A hybrid model for predicting spatial distribution of soil organic matter in a bamboo forest based on general regression neural network and interative algorithm

A general regression neural network model,combined with an interative algorithm(GRNNI)using sparsely distributed samples and auxiliary environmental variables was proposed to predict both spatial distribution and variability of soil organic matter(SOM)in a bamboo forest.The auxiliary environmental variables were:elevation,slope,mean annual temperature,mean annual precipitation,and normalized difference vegetation index.The prediction accuracy of this model was assessed via three accuracy indices,mean error(ME),mean absolute error(MAE),and root mean squared error(RMSE)for validation in sampling sites.Both the prediction accuracy and reliability of this model were compared to those of regression kriging(RK)and ordinary kriging(OK).The results show that the prediction accuracy of the GRNNI model was higher than that of both RK and OK.The three accuracy indices(ME,MAE,and RMSE)of the GRNNI model were lower than those of RK and OK.Relative improvements of RMSE of the GRNNI model compared with RK and OK were 13.6%and 17.5%,respectively.In addition,a more realistic spatial pattern of SOM was produced by the model because the GRNNI model was more suitable than multiple linear regression to capture the nonlinear relationship between SOM and the auxiliary environmental variables.Therefore,the GRNNI model can improve both prediction accuracy and reliability for determining spatial distribution and variability of SOM.

Numerical analysis for permafrost temperature field in the short term of permafrost subgrade filling

Purpose-It is of great significance to study the influence of subgrade filling on permafrost temperature field in permafrost area for the smooth construction and safe operation of railway.Design/methodology/approach-The paper builds up the model for the hydrothermal coupling calculation of permafrost using finite element software COMSOL to study how permafrost temperature field changes in the short term after subgrade filling,on which basis it proposes the method of calculation for the concave distortion of freezing front in the subgrade-covered area.Findings-The results show that the freezing front below the subgrade center sinks due to the thermal effect of subgrade filling,which will trigger hydrothermal erosion in case of sufficient moisture inflows,leading to the thawing settlement or the cracking of the subgrade,etc.The heat output of soil will be hindered the most in case of July filling,in which case the sinking and the distortion of the freezing front is found to be the most severe,which the recovery of the permafrost temperature field,the slowest,constituting the most unfavorable working condition.The concave distortion of the freezing front in the subgrade area increases with the increase in temperature difference between the filler and ground surface,the subgrade height,the subgrade width and the volumetric thermal capacity of filler,while decreases with the increase of the thermal conductivity of filler.Therefore,the filler chose for engineering project shall be of small volumetric thermal capacity,low initial temperature and high thermal conductivity whenever possible.Originality/value-The concave distortion of the freezing front under different working conditions at different times after filling can be calculated using the method proposed.

Ag Nanowire-Ag Nanoparticle Hybrids for the Highly Enhanced Fluorescene Detection of Protoporphyrin IX Based on Surface Plasmon-Enhanced Fluorescence

In this study, we developed an approach to fabricate novel 1D Ag NWs-Ag NPs hybrid substrate for enhanced fluorescene detection of protoporphyrin 1X （PplX） based on surface plasmon-enhanced fluorescence. The Ag NWs-Ag NPs hybrid was synthesized by combining the hydrothermal method and self-assembly method with the asisstance of polyvinylpyrrolidone （PVP）. When the Ag NWs-Ag NPs hybrid was deposited on the glass substrate and employed as active substrate to detect PplX, the fluorescence intensity of PplX was enhanced greatly due to the coupling effect of localized surface plasmon-localized surface plasmon （LSP-LSP） and localized surface plasmon- surface plasmon propagation （LSP-SPP） which induced great enhancement of the electromagnetic field. Furthermore, the enhancement effect was approximately linear when the concentration of PpIX was ranged from 1×10^-7 mol/L to 2×10^-5 mol/L, and the photobleaching phenomenon of PplX was reduced greatly, indicating that the fab- ricated Ag NWs-Ag NPs hybrid substrate had well performance for PplX imaging. This work provides an effective approach to prepare highly sensitive and stable fluorescence enhancement substrate, and has great potential application in fluorescence imaging.

Promotion effect of FGF23 on osteopenia in congenital scoliosis through FGFr3/TNAP/OPN pathway

Background:Congenital scoliosis(CS)is a complex spinal malformation of unknown etiology with abnormal bone metabolism.Fibroblast growth factor 23(FGF23),secreted by osteoblasts and osteocytes,can inhibit bone formation and mineralization.This research aims to investigate the relationship between CS and FGF23.Methods:We collected peripheral blood from two pairs of identical twins for methylation sequencing of the target region.FGF23 mRNA levels in the peripheral blood of CS patients and age-matched controls were measured.Receiver operator characteristic(ROC)curve analyses were conducted to evaluate the specificity and sensitivity of FGF23.The expression levels of FGF23 and its downstream factors fibroblast growth factor receptor 3(FGFr3)/tissue non-specific alkaline phosphatase(TNAP)/osteopontin(OPN)in primary osteoblasts from CS patients(CS-Ob)and controls(CT-Ob)were detected.In addition,the osteogenic abilities of FGF23-knockdown or FGF23-overexpressing Ob were examined.Results:DNA methylation of the FGF23 gene in CS patients was decreased compared to that of their identical twins,accompanied by increased mRNA levels.CS patients had increased peripheral blood FGF23 mRNA levels and decreased computed tomography(CT)values compared with controls.The FGF23 mRNA levels were negatively correlated with the CT value of the spine,and ROCs of FGF23 mRNA levels showed high sensitivity and specificity for CS.Additionally,significantly increased levels of FGF23,FGFr3,OPN,impaired osteogenic mineralization and lower TNAP levels were observed in CS-Ob.Moreover,FGF23 overexpression in CT-Ob increased FGFr3 and OPN levels and decreased TNAP levels,while FGF23 knockdown induced downregulation of FGFr3 and OPN but upregulation of TNAP in CS-Ob.Mineralization of CS-Ob was rescued after FGF23 knockdown.Conclusions:Our results suggested increased peripheral blood FGF23 levels,decreased bone mineral density in CS patients,and a good predictive ability of CS by peripheral blood FGF23 levels.FGF23 may contribute to osteopenia in CS patients through FGFr3/TNAP/OPN pathway.

Supplementary open dataset for WiFi indoor localization based on received signal strength

Several Wireless Fidelity(WiFi)fingerprint datasets based on Received Signal Strength(RSS)have been shared for indoor localization.However,they can’t meet all the demands of WiFi RSS-based localization.A supplementary open dataset for WiFi indoor localization based on RSS,called as SODIndoorLoc,covering three buildings with multiple floors,is presented in this work.The dataset includes dense and uniformly distributed Reference Points(RPs)with the average distance between two adjacent RPs smaller than 1.2 m.Besides,the locations and channel information of pre-installed Access Points(APs)are summarized in the SODIndoorLoc.In addition,computer-aided design drawings of each floor are provided.The SODIndoorLoc supplies nine training and five testing sheets.Four standard machine learning algorithms and their variants(eight in total)are explored to evaluate positioning accuracy,and the best average positioning accuracy is about 2.3 m.Therefore,the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format.The dataset can be used for clustering,classification,and regression to compare the performance of different indoor positioning applications based on WiFi RSS values,e.g.,high-precision positioning,building,floor recognition,fine-grained scene identification,range model simulation,and rapid dataset construction.