omega 38(3-4):145–156. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.omega.2009.07.003\nTone K, Tsutsui M (2014) Dynamic DEA with network structure: a slacks-based measure approach. 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Asian J Technol Innov 24(2):161–178. https:\u002F\u002Fdoi.org\u002F10.1080\u002F19761597.2016.1196009\nYang Q, Duan X (2014) The time-space measurement of efficiency of development of high-technology industry and provincial differences based on the DEA-Malmquist TFP Index. Economic Geography (7):103–110. https:\u002F\u002Fdoi.org\u002F10.15957\u002Fj.cnki.jjdl.2014.07.045\nYao H, Wu T (2011) Deconstruction of innovative industrial clusters. Technology Today 000(012):32–34. https:\u002F\u002Fdoi.org\u002F10.3969\u002Fj.issn.1003-7438.2004.12.012\nZeng J, Liu D, Yi H (2019) Agglomeration, structural embeddedness, and enterprises' innovation performance: an empirical study of Wuhan Biopharmaceutical Industrial Cluster Network. Sustainability 11. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fsu11143922\nZhang J, Wang Y (2019) Technology efficiency of strategic emerging industries from innovative industrial clusters. Studies in Science of Science 37(08). https:\u002F\u002Fdoi.org\u002F10.16192\u002Fj.cnki.1003-2053.2019.08.005\nZhang B, Luo Y, Chiu YH (2019) Efficiency evaluation of china's high-tech industry with a multi-activity network data envelopment analysis approach. 66(6):2–9. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.seps.2018.07.013\nZhao Y, Hao L, Yang Z (2010) Regional differentiation of energy efficiency and its causes in Jiangsu. CATA Geographical Sinica 65(008):919–928. https:\u002F\u002Fdoi.org\u002F10.11821\u002Fxb201008003\nZhu S, He C (2016) Global and local governance, industrial and geographical dynamics: a tale of two clusters. Environ Plan 34(8):1453–1473. https:\u002F\u002Fdoi.org\u002F10.1177\u002F0263774X15621760",{"VOID":94},"10.1007\u002Fs12517-021-06927-5","PUBLICATION","VERIFIED","2024-12-07T00:55:57.170+00:00","Auto Verify",[100],"VI","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-021-06927-5",[103,119,132],{"id":104,"sortIndex":23,"researcher":22,"roles":105,"affiliations":107,"properties":116,"displayName":118,"givenName":22,"familyName":22},"bfc57d68-fa93-46f1-ac87-80cb4e05c8e6",[106],"AUTHOR",[108],{"id":109,"sortIndex":23,"affiliation":110,"properties":22},"9c4c81bf-79fe-482b-8bc4-adf25ebb91ad",{"id":109,"createTime":22,"updateTime":22,"relativeEntities":111,"slug":22,"properties":112,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":115,"statistic":22},[],{"title":113},{"VI":114},"School of Economics and Management, Fuzhou University, Fuzhou, China",[],{"title":117},{"VI":118},"Meiying Kong",{"id":120,"sortIndex":46,"researcher":22,"roles":121,"affiliations":122,"properties":129,"displayName":131,"givenName":22,"familyName":22},"ac5208a2-e355-4424-9c30-75160e102690",[106],[123],{"id":109,"sortIndex":23,"affiliation":124,"properties":22},{"id":109,"createTime":22,"updateTime":22,"relativeEntities":125,"slug":22,"properties":126,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":128,"statistic":22},[],{"title":127},{"VI":114},[],{"title":130},{"VI":131},"Xiaoqing Wang",{"id":133,"sortIndex":45,"researcher":22,"roles":134,"affiliations":135,"properties":142,"displayName":144,"givenName":22,"familyName":22},"1c293d15-1897-4b69-a563-6328045e2538",[106],[136],{"id":109,"sortIndex":23,"affiliation":137,"properties":22},{"id":109,"createTime":22,"updateTime":22,"relativeEntities":138,"slug":22,"properties":139,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":141,"statistic":22},[],{"title":140},{"VI":114},[],{"title":143},{"VI":144},"Qiuming Wu","ARTICLE",{"url":101,"publisher":147,"properties":167},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":148,"slug":10,"properties":149,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":153,"manageAffiliations":154,"indexDatabases":155,"url":22,"thumbnailPath":22,"statistic":162,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":150,"title":151,"eissn":152},{"VOID":15},{"EN":17},{"VOID":13},[],[],[156],{"id":28,"indexDatabase":157,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":158,"label":159,"description":160,"key":36,"publicationTags":161,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":163,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":164,"totalCitation":65,"totalCitationByYear":165,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":166,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":168,"volume":170},{"VOID":169},"1-15",{"VOID":171},"14","2021-03-30",2021,[38],false,{"id":177,"createTime":178,"updateTime":179,"relativeEntities":180,"slug":181,"properties":182,"entityType":95,"verifyStatus":96,"verifyTime":192,"verifyNote":98,"languages":22,"translateLanguages":193,"viewCount":23,"primaryUrl":194,"fullTextUrl":22,"authors":195,"publicationType":145,"publisherRelationship":226,"citationCount":22,"citationInfo":22,"publishDate":251,"publishYear":173,"citationAnalyzeStatus":21,"lastCitationAnalyze":22,"indexDatabases":252,"openAccess":22,"references":22,"isForceReanalyzing":175},"40028ae9-888a-4fb6-8405-a5708505fbb8","2024-01-29T06:30:31.801+00:00","2026-09-08T04:13:50.685+00:00",[],"Influence-of-nano-titanium-dioxide-particles-TiO2-NPs-on-improving-phytoremediation-efficiency-of-As-Cu-Cd-from-copper-mine-wastewaters-using-Lemna-minor",{"abstract":183,"title":185,"references":188,"doi":190},{"EN":184},"Phytoremediation technology is an appropriate and eco-friendly technique with high efficiency to reduce heavy metal hazards from a contaminated environment. This study analyzed the effects of individual titanium dioxide nano-particles (TiO2 NPs) and combined with EDTA (TiO2 NPs+ EDTA) on phytoremediation of arsenic (As), copper (Cu), and cadmium (Cd) from copper mine wastewater using Lemna minor. The experiments were exposed using different concentrations of TiO2 NPs (200 and 300 mg kg−1) and EDTA (2 g kg−1) in seven treatments during 10 days. The content of As, Cu, and Cd was measured in plant tissues to calculate the total metal extraction (Ct), bioaccumulation factor (BF), and removal efficiency (RE). The content of As and Cu in the plant showed that uptake depended upon TiO2 NP concentration, and the Ct, BF, and RE values were affected by concentration of TiO2 NPs in wastewater. Adding 200 mg kg−1 TiO2 NPs to mine wastewater significantly increased the plant biomass (66.5%), relative growth rate (42.2%), relative growth factor (33.9%), and tolerance index (39.2%) compared to the individual wastewater treatment. Adding 200 mg kg−1 TiO2 NPs enhanced the As content, Ct, and BF values to 382.5± 41.2 mg kg−1, 122.1± 17.9 μg plant−1, and 664.2±161.2, respectively, while the combined use of TiO2 NPs+ EDTA and individual application of EDTA significantly decreased As uptake compared with wastewater treatment. For As, the RE (54.7%) and total absorbed (1786 kg ha−1 year−1) reached the maximum values following the application of 300 mg kg−1 TiO2 NPs, which were 25.8% and 30.1% higher than the control wastewater treatment, respectively. Furthermore, the BF values in control wastewater treatment showed that L. minor was known as a hyper-accumulate plant for the Cu metal, whereas the Ct, RE, and BF values increased due to applying individual TiO2 NPs and combined TiO2 NPs+EDTA. These results suggest that using TiO2 NPs for phytoremediation of contaminated water is helpful in enhancing As accumulation, whereas the L. minor is also a useful plant for Cu accumulation.",{"EN":186,"VI":187},"Influence of nano-titanium dioxide particles (TiO2 NPs) on improving phytoremediation efficiency of As\u002FCu\u002FCd from copper mine wastewaters using Lemna minor","Ảnh hưởng của các hạt nano titan dioxide (TiO2 NPs) đến việc nâng cao hiệu quả xử lý thực vật As\u002FCu\u002FCd từ nước thải mỏ đồng bằng Lemna minor",{"VOID":189},"Alfano OM, Bahnemann D, Cassano AE, Dillert R, Goslich R (2000) Photocatalysis in water environments using artificial and solar light. Catalysis Today 58(2-3):199–230\nAlvarado S, Guédez M, Lué-Merú MP, Nelson G, Alvaro A, Jesús AC, Gyula Z (2008) Arsenic removal from waters by bioremediation with the aquatic plants water hyacinth (Eichhorniacrassipes) and lesser duckweed (L. minor). 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Environ Pollut 157(5):1533–1543",{"VOID":191},"10.1007\u002Fs12517-021-06842-9","2024-12-07T00:33:20.049+00:00",[100],"https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-021-06842-9",[196,211],{"id":197,"sortIndex":23,"researcher":22,"roles":198,"affiliations":199,"properties":208,"displayName":210,"givenName":22,"familyName":22},"06cfbf56-e93d-408f-a106-f3e7bf540735",[106],[200],{"id":201,"sortIndex":23,"affiliation":202,"properties":22},"8e249bc9-470f-4d38-b4c3-74da7855f3fe",{"id":201,"createTime":22,"updateTime":22,"relativeEntities":203,"slug":22,"properties":204,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":207,"statistic":22},[],{"title":205},{"VI":206},"Department of Water Science & Engineering, Faculty of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran",[],{"title":209},{"VI":210},"Akram Seifi",{"id":212,"sortIndex":46,"researcher":22,"roles":213,"affiliations":214,"properties":223,"displayName":225,"givenName":22,"familyName":22},"dcd065ab-ddab-4495-8c2f-6cd7043b31c0",[106],[215],{"id":216,"sortIndex":23,"affiliation":217,"properties":22},"20463b83-9e80-4881-bce8-44ded4bdc7cb",{"id":216,"createTime":22,"updateTime":22,"relativeEntities":218,"slug":22,"properties":219,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":222,"statistic":22},[],{"title":220},{"VI":221},"Department of Civil Engineering, Faculty of Technical and Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran",[],{"title":224},{"VI":225},"Majid Dehghani",{"url":194,"publisher":227,"properties":247},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":228,"slug":10,"properties":229,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":233,"manageAffiliations":234,"indexDatabases":235,"url":22,"thumbnailPath":22,"statistic":242,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":230,"title":231,"eissn":232},{"VOID":15},{"EN":17},{"VOID":13},[],[],[236],{"id":28,"indexDatabase":237,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":238,"label":239,"description":240,"key":36,"publicationTags":241,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":243,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":244,"totalCitation":65,"totalCitationByYear":245,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":246,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":248,"volume":250},{"VOID":249},"1-14",{"VOID":171},"2021-03-12",[38],{"id":254,"createTime":255,"updateTime":256,"relativeEntities":257,"slug":258,"properties":259,"entityType":95,"verifyStatus":96,"verifyTime":270,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":271,"fullTextUrl":22,"authors":272,"publicationType":145,"publisherRelationship":367,"citationCount":393,"citationInfo":394,"publishDate":397,"publishYear":395,"citationAnalyzeStatus":398,"lastCitationAnalyze":256,"indexDatabases":399,"openAccess":22,"references":22,"isForceReanalyzing":175},"2a542b5e-cb10-4aa2-8103-408e19f3811f","2024-01-29T00:10:59.876+00:00","2026-08-24T20:06:28.492+00:00",[],"Sedimentology-and-reservoir-characteristics-of-Jurassic-Samanasuk-Formation-of-Salt-Range-and-Hazara-areas-Upper-Indus-Basin-Pakistan",{"abstract":260,"title":262,"gsPaper":264,"references":266,"doi":268},{"EN":261},"The present study focuses on the Samanasuk Formation, which is exposed in Chichali Gorge (Surghar Range), Nammal Gorge (Western Salt Range), and Sangar Gali (Hazara area). The study aims to analyze the lithofacies, petrographic characteristics, diagenetic features, ooid types, and depositional settings of the formation. Outcrop data revealed four distinct lithofacies have been identified: limestone facies (SF1), dolomitic limestone facies (SF2), oolitic limestone facies (SF3), and sandy limestone facies (SF4). In the Chichali Gorge and Nammal Gorge sections, the petrographic study further classified the formation into three facies: mudstone, wackestone, and packstone. This classification reveals a progradational stacking pattern, where the coarse-grained facies predominantly overlie the fine-grained facies. Additionally, the petrographic analysis indicates various depositional environments ranging from the beach to the inner ramp. The Samanasuk Formation in the studied sections exhibits five major diagenetic features, representing five significant diagenetic processes: micritic envelopes, cementation, hard ground surfaces, physical and chemical compaction, and dolomitization. Furthermore, two types of ooids have been identified within the formation, namely Radial Ooids and Concentric Ooids. Radial Ooids are formed in the beach-to-inner ramp environment, while Concentric Ooids are formed in the inner ramp environment. To assess the petrophysical properties, wireline logs from the Chanda deep-01 and Isakhel-01 wells were interpreted. The analysis and log interpretation aided in characterizing lithologic units of reservoir zones, determining reservoir properties, differentiating shale-bearing zones from non-shale-bearing zones, and understanding the depositional settings. The petrophysical evaluation of the Samanasuk Formation, conducted through outcrop and thin section studies, reveals shoaling upward cycles, indicating that the formation belongs to the High Stand System Tract (HST). The depositional model suggests that the Samanasuk Formation was deposited during the Middle Jurassic in a shallow marine environment extending from the beach to the inner ramp.",{"EN":263},"Sedimentology and reservoir characteristics of Jurassic Samanasuk Formation of Salt Range and Hazara areas, Upper Indus Basin, Pakistan",{"VOID":265},"[\"15765835619047028190\"]",{"VOID":267},"Ali F, Haneef M, Zhang S, Latif K (2019) Ooid fabric in the Jurassic of the Indus Basin, Pakistan: control on the original mineralogy. Curr Sci 119(5):837\nAmel H, Jafarian A, Husinec A, Koeshidayatullah A, Rudy S (2015) Microfacies, depositional environment and diagenetic evolution controls on the reservoir quality of the Permian Upper Dalan Formation, Kish Gas Field, Zagros Basin. 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Palaeogeogr Palaeoclimatol Palaeoecol 37(1):17–42. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0031-0182(82)90056-6\nKavoosi MA, Lasemi Y, Sherkati S, Moussavi-Harami R (2009) Facies analysis and depositional sequences of the Upper Jurassic Mozduran Formation, a carbonate reservoir in the Kopet Dagh Basin, NE Iran. J Pet Geol 32(3):235–259\nKazmi AH (1979) Active fault systems in Pakistan. In: Farah A, Dejong KA (eds) Geodynamics of Pakistan, Geol, Sur Pakistan, pp 285–294\nKazmi AH, Rana MA (1982) Geology and tectonics of the Salt Range and Trans Indus Ranges, Pakistan. University of Peshawar and Geological Survey of Pakistan, Islamabad\nLasemi Y (1995) Platform carbonates of the Upper Jurassic Mozduran formation in the Kopet Dagh Basin, NE Iran—facies, palaeoenvironments and sequences. 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Blackwell Scientific Publication, London\nWadia DN (1957) Geology of India, vol 536. Mcmillan and Co., London\nWadood B, Khan S, Li H, Yiqun L, Ahmad S, Jiao X (2020) Sequence stratigraphic framework of the Jurassic Samana Suk carbonate formation, North Pakistan: implications for reservoir potential. Arab J Sci Eng 46:525–542. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs13369-020-04654-9\nWanas HA (2008) Cenomanian rocks in the Sinai Peninsula, Northeast Egypt: facies analysis and sequence stratigraphy. J Afr Earth Sc 52:125–138\nWilson JL (1975a) The lower carboniferous Waulsortian facies. In: Carbonate Facies in Geologic History. Springer, Berlin, pp 148–168\nWilson JL (1975b) Calcareous facies in geological history. Springer-Verlag, Berlin, Heidelberg, New York\nWynne A. B., (1877). Note on the Tertiary zone and underlying rocks of the north-west Panjab Records of the Geological Survey of India, 7, 2, 107-132.",{"VOID":269},"10.1007\u002Fs12517-023-11530-x","2024-05-16T02:24:16.457+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-023-11530-x",[273,290,303,318,334,351],{"id":274,"sortIndex":23,"researcher":22,"roles":275,"affiliations":276,"properties":285,"displayName":287,"givenName":22,"familyName":22},"7942e62e-3d76-41c4-aa94-e7ae922dcd26",[106],[277],{"id":278,"sortIndex":23,"affiliation":279,"properties":22},"807dfaf8-2dc9-4994-82db-029644f28ca1",{"id":278,"createTime":22,"updateTime":22,"relativeEntities":280,"slug":22,"properties":281,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":284,"statistic":22},[],{"title":282},{"VI":283},"Institute of Geology, University of Azad Jammu and Kashmir, Muzaffarabad, 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burst is a serious geological hazard in deep underground mines affecting progress of mining operations. Although rock burst is a complex process, a distribution law of fractal characteristics can explain the rock failure mechanism. Using a servo-controlled testing system, uniaxial cyclic loading tests on coal rock specimens were conducted to investigate the fractal characteristics of the fragments under different loading rates. To comprehensively characterize the coal fragments of different sizes, samples were divided into four groups of different size: particles, fine, medium-size, and coarse fragments. The distribution of the fragments under uniaxial cyclic loading conditions was then investigated based on the theory of fractal geometry, and the relationships between fractal dimensions and loading rates. Under uniaxial cyclic loading and unloading conditions, most of the fragments are irregular wedges and bulks, exhibiting obvious shape characteristics. Under various loading rates, the length-quantity fractal dimensions of the coal fragments ranged from 0.74 to 1.44, the width-quantity fractal dimensions range from 0.44 to 1.65, and the thickness-cumulative mass fractal dimensions range from 1.0 to 1.33. The coal rock’s crushing size-mass fractal dimensions under different loading rates were 2.27, 2.30, 2.32, and 2.35, respectively. Under a small loading rate, the dimension-quantity fractal dimensions are relatively small, suggesting that the coal rock was less crushed, with large fragments differing greatly in length, width, and thickness. The results show that the coal rock fragments exhibit certain shape characteristics after the cyclic loading, like irregular shapes and wedges. Under a larger loading rate, the fragments showed greater fractal dimensions of both size and mass; the coal samples crushed more thoroughly with more uniform fragments in length, width, thickness and mass. The conclusions obtained in this study confirm the classification and fractal characteristics of coal rock fragments by uniaxial cyclic loading conditions in laboratory test and provide the basis for further study on the mechanism of rock burst. This study is helpful for us to make a thorough inquiry the danger degree of rock burst in coal mine by using fractal geometry, understand the effects of methane to coal and the evolution mechanism of cracks, and it can be applied to the research on occurrence mechanism and early warning of rock burst.",{"EN":410},"Classification and fractal characteristics of coal rock fragments under uniaxial cyclic loading conditions",{"VOID":412},"[\"8398432228407079403\"]",{"VOID":414},"10.1007\u002Fs12517-018-3534-2","2024-05-02T22:17:21.780+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-018-3534-2",[418,433,446],{"id":419,"sortIndex":23,"researcher":22,"roles":420,"affiliations":421,"properties":430,"displayName":432,"givenName":22,"familyName":22},"87143b05-89dd-41e2-a5cf-0fc7d0426582",[106],[422],{"id":423,"sortIndex":23,"affiliation":424,"properties":22},"b165ab52-8013-4f5d-962e-f50d7baa0817",{"id":423,"createTime":22,"updateTime":22,"relativeEntities":425,"slug":22,"properties":426,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":429,"statistic":22},[],{"title":427},{"VI":428},"State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao, China",[],{"title":431},{"VI":432},"Yangyang Li",{"id":434,"sortIndex":46,"researcher":22,"roles":435,"affiliations":436,"properties":443,"displayName":445,"givenName":22,"familyName":22},"15a6ca91-9f07-4e1e-a960-c2595a1241cc",[106],[437],{"id":423,"sortIndex":23,"affiliation":438,"properties":22},{"id":423,"createTime":22,"updateTime":22,"relativeEntities":439,"slug":22,"properties":440,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":442,"statistic":22},[],{"title":441},{"VI":428},[],{"title":444},{"VI":445},"Shichuan Zhang",{"id":447,"sortIndex":45,"researcher":22,"roles":448,"affiliations":449,"properties":456,"displayName":458,"givenName":22,"familyName":22},"32968c8a-04ed-457f-8658-69bf2675ae82",[106],[450],{"id":423,"sortIndex":23,"affiliation":451,"properties":22},{"id":423,"createTime":22,"updateTime":22,"relativeEntities":452,"slug":22,"properties":453,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":455,"statistic":22},[],{"title":454},{"VI":428},[],{"title":457},{"VI":458},"Xin Zhang",{"url":416,"publisher":460,"properties":480},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":461,"slug":10,"properties":462,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":466,"manageAffiliations":467,"indexDatabases":468,"url":22,"thumbnailPath":22,"statistic":475,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":463,"title":464,"eissn":465},{"VOID":15},{"EN":17},{"VOID":13},[],[],[469],{"id":28,"indexDatabase":470,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":471,"label":472,"description":473,"key":36,"publicationTags":474,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":476,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":477,"totalCitation":65,"totalCitationByYear":478,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":479,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":481,"volume":483},{"VOID":482},"1-12",{"VOID":484},"11",{"total":23,"publishYear":486,"statisticByYear":487},2018,{},"2018-04-30","2026-08-24T12:30:32.460+00:00",[38],[492,498,501,507,510,513,516,519,522,525,531,537,540,543,546,549,552,555,558],{"id":493,"text":494,"url":495,"identifiers":496},"7d07dd9c-6f2c-4c5f-ac36-2e915b21f989","Akdag S, Karakus M, Taheri A, Nguyen G, Manchao H (2018) Effects of thermal damage on strain burst mechanism for brittle rocks under true-triaxial loading conditions. 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Acta Mech 226(11):3623–3637",{"doi":506},{"id":22,"text":520,"url":22,"identifiers":521},"Jevric M, Knezevic M, Kalezic J, Kopitovic-Vukovic N, Cipranic I (2014) Application of fractal geometry in urban pattern design. Tehnicki Vjesnik 21(4):873–879",{},{"id":502,"text":523,"url":504,"identifiers":524},"Kong X, Wang E, Hu S, Shen R, Li X, Zhan T (2016) Fractal characteristics and acoustic emission of coal containing methane in triaxial compression failure. J Appl Geophys 124:139–147",{"doi":506},{"id":526,"text":527,"url":528,"identifiers":529},"09978a22-1ba1-4d11-bbc4-cafbf0d2e6c6","Liu R, Jiang Y, Li B, Wang X (2015) A fractal model for characterizing fluid flow in fractured rock masses based on randomly distributed rock fracture networks. Comput Geotech 65:45–55","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0266352X14002092",{"doi":530},"10.1016\u002Fj.compgeo.2014.11.004",{"id":532,"text":533,"url":534,"identifiers":535},"e8737b99-2774-476c-afb1-fa72fb045763","Liu R, Li B, Jiang Y (2016) A fractal model based on a new governing equation of fluid flow in fractures for characterizing hydraulic properties of rock fracture networks. Comput Geotech 75:57–68","https:\u002F\u002Flinkinghub.elsevier.com\u002Fretrieve\u002Fpii\u002FS0266352X16300076",{"doi":536},"10.1016\u002Fj.compgeo.2016.01.025",{"id":502,"text":538,"url":504,"identifiers":539},"Mahjani M, Moshrefi R, Sharifi-Viand A, Taherzad A, Jafarian M, Hasanlou F (2016) Surface investigation by electrochemical methods and application of chaos theory and fractal geometry. Chaos, Solitons Fractals 91:598–603",{"doi":506},{"id":22,"text":541,"url":22,"identifiers":542},"Nagahama H (2000) Fractal scalings of rock fragmentation. Earth Sci Fron 7(1):169–177",{},{"id":22,"text":544,"url":22,"identifiers":545},"Sui L, Yang Y, Ju Y (2014) Fractal description of rock fracture behavior. Mec Pract 36:753–756",{},{"id":502,"text":547,"url":504,"identifiers":548},"Tian B, Liu S, Zhang Y, Wang Z (2016) Analysis of fractal characteristic of fragments from rock burst tests under different loading rates. Tehnicki Vjesnik 23:1269–1276",{"doi":506},{"id":22,"text":550,"url":22,"identifiers":551},"Wang G, Gong S, Li Z, Dou L, Cai W, Mao Y (2016) Evolution of stress concentration and energy release before rock bursts: two case studies from Xingan coal mine, Hegang, China. Rock Mech Rock Eng 49(8):3393–3401",{},{"id":502,"text":553,"url":504,"identifiers":554},"Wold M, Connell L, Choi S (2008) The role of spatial variability in coal seam parameters on gas outburst behaviour during coal mining. Int J Coal Geol 75(1):1–14",{"doi":506},{"id":502,"text":556,"url":504,"identifiers":557},"Wong T-f, Wong RHC, Chau KT, Tang CA (2006) Microcrack statistics, Weibull distribution and micromechanical modeling of compressive failure in rock. Mech Mater 38(7):664–681",{"doi":506},{"id":502,"text":559,"url":504,"identifiers":560},"Wu Q, Liu Y, Yang L (2011) Using the vulnerable index method to assess the likelihood of a water inrush through the floor of a multi-seam coal mine in China. Mine Water Environ 30(1):54–60",{"doi":506},{"id":562,"createTime":563,"updateTime":564,"relativeEntities":565,"slug":566,"properties":567,"entityType":95,"verifyStatus":96,"verifyTime":578,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":579,"fullTextUrl":22,"authors":580,"publicationType":145,"publisherRelationship":613,"citationCount":23,"citationInfo":639,"publishDate":642,"publishYear":640,"citationAnalyzeStatus":398,"lastCitationAnalyze":643,"indexDatabases":644,"openAccess":22,"references":22,"isForceReanalyzing":175},"7a0e8031-7573-4595-8103-fa6bda5fe803","2024-02-10T21:15:58.059+00:00","2026-08-18T19:18:33.196+00:00",[],"A-new-approach-for-the-geological-risk-evaluation-of-coal-resources-through-a-geostatistical-simulation",{"abstract":568,"title":570,"gsPaper":572,"references":574,"doi":576},{"EN":569},"Estimations of mineral resources and ore reserves have been recently widely used by mining engineers and investors. The classification framework based on the prepared code by the Joint Ore Reserves Committee of The Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia (JORC code), which is one of the international standards for mineral resource and ore reserve reporting, provides a template system that conforms to international society requirements. Recent research has shown that an existing fault risk can affect the mineral resource and ore reserve estimation. According to this research, the faulted area that is involved in the effect on the estimated region is so extensive that it is not distinguishable. In this research, a new method called FGT (F for fault, G for grade and T for thickness) is introduced and presented for the estimation of mineral resources. The proposed method can provide an error map of a particular aspect of the combination of coal accumulation (G), fault risk (F) and thickness (T), and its output would categorise the mineral resources. This method was implemented in the Parvadeh Ш coal deposit, which is located in the eastern portion of Central Iran. The deposit contains five seams named B1, B2, C1, C2 and D; of these, C1 was selected as the most important seam in the exploratory grid analysis. Thus, C1 alone can reflect the properties of the Parvadeh Ш deposit. In this study, we compared the conventional method and the FGT method. This comparison indicated that the areas that should be rejected from the region in the FGT method are less and more distinguishable than those determined with the conventional method. Therefore, the inferred resources can be completely differentiated from the indicated and measured resources with a high resolution. The conventional method cannot distinguish between these three categories at this level of resolution. Therefore, the FGT approach has high precision in classifying the coal resource compared to the conventional method.",{"EN":571},"A new approach for the geological risk evaluation of coal resources through a geostatistical simulation",{"VOID":573},"[\"13598003515308668754\"]",{"VOID":575},"Basarir H, Kumral M, Karpuz C, Tutluoglu L (2010) Geostatistical modeling of spatial variability of SPT data for a borax stockpile site. Eng Geol 114:154–163\nDimitrakopoulos R, Li S (2001) Quantification of fault uncertainty and risk management in underground long wall coal mining. In: Proceedings Geological Hazards (eds: R Doule and J Moloney), pp 175–182\nDimitrakopoulos R, Luo X (2004) Generalized sequential Gaussian simulation on group size v and screen-effect approximations for large field simulations. Math Geol 36(5):567–591\nDimitrakopoulos R, Scott J, Li S (2005) Quantification of geological uncertainty and risk assessment in coal resource\u002Freserve classification. ACARP Project C11042 Report, Volume I, W H Bryan Mining Geology Research Centre, The University of Queensland, 250 pp\nDowd PA (1993) Geostatistical simulation. Course notes for the MSc in Mineral Resource and Environmental Geostatistics, University of Leeds, 123 pp\nEmery X (2008) Statistical tests for validating geostatistical simulation algorithms. Comput Geosci 34:1610–1620\nEscuder J, Carbonell R, Matri D, Perez-Estaun A (2003) 3-D stochastic modeling and simulation of fault zones in the Albala granitic pluton, SW Iberian Variscan Massif. J Struct Geol 25:1487–1506\nJoint Ore Reserves Committee of The Australasian Institute of Mining and Metallurgy, Australian Institute of Geoscientists and Minerals Council of Australia (JORC) (2004) Australasian Code for Reporting of Exploration Results, Mineral Resources and Ore Reserves (The Australasian Institute of Mining and Metallurgy, Melbourne) [online]. Available at: http:\u002F\u002Fwww.ausimm.com.au\u002Fmain\u002Fabout\u002Fdocs\u002FJorc0105.pdf. Accessed: 7 May 2007\nJournel AG (1989) Fundamentals of geostatistics in five lessons. American Geophysical Union Publication, Washington, DC, 40 pp\nLi S, Dimitrakopoulos R (2002) Quantification and assessment of fault uncertainty and risk using stochastic conditional simulations. J Coal Sci Eng 8:1–11\nLi S, Dimitrakopoulos R, Scott J, Dunn D (2008) Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry. Ore Body Model Strategic Mine Plann Spectrum Ser 14:253–260\nMwasinga PP (2001) Approaching resource Classification: general practices and the integration of geostatistics. Comput Appl Min Ind J 97–104\nTabas Unit Exploration (1999) Report of primary exploration in Parvadeh III, Vol. 1, 517 pp\nYu Y (2010) Geostatistical interpolation and simulation of RQD measurement. MSc thesis in Mining Engineering, University of British Columbia, 90 pp",{"VOID":577},"10.1007\u002Fs12517-011-0391-7","2024-05-16T06:39:14.262+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-011-0391-7",[581,596],{"id":582,"sortIndex":23,"researcher":22,"roles":583,"affiliations":584,"properties":593,"displayName":595,"givenName":22,"familyName":22},"2052d647-ddab-4a1a-a810-4d0bc36b4791",[106],[585],{"id":586,"sortIndex":23,"affiliation":587,"properties":22},"bd08c0d4-da51-40cf-a678-b68585941ae5",{"id":586,"createTime":22,"updateTime":22,"relativeEntities":588,"slug":22,"properties":589,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":592,"statistic":22},[],{"title":590},{"EN":591},"Department of Mining Engineering, University of Tehran, Tehran, Iran",[],{"title":594},{"VI":595},"Omid Ashgari",{"id":597,"sortIndex":46,"researcher":22,"roles":598,"affiliations":599,"properties":608,"displayName":610,"givenName":22,"familyName":22},"203045e3-978c-4615-ab74-f955a4af8200",[106],[600],{"id":601,"sortIndex":23,"affiliation":602,"properties":22},"e8332273-a70a-44ad-8774-25e96817cc91",{"id":601,"createTime":22,"updateTime":22,"relativeEntities":603,"slug":22,"properties":604,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":607,"statistic":22},[],{"title":605},{"VI":606},"Young Researchers Club, South Tehran Branch, Islamic Azad University, Tehran, Iran",[],{"title":609,"gsAuthor":611},{"VI":610},"Nasser Madani Esfahani",{"VOID":612},"[\"MghExQgAAAAJ\"]",{"url":579,"publisher":614,"properties":634},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":615,"slug":10,"properties":616,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":620,"manageAffiliations":621,"indexDatabases":622,"url":22,"thumbnailPath":22,"statistic":629,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":617,"title":618,"eissn":619},{"VOID":15},{"EN":17},{"VOID":13},[],[],[623],{"id":28,"indexDatabase":624,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":625,"label":626,"description":627,"key":36,"publicationTags":628,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":630,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":631,"totalCitation":65,"totalCitationByYear":632,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":633,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":635,"volume":637},{"VOID":636},"929-943",{"VOID":638},"6",{"total":23,"publishYear":640,"statisticByYear":641},2011,{},"2011-09-10","2026-08-18T19:18:33.195+00:00",[38],{"id":646,"createTime":647,"updateTime":648,"relativeEntities":649,"slug":650,"properties":651,"entityType":95,"verifyStatus":96,"verifyTime":662,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":663,"fullTextUrl":22,"authors":664,"publicationType":145,"publisherRelationship":702,"citationCount":22,"citationInfo":22,"publishDate":727,"publishYear":173,"citationAnalyzeStatus":398,"lastCitationAnalyze":728,"indexDatabases":729,"openAccess":22,"references":22,"isForceReanalyzing":175},"8a72b9d9-b1c0-46b3-80f8-e6a0807dfa81","2024-01-22T21:09:25.383+00:00","2026-08-17T21:16:11.929+00:00",[],"RETRACTED-ARTICLE-Detection-of-soil-pollution-in-hilly-area-based-on-Bayesian-network-and-optimization-of-child-allowance-system",{"abstract":652,"title":654,"gsPaper":656,"references":658,"doi":660},{"EN":653},"As one of the basic components of deep learning, artificial neural network algorithm has been greatly affected in recent years, and has been widely used in many fields. The difference of Bayesian network is that its weight and threshold are expressed by probability distribution, compared with neural network with fixed weight. It is equivalent to the introduction of uncertainty and plays the role of regularization, therefore, to find a reasonable way to express the weights and thresholds of Bayesian network, so that it has a certain engineering application value. In recent years, soil pollution in hilly areas has become a major threat to food security and ecological environment. Therefore, it is very important to effectively obtain the relevant information of soil pollution components. The traditional detection method is complex and takes a long time, but the laser probe technology has become a research hotspot in the field of soil pollution detection in hilly areas because of its fast analysis speed and convenient operation. In order to promote the healthy growth of children, the government and society have implemented a series of policies and services to optimize the child allowance system, and the application of child allowance system in optimization is described in detail.",{"EN":655},"RETRACTED ARTICLE: Detection of soil pollution in hilly area based on Bayesian network and optimization of child allowance system",{"VOID":657},"[\"15028041502091805335\"]",{"VOID":659},"Aminu M, Matori AN, Yusof KW, Malakahmad A, Zainol RB (2017) Analytic network process (ANP) -based spatial decision support system (SDSS) for sustainable tourism planning in Cameron Highlands, Malaysia. Arab J Geosci 10(13). https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-017-3067-0\nBanerjee P, Ghose MK, Pradhan R (2018) Analytic hierarchy process and information value method-based landslide susceptibility mapping and vehicle vulnerability assessment along a highway in Sikkim Himalaya. Arab J Geosci 11(7):1–8. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-018-3488-4\nBibri SE (2018) A foundational framework for smart sustainable city development: theoretical, disciplinary, and discursive dimensions and their synergies. Sustain Cities Soc 38:758–794. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scs.2017.12.032\nCanepa B (2007) Bursting the bubble: determining the transit-oriented development’s walkable limits. Transp Res Rec 1992(1):28–34. https:\u002F\u002Fdoi.org\u002F10.3141\u002F1992-04\nCelik E, Aydin N, Gumus AT (2014) A multiattribute customer satisfaction evaluation approach for rail transit network: a real case study for Istanbul, Turkey. Transp Policy 36:283–293. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tranpol.2014.09.005\nEwing R, Cervero R (2010) Travel and the built environment. J Am Plan Assoc 76(3):265–294. https:\u002F\u002Fdoi.org\u002F10.1080\u002F01944361003766766\nHabibian M, Hosseinzadeh A (2018) Walkability index across trip purposes. Sustain Cities Soc 42:216–225. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.scs.2018.07.005\nHollevoet J, De Witte A, Macharis C (2011) Improving insight in modal choice determinants: an approach towards more sustainable transport. WIT Transa Built Environ 116:129–141. https:\u002F\u002Fdoi.org\u002F10.2495\u002FUT110121\nJones E (2003) Walkable towns: the liveable neighbourhoods strategy. Sustain Transport:314–325. https:\u002F\u002Fdoi.org\u002F10.1016\u002FB978-1-85573-614-6.50029-X\nKamruzzaman M, Baker D, Washington S, Turrell G (2014) Advance transit oriented development typology: case study in brisbane. Australia J Transp Geogr 34:54–70. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jtrangeo.2013.11.002\nLoo BPY, Du Verle F (2016) Transit-oriented development in future cities : towards a two-level sustainable mobility strategy. Int J Urban Sci 21(sup1):54–67. https:\u002F\u002Fdoi.org\u002F10.1080\u002F12265934.2016.1235488\nMalczewski J (2006) GIS-based multicriteria decision analysis: a survey of the literature. Int J Geogr Inf Sci 20(7):703–726. https:\u002F\u002Fdoi.org\u002F10.1080\u002F13658810600661508\nMotlagh MG, Amraei B, Halimi M (2020) Evaluating the hazardous potential of the dieback of the Zagros Oak forests using the multi-criteria decision-making methods. Arab J Geosci 13:995. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-020-05992-6\nSchlossberg M, Brown N (2004) Comparing transit-oriented development sites by walkability performance measures. Transp Res Rec 34–42:34–42. https:\u002F\u002Fdoi.org\u002F10.3141\u002F1887-05\nSingh MP, Singh P (2017) Multi-criteria GIS modeling for optimum route alignment planning in outer region of Allahabad City, India. Arab J Geosci 10:294. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-017-3076-z\nSingh YJ, Fard P, Zuidgeest M, Brussel M, van Maarseveen M (2014) Measuring transit oriented development: a spatial multi criteria assessment approach for the City Region Arnhem and Nijmegen. J Transp Geogr 35:130–143. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jtrangeo.2014.01.014\nSingh YJ, Lukman A, Flacke J, Zuidgeest M, Van Maarseveen MF (2017) Measuring TOD around transit nodes-towards TOD policy. Transp Policy 56:5696–5111. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tranpol.2017.03.013\nZhao C, Nielsen TA, Olafsson AS, Carstensen TA, Meng X (2018) Urban form, demographic and socio-economic correlates of walking, cycling, and e-biking: evidence from eight neighborhoods in Beijing. Transp Policy 64:102–112. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.tranpol.2018.01.018",{"VOID":661},"10.1007\u002Fs12517-021-08030-1","2024-05-14T16:46:24.799+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-021-08030-1",[665,689],{"id":666,"sortIndex":23,"researcher":22,"roles":667,"affiliations":668,"properties":686,"displayName":688,"givenName":22,"familyName":22},"01c11699-9d77-43b0-a0a8-4558956b2eb1",[106],[669,677],{"id":670,"sortIndex":23,"affiliation":671,"properties":22},"4387fea2-1a2f-4ac8-8373-ef966fe29590",{"id":670,"createTime":22,"updateTime":22,"relativeEntities":672,"slug":22,"properties":673,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":676,"statistic":22},[],{"title":674},{"VI":675},"School of Philosophy, Zhongnan University of Economics and Law, Wuhan, China",[],{"id":678,"sortIndex":46,"affiliation":679,"properties":685},"cfb9ff04-ee60-4c31-8af8-982136ffe6b4",{"id":678,"createTime":22,"updateTime":22,"relativeEntities":680,"slug":22,"properties":681,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":684,"statistic":22},[],{"title":682},{"VI":683},"School of Sociology, Huazhong University of Science and Technology, Wuhan, China",[],{},{"title":687},{"VI":688},"Wei Li",{"id":690,"sortIndex":46,"researcher":22,"roles":691,"affiliations":692,"properties":699,"displayName":701,"givenName":22,"familyName":22},"99a560f8-15e7-418d-9d92-6b576e6ba758",[106],[693],{"id":670,"sortIndex":23,"affiliation":694,"properties":22},{"id":670,"createTime":22,"updateTime":22,"relativeEntities":695,"slug":22,"properties":696,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":698,"statistic":22},[],{"title":697},{"VI":675},[],{"title":700},{"VI":701},"Qi 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Han",{"VOID":770},"A5070953313",{"url":22,"publisher":772,"properties":22},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":773,"slug":10,"properties":774,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":778,"manageAffiliations":779,"indexDatabases":780,"url":22,"thumbnailPath":22,"statistic":787,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":775,"title":776,"eissn":777},{"VOID":15},{"EN":17},{"VOID":13},[],[],[781],{"id":28,"indexDatabase":782,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":783,"label":784,"description":785,"key":36,"publicationTags":786,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":788,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":789,"totalCitation":65,"totalCitationByYear":790,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":791,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"total":73,"publishYear":793,"statisticByYear":794},2020,{},"2020-10-01","2026-08-16T22:20:36.930+00:00",[38],[799,803,807,811,815,819,823,827,830,834,838,842,845,849,852,855,859,862,865,869,872,875,879,882,886],{"id":22,"text":800,"url":22,"identifiers":801},"Campana EF, Diez M, Iemma U, Liuzzi G, Lucidi S, Rinaldi F, Serani A (2016) Derivative-free global ship design optimization using global\u002Flocal hybridization of the DIRECT algorithm. Optim Eng 17:127–156",{"doi":802},"10.1007\u002Fs11081-015-9303-0",{"id":22,"text":804,"url":22,"identifiers":805},"Cao SS, Hu DY, Hu ZW, Zhao WJ, Chen SS, Yu C (2018) Comparison of spatial structures of urban agglomerations between the Beijing-Tianjin-Hebei and Boswash based on the subpixel-level impervious surface coverage product. J Geogr Sci 28:306–322",{"doi":806},"10.1007\u002Fs11442-018-1474-0",{"id":22,"text":808,"url":22,"identifiers":809},"Chen XZ, Jie Y (2018) Acquisition and optimization of weld trajectory and pose information for robot welding of spatial corrugated web sheet based on laser sensing. Int J Adv Manuf Technol 96:3033–3041",{"doi":810},"10.1007\u002Fs00170-018-1716-4",{"id":22,"text":812,"url":22,"identifiers":813},"Chen PY, Tung CP, Lin WC, Li YH (2016) Spatial optimization procedure for land-use arrangement in a community based on a human comfort perspective. Paddy Water Environ 14:71–83",{"doi":814},"10.1007\u002Fs10333-015-0479-x",{"id":22,"text":816,"url":22,"identifiers":817},"Gang G, Siewerdsen J, Stayman J (2016) TH-CD-207B-09: Task-driven fluence field modulation design for model-based iterative reconstruction in CT. Med Phys 43:3890–3891",{"doi":818},"10.1118\u002F1.4958215",{"id":22,"text":820,"url":22,"identifiers":821},"Gu JJ, Guo P, Huang GH (2016) Achieving the objective of ecological planning for arid inland river basin under uncertainty based on ecological risk assessment. Stoch Env Res Risk A 30:1485–1501",{"doi":822},"10.1007\u002Fs00477-015-1159-5",{"id":22,"text":824,"url":22,"identifiers":825},"Li TX, Zhou XC, Ikhumhen HO, Difei A (2018) Research on the optimization of air quality monitoring station layout based on spatial grid statistical analysis method. Environ Technol 39:1–27",{"doi":826},"10.1080\u002F09593330.2018.1508251",{"id":22,"text":828,"url":22,"identifiers":829},"Rong F, Li WJ, Rao H, Zhou BR, Huang SD (2018) Optimization method for switching frequency of converter valve in mmc flexible dc transmission system. J Power Supply 16:84–91",{},{"id":22,"text":831,"url":22,"identifiers":832},"Sheng Y, Zhang Y, Guo H, Bose SK, Shen GX (2018) Benefits of unidirectional design based on decoupled transmitters and receivers in tackling traffic asymmetry for elastic optical networks. J Optical Commun Netw 10:C1–C14",{"doi":833},"10.1364\u002FJOCN.10.0000C1",{"id":22,"text":835,"url":22,"identifiers":836},"Shi Y, Lin F, Dai L, Jing XW (2016) Medical cloud computing risk prediction method based on analytic hierarchy process and MRHGA-RBF neural networking optimization. Journal of Medical Imaging & Health Informatics 6:1076–1087",{"doi":837},"10.1166\u002Fjmihi.2016.1804",{"id":22,"text":839,"url":22,"identifiers":840},"Teng YJ, Zhang M, Tong SF (2018) The optimization design of sub-regions scanning and vibration analysis for beaconless spatial acquisition in the inter-satellite laser communication system. IEEE Photonics Journal 10:1–11",{"doi":841},"10.1109\u002FJPHOT.2018.2884718",{"id":22,"text":843,"url":22,"identifiers":844},"Wang XP, Zhang Y (2017) Perturbation of mei symmetries and adiabatic invariants for dynamical systems based on nonstandard lagrangians. J Jilin Univ (Science Edition) 55:1569–1574",{},{"id":22,"text":846,"url":22,"identifiers":847},"Xu KP, Wang JJ, Chi YY, Liu M, Lu HJ (2016) Spatial optimization and sustainable use of land based on an integrated ecological risk in the Yun-Gui plateau region. Acta Ecol Sin 36:821–827",{"doi":848},"10.1016\u002Fj.chnaes.2016.09.007",{"id":22,"text":850,"url":22,"identifiers":851},"Ya L (2017) Simulation model of rationality evaluation of construction cost of tall buildings. Computer Simulation 34:416–419",{},{"id":22,"text":853,"url":22,"identifiers":854},"Yang TR, Kuang WH, Liu WD, Liu AL, Pan T (2017) Optimizing the layout of eco-spatial structure in Guanzhong urban agglomeration based on the ecological security pattern. Geogr Res 36:441–452",{},{"id":22,"text":856,"url":22,"identifiers":857},"Yang YQ, Wang H, Wang HQ, Gu SQ, Xu DL, Quan SL (2018) Reply to “comments on ‘optimization of sparse frequency diverse array with time-invariant spatial-focusing beampattern’”. IEEE Antennas and Wireless Propagation Letters 17:2522–2522",{"doi":858},"10.1109\u002FLAWP.2018.2870513",{"id":22,"text":860,"url":22,"identifiers":861},"Ye J, Xie QQ, Tan NY (2017a) National land spatial pattern distribution method based on ecological carrying capacity. Trans Chin Soc Agric Eng 33:262–271",{},{"id":22,"text":863,"url":22,"identifiers":864},"Ye YC, Sun K, Kuang LH, Zhao XM, Guo X (2017b) Spatial layout optimization of urban space and agricultural space based on spatial decision-making. Trans Chin Soc Agric Eng 33:256–266",{},{"id":22,"text":866,"url":22,"identifiers":867},"Yu J, Wen JH (2016) Multi-criteria satisfaction assessment of the spatial distribution of urban emergency shelters based on high-precision population estimation. Int J Disaster Risk Sci 7:413–429",{"doi":868},"10.1007\u002Fs13753-016-0111-8",{"id":22,"text":870,"url":22,"identifiers":871},"Yue DP, Yu Q, Zhang QB (2017) Progress in research on regional ecological security pattern optimization. Transactions of the Chinese Society for Agricultural Machinery 48:1–10",{},{"id":22,"text":873,"url":22,"identifiers":874},"Zhang DS (2016) Computer network design and implementation based on relational database technology. Automation & Instrumentation 4:229–230",{},{"id":22,"text":876,"url":22,"identifiers":877},"Zhang F, Song KJ, Fan Y (2017) A terahertz spatial power combiner based on 2D periodic hole-shaped grating using nongradient optimization method. Electromagnetics 37:538–549",{"doi":878},"10.1080\u002F02726343.2017.1395582",{"id":22,"text":880,"url":22,"identifiers":881},"Zhu J, Wen XH (2017) Intelligent power monitoring system of building equipment based on Internet of things. Chinese Journal of Power Sources 41:1775–1777",{},{"id":22,"text":883,"url":22,"identifiers":884},"Zhu DZ, Werner PL, Werner DH (2017) Design and optimization of 3d frequency selective surfaces based on a multi-objective lazy ant colony optimization algorithm. IEEE Transactions on Antennas & Propagation 65:7137–7149",{"doi":885},"10.1109\u002FTAP.2017.2766660",{"id":22,"text":887,"url":22,"identifiers":888},"Zou KM, Wu YG (2017) The comparative study between two types of boron silicate glass-ceramics preparation methods. Journal of China Academy of Electronics and Information Technology 12:193–196",{},{"id":890,"createTime":891,"updateTime":892,"relativeEntities":893,"slug":894,"properties":895,"entityType":95,"verifyStatus":96,"verifyTime":906,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":907,"fullTextUrl":22,"authors":908,"publicationType":145,"publisherRelationship":954,"citationCount":23,"citationInfo":979,"publishDate":981,"publishYear":173,"citationAnalyzeStatus":398,"lastCitationAnalyze":982,"indexDatabases":983,"openAccess":22,"references":22,"isForceReanalyzing":175},"2b3cc8ac-fd36-49d9-8104-c1abb9d02850","2024-02-09T21:58:00.552+00:00","2026-08-16T03:06:33.749+00:00",[],"Iron-in-groundwater-quality-evaluation-health-risk-and-spatial-distribution-in-Rangpur-City-Corporation",{"abstract":896,"title":898,"gsPaper":900,"references":902,"doi":904},{"EN":897},"High iron (Fe)–bearing groundwater in Bangladesh contaminates the groundwater aquifer with subsequent health risk. All the populations of Rangpur City Corporation (RpCC) depend on groundwater for their drinking, domestics, and irrigation purposes. This study investigates the concentration of Fe in groundwater of RpCC with special attention on the associated health risk of the dwellers residing in it. Stratified random sampling was adopted to collect 101 water samples from 33 wards of which each ward has 3 samples from tube well to identify Fe concentration using atomic absorption spectroscopy (AAS). The data are analyzed following several statistical technique and compared with Bangladesh and WHO standard values. Maximum mean concentration was found in ward no. 11which is 1.620 mg\u002FL and minimum mean concentration was found in ward no. 6 which is 0.006 mg\u002FL. Single Factor Index (SFI) and Nemerow Index (NI) were applied to analyze the quality of groundwater for the whole data set. Hazard Quotient (HQ) and Hazard Index (HI) were used to assess health risk and it ensures that the females of the study area are more susceptible to iron health risk than the males. However, geostatistical analyses using ArcGIS-10.5 were used to show the spatial distribution and autocorrelation among several variables. Spatial distribution maps show important visualization for understanding Fe concentration zoning, distribution of quality indices, and health risk. The findings of this work will help the city corporation authority, environmental planner, and policymaker to understand the potential health risk due to Fe concentration.",{"EN":899},"Iron in groundwater: quality evaluation, health risk, and spatial distribution in Rangpur City Corporation",{"VOID":901},"[\"5495076868918942245\"]",{"VOID":903},"Adhikary PP, Chandrasekharan H, Chakraborty D, Kamble K (2010) Assessment of groundwater pollution in West Delhi, India using geostatistical approach. Environ Monit Assess 167(1–4):599–615\nAhmed Z, Panaullah G, DeGloria S, Duxbury J (2011) Factors affecting paddy soil arsenic concentration in Bangladesh: prediction and uncertainty of geostatistical risk mapping. Sci Total Environ 412-413:324–335\nAzad A (2003) Impacts of Farakka Barrage on surface water resources in Bangladesh. World Environment Day, Report of Department of Environment, Government of the People’s Republic of Bangladesh, pp 40-43\nBader J (1973) Ground-water contamination, The United State of America and Puerto Rica. U. S. Geological Survey, Washington DC, p 103\nBahar M, Reza M (2010) Hydrochemical characteristics and quality assessment of shallow groundwater in a coastal area of Southwest Bangladesh. Environ Earth Sci 61(5):1065–1073\nBallentine R (1972) Subsurface pollution problems in the United States. Tech. Studies Rept. TS-00-72-02, U. S. Environmental Protection Agency, Washington DC, p 29\nBanglapedia (2015) Rangpur district. [Online] Available at: http:\u002F\u002Fen.banglapedia.org\u002Findex.php?title=Rangpur_District. Accessed 24 February, 2019\nBhuiyan M, Bodrud-Doza M, Islam A, Rakib M, Rahman M, Ramanathan A (2016) Assessment of groundwater quality of Lakshimpur district of Bangladesh using water quality indices, geostatistical methods, and multivariate analysis. Environmental Earth Sciences 75(12)\nBiswas R, Roy D, Towfiqul Islam A, Rahman M, Ali M (2014) Assessment of drinking water related to arsenic and salinity hazard in Patuakhali district, Bangladesh. Int J Adv Geosci 2(2)\nBodrud-Doza M, Islam A, Ahmed F, Das S, Saha N, Rahman M (2016) Characterization of groundwater quality using water evaluation indices, multivariate statistics and geostatistics in central Bangladesh. Water Sci 30(1):19–40\nBureau of Indian Standards (2012) Indian standard drinking water specifications, 1st revision, pp 1-8\nDavid M (1977) Geostatistical ore reserve estimation. Elsevier, Amsterdam\nDelhomme J (1978) Kriging in the hydrosciences. Adv Water Res 1:251–266\nDepartment of environment (1997) The environment conservation rules. Government of the People’s Republic of Bangladesh, Dhaka\nElinder C, Iron I, Friberg L, Nordberg G, Vouk V (1986) Handbook on the technology of metals. Elsevier, Amsterdam, pp 276–297\nESRI (2009) ArcGIS Desktop Software. Redlands, CA: ArcGIS Desktop 9.3\nGoovaerts P (1997) Comparative performance of indicator algorithms for modeling conditional probability distribution function. Math Geol 26:389–411\nGorai A, Kumar S (2013) Spatial distribution analysis of groundwater quality index using GIS: a case study of Ranchi Municipal Corporation (RMC) area. Geoinformat Geostat Overview 1:2\nGunatilaka A (2005) Groundwater woes of Asia. Asian Water, January\u002FFebruary\nHong-gui D, Teng-feng G, Ming-hui L, Xu D (2012) Comprehensive assessment model on heavy metal pollution in soil. Int J Electrochem Sci 7:5286–5296\nHossain D, Islam M, Sultana N, Tusher T (2013) Assessment of iron contamination in groundwater at Tangail municipality, Bangladesh. J Environ Sci Nat Resour 6(1):117–121.\nImam B (2005) Energy resources of Bangladesh. Published University Grants Commission of Bangladesh\nIslam ARMT, Shen S, Bodrud-Doza M, Rahman M, Das S (2017) Assessment of trace elements of groundwater and their spatial distribution in Rangpur district, Bangladesh. Arab J Geosci 10(4):95. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-017-2126-3\nJahan CS, Mazumder QH, Islam ATMM, Adham MI (2010) Impact of irrigation in Barind area, NW Bangladesh- an evaluation based on the meteorological parameters and fluctuation trend in groundwater table. J Geol Soc India 76:134–142. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12594-010-0085-x\nKnepper W (1981) Iron. In: Kirk-Othmer encyclopedia of chemical technology, vol 13. Wiley Interscience, New York, pp 735–753\nKubo S (1993) Geomorphological features of northwestern Bangladesh and some problems on flood mitigation. GeoJournal 31(4):313–318\nLian F, Wang Y (2011) July frontiers of green building, materials and civil engineering. Appl Mech Mater\nMarko K, Al-Amri N, Elfeki A (2013) Geostatistical analysis using GIS for mapping groundwater quality: case study in the recharge area of Wadi Usfan, western Saudi Arabia. Arab J Geosci 7:5239–5252. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12517-013-1156-2\nMeyer C (1973) Polluted ground: some causes, effects, controls, and monitoring. U.S. Environmental Protection Agency, Washington, D. C., p 282\nMridha M, Rashid M, Talukder K (1996) Quality of groundwater for irrigation in Natore district, Bangladesh. J Agric Res 2(1):25–252\nPanaskar D, Wagh V, Muley A, Mukate S, Pawar R, Aamalawar M (2016) Evaluating groundwater suitability for the domestic, irrigation, and industrial purposes in Nanded Tehsil, Maharashtra, India, using GIS and statistics. Arab J Geosci 9(13):615\nQureshi A, Ahmed Z, Krupnik T (2014) Groundwater management in Bangladesh: an analysis of problems and opportunities. Cereal Systems Initiative for South Asia Mechanization and Irrigation (CSISA-MI) Project, Research Report No. 2, Dhaka, Bangladesh: CIMMYT\nRahman M, Islam M, Bodrud-Doza M, Muhib M, Zahid A, Shammi M, Tareq S, Kurasaki M (2017) Spatio-temporal assessment of groundwater quality and human health risk: a case study in Gopalganj, Bangladesh. Expo Health. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12403-017-0253-y\nShahid S, Chen X, Hazarika M (2014) Evaluation of groundwater quality for irrigation in Bangladesh using geographic information system. J Hydrol Hydromech 54(1):3–14\nShahidullah S, Hakim M, Alam M, Shansuddoha A (2000) Assessment of groundwater quality in a selected area of Bangladesh. Pak J Biol Sci 3(2):246–249\nShi J, Wang H, Xu J, Wu J, Liu X, Zhu H, Yu C (2007) Spatial distribution of heavy metals in soils: a case study of Changxing, China. Environ Geol 52(1):1–10\nSingh P, Tiwari A, Panigarhy B, Mahato M (2013) Water quality indices used for water resources vulnerability assessment using GIS technique: a review. Int J Earth Sci Eng 6(6–1):1594–1600\nSmith A, Lingas E, Rahman M (2000) Contamination of drinking water by arsenic in Bangladesh: a public health emergency. Bull World Health Organ 78:1093–1103\nTiwari A, Singh P, Mahato M (2014) GIS-based evaluation of water quality index of groundwater resources in west Bokaro Coalfield, India. Curr World Environ 9(3):73–79\nUS Environmental Protection Agency (1989) Risk assessment guidance for superfund, volume 1: human health evaluation manual (part A)\nVasanthavigar M, Srinivasamoorthy K, Vijayaragavan K, Ganthi R, Chidambaram S, Anandhan P, Manivannan R, Vasudevan S (2010) Application of water quality index for groundwater quality assessment: Thirumanimuttar sub-basin, Tamilnadu, India. Environ Monit Assess 171(1–4):595–609. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10661-009-1302-1\nWagh V, Panaskar D, Muley A, Mukate S (2017) Groundwater suitability evaluation by CCME WQI model for Kadava River Basin, Nashik, Maharashtra, India. Model Earth Syst Environ. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs40808-017-0316-x\nWebster R, Oliver M (2001) Geostatistics for environmental scientists. Wiley, Chichester\nWorld Health Organization (2004) Guidelines for drinking-water quality, 3rd edn. WHO, Geneva",{"VOID":905},"10.1007\u002Fs12517-021-06450-7","2024-05-16T08:12:53.637+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-021-06450-7",[909,924,937],{"id":910,"sortIndex":23,"researcher":22,"roles":911,"affiliations":912,"properties":921,"displayName":923,"givenName":22,"familyName":22},"2a930cb4-4333-4d68-8ef3-6ffff14c0cd3",[106],[913],{"id":914,"sortIndex":23,"affiliation":915,"properties":22},"b1f73a6b-46bd-489d-8b8f-1d1a5ef46de8",{"id":914,"createTime":22,"updateTime":22,"relativeEntities":916,"slug":22,"properties":917,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":920,"statistic":22},[],{"title":918},{"VI":919},"Department of Disaster Management, Begum Rokeya University, Rangpur, Bangladesh",[],{"title":922},{"VI":923},"Md. Emdadul Haque",{"id":925,"sortIndex":46,"researcher":22,"roles":926,"affiliations":927,"properties":934,"displayName":936,"givenName":22,"familyName":22},"b9ac0eea-cc72-4ebf-b0c6-bbcb94323f2b",[106],[928],{"id":914,"sortIndex":23,"affiliation":929,"properties":22},{"id":914,"createTime":22,"updateTime":22,"relativeEntities":930,"slug":22,"properties":931,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":933,"statistic":22},[],{"title":932},{"VI":919},[],{"title":935},{"VI":936},"Rubaiya Nusrat Zahan",{"id":938,"sortIndex":45,"researcher":22,"roles":939,"affiliations":940,"properties":949,"displayName":951,"givenName":22,"familyName":22},"3d8974d3-7753-422d-9fbc-69443c615435",[106],[941],{"id":942,"sortIndex":23,"affiliation":943,"properties":22},"c1edc25b-6a72-484e-bd3f-4dd4298643da",{"id":942,"createTime":22,"updateTime":22,"relativeEntities":944,"slug":22,"properties":945,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":948,"statistic":22},[],{"title":946},{"VI":947},"Department of Geology and Mining, University of Rajshahi, Rajshahi, Bangladesh",[],{"title":950,"gsAuthor":952},{"VI":951},"Selim Reza",{"VOID":953},"[\"MBRBev0AAAAJ\"]",{"url":907,"publisher":955,"properties":975},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":956,"slug":10,"properties":957,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":961,"manageAffiliations":962,"indexDatabases":963,"url":22,"thumbnailPath":22,"statistic":970,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":958,"title":959,"eissn":960},{"VOID":15},{"EN":17},{"VOID":13},[],[],[964],{"id":28,"indexDatabase":965,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":966,"label":967,"description":968,"key":36,"publicationTags":969,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":971,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":972,"totalCitation":65,"totalCitationByYear":973,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":974,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":976,"volume":978},{"VOID":977},"1-13",{"VOID":171},{"total":23,"publishYear":173,"statisticByYear":980},{},"2021-03-19","2026-08-16T03:06:33.748+00:00",[38],{"id":985,"createTime":986,"updateTime":987,"relativeEntities":988,"slug":989,"properties":990,"entityType":95,"verifyStatus":96,"verifyTime":1001,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1002,"fullTextUrl":22,"authors":1003,"publicationType":145,"publisherRelationship":1036,"citationCount":23,"citationInfo":1062,"publishDate":1065,"publishYear":1063,"citationAnalyzeStatus":398,"lastCitationAnalyze":1066,"indexDatabases":1067,"openAccess":22,"references":22,"isForceReanalyzing":175},"54c12852-8ffc-40c3-80e1-8f589e3939e0","2023-12-25T15:41:01.768+00:00","2026-08-15T22:38:47.115+00:00",[],"Geochemical-assessment-and-speciation-of-metals-in-sediments-of-Osun-and-Erinle-Rivers-Southwestern-Nigeria",{"abstract":991,"title":993,"gsPaper":995,"references":997,"doi":999},{"EN":992},"Fifty sediment samples were collected from Osun (urban) and Erinle (suburban) rivers in addition to ten samples of the underlying rock types (schist and gneiss) and analyzed for elemental constituents while speciation of metals was determined by sequential analysis. Data were geochemically evaluated and ArcGIS was used to generate geochemical maps. Metal concentrations (ppm) in sub-urban and urban areas were Cd (0.2–0.2, 0.2–1.1), Cu (37.0–272.0, 49.0–970.0), Ni (6.0–27.0, 3.0–43.0), Pb (16.0–67.0, 15.0–2650.0), Zn (32.0–170.0, 50.0–987.0), Co (8.0–60.0, 2.0–86.0), Cr (26.0–153.0, 9.0–128.0), V (30.0–142.0, 9.0–135.0), and Mn (442.0–5100.0, 107.0–3930.0), respectively. In the rocks, Cu, Ni, Pb, Co, Cr, V, and Zn, concentrations (ppm) were below detection limit (BDL)-0.05, BDL-38.00; 6.23–12.00, BDL-20.00; 3.78–6.23, BDL-5.00; BDL-0.20, BDL-4.00; 5.00–9.00, BDL-66.00; 15.99–32.00, BDL-130.00; and 18.00–26.00, BDL-48.00, respectively, with Cu, Pb, Zn, Cd, and Mn of elevated concentrations in sediments compared with that of the rocks, being indication of additional anthropogenic sourcing. Calculated contamination indices revealed contamination for sediment from the urban areas compared to those from the sub-urban. High percentage of Pb (2.94–81.92%), Cu (31.69–45.95%), Zn (49.2–65.5%), Cd (31.69–45.95%), and Mn (12.13–37.50%) are hosted by the bio-available phases (carbonate, organic, and sulfide). The geochemical distribution of metals in the sediments of the Osun and Erinle rivers is governed by both geogenic (Ni-Cr-Co-V) and anthropogenic (Pb-Cd-Zn) activities. Elevated concentration and occurrences of the selected metals in the bio-available phases pose potential health risk to people in the urban area.",{"EN":994},"Geochemical assessment and speciation of metals in sediments of Osun and Erinle Rivers, Southwestern Nigeria",{"VOID":996},"[\"10079252170624580665\"]",{"VOID":998},"Buat-Menard P, Chesselet R (1979) Variable influence of atmospheric flux on the trace metal chemistry of oceanic suspended matter. Earth Planet Sci Lett 42:398–411. doi:10.1016\u002F0012-821X(79)90049-9\nChon HT, Kim KW, Kim JY (1995) Environ Geochem Health 17:139. doi:10.1007\u002FBF00126082\nDurn G, Ottner F, Slovenec D (1999) Mineralogy and geochemical indicators of the polygenetic nature of Terra Rosa in Istria, Croatia. Geoderma 91:125–150. doi:10.1016\u002FS0016-7061(98)00130-X\nFakayode SO, Olu-Owolabi BI (2003) Heavy metal contamination of roadside topsoil in Osogbo, Nigeria: its relationship to traffic density and proximity to highways. Environ Geol 44:150–157\nHakanson L (1980) An ecological risk index for aquatic pollution control: a sedimentological approach. Water Res 14:975–1001. doi:10.1016\u002F0043-1354(80)90143-8\nKarbassi AR, Amirnezhad R (2004) Int J Environ Sci Technol 1:191. doi:10.1007\u002FBF03325832\nLee SV, Cundy AB (2001) Heavy metal contamination and mixing processes in sediments from the Humber estuary, Eastern England. Estuar Coast Shelf Sci 53:619–636\nLoska K, Wiechula D, Korus I (2004) Metal contamination of farming soils affected by industry. Environ Int 30:159–165\nMuller G (1979) Schwermetalle in den Sedimenten des Rheins- Verenderungen Seit 1979. Umschau 79:778–783\nMuller G (1981) Index of geo-accumulation in sediments of the Rhine River. GeoJournal 2:108–118\nNishida H, Miyai M, Tada F, Suzuki S (1982) Computation of the index of pollution caused by heavy metals in river sediments. Environ Pollut B 4:241–248\nSutherland RA (2000) Bed sediment-associated trace metal in an urban stream, Ohau, Hawaii. Environ Geol 39:611–627\nTurekian KK, Wedepohl LH (1964) Distribution of the elements in major units of the earth’s crust. Bull Geol Soc Am 72:175–191\nWindom H, Schropp S, Calder F, Smith RJR, Burney L, Lewis F, Rawilson C (1989) Natural trace metal concentration in estuarine and coastal marine elements of the southern US. Environ Sci Technol 23(3):314–320",{"VOID":1000},"10.1007\u002Fs12517-017-3110-1","2024-05-16T21:52:57.410+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs12517-017-3110-1",[1004,1019],{"id":1005,"sortIndex":23,"researcher":22,"roles":1006,"affiliations":1007,"properties":1016,"displayName":1018,"givenName":22,"familyName":22},"913d615d-3f8c-4401-a74d-609f447808d3",[106],[1008],{"id":1009,"sortIndex":23,"affiliation":1010,"properties":22},"147eb89b-9996-4409-a033-8659512ce17d",{"id":1009,"createTime":22,"updateTime":22,"relativeEntities":1011,"slug":22,"properties":1012,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1015,"statistic":22},[],{"title":1013},{"VI":1014},"Nigerian Geological Survey Agency, Abeokuta office, Abuja, Nigeria",[],{"title":1017},{"VI":1018},"O. W. Okunola",{"id":1020,"sortIndex":46,"researcher":22,"roles":1021,"affiliations":1022,"properties":1031,"displayName":1033,"givenName":22,"familyName":22},"80110d4c-6954-4dff-9962-57869f37adb9",[106],[1023],{"id":1024,"sortIndex":23,"affiliation":1025,"properties":22},"c9df259a-a2cc-4ac6-8886-e3923f541111",{"id":1024,"createTime":22,"updateTime":22,"relativeEntities":1026,"slug":22,"properties":1027,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1030,"statistic":22},[],{"title":1028},{"VI":1029},"Department of Geology, University of Ibadan, Ibadan, Nigeria",[],{"title":1032,"gsAuthor":1034},{"VI":1033},"A. S. Olatunji",{"VOID":1035},"[\"3Jzo168AAAAJ\"]",{"url":1002,"publisher":1037,"properties":1057},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1038,"slug":10,"properties":1039,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":1043,"manageAffiliations":1044,"indexDatabases":1045,"url":22,"thumbnailPath":22,"statistic":1052,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":1040,"title":1041,"eissn":1042},{"VOID":15},{"EN":17},{"VOID":13},[],[],[1046],{"id":28,"indexDatabase":1047,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":1048,"label":1049,"description":1050,"key":36,"publicationTags":1051,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":1053,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":1054,"totalCitation":65,"totalCitationByYear":1055,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":1056,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":1058,"volume":1060},{"VOID":1059},"1-16",{"VOID":1061},"10",{"total":23,"publishYear":1063,"statisticByYear":1064},2017,{},"2017-08-18","2026-08-15T22:38:47.114+00:00",[38],{"id":1069,"createTime":1070,"updateTime":1071,"relativeEntities":1072,"slug":1073,"properties":1074,"entityType":95,"verifyStatus":96,"verifyTime":1085,"verifyNote":98,"languages":22,"translateLanguages":22,"viewCount":23,"primaryUrl":1086,"fullTextUrl":22,"authors":1087,"publicationType":145,"publisherRelationship":1127,"citationCount":23,"citationInfo":1153,"publishDate":1156,"publishYear":1154,"citationAnalyzeStatus":21,"lastCitationAnalyze":1157,"indexDatabases":1158,"openAccess":22,"references":22,"isForceReanalyzing":175},"cade2cc9-09c9-4e1e-80bb-bdd40575c60b","2023-11-25T03:06:49.299+00:00","2026-07-30T19:55:13.719+00:00",[],"Missing-rainfall-data-estimation-an-approach-to-investigate-different-methods-case-study-of-Baghdad",{"abstract":1075,"title":1077,"gsPaper":1079,"references":1081,"doi":1083},{"EN":1076},"The missing of the meteorological data in Iraq is common due to malfunction of measuring devices, security status, and human effects. The study tested 17 missing precipitation data estimation methods in Baghdad city as a case study, where, all the surrounding stations around Baghdad experienced the missing of data for various reasons, and some of the missing data are for a full year record. The methods examined in this study are based on different approaches, some of the methods are based upon the distances to the targeted station, others are upon regression factors, and there are also methods that combine several factors. There are also other types of missing data filling methods which depend on imputation and artificial intelligence. The investigation of the most accurate method to find the missing data will assist researchers and decision makers to fill the gap in their analysis in one of the most vulnerable countries in terms of drought and climate changes impacts. Results showed that Expectation Maximization (EM) method utilization has the best results with the least errors, and Multiple Linear Regression (MLR) method was ranked the second best method. In general, all of the applied methods had resulted acceptable interpolations, and it was clear that the combined methods have low significance on the results in comparison with others. All of these findings are limited to the study area meteorological and spatial conditions.",{"EN":1078},"Missing rainfall data estimation—an approach to investigate different methods: case study of Baghdad",{"VOID":1080},"[\"18179285460098197805\"]",{"VOID":1082},"(IMOaS), Iraqi Meteorological Organization and Seismology (2021) Unpublished Data of Meteorological Stations in Iraq\nAbdullah M, Al-Ansari N (2021) Irrigation projects in Iraq. J Earth Sci Geotech Eng 11(2):35–160. https:\u002F\u002Fdoi.org\u002F10.47260\u002Fjesge\u002F1123\nAbdulrazzaq Z (2020) The feasibility of using TRMM satellite data for missing terrestrial stations in Iraq for mapping the rainfall contour lines. Civ Eng Beyond Limits 1:15–19. https:\u002F\u002Fdoi.org\u002F10.36937\u002Fcebel.2020.003.003\nAbdulrida MA, Al-Jumaily K (2016) Comparisons of monthly rainfall data with satellite estimates of TRMM 3B42 over Iraq. Int J Sci Res Publications 6(1):494–501\nAl-Salihi AM, Al-Lami AM, Mohammed AJ (2013) Prediction of monthly rainfall for selected meteorological stations in Iraq using back propagation algorithms. J Environ Sci Technol 6(1):16–28. https:\u002F\u002Fdoi.org\u002F10.3923\u002Fjest.2013.16.28\nAlozeer A (2020) Estimation of mean areal rainfall and missing data by using GIS in Nineveh, Northern Iraq. Iraqi Geological Journal 53:93–103. https:\u002F\u002Fdoi.org\u002F10.46717\u002Figj.53.1E.7Ry-2020-07.07\nAnderson EA (1972) National weather service river forecast system forecast procedures. NOAA Tech Memo NWS HYDRO-14\nArmanuos A, Al-Ansari N, Yaseen Z (2020) Cross assessment of twenty-one different methods for missing precipitation data estimation. Atmosphere 11:1–35. https:\u002F\u002Fdoi.org\u002F10.3390\u002Fatmos11040389\nAzman MA-Z, Zakaria R, Ahmad Radi NF (2015) Estimation of missing rainfall data in Pahang using modified spatial interpolation weighting methods. AIP Conference Proceedings 1643(2015):65–72. https:\u002F\u002Fdoi.org\u002F10.1063\u002F1.4907426\nBarbalho F, Silva G, Formiga K (2014) Average rainfall estimation: methods performance comparison in the Brazilian semi-arid. J Water Resour Prot 06:97–103. https:\u002F\u002Fdoi.org\u002F10.4236\u002Fjwarp.2014.62014\nBárdossy A, Pegram G (2014) Infilling missing precipitation records—a comparison of a new copula-based method with other techniques. J Hydrol 519:1162–1170. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2014.08.025\nCarvalho J, Nakai A, Monteiro JE (2016) Spatio-temporal modeling of data imputation for daily rainfall series in homogeneous zones. Revista Brasileira De Meteorologia 31:196–201. https:\u002F\u002Fdoi.org\u002F10.1590\u002F0102-778631220150025\nDempster A, Laird N, Rubin D (1977) Maximum likelihood from incomplete data via the EMalgorithm. J Roy Stat Soc 39(1):1–38\nFrenken K (2009) Irrigation in the Middle East region in figures AQUASTAT Survey-2008. Water Reports. Food and Agriculture Organization of the United Nations, Rome\nKanda N, Negi H, Shekhar M, Rishi M (2017) Performance of various techniques in estimating missing climatological data over snowbound mountainous areas of Karakoram Himalaya. Meteorol Appl 25. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fmet.1699\nKashani M, Dinpashoh Y (2011) Evaluation of efficiency of different estimation methods for missing climatological data. Stoch Environ Res Risk Assess 26. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00477-011-0536-y\nLinsley Jr RK, Kohler MA, Paulhus JL (1975) Hydrology for engineers. McGraw Hill\nMurad S, Jaff Y (2020) Comparable investigation for rainfall forecasting using different data mining approaches in Sulaymaniyah city in Iraq. Int J Environ Sci Technol. https:\u002F\u002Fdoi.org\u002F10.18488\u002Fjournal.72.2020.41.11.18\nPaulhus JL, Kohler MA (1952) Interpolation of missing precipitation records. J Monthly Weather Review 80(8):129–133\nRubin DB (1988) An overview of multiple imputation. Proceedings of the survey research methods section of the American statistical association. Citeseer, pp 79–84\nSattari M, Rezazadeh Joudi A (2016) Assessment of different methods for estimation of missing data in precipitation studies. Hydrol Res 48. https:\u002F\u002Fdoi.org\u002F10.2166\u002Fnh.2016.364\nSchneider T (2001) Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J Clim 14:853–871. https:\u002F\u002Fdoi.org\u002F10.1175\u002F1520-0442(2001)014%3c0853:AOICDE%3e2.0.CO;2\nSyed Jamaludin SS, Deni S, Jemain A (2008) Revised spatial weighting methods for estimation of missing rainfall data. Asia-Pac J Atmos Sci 44:93–104\nTang W, Kassim A, Abubakar S (1996) Comparative studies of various missing data treatment methods—Malaysian experience. Atmos Res 42:247–262. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0169-8095(95)00067-4\nTeegavarapu R (2009) Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules. J Hydro 11.https:\u002F\u002Fdoi.org\u002F10.2166\u002Fhydro.2009.009\nTeegavarapu R, Chandramouli V (2005) Improved weighting methods, deterministic and stochastic data-driven models for estimation of missing precipitation records. J Hydrol 312:191–206. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jhydrol.2005.02.015\nWillmott C, Matsuura K, Robeson S (2009) Ambiguities inherent in sums-of-squares-based error statistics. Atmos Environ 43:749–752. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atmosenv.2008.10.005\nWillmott CJ (1981) On the validation of models. J Physical Geography 2(2):184–194\nWold HOA (1968) Nonlinear estimation by iterative least square procedures\nYozgatligil C, Aslan S, Iyigun C, Batmaz I (2012) Comparison of missing value imputation methods in time series: the case of Turkish meteorological data. Theoret Appl Climatol 112. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs00704-012-0723-x",{"VOID":1084},"10.1007\u002Fs12517-022-10995-6","2024-05-13T13:07:06.882+00:00","https:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs12517-022-10995-6",[1088,1112],{"id":1089,"sortIndex":23,"researcher":22,"roles":1090,"affiliations":1091,"properties":1109,"displayName":1111,"givenName":22,"familyName":22},"1529caeb-636c-4f73-9f11-e4c612d640a0",[106],[1092,1100],{"id":1093,"sortIndex":23,"affiliation":1094,"properties":22},"8c0d40e0-e615-42f4-ad75-3fc24e727a2d",{"id":1093,"createTime":22,"updateTime":22,"relativeEntities":1095,"slug":22,"properties":1096,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1099,"statistic":22},[],{"title":1097},{"EN":1098},"Baghdad, Iraq",[],{"id":1101,"sortIndex":46,"affiliation":1102,"properties":1108},"9fba8e3f-72a1-455c-8820-e95963e89775",{"id":1101,"createTime":22,"updateTime":22,"relativeEntities":1103,"slug":22,"properties":1104,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1107,"statistic":22},[],{"title":1105},{"VI":1106},"Lulea University of Technology, Lulea, Sweden",[],{},{"title":1110},{"VI":1111},"Mukhalad Abdullah",{"id":1113,"sortIndex":46,"researcher":22,"roles":1114,"affiliations":1115,"properties":1122,"displayName":1124,"givenName":22,"familyName":22},"5daabe01-a523-4382-9e4e-52b32e63f5ae",[106],[1116],{"id":1101,"sortIndex":23,"affiliation":1117,"properties":22},{"id":1101,"createTime":22,"updateTime":22,"relativeEntities":1118,"slug":22,"properties":1119,"entityType":22,"verifyStatus":22,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":22,"url":22,"parentIds":1121,"statistic":22},[],{"title":1120},{"VI":1106},[],{"title":1123,"gsAuthor":1125},{"VI":1124},"Nadhir Al-Ansari",{"VOID":1126},"[\"GNkD1HYAAAAJ\"]",{"url":1086,"publisher":1128,"properties":1148},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1129,"slug":10,"properties":1130,"entityType":20,"verifyStatus":21,"verifyTime":22,"verifyNote":22,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":1134,"manageAffiliations":1135,"indexDatabases":1136,"url":22,"thumbnailPath":22,"statistic":1143,"gsStatistic":22,"type":74,"analyzePriority":22},[],{"issn":1131,"title":1132,"eissn":1133},{"VOID":15},{"EN":17},{"VOID":13},[],[],[1137],{"id":28,"indexDatabase":1138,"url":39,"indexYears":40,"academicFieldIds":22,"indexDatabaseRanking":41},{"id":30,"createTime":22,"updateTime":22,"relativeEntities":1139,"label":1140,"description":1141,"key":36,"publicationTags":1142,"standard":22},[],{"EN":33,"VI":33},{"EN":33,"VI":35},[38],{"impactFactor":23,"impactFactorByYear":1144,"i10Index":45,"i10IndexLast5Year":46,"totalPublication":47,"totalPublicationByYear":1145,"totalCitation":65,"totalCitationByYear":1146,"totalCitationPerPublication":44,"totalCitationPerPublicationByYear":1147,"hindexLast5Year":73,"hindex":73},{"2019":44,"2020":44,"2021":23,"2022":23,"2023":23},{"2008":49,"2009":50,"2010":51,"2011":52,"2012":53,"2013":54,"2014":55,"2015":56,"2016":57,"2017":58,"2018":59,"2019":60,"2020":61,"2021":62,"2022":63,"2023":64,"2024":65},{"2018":67,"2019":46,"2020":68,"2021":69},{"2018":71,"2019":23,"2020":72,"2021":44},{"pages":1149,"volume":1151},{"VOID":1150},"1-17",{"VOID":1152},"15",{"total":23,"publishYear":1154,"statisticByYear":1155},2022,{},"2022-11-30","2026-07-30T19:55:13.718+00:00",[]]