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Mmetọ ala bụ nnukwu nsogbu nke ihe omume mmadụ na-eme. Nkesa mbara igwe nke ihe nwere ike igbu egbu (PTE) dịgasị iche iche n'ọtụtụ obodo na mpaghara ime obodo. Ya mere, ọ na-esiri ike ịkọwapụta ihe dị n'ime PTE dị n'ime ala ndị dị otú ahụ. Ngụkọta nke 115 samples nwetara site na Frydek Mistek na Czech Republic. Calcium (Calcium) na potassium (Calcium) (Calcium) na potassium (Calcium) na potassium (Calcium) , magnesium (Calcium) na di na nwunye (Calcium) kpebisiri ike, magnesium (Calcium na potassium). d plasma emission spectrometry. Nzaghachi nzaghachi bụ Ni na ndị na-ebu amụma bụ Ca, Mg, na K. Nkwekọrịta mmekọrịta dị n'etiti mgbanwe nzaghachi na mgbanwe amụma na-egosi mmekọrịta na-eju afọ n'etiti ihe ndị ahụ. Nchọpụta amụma gosiri na Nkwado Vector Machine Regression (SVMR) rụrụ nke ọma, ọ bụ ezie na e mere atụmatụ mgbọrọgwụ ya pụtara njehie square (RMSE) (235.974 mg / kkso) na 6 mg / k dị elu karịa usoro ndị ọzọ etinyere. Ụdị ngwakọta maka Empirical Bayesian Kriging-Multiple Linear Regression (EBK-MLR) na-arụ ọrụ na-adịghị mma, dị ka ihe àmà na-egosi na ọnụọgụ nke mkpebi siri ike na-erughị 0.1. The Empirical Bayesian Kriging-Support Vector Machine Regression (EBK-SVMR) nlereanya bụ obere ihe nlereanya (EBK-SVMR) . 8 mg / kg) ụkpụrụ na ọnụ ọgụgụ dị elu nke mkpebi siri ike (R2 = 0.637) The EBK-SVMR modeling technique output is visualized using a self-organizing map.Clustered neurons in the plane of the hybrid model CakMg-EBK-SVMR akụrụngwa na-egosi multiple agba ụkpụrụ na-ebu amụma Niile nhazi n'ime obodo na peri-n'ịtụgharị uche bụ amụma SV. s n'ime obodo mepere emepe na ala ime obodo.
Nickel (Ni) a na-ewere a micronutrient maka osisi n'ihi na ọ na-eme ka ikuku nitrogen fixation (N) na urea metabolism, nke abụọ nke a chọrọ maka mkpụrụ germination.Na mgbakwunye na ya onyinye na mkpụrụ germination, Ni nwere ike ime dị ka a fungal na nje inhibitor na-akwalite osisi development.The enweghị nickel n'ime ala na-enye ohere osisi na-amịkọrọ ya, ihe atụ nke green chlorosis na-eme ka ihe atụ nke akwụkwọ ndụ akwụkwọ ndụ akwụkwọ ndụ akwụkwọ ndụ. fatịlaịza dabeere na-ebuli nitrogen fixation2.Na-aga n'ihu ngwa nke nickel dabeere na fatịlaịza na-eme ka ala na-amụba ikike nke mkpo ọkụ na-edozi nitrogen n'ala na-anọgide na-enwe nickel ịta n'ime ala. Ọ bụ ezie na nickel bụ a micronutrient maka osisi, ya oké oriri na ala nwere ike ime ihe ọjọọ karịa good.The toxicity nke nickel na ala dị mkpa n'ihi na ihe ọkụkụ na-eto eto n'ime ala. Dị ka Liu3 si kwuo, Ni achọpụtala na ọ bụ ihe dị mkpa nke 17 dị mkpa maka mmepe osisi na uto.In mgbakwunye na ọrụ nickel na mmepe osisi na uto, ụmụ mmadụ chọrọ ya maka ngwa dị iche iche.Electroplating, mmepụta nke nickel-based alloys, na mmepụta nke mgbanye ngwaọrụ na spark plọg na ụlọ ọrụ mmepụta ihe niile chọrọ ntinye nickel dị iche iche na ụlọ ọrụ mmepụta ihe. ọtụtụ ebe eji kichinware, ballroom ngwa, nri ụlọ ọrụ onunu, eletriki, waya na cable, jet turbines, ịwa implants, textiles, na shipbuilding5.Ni-ọgaranya etoju na ala (ie, elu ala) e weere na ma anthropogenic na eke isi mmalite, ma isi, Ni bụ a eke isi iyi kama anthropogenic4,6.Natural isi mmalite nke nickel, ọkụ na-agụnye ọkụ na-eme ka ọkụ na-agụnye ọkụ ọkụ Filiks;Otú ọ dị, isi mmalite anthropogenic gụnyere batrị nickel / cadmium na ụlọ ọrụ nchara, electroplating, arc welding, diesel na mmanụ mmanụ, na ikuku ikuku sitere na combustion coal na mkpofu na sludge incineration Nickel accumulation7,8. Dị ka Freedman na Hutchinson9 na Manyiwa et al.10, isi mmalite nke elu ala mmetọ na ozugbo na n'akụkụ gburugburu bụ tumadi nickel-ọla kọpa dabeere smelters na mines. The top ala gburugburu Sudbury nickel-ọla refinery na Canada nwere kasị elu etoju nke nickel mmetọ na 26,000 mg/kg11. N'ụzọ dị iche, mmetọ si Russia na-arụpụtara nickel mmepụta n'elu Norwegian mmepụta nke nickel1. et al.12, ego nke HNO3-extractable nickel na mpaghara n'elu ala ubi (nickel mmepụta na Russia) sitere na 6.25 ruo 136.88 mg / kg, kwekọrọ na 30.43 mg / kg na a baseline ịta nke 25 mg / kg. Dị ka ala kabata 11 nke fatịlaịza na-emepe emepe na-emepe emepe site na fatịlaịza n'oge ime obodo nke phosphorus. s nwere ike igbanye ma ọ bụ mebie ala. Mmetụta nke nickel nwere ike ịkpata ọrịa cancer site na mutagenesis, mmebi nke chromosomal, ọgbọ Z-DNA, igbochi DNA excision mmezi, ma ọ bụ usoro epigenetic.
Ntụle mmetọ nke ala na-aga n'ihu n'oge na-adịbeghị anya n'ihi ọtụtụ nsogbu ahụike metụtara ahụike sitere na mmekọrịta ala-osisi, ala na mmekọrịta ndụ nke ala, mmebi gburugburu ebe obibi, na ntule mmetụta gburugburu ebe obibi. Ruo taa, amụma gbasara oghere nke ihe nwere ike igbu egbu (PTEs) dị ka Ni na ala na-arụsi ọrụ ike ma na-ewe oge site n'iji usoro ala dijitalụ emeziwanye ihe ịga nke ọma ugbu a. PSM) .Dịka Minasny na McBratney16 si kwuo, amụma ala nkewa (DSM) egosila na ọ bụ subdiscipline a ma ama nke sayensị ala.Lagacherie na McBratney, 2006 kọwaa DSM dị ka "mmepụta na njuputa nke usoro ihe ọmụma gbasara mbara ala site n'iji ọnọdụ na ụlọ nyocha nyocha ụzọ na mbara ala na usoro na-abụghị nke mbara igwe".17 na-akọwapụta na DSM ma ọ bụ PSM nke oge a bụ usoro kachasị dị irè maka ịkọ ma ọ bụ maapụ nkesa mbara igwe nke PTE, ụdị ala na ihe onwunwe ala.Geostatistics and Machine Learning Algorithms (MLA) bụ usoro nhazi nke DSM nke na-emepụta maapụ digitized site n'enyemaka nke kọmputa na-eji data dị ịrịba ama na nke ntakiri.
Deutsch18 na Olea19 na-akọwa geostatistics dị ka "nchịkọta usoro ọnụọgụgụ nke na-ekwu maka nnochite anya njirimara oghere, na-ejikarị ụdị stochastic eme ihe, dị ka otu nyocha usoro oge si akọwa data nwa oge."N'ụzọ bụ isi, geostatistics gụnyere nyocha nke variograms, nke na-enye ohere Quantify na kọwapụta ndabere nke ụkpụrụ oghere site na dataset ọ bụla20.Gumiaux et al.20 na-egosikwa na nyocha nke variograms na geostatistics dabere na ụkpụrụ atọ, gụnyere (a) ịgbakọ ọnụ ọgụgụ nke njikọ data, (b) ịchọpụta na ịgbakọ anisotropy na disparity dataset na (c) na mgbakwunye na iburu n'uche njehie dị n'ime nke data nha nke kewapụrụ na mmetụta mpaghara, a na-eji ọtụtụ ihe eji eme ihe na mpaghara geostat. kriging izugbe, ngalaba-kriging, kriging nkịtị, empirical Bayesian kriging, usoro kriging dị mfe na usoro mmekọrịta ndị ọzọ ama ama nke ọma iji mapụta ma ọ bụ ịkọ PTE, njirimara ala na ụdị ala.
Machine Learning Algorithms (MLA) bụ a dịtụ ọhụrụ usoro na-ewe ndị ibu na-abụghị linear data klas, fueled site algọridim bụ isi eji maka data Ngwuputa, na-achọpụta ụkpụrụ na data, na ugboro ugboro etinyere na nhazi ọkwa na sayensị ubi dị ka ala sayensị na nloghachi tasks.Ọtụtụ nnyocha akwụkwọ na-adabere na MLA ụdị ka amụma PTE na ala, dị ka Tan et al.22 (oke ọhịa na-enweghị usoro maka ntule igwe dị arọ na ala ugbo), Sakizadeh et al.23 (ihe nlere anya site na iji igwe nkwado vector na netwọk akwara artificial) mmetọ ala .Na mgbakwunye, Vega et al.24 (CART maka ịmegharị njigide ọla dị arọ na mgbasa ozi na ala) Sun et al.25 (ngwa nke cubist bụ nkesa nke Cd na ala) na algọridim ndị ọzọ dị ka onye agbata obi kacha nso, nkwụghachi azụ agbagoro agbagoro, na nkwụghachi azụ Osisi tinyekwara MLA iji buo PTE na ala.
Ngwa nke DSM algọridim na amụma ma ọ bụ nkewa na-eche ọtụtụ nsogbu ihu. Ọtụtụ ndị na-ede akwụkwọ kwenyere na MLA dị elu karịa geostatistics na nke ọzọ. Ọ bụ ezie na otu dị mma karịa nke ọzọ, nchikota nke abụọ ahụ na-eme ka ọkwa nke ziri ezi nke eserese ma ọ bụ amụma dị na DSM15.Woodcock na Gopal26 Finke27;Pontius na Cheuk28 na Grunwald29 na-ekwu banyere erughị eru na ụfọdụ njehie na amụma ala nkewa. Ndị ọkà mmụta sayensị ala agbalịwo usoro dị iche iche iji kwalite irè, izi ezi, na amụma amụma nke DSM maapụ na ịkọ amụma. Nchikota nke ejighị n'aka na nkwenye bụ otu n'ime ọtụtụ akụkụ dị iche iche agbakwunyere na DSM iji mee ka arụmọrụ dị mma na belata.15 na-akọwapụta na omume nkwado na ejighị n'aka ewepụtara site na imepụta map na amụma kwesịrị ịkwado onwe ya iji melite ogo map. Oke nke DSM bụ n'ihi ọdịdị ala gbasasịrị agbasasị na mpaghara, nke gụnyere akụkụ nke ejighị n'aka;Otú ọ dị, enweghị nke doro anya na DSM nwere ike ibili site na ọtụtụ isi mmalite nke njehie, ya bụ covariate njehie, nlereanya njehie, ebe njehie, na analytical Error 31. Modelling na-ezighị ezi na-ebute na MLA na geostatistical usoro na-ejikọta ya na enweghi nghọta, n'ikpeazụ na-eduga na oversimplification nke ezigbo process32. N'agbanyeghị ọdịdị nke ihe nlereanya, ma ọ bụ ihe nlereanya nwere ike ịdị na-eme ihe nlereanya, na-enweghị ike ime ka ihe nlereanya, ma ọ bụ ihe nlereanya, na-agbanyeghị ọdịdị nke ihe nlereanya ma ọ bụ ihe nlereanya, na-enweghị ike ịme ihe nlereanya nke ihe nlereanya. 33. N'oge na-adịbeghị anya, usoro DSM ọhụrụ apụtala nke na-akwalite njikọ nke geostatistics na MLA na map na ịkọ amụma. Ọtụtụ ndị ọkà mmụta sayensị na ndị na-ede akwụkwọ, dị ka Sergeev et al.34;Subbotina et al.35;Tarasov et al.36 na Tarasov et al.37 ejirila ezigbo geostatistics na mmụta igwe iji mepụta ụdị ngwakọ na-eme ka arụmọrụ amụma na eserese dị mma.àgwà.Ụfọdụ n'ime ụdị ngwakọ ndị a ma ọ bụ ngwakọta algọridim bụ Artificial Neural Network Kriging (ANN-RK), Multilayer Perceptron Residual Kriging (MLP-RK), Generalized Regression Neural Network Residual Kriging (GR- NNRK) 36, Artificial Neural Network Kriging-Multilayer Perceptron (ANN-K-7 na Ressional-MLP) 36.
Dị ka Sergeev et al., ijikọta dị iche iche modeling usoro nwere ikike ikpochapụ ntụpọ na-amụba arụmọrụ nke dapụtara ngwakọ nlereanya kama ịzụlite ya otu model.Na nke a gburugburu, a ọhụrụ akwụkwọ na-arụ ụka na ọ dị mkpa itinye a jikọtara algọridim nke geostatistics na MLA na-emepụta ezigbo ngwakọ ụdị iji amụma Ni enrichment n'ime obodo na peri-urban nke obodo Bay na-eme ka ihe nlereanya nke KESI na mpaghara obodo na mpaghara ime obodo nke KES (Kesisi). mix ya na Nkwado Vector Machine (SVM) na Multiple Linear Regression (MLR) model.Hybridization nke EBK na ihe ọ bụla MLA na-amaghị.The multiple agwakọta ụdị hụrụ bụ nchikota nke nkịtị, residual, regression kriging, na MLA.EBK bụ a geostatistical interpolation usoro na utilizes a spatially stochastic usoro na-na-na-n'ebe ubi na-enye ohere n'elu stochastic usoro na-n'ógbè ahụ paramita nke na-abụghị nke mpaghara. ing for spatial variation39.EBK ejirila n'ọtụtụ ọmụmụ, gụnyere nyocha nkesa carbon organic n'ime ala ugbo40, na-enyocha mmetọ ala41 na maping ala Njirimara42.
N'aka nke ọzọ, Self-Organizing Graph (SeOM) bụ mmụta mmụta nke etinyere n'akwụkwọ dị iche iche dị ka Li et al.43, Wang et al.44, Hossain Bhuiyan et al.45 na Kebonye et al.46 Kpebie àgwà gbasara mbara igwe na nhazi nke ihe.Wang et al.44 na-akọwapụta na SeOM bụ usoro mmụta dị ike nke a maara maka ikike ijikọta na icheta nsogbu ndị na-abụghị nke linear. N'adịghị ka usoro njirimara ndị ọzọ dị ka nyocha nke isi akụrụngwa, nchịkọta na-enweghị isi, nchịkọta nhazi, na ime mkpebi dị iche iche, SeOM ka mma n'ịhazi na ịchọpụta ụkpụrụ PTE. Dị ka Wang et al.44, SeOM nwere ike ikpokọta nkesa nke neurons ndị metụtara ya wee nye nhụta data dị elu.
Akwụkwọ a aims n'ịwa a siri ike nkewa nlereanya na ezigbo ziri ezi maka ịkọ ọdịnaya nickel n'ime obodo na mpụta obodo ala. Anyị na-eche na a pụrụ ịdabere na nke ngwakọta nlereanya tumadi dabere na mmetụta nke ndị ọzọ ụdị mmasị na isi model. Anyị na-ekweta ihe ịma aka chere DSM ihu, na mgbe nsogbu ndị a na-egbo n'ọtụtụ ihu, Nchikota ọganihu na geostatistics na-egosi na MLA.Ya mere, anyị ga-anwa ịza ajụjụ nyocha nke nwere ike ịmepụta ụdị agwakọta. Otú ọ dị, olee otú ihe nlereanya ahụ si buru amụma ihe mgbaru ọsọ? Ọzọkwa, gịnị bụ ọkwa nke nyocha nke arụmọrụ dabeere na nkwenye na nyocha ziri ezi? Ya mere, ihe mgbaru ọsọ kpọmkwem nke ọmụmụ ihe a bụ (a) ịmepụta ngwakọta ngwakọta maka SVMR ma ọ bụ MLR site na iji EBK dị ka ihe nlereanya isi ma ọ bụ ihe atụ nke ime obodo (b) atụnyere ihe nlereanya kachasị mma maka ime obodo. ala obodo mepere emepe , na (d) ntinye nke SeOM iji mepụta maapụ dị elu nke mgbanwe nickel spatial.
A na-eme ọmụmụ ihe na Czech Republic, kpọmkwem na mpaghara Frydek Mistek na mpaghara Moravia-Silesian (lee foto 1) . Geography nke ebe a na-amụ ihe dị nnọọ mgbagwoju anya ma bụrụ akụkụ nke mpaghara Moravia-Silesian Beskidy, nke bụ akụkụ nke mpụta nke ugwu Carpathian. Ebe ọmụmụ ihe dị n'etiti 49 ° ′ 0 ′ 0 ′ 2 ′ 49º na 49º ′ 1 ′ 2 ′ 2 ′ 2 ′ 2 ′ 49° na 49º ′ 1 ′ 2 n’etiti 49º na 49 ′ 1 ′ 1 ′ 2 ′ 49º na 49 ′ 1 ′ 2 ′ 49º na 49 ′ 1. n'etiti 225 na 327 m;Otú ọ dị, usoro nhazi nke Koppen maka ọnọdụ ihu igwe nke mpaghara ahụ bụ Cfb = ihu igwe na-ekpo ọkụ, e nwere ọtụtụ mmiri ozuzo ọbụna n'ime ọnwa akọrọ. Okpomọkụ na-adịgasị iche iche n'ime afọ n'etiti -5 °C na 24 Celsius C, ọ na-adịkarịghị ịda n'okpuru -14 °C ma ọ bụ n'elu 30 °C, ebe nkezi nke afọ 7 na preci 5 mm2. ebe a bụ 1,208 square kilomita, na 39.38% nke ala akọ na 49.36% nke mkpuchi ọhịa. N'aka nke ọzọ, ebe a na-eji ọmụmụ ihe a bụ banyere 889.8 square kilomita. Na gburugburu Ostrava, ígwè ọrụ na ígwè ọrụ na-arụsi ọrụ ike. enwekwu ike nke alloy mgbe ịnọgide na-enwe ya mma ductility na toughness), na kpụ ọkụ n'ọnụ agriculture dị ka phosphate fatịlaịza ngwa na anụ ụlọ mmepụta bụ research nwere ike isi mmalite nke nickel na mpaghara (eg, na-agbakwụnye nickel ka atụrụ na-amụba ibu udu na atụrụ na ala-eri nri ehi) .Other ulo oru ojiji nke nickel na nnyocha ebe na-agụnye ya ojiji na electroplating nickel n'ụzọ dị mfe na-agụnye electroplating n'ime ala nke electroplating. agba, Ọdịdị, na carbonate ọdịnaya.The ala udidi bụ ọkara na ezi, ewepụtara site na nne na nna material.Ha bụ colluvial, alluvial ma ọ bụ aeolian na nature.Some ala ebe na-egosi mottled n'elu na subsoil, mgbe na ihe na bleaching. Otú ọ dị, cambisols na stagnosols bụ ndị kasị nkịtị ala ụdị na region48.5 ranging site elu cambi4. Czech Republic49.
Maapụ mpaghara ọmụmụ [E ji ArcGIS Desktop mepụta maapụ mpaghara ọmụmụ (ESRI, Inc, ụdị 10.7, URL: https://desktop.arcgis.com).]
A na-enweta mkpokọta 115 n'elu ala site na obodo ukwu na obodo nta dị na mpaghara Frydek Mistek. Ụdị ihe atụ a na-eji eme ihe bụ grid mgbe niile na ihe atụ nke ala nke dị na 2 × 2 km iche, a na-atụkwa elu ala n'ime omimi nke 0 ruo 20 cm site na iji ngwaọrụ aka (Leica Zeno 5 GPS, nke a na-arụ ọrụ GPS, nke a na-agbanye n'ime ụgbọ ala zips na nke ọma). s na-ekpo ọkụ na-ekpo ọkụ iji mepụta ihe ndị a kpụrụ akpụ, nke a na-emegharị site na usoro ígwè ọrụ (Fritsch diski igwe igwe), na sieved (sieve size 2 mm) .Ebe 1 gram nke a mịrị amị, homogenized na sieved ala samples n'ụzọ doro anya kpọrọ teflon karama. Na nke ọ bụla Teflon arịa, na-ekesa 7 ml nke 35% NO 3 ml nke HCl na-akpaghị aka na-ekpuchi ọkụ acid na 6. ly ma kwe ka samples guzoro n'abali maka mmeghachi omume (aqua regia program) . Tinye ihe dị elu na efere ígwè na-ekpo ọkụ (okpomọkụ: 100 W na 160 Celsius C) maka 2 h iji mee ka usoro mgbaze nke ihe ndị ahụ dị mfe, wee mee ka ọ dị jụụ. Nyefee supernatant na 50 ml volumetric flask na diluted na 5 ml nke PVC mmiri na-ekpo ọkụ. tube na mmiri deionized. Ọzọkwa, 1 ml nke ihe ngwọta dilution na-eji 9 ml nke mmiri mmiri na-ekpo ọkụ na-agbanye n'ime mmiri 12 ml nke a kwadebere maka PTE pseudo-concentration. Ihe nchịkọta nke PTEs (Dị ka, Cd, Cr, Cu, Mn, Ni, Pb, Zn, Ca, Mg, K) ekpebisiri ike site na ICP Plactive (ICP) rmo Fisher Scientific, USA) dị ka usoro ọkọlọtọ na nkwekọrịta si dị. Gbaa mbọ hụ na njikwa mma na njikwa (QA / QC) usoro (SRM NIST 2711a Montana II Ala) .PTEs nwere oke nchọpụta dị n'okpuru ọkara ka ewepụrụ n'ọmụmụ ihe a. Oke nchọpụta nke PTE ejiri mee ihe n'ọmụmụ ihe a bụ 0.0004. (Ịtụle usoro nyocha nke ọ bụla). hụ na e belatara mperi, a na-eme nyocha ugboro abụọ.
Empirical Bayesian Kriging (EBK) bụ otu n'ime ọtụtụ geostatistical interpolation usoro eji na modeling na iche iche ubi dị ka ala sayensị. N'adịghị ka ndị ọzọ kriging interpolation usoro, EBK dị iche na omenala kriging ụzọ site n'ịtụle njehie e mere atụmatụ site semivariogram model.In EBK interpogram, ọtụtụ semivariogram-eme ka usoro n'oge interpovarioter na-abụghị otu. doro anya na mmemme jikọtara na nke a ibé nke semivariogram nke mejupụtara a ukwuu mgbagwoju akụkụ nke a zuru ezu kriging usoro.The interpolation usoro nke EBK na-eso atọ njirisi tụrụ aro site Krivoruchko50, (a) ihe nlereanya na-eme atụmatụ na semivariogram si na ntinye dataset (b) ọhụrụ buru amụma uru maka onye ọ bụla ntinye dataset ọnọdụ dabere na emepụtara semivarioset data sitere na emepụtara semivarioset. A na-enye iwu quation dị ka azụ
Ebe \ (Prob \ ekpe (A \ nri) \) na-anọchi anya tupu, \ (Prob \ ekpe (B \ nri) \) oke ihe gbasara nke puru omume na-eleghara anya n'ọtụtụ ọnọdụ, \ (Prob (B, A) \ ) .The semivariogram ngụkọta oge dabeere Bayes 'iwu, nke na-egosi propensity nke observation datasets na ike ike kere si semivariogram nke na-ekwu, otú e si ekpebi nke semivariogram. mepụta dataset nke nlebanya sitere na semivariogram.
Igwe na-akwado vector igwe bụ igwe mmụta algọridim nke na-emepụta hyperplane nkewa dị mma iji mata ọdịiche dị n'otu ma ọ bụghị klas nke kwụ ọtọ.Vapnik51 kere ebumnuche nhazi ọkwa algorithm, mana ọ na-adịbeghị anya ejiri ya dozie nsogbu ndị na-atụgharị uche. Dị ka Li et al.52 si kwuo, SVM bụ otu n'ime usoro nhazi ọkwa kachasị mma ma jiri ya mee ihe dị iche iche nke SVM Regression. ) e ji mee ihe na nyocha a.Cherkassky na Mulier53 sụrụ ụzọ SVMR dị ka kernel regression dabeere na kernel, nke a na-eme ka a na-eme ya site na iji usoro nhazi nke na-arụ ọrụ na-arụ ọrụ nke ọtụtụ mba.55, epsilon (ε) -SVMR na-eji dataset a zụrụ azụ nweta ihe nnochite anya dị ka ọrụ epsilon-enweghị mmetụta nke etinyere na eserese data ahụ n'onwe ya na nke kacha mma epsilon bias si ọzụzụ na data correlated. A na-eleghara njehie anya preset anya site na uru ahụ n'ezie, ma ọ bụrụ na njehie ahụ karịrị ε (ε), ihe ndị dị n'ala na-akwụ ụgwọ ya. nke Vapnik51 tụrụ aro ka egosiri n'okpuru.
ebe b na-anọchi anya ọnụ ụzọ scalar, \ (K \ ekpe ({x}_{,}{ x}_{k}\nri) \) na-anọchi anya ọrụ kernel, \(\ alpha \) na-anọchi anya Lagrange multiplier, N na-anọchi anya ọnụọgụ dataset, \ ({x}_{k} \) na-anọchite anya ntinye data, na \(y\) bụ isi ọrụ bụ SMR, na \(y\) bụ isi ọrụ bụ S. ihe radial ndabere ọrụ (RBF) .The RBF kernel na-etinyere iji chọpụta ezigbo SVMR nlereanya, nke dị oké mkpa iji nweta kasị aghụghọ penalty set factor C na kernel parameter gamma (γ) maka PTE ọzụzụ data. Mbụ, anyị nyochaa ọzụzụ set na mgbe ahụ nwalere ihe nlereanya arụmọrụ na nkwado set. The steering paramita na Radi usoro bụ sig.
A multiple linear regression model (MLR) bụ regression nlereanya nke na-anọchi anya mmekọrịta dị n'etiti nzaghachi mgbanwe na a ọnụ ọgụgụ nke amụma variables site na iji linear pooled parameters gbakọọ na-eji kacha nta square usoro. atory variables.Nha nhata MLR bụ
ebe y bụ mgbanwe nzaghachi, \ (a \) bụ intercept, n bụ ọnụọgụ ndị amụma, \ ({b}_{1}\) bụ akụkụ azụ azụ nke ọnụọgụgụ, \ ({x}_{ i} \) na-anọchite anya amụma ma ọ bụ mgbanwe nkọwa, na \ ({\varepsilon}_{i}\) na-anọchi anya njehie dị na ihe nlereanya ahụ.
A na-enweta ụdị ndị a gwakọtara site na sandwiching EBK na SVMR na MLR. Nke a na-eme site n'iwepụta ụkpụrụ amụma sitere na interpolation EBK. A na-enweta ụkpụrụ ndị a na-enweta site na Ca, K, na Mg na-ejikọta ya site na usoro nchịkọta iji nweta mgbanwe ọhụrụ, dị ka CaK, CaMg, na KMg. A na-enweta ihe anọ, CaKM na-agbanwe agbanwe. Ca, K, Mg, CaK, CaMg, KMg na CaKMg. Ndị a na-agbanwe agbanwe ghọrọ ndị amụma anyị, na-enyere aka ịkọ ọkwa nickel na obodo mepere emepe na ala obodo.The SVMR algọridim e rụrụ na amụma iji nweta a gwakọtara nlereanya Empirical Bayesian Kriging-Support Vector Vector Machine (EBK_SVM) N'otu aka ahụ, agbanwe agbanwe na-emepụta ihe nlereanya nke Empir Bayesian (EBK_SVM). -Multiple Linear Regression (EBK_MLR) .Ọtụtụ, mgbanwe Ca, K, Mg, CaK, CaMg, KMg, na CaKMg na-eji dị ka covariates dị ka amụma nke ọdịnaya Ni n'ime obodo mepere emepe na peri-urban ala. The kasị anabata nlereanya enwetara (EBK_SVM ma ọ bụ EBK_MLR) ga-ahụ na-arụ ọrụ na-arụ ọrụ na-eji a selfflow.
Iji SeOM aghọwo ngwá ọrụ na-ewu ewu maka ịhazi, nyochaa, na ịkọ amụma data na mpaghara ego, ahụike, ụlọ ọrụ, ọnụ ọgụgụ, sayensị ala, na ndị ọzọ.SeOM na-emepụta site na iji netwọk neural na-enweghị nlekọta na usoro mmụta nke a na-ejighị n'aka maka nhazi, nyocha, na amụma.N'ime ọmụmụ ihe a, a na-eji SeOM iji anya nke uche na ntinye uche nke Nii dabere na nhazi nke obodo mepere emepe na usoro NiOM nke na-ebu amụma n'ime usoro nhazi nke obodo na-adabere na usoro nhazi nke obodo SeOM. na-eji dị ka n ntinye-akụkụ vector variables43,56.Melssen et al.57 na-akọwa njikọ nke vector ntinye n'ime netwọk neural site na otu ntinye oyi akwa na vector mmepụta na otu vector dị arọ. Ihe mmepụta nke SeOM na-emepụta bụ map nke abụọ nwere akụkụ dị iche iche nke neurons ma ọ bụ nodes kpara n'ime hexagonal, okirikiri, ma ọ bụ square topological map dị ka ha nso. Comparing map sizes based on error topographic (QM) 0. 6 na 0.904, n'otu n'otu, a na-ahọrọ, nke bụ akụkụ 55-map (5 × 11) . A na-ekpebi nhazi nke neuron dị ka ọnụ ọgụgụ nke ọnụ ọgụgụ dị na nha anya nhụsianya.
Ọnụ ọgụgụ nke data e ji mee ihe n'ime ọmụmụ ihe a bụ 115 samples. A random obibia na-eji kewaa data n'ime ule data (25% maka nkwado) na ọzụzụ data sets (75% maka calibration) .The ọzụzụ dataset na-eji n'ịwa regression nlereanya (calibration), na ule dataset na-eji iji nyochaa generalization ikike58.E mere nke a iji nyochaa adabara nke ụdị dị iche iche nke cross-cross e ji mee ihe n'ime ala iri. Usoro nhazi, ugboro ise ugboro ise. A na-eji mgbanwe ndị a na-emepụta site na EBK interpolation dị ka ndị na-ebu amụma ma ọ bụ nkọwa nkọwa iji buru amụma mgbanwe (PTE) . A na-edozi ihe nlere anya na RStudio site na iji nchịkọta ngwugwu (Kohonen), ọba akwụkwọ (nlekọta), ọbá akwụkwọ (modelr), ụlọ akwụkwọ ("e1071"), ọbá akwụkwọ ("plyr")), ụlọ akwụkwọ ("mere" prospectries).
A na-eji paramita nkwenye dị iche iche iji chọpụta ihe nlereanya kachasị mma maka ịkọ ọkwa nickel na ala na iji nyochaa izi ezi nke ihe nlereanya ahụ na nkwenye ya. A na-enyocha ụdị hybridization site na iji njehie zuru oke (MAE), mgbọrọgwụ pụtara square njehie (RMSE), na R-squared ma ọ bụ ọnụọgụ mkpebi (R2) .RMSE na-akọwa ọdịiche nke nha nha na nzaghachi na nzaghachi, na-anọchi anya ihe atụ nke onwe ya. ike nke ihe nlereanya, mgbe MAE na-ekpebi n'ezie quantitative uru. The R2 uru ga-adị elu iji nyochaa kacha mma ngwakọta nlereanya na-eji nkwado parameters, na nso uru bụ 1, elu nke ziri ezi. Dị ka Li et al.59, uru R2 nke 0.75 ma ọ bụ karịa ka a na-ewere dị ka ezigbo amụma;site na 0.5 ruo 0.75 bụ ihe nlereanya a na-anabata nke ọma, na n'okpuru 0.5 bụ arụmọrụ nlereanya na-adịghị anabata. Mgbe ị na-ahọrọ ihe nlereanya site na iji usoro nyocha nke RMSE na MAE, ụkpụrụ ndị dị ala enwetara zuru oke ma weere ya dị ka nhọrọ kacha mma. Ngụkọta na-esonụ na-akọwa usoro nkwenye.
ebe n na-anọchi anya nha nke uru hụrụ\({Y}_{i}\) na-anọchi anya nzaghachi atụnyere, na \({\ widehat{Y}}_{i}\) na-anọchikwa anya uru nzaghachi e buru n'amụma, ya mere, maka nke mbụ i nhụbanya.
A na-egosi nkọwa ndekọ ọnụ ọgụgụ nke ndị amụma amụma na mgbanwe nzaghachi na Tebụl 1, na-egosi pụtara, ọkọlọtọ deviation (SD), ọnụọgụ nke mgbanwe (CV), kacha nta, kacha, kurtosis, na skewness. The kacha nta na kacha ụkpụrụ nke ihe ndị dị na mbelata n'usoro nke Mg
Njikọ nke mgbanwe ndị na-ebu amụma na ihe mmeghachi omume na-egosi njikọ dị mma n'etiti ihe ndị ahụ (lee ihe oyiyi 3) . Mmekọrịta ahụ gosiri na CaK gosipụtara mmekọrịta dị oke ọnụ na uru r = 0.53, dị ka CaNi. Ọ bụ ezie na Ca na K na-egosi mkpakọrịta dị umeala n'obi na ibe ha, ndị nchọpụta dị ka Kingston et al.68 na Santo69 na-atụ aro na ọkwa ha na-eme ka ọ dị na magnesium (km r =) A na-etinye ya, potassium magsium nitrate, na potassium na-eme ka ihe dị ka nickel, magnesium na-egbochi ihe ọ bụla. Essium, na magnesium ma calsium belata mmetụta na-egbu egbu nke nickel na ala.
Matrix njikọ maka ihe ndị na-egosi mmekọrịta dị n'etiti ndị amụma na nzaghachi (Rịba ama: ọnụ ọgụgụ a na-agụnye ihe mgbagwoju anya n'etiti ihe ndị dị mkpa, ọkwa dị mkpa dabeere na p <0,001).
Ọnụ ọgụgụ 4 na-egosipụta nkesa nkesa nke ihe dị iche iche. Dị ka Burgos et al70 si kwuo, ntinye nkesa nkesa bụ usoro eji akọwapụta ma gosipụta ebe dị ọkụ na mpaghara ndị rụrụ arụ. A na-ahụ ọkwa nkwalite nke Ca na Fig. 4 n'ebe ugwu ọdịda anyanwụ nke map nkesa mbara igwe. Ọnụ ọgụgụ ahụ na-egosi ngwa ngwa na elu Ca enrichment hotspot nke n'ebe ugwu nke na-eme ka map nke ebe ugwu na-eme ka ọ bụrụ ihe na-eme ka ọ bụrụ ihe na-eme ka calcium dịkwuo elu. cium oxide) iji belata ala acidity na ojiji na ígwè igwe igwe dị ka alkaline oxygen na steelmaking usoro.N'aka nke ọzọ, ndị ọrụ ugbo na-ahọrọ iji calcium hydroxide na acidic Ona ka neutralize pH, nke nwekwara enwekwu calcium ọdịnaya nke ala71.Potassium na-egosikwa na-ekpo ọkụ tụrụ na n'ebe ugwu ọdịda anyanwụ na n'ebe ọwụwa anyanwụ nke map.The Northwest bụ a isi nke agricultural ụkpụrụ nke potassium na-enwe ike ịbụ agricultural obodo. kwekọrọ na ọmụmụ ihe ndị ọzọ, dị ka Madaras na Lipavský72, Madaras et al.73, Pulkrabova et al.74, Asare et al.75, bụ ndị chọpụtara na nkwụsi ike ala na ọgwụgwọ na KCl na NPK mere ka ọdịnaya K dị elu na ala.Ọganihu Potassium gbasara ohere na ugwu ọdịda anyanwụ nke map nkesa nwere ike ịbụ n'ihi iji fatịlaịza dabeere na potassium dị ka potassium chloride, potassium sulfate, potassium nitrate, potash, na potash iji mee ka ọdịnaya potassium dị n'ime ala dara ogbenye.Zádorová et al.76 na Tlustoš et al.77 kwuputara na ntinye nke fatịlaịza sitere na K na-abawanye ọdịnaya K n'ime ala ma ga-abawanye ụbara nri nri ala n'ime ogologo oge, ọkachasị K na Mg na-egosi ebe dị ọkụ n'ime ala. Ọ dịtụ oke ọkụ n'ebe ugwu ọdịda anyanwụ nke map na n'ebe ndịda ọwụwa anyanwụ nke map. Colloidal fixation na ala na-ebelata mkpokọta magnesium na fatịlaịza n'ime ala na-eme ka fatịlaịza chlorosis na-eme ka ọ ghara ịdị na-egosipụta. dị ka potassium magnesium sulfate, magnesium sulfate, na Kieserite, na-emeso adịghị ike (osisi na-apụta na-acha odo odo, acha ọbara ọbara, ma ọ bụ aja aja, na-egosi ụkọ magnesium) na ala nke nwere pH nkịtị6. Nchikota nke nickel n'elu obodo mepere emepe na nke ime obodo nwere ike ịbụ n'ihi ọrụ anthropogenic dị ka ọrụ ugbo na mkpa nke mmepụta nickel78.
Nkesa ihe gbasara mbara ala [e ji ArcGIS Desktop mepụta maapụ nkesa oghere (ESRI, Inc, Version 10.7, URL: https://desktop.arcgis.com).]
N'aka nke ọzọ, RMSE na MAE nke Ni dị nso na zero (0.86 RMSE, -0.08 MAE) .N'aka nke ọzọ, ma RMSE na MAE ụkpụrụ nke K na-anabata .RMSE na MAE. Nsonaazụ dị ukwuu maka calcium na magnesium na ihe dị iche iche nke MAE nke MAE mere ka ọmụmụ ihe a dị ukwuu. iji EBK na-ebu amụma Ni achọpụtara na ọ dị mma karịa nsonaazụ John et al.54 na-eji synergistic kriging na-ebu amụma gbasara mkpokọta S na ala site na iji otu data anakọtara. Ihe EBK anyị mụrụ na-ejikọta na nke Fabijaczyk et al.41, Yan et al.79, Beguin et al.80, Adhikary et al.81 na John et al.82, ọkachasị K na Ni.
A na-enyocha arụmọrụ nke ụzọ onye ọ bụla maka ịkọ ọdịnaya nickel n'ime obodo na obodo dịpụrụ adịpụ site na iji arụmọrụ nke ụdị (Table 3) .Nkwenye ihe nlereanya na nyocha ziri ezi kwadoro na Ca_Mg_K amụma jikọtara ya na EBK SVMR nlereanya na-arụpụta ihe kasị mma.Calibration nlereanya Ca_Mg_K-EBK_SVMR nlereanya ab. ), 95.479 mg / kg (RMSE) na 77.368 mg / kg (MAE) Ca_Mg_K-SVMR bụ 0.663 (R2), 235.974 mg / kg (RMSE) na 166.946 mg / kg (MAE) . Otú o sina dị, ezi R2-R2S values (MAE) . na Ca_Mg-EBK_SVMR (0.643 = R2);nsonaazụ RMSE na MAE ha dị elu karịa nke Ca_Mg_K-EBK_SVMR (R2 0.637) (lee Isiokwu 3) . Tụkwasị na nke ahụ, RMSE na MAE nke Ca_Mg-EBK_SVMR (RMSE = 1664.64 na MAE = 1031.49) na-asọpụrụ 137.5, na Ca_g EBK_SVMR. N'otu aka ahụ, RMSE na MAE nke Ca_Mg-K SVMR (RMSE = 235.974 na MAE = 166.946) nlereanya bụ 2.5 na 2.2 ibu karịa ndị nke Ca_Mg_K-EBK_SVMR RMSE na MAE, na-egosi na nke kacha mma akara na-egosi na concentrated line na-akọwa otú e si gbakọọ arụpụta ihe. A hụrụ ME na MAE. Dị ka Kebonye et al.46 na John et al.54, ka RMSE na MAE dị nso na efu, ihe ka mma.SVMR na EBK_SVMR nwere ọnụ ọgụgụ dị elu nke RSME na MAE. Achọpụtara na atụmatụ RSME na-anọgide na-adị elu karịa ụkpụrụ MAE, na-egosi ọnụnọ nke outliers. Dị ka Legates na McCabe83 si kwuo, njedebe nke SE na-atụ aro ka ọ bụrụ ihe na-egosi na ọ bụ ihe na-egosi na ọ bụ ihe na-eme ka ọ bụrụ ihe na-egosi na ọ bụ ihe na-eme ka ọ bụrụ ihe na-egosi na njedebe nke njedebe nke MAE na McCabe83. Nke outliers.Nke a pụtara na ndị ọzọ heterogeneous na dataset, na elu MAE na RMSE ụkpụrụ.The izi ezi nke cross-validation ntule nke Ca_Mg_K-EBK_SVMR agwakọta nlereanya maka ịkọ Ni ọdịnaya n'ime obodo mepere emepe na ala ala bụ 63.70%.Dịka Li et al.59, ọkwa a nke ziri ezi bụ ihe nlereanya a na-anabata nke ọma. A na-atụnyere nsonaazụ dị ugbu a na ọmụmụ ihe gara aga nke Tarasov et al.36 onye ngwakọ ụdị kere MLPRK (Multilayer Perceptron Residual Kriging), metụtara EBK_SVMR ziri ezi nwale index kọrọ na nke ugbu a ọmụmụ, RMSE (210) na The MAE (167.5) bụ elu karịa anyị pụta na nke ugbu a ọmụmụ (RMSE 95.479, MAE 77.4 na nke ahụ mgbe nke 6) . Tarasov et al.36 (0.544), o doro anya na ọnụọgụ nke mkpebi siri ike (R2) dị elu na ihe nlereanya a gwakọtara ọnụ. Oke njehie (RMSE na MAE) (EBK SVMR) maka ụdị agwakọta bụ okpukpu abụọ dị ala. N'otu aka ahụ, Sergeev et al.34 dekọrọ 0.28 (R2) maka mmepụta ngwakọ nke e dekọrọ (Multilayer na KR2). Ọkwa amụma ziri ezi nke ihe nlereanya a (EBK SVMR) bụ 63.7%, ebe amụma amụma Sergeev et al nwetara.34 bụ 28%.Map ikpeazụ (Fig 5) nke e kere site na iji ụdị EBK_SVMR na Ca_Mg_K dị ka onye amụma na-egosi amụma nke ebe dị ọkụ ma na-agafe agafe na nickel n'elu ebe ọmụmụ ihe dum. Nke a pụtara na ntinye nke nickel na ebe ọmụmụ ihe na-abụkarị agafeghị oke, na-enwe oke dị elu na mpaghara ụfọdụ.
A na-anọchi anya maapụ amụma ikpeazụ site na iji ụdị ngwakọ EBK_SVMR yana iji Ca_Mg_K dị ka onye amụma.
Egosiputara na onu ogugu 6 bu ihe omuma nke PTE dika ihe eji eme ihe nke oma nke nwere neurons n'otu n'otu. Ọ dịghị nke ụgbọ elu ndị na-emepụta ihe na-egosipụta otu ụkpụrụ agba dị ka egosipụtara. Otú ọ dị, ọnụ ọgụgụ kwesịrị ekwesị nke neurons kwa eserese eserese bụ 55. SeOM na-emepụta site na iji ụdị dị iche iche nke agba, na ihe yiri nke agba agba, na-atụnyere ihe onwunwe nke ihe atụ. elu neurons na ọtụtụ obere neurons.Ya mere, CaK na CaMg na-ekere òkè ụfọdụ myirịta na nnọọ elu-iji neurons na ala-na-agafeghị oke agba ụkpụrụ.Ma ụdị amụma amụma ịta nke Ni na ala site n'igosipụta ọkara ka elu hues nke na agba ndị dị otú ahụ dị ka red, oroma na yellow.The KMg nlereanya na-egosiputa ọtụtụ elu agba ụkpụrụ dabere na kpọmkwem n'ike-n'ike na agba na ala na-eme ka atụmatụ nke Ni na ala site n'igosipụta ọkara na elu hues nke na agba ndị dị otú ahụ dị ka red, oroma na yellow.The KMg nlereanya na-egosiputa ọtụtụ elu agba ụkpụrụ dabere na kpọmkwem n'ike-n'ike na agba agba agba agba agba agba agba agba. ihe nlereanya ahụ gosipụtara ụkpụrụ agba agba dị elu nke na-egosi ike ịta ahụhụ nke nickel n'ime ala (lee foto 4) . Ụgbọ elu nke ihe nlereanya CakMg na-egosi ụdị agba dị iche iche site na ala ruo elu dị ka ọnụ ọgụgụ nke agba ziri ezi. Ọzọkwa, amụma nke ihe nlereanya nke ọdịnaya nickel (CakMg) yiri nkesa mbara igwe nke nickel egosipụtara na Figure 5., abụọ na-egosi n'ime ime obodo na nke dị ala. ure 7 na-egosi usoro contour na k-pụtara na-achịkọta na map, kewara n'ime ụyọkọ atọ dabere na uru e buru n'amụma na ụdị nke ọ bụla. Usoro contour na-anọchi anya ọnụ ọgụgụ kachasị mma nke ụyọkọ. N'ime ihe atụ nke ala 115 anakọtara, ụdị 1 nwetara ọtụtụ ihe atụ nke ala, 74.Cluster 2 natara 33 samples, ebe 8-comp plan nyere 33 samples. iji nye ohere maka nkọwa ụyọkọ ziri ezi. N'ihi ọtụtụ usoro anthropogenic na usoro okike na-emetụta nhazi ala, ọ na-esiri ike ịnwe ụkpụrụ ụyọkọ dị iche iche nke ọma na map SeOM nke ekesara.
Mbupụta ụgbọ elu nke ọ bụla Empirical Bayesian Kriging Support Vector Machine (EBK_SVM_SeOM) na-agbanwe.[Ejiri maapụ SeOM site na iji RStudio (ụdị 1.4.1717: https://www.rstudio.com/).]
Akụkụ nkewa ụyọkọ dị iche iche [Ejiri maapụ SeOM site na iji RStudio (ụdị 1.4.1717: https://www.rstudio.com/)]
Ọmụmụ ihe a na-egosi n'ụzọ doro anya usoro nhazi maka nhazi nke nickel n'ime obodo na obodo dịpụrụ adịpụ. Ihe ọmụmụ ahụ nwalere usoro nhazi dị iche iche, na-ejikọta ihe na usoro nhazi, iji nweta ụzọ kachasị mma iji buru amụma gbasara nickel n'ime ala. tial nkesa nke components exhibited by EBK_SVMR (lee Figure 5) .The results na-egosi na support vector igwe regression nlereanya (Ca Mg K-SVMR) amụma ịta nke Ni na ala dị ka otu nlereanya, ma nkwado na ziri ezi nwale parameters na-egosi nnọọ elu njehie n'usoro nke RMSE na MAE.N'aka nke ọzọ, ndị nlereanya Usoro na-arụ ọrụ nke ọma na-arụ ọrụ nke ọma EBK n'ọrụ na ala flawed n'ọrụ na ihe nlereanya nke EBK n'ihi na n'aka nke ọzọ. (R2) .Enwetara ezigbo nsonaazụ site na iji EBK SVMR na ihe ndị jikọtara ọnụ (CaKMg) na RMSE dị ala na njehie MAE na ihe ziri ezi nke 63.7% .Ọ na-atụgharị na ijikọta EBK algorithm na igwe mmụta algorithm nwere ike ịmepụta ngwakọ algọridim nke nwere ike ibu amụma ntinye nke PTE na ala. Nke a na-egosi na iji Ca Mg K dị ka amụma amụma na mpaghara Nick nke na-aga n'ihu n'ịmụta Ni. fatịlaịza dabeere na fatịlaịza na mmetọ ụlọ ọrụ nke ala site na ụlọ ọrụ nchara nwere ọchịchọ ime ka ntinye nke nickel dị n'ime ala. Ọmụmụ ihe a gosiri na ụdị EBK nwere ike ibelata ọkwa nke njehie ma melite izi ezi nke ihe nlereanya nke nkesa mbara ala n'ime obodo ma ọ bụ obodo obodo. N'ozuzu, anyị na-atụ aro ka itinye ihe nlereanya EBK-SVMR iji nyochaa na ịkọ PTE na ala;na mgbakwunye, anyị na-atụ aro iji EBK ka hybridize dị iche iche igwe mmụta algọridim.Ni ịta e buru amụma iji ọcha dị ka covariates;Otú ọ dị, iji ndị ọzọ covariates ga-eme ka arụmọrụ nke ihe nlereanya ahụ dịkwuo mma, nke a pụrụ iwere dị ka njedebe nke ọrụ ugbu a. Mbelata ọzọ nke ọmụmụ ihe a bụ na ọnụ ọgụgụ nke datasets bụ 115. Ya mere, ọ bụrụ na a na-enyekwu data, a pụrụ imeziwanye arụmọrụ nke usoro hybridization nke a na-atụ aro.
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Oge nzipu: Jul-22-2022