One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes...One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using in...The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.展开更多
[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the ste...[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the stem and leaf, and the surface of the leaf and the powder of the root, stem and leaf were made by the conventional method, which were observed under the optical microscope. Ultraviolet-visible spectrum identification was carried out according to a conventional method. [Results] The microscopic identification and ultraviolet-visible absorption characteristics of P. venusta (Ker-Gawler) Miers were described in detail. [Conclusions] This study is expected to provide a reference for the identification of P. venusta(KerGawler)Miers and the establishment of the related quality standard.展开更多
Several socio-environmental needs(medicine,industry,engineering,orogenesis,genesis,etc.)require minerals to be more precisly defined and characterised.The identification of minerals plays a crucial role for researcher...Several socio-environmental needs(medicine,industry,engineering,orogenesis,genesis,etc.)require minerals to be more precisly defined and characterised.The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis.However,traditional methods relied heavily on expert knowledge and specialised equipment,making them labour-intensive,costly and time-consuming.This depen-dence is often labour-intensive,not to mention costly and time-consuming.To address this issue,some re-searchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks.However this approch does not correspond to patterns of mineral distribution,where minerals are typically found in associations.These associations make it difficult to accurately identify minerals using con-ventional machine learning algorithms.This paper introduces a deep neural learning model based on multi-label classification,utilizing the problem adaptation method to analyse microscopic images of rock thin sections.The model is based on the ResNet50 architecture,which is designed to analyse minerals and generates the probability of a mineral presence in an image.This method provides a solution to the dependence between associated minerals.Experiments on many test images showed a model confidence,achieving average precision,recall and F1_score 97.15%,96.25%and 96.69%,respectively.Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively.In this way,the importance of each pixel with the class of interest can be assessed using heat maps.The recorded results,in terms of both performance and pixel_level evaluation,demonstrate the promising potential of the model used.It can therefore be considered for multi-labels image classification,particulary for images representing rock minerals.This approach serves as a valuable support tool for geological studies.展开更多
At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standar...At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.展开更多
Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.F...Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.展开更多
The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method ...The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.展开更多
Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock m...Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.展开更多
Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ mod...Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.展开更多
The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of differen...The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of different inorganic components have not yet been fully developed.For this reason,a quantitative characterization method of inorganic pores using pixel information was proposed in this study.A machine learning algorithm was used to assist the field emission scanning electron microscopy(FE-SEM)image processing of shale to realize the accurate identification and quantitative characterization of inorganic pores on the surface of high-precision images of shale with a small view.Moreover,large-view image splicing technology,combined with quantitative evaluation of minerals by scanning electron microscopy(QEMSCAN)image joint characterization technology,was used to accurately analyze the distribution characteristics of inorganic pores under different mineral components.The quantitative methods of pore characteristics of different inorganic components under the pixel information of shale were studied.The results showed that(1)the Waikato Environment for Knowledge Analysis(WEKA)machine learning model can effectively identify and extract shale mineral components and inorganic pore distribution,and the large-view FE-SEM images are representative of samples at the 200μm×200μm view scale,meeting statistical requirements and eliminating the influence of heterogeneity;(2)the pores developed by different mineral components of shale had obvious differences,indicating that the development of inorganic pores is highly correlated with the properties of shale minerals themselves;and(3)the pore-forming ability of different mineral components is calculated by the quantitative method of single component pore-forming coefficient.Chlorite showed the highest pore-forming ability,followed by(in descending order)illite,pyrite,calcite,dolomite,albite,orthoclase,quartz,and apatite.This study contributes to advancing our understanding of inorganic pore characteristics in shale.展开更多
Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric featur...Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.展开更多
Sesame is one of the eight major allergens that cause food allergies.Study of the epitopes of sesame allergens is important for understanding their sensitization mechanisms.Currently,less information is available on t...Sesame is one of the eight major allergens that cause food allergies.Study of the epitopes of sesame allergens is important for understanding their sensitization mechanisms.Currently,less information is available on the epitope studies of sesame allergens.In this study,we analyzed the molecular characteristics,structure and homology of Ses i 3,one of the important sesame allergens.We predicted the B-cell linear epitopes of Ses i 3 using bioinformatics tools and characterized them by slot blot immuno-microarrays technology.Eight peptides as B-cell linear epitopes of Ses i 3 were identified,in addition,key amino acids in these epitopes were predicted and leucine 422 was identified as a key amino acid.The present work will contribute to further understanding of the sesame allergen and provide some help in the prevention and treatment of sesame allergy.展开更多
Expanding the specific surface area of substrates and carrying out precise surface engineering of imprinted nanocavities are crucial methods for enhancing the identification efficiency of molecularly imprinted polymer...Expanding the specific surface area of substrates and carrying out precise surface engineering of imprinted nanocavities are crucial methods for enhancing the identification efficiency of molecularly imprinted polymers(MIPs).To implement this synergistic strategy,bioinspired surface engineering was used to incorporate dual covalent receptors via precise post-imprinting modifications(PIMs)onto mesoporous silica nanosheets.The prepared sorbents(denoted as‘‘D-PMIPs”)were utilized to improve the specific identification of adenosine 5-monophosphate(AMP).Significantly,the mesoporous silica nanosheets possess a high surface area of approximately 498.73 m^(2)·g^(-1),which facilitates the formation of abundant specific recognition sites in the D-PMIPs.The dual covalent receptors are valuable for estab-lishing the spatial orientation and arrangement of AMP through multiple cooperative interactions.PIMs enable precise site-specific functionalization within the imprinted cavities,leading to the tailor-made formation of complementary binding sites.The maximum number of high-affinity binding sites(Nmax)of the D-PMIPs is 39.99 lmol·g^(-1),which is significantly higher than that of imprinted sorbents with a sin-gle receptor(i.e.,S-BMIPs or S-PMIPs).The kinetic data of the D-PMIPs can be effectively described by a pseudo-second-order model,indicating that the main binding mechanism involves synergistic chemisorption from boronate affinity and the pyrimidine base.This study suggests that using dual cova-lent receptors and PIMs is a reliable approach for creating imprinted sorbents with high selectivity,allow-ing for the controlled engineering of imprinted sites.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
基金supported by the Shennong Laboratory Project of Henan Province,China(SN01-2022-01)the China Postdoctoral Science Foundation(2023M731006)the Project of Science and Technology of Henan Province,China(232102111104)。
文摘One of agriculture’s major challenges is the low efficiency of phosphate(Pi)use,which leads to increased costs,harmful environmental impacts,and the depletion of phosphorus(P)resources.The TaPHT1;6 gene,which encodes a high-affinity Pi transporter(PHT),plays a crucial role in Pi absorption and transport.In this study,the promoter and coding regions of three TaPHT1;6 gene copies on chromosomes 5A,5B,and 5D were individually amplified and sequenced from 167 common wheat(Triticum aestivum L.)cultivars.Sequence analysis revealed 16 allelic variation sites within the promoters of TaPHT1;6-5B among these cultivars,forming three distinct haplotypes:Hap1,Hap2,and Hap3.Field trials were conducted over two years to compare wheat genotypes with these haplotypes,focusing on assessing plant dry weight,grain yield,P content,Pi fertilizer absorption efficiency,and Pi fertilizer utilization efficiency.Results indicated that Hap3 represented the favored Pi-efficient haplotype.Dual-luciferase reporter assay demonstrated that the Hap3 promoter,carrying the identified allelic variation sites,exhibited higher gene-driven capability,leading to increased expression levels of the TaPHT1;6-5B gene.We developed a distributed cleaved amplified polymorphic site marker(dCAPS-571)to distinguish Hap3 from the other two haplotypes based on these allelic variation sites,presenting an opportunity for breeding Pi-efficient wheat cultivars.This study successfully identified polymorphic sites on TaPHT1;6-5B associated with Pi efficiency and developed a functional molecular marker to facilitate future breeding endeavors.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金funded by Vice Chancellor of Research at Shiraz University(grant 3GFU2M1820).
文摘The identification of the traction acting on a portion of the surface of an anisotropic solid is very important in structural health monitoring and optimal design of structures. The traction can be determined using inverse methods in which displacement or strain measurements are taken at several points on the body. This paper presents an inverse method based on the method of fundamental solutions for the traction identification problem in two-dimensional anisotropic elasticity. The method of fundamental solutions is an efficient boundary-type meshless method widely used for analyzing various problems. Since the problem is linear, the sensitivity analysis is simply performed by solving the corresponding direct problem several times with different loads. The effects of important parameters such as the number of measurement data, the position of the measurement points, the amount of measurement error, and the type of measurement, i.e., displacement or strain, on the results are also investigated. The results obtained show that the presented inverse method is suitable for the problem of traction identification. It can be concluded from the results that the use of strain measurements in the inverse analysis leads to more accurate results than the use of displacement measurements. It is also found that measurement points closer to the boundary with unknown traction provide more reliable solutions. Additionally, it is found that increasing the number of measurement points increases the accuracy of the inverse solution. However, in cases with a large number of measurement points, further increasing the number of measurement data has little effect on the results.
基金Supported by Scientific Research Program of Guangxi University of Chinese Medicine(P200246).
文摘[Objectives] To identify Pyrostegia venusta (Ker-Gawler.) Miers by microscope and ultraviolet spectrum. [Methods] The paraffin section, slide section and freehand section were used to make the cross section of the stem and leaf, and the surface of the leaf and the powder of the root, stem and leaf were made by the conventional method, which were observed under the optical microscope. Ultraviolet-visible spectrum identification was carried out according to a conventional method. [Results] The microscopic identification and ultraviolet-visible absorption characteristics of P. venusta (Ker-Gawler) Miers were described in detail. [Conclusions] This study is expected to provide a reference for the identification of P. venusta(KerGawler)Miers and the establishment of the related quality standard.
文摘Several socio-environmental needs(medicine,industry,engineering,orogenesis,genesis,etc.)require minerals to be more precisly defined and characterised.The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis.However,traditional methods relied heavily on expert knowledge and specialised equipment,making them labour-intensive,costly and time-consuming.This depen-dence is often labour-intensive,not to mention costly and time-consuming.To address this issue,some re-searchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks.However this approch does not correspond to patterns of mineral distribution,where minerals are typically found in associations.These associations make it difficult to accurately identify minerals using con-ventional machine learning algorithms.This paper introduces a deep neural learning model based on multi-label classification,utilizing the problem adaptation method to analyse microscopic images of rock thin sections.The model is based on the ResNet50 architecture,which is designed to analyse minerals and generates the probability of a mineral presence in an image.This method provides a solution to the dependence between associated minerals.Experiments on many test images showed a model confidence,achieving average precision,recall and F1_score 97.15%,96.25%and 96.69%,respectively.Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively.In this way,the importance of each pixel with the class of interest can be assessed using heat maps.The recorded results,in terms of both performance and pixel_level evaluation,demonstrate the promising potential of the model used.It can therefore be considered for multi-labels image classification,particulary for images representing rock minerals.This approach serves as a valuable support tool for geological studies.
基金supported by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX22_1136)the National Natural Scientific Foundation of China(No.42275037)+2 种基金the Basic Research Fund of CAMS(No.2023Z016)the Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(No.SCSF202202)supported by the Jiangsu Collaborative Innovation Center for Climate Change。
文摘At present,the identification of tropical cyclone remote precipitation(TRP)requires subjective participation,leading to inconsistent results among different researchers despite adopting the same identification standard.Thus,establishing an objective identification method is greatly important.In this study,an objective synoptic analysis technique for TRP(OSAT_TRP)is proposed to identify TRP using daily precipitation datasets,historical tropical cyclone(TC)track data,and the ERA5 reanalysis data.This method includes three steps:first,independent rain belts are separated,and those that might relate to TCs'remote effects are distinguished according to their distance from the TCs.Second,the strong water vapor transport belt from the TC is identified using integrated horizontal water vapor transport(IVT).Third,TRP is distinguished by connecting the first two steps.The TRP obtained through this method can satisfy three criteria,as follows:1)the precipitation occurs outside the circulation of TCs,2)the precipitation is affected by TCs,and 3)a gap exists between the TRP and TC rain belt.Case diagnosis analysis,compared with subjective TRP results and backward trajectory analyses using HYSPLIT,indicates that OSAT_TRP can distinguish TRP even when multiple TCs in the Northwest Pacific are involved.Then,we applied the OSAT_TRP to select typical TRPs and obtained the synoptic-scale environments of the TRP through composite analysis.
基金financially supported by the National Key R&D Program of China(No.2024YFC3012701)the National Natural Science Foundation of China(No.42307269)+1 种基金the Growth of Young Scientific and Technological Talents of Guizhou Educational Commission[Qianjiaoji,Grant No.[2024]348]the Foundation Research Project of Kaili University(No.YTH-TD20253I)。
文摘Anti-slide piles are commonly used to stabilise high and steep slopes in earthquake-prone areas in southwestern China.Herein,we investigate the impact of initial damage on the seismic performance of anti-slide piles.For this purpose,we selected a representative slope adjacent to the Jiuzhaigou Bridge in the Sichuan–Qinghai Railway;we employed a three-dimensional dynamic finite element method combined with the local stiffness reduction approach to simulate three different initial-damage scenarios:intact,slightly damaged and heavily damaged.The dynamic displacement,bending moment and shear stress responses of the piles were comprehensively analysed.Using wavelet packet energy spectrum(WPES)analysis,we introduced two indices:the damage index(DPERV)and its increment(|△DPERV|).The results showed that both the initial damage and seismic energy control the peak dynamic response of the piles.Specifically,high initial damage accelerates stiffness degradation,leading to large horizontal displacements,whereas intact piles sustain high bending moments and shear forces.The distribution of|△DPERV|along a pile reveals three post-earthquake performance stages(i.e.minor,moderate and severe),which agree well with the observed mechanical response characteristics and form the basis for targeted reinforcement strategies.The main innovation of this study is the combined use of initial-damage simulation with WPES analysis,thereby establishing a quantitative diagnostic framework(DPERV and|△DPERV|)for anti-slide piles.This framework determines the non-linear relationship between seismic response and damage evolution and provides a rapid,usable tool for health monitoring and post-earthquake decision-making in landslide-prone mountainous railway areas.
基金supported by the National Key Research and Development Program(No.2022YFA1603300)the National Natural Science Foundation of China(Nos.U2230133,12305266,11921006,12405282)National Grand Instrument Project(No.2019YFF01014400)。
文摘The illicit trafficking of special nuclear materials(SNMs)poses a grave threat to global security and necessitates the development of effective nuclear material identification methods.This study investigated a method to isotopically identify the SNMs,including^(233,235,238)U,^(239-242)Pu,and^(232)Th,based on the detection of delayedγ-rays from photofission fragments.The delayedγ-ray spectra resulting from the photofission of SNMs irradiated by a 14 MeVγbeam with a total of 10~9 were simulated using Geant4.Three high-yield fission fragments,namely^(138)Cs,^(89)Rb,and^(94)Y,were selected as candidate fragments for SNM identification.The yield ratios of these three fragments were calculated,and the results from the different SNMs were compared.The yield ratio of^(138)Cs/^(89)Rb was used to identify most SNMs,including^(233,235,238)U,^(242)Pu,and^(232)Th,with a confidence level above 95%.To identify^(239-241)Pu with the same confidence,a higher total number of 10^(11)γbeams is required.However,although the^(94)Y/^(89)Rb ratio is suitable for elementally identifying SNMs,isotopic identification is difficult.In addition,the count rate of the delayedγabove 3 MeV can be used to rapidly detect the presence of nuclear materials.
基金supported by grants from Key R&D Program of Shandong Province(2022LZGC003)China Agriculture Research System of MOF and MARA,the National Key Research and Development Project(2023YFD1300200 and 2023YFF1001104)+1 种基金the Science and Technology Program of Sichuan Province(2024ZHCG0109)the 2115 Talent Development Program of China Agricultural University.
文摘Background Breed identification plays an important role in conserving indigenous breeds,managing genetic resources,and developing effective breeding strategies.However,researches on breed identification in livestock mainly focused on purebreds,and they yielded lower predict accuracy in hybrid.In this study,we presented a Multi-Layer Perceptron(MLP)model with multi-output regression framework specifically designed for genomic breed composition prediction of purebred and hybrid in pigs.Results We utilized a total of 8,199 pigs from breeding farms in eight provinces in China,comprising Yorkshire,Landrace,Duroc and hybrids of Yorkshire×Landrace.All the animals were genotyped with 1K,50K and 100K SNP chips.Comparing with random forest(RF),support vector regression(SVR)and Admixture,our results from five replicates of fivefold cross validation demonstrated that MLP achieved a breed identification accuracy of 100%for both hybrid and purebreds in 50K and 100K SNP chips,SVR performed comparable with MLP,they both outperformed RF and Admixture.In the independent testing,MLP yielded accuracy of 100%for all three pure breeds and hybrid across all SNP chips and panel,while SVR yielded 0.026%–0.121%lower accuracy than MLP.Compared with classification-based framework,the new strategy of multi-output regression framework in this study was helpful to improve the predict accuracy.MLP,RF and SVR,achieved consistent improvements across all six SNP chips/panel,especially in hybrid identification.Our results showed the determination threshold for purebred had different effects,SVR,RF and Admixture were very sensitive to threshold values,their optimal threshold fluctuated in different scenarios,while MLP kept optimal threshold 0.75 in all cases.The threshold of 0.65–0.75 is ideal for accurate breed identification.Among different density of SNP chips,the 1K SNP chip was most cost-effective as yielding 100%accuracy with enlarging training set.Hybrid individuals in the training set were useful for both purebred and hybrid identification.Conclusions Our new MLP strategy demonstrated its high accuracy and robust applicability across low-,medium-,and high-density SNP chips.Multi-output regression framework could universally enhance prediction accuracy for ML methods.Our new strategy is also helpful for breed identification in other livestock.
基金supported by the National Natural Science Foundation of China(Nos.52174099 and 52474168)the Science and Technology Innovation Program of Hunan Province,China(No.2023RC3050)+1 种基金the Natural Science Foundation of Hunan,China(No.2024JJ4064)the Open Fund of the State Key Laboratory of Safety Technology of Metal Mines(No.kfkt2023-01).
文摘Real-time identification of rock strength and cuttability based on monitoring while cutting during excavation is essential for key procedures such as the precise adjustment of excavation parameters and the in-situ modification of hard rocks.This study proposes an in-telligent approach for predicting rock strength and cuttability.A database comprising 132 data sets is established,containing cutting para-meters(such as cutting depth and pick angle),cutting responses(such as specific energy and instantaneous cutting rate),and rock mech-anical parameters collected from conical pick-cutting experiments.These parameters serve as input features for predicting the uniaxial compressive strength and tensile strength of rocks using regression fitting and machine learning methodologies.In addition,rock cuttabil-ity is classified using a combination of the analytic hierarchy process and fuzzy comprehensive evaluation method,and subsequently iden-tified through machine learning approaches.Various models are compared to determine the optimal predictive and classification models.The results indicate that the optimal model for uniaxial compressive strength and tensile strength prediction is the genetic algorithm-optimized backpropagation neural network model,and the optimal model for rock cuttability classification is the radial basis neural network model.
基金supported by the National Natural Science Foundation of China(42372144)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01E09)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-01-05).
文摘The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of different inorganic components have not yet been fully developed.For this reason,a quantitative characterization method of inorganic pores using pixel information was proposed in this study.A machine learning algorithm was used to assist the field emission scanning electron microscopy(FE-SEM)image processing of shale to realize the accurate identification and quantitative characterization of inorganic pores on the surface of high-precision images of shale with a small view.Moreover,large-view image splicing technology,combined with quantitative evaluation of minerals by scanning electron microscopy(QEMSCAN)image joint characterization technology,was used to accurately analyze the distribution characteristics of inorganic pores under different mineral components.The quantitative methods of pore characteristics of different inorganic components under the pixel information of shale were studied.The results showed that(1)the Waikato Environment for Knowledge Analysis(WEKA)machine learning model can effectively identify and extract shale mineral components and inorganic pore distribution,and the large-view FE-SEM images are representative of samples at the 200μm×200μm view scale,meeting statistical requirements and eliminating the influence of heterogeneity;(2)the pores developed by different mineral components of shale had obvious differences,indicating that the development of inorganic pores is highly correlated with the properties of shale minerals themselves;and(3)the pore-forming ability of different mineral components is calculated by the quantitative method of single component pore-forming coefficient.Chlorite showed the highest pore-forming ability,followed by(in descending order)illite,pyrite,calcite,dolomite,albite,orthoclase,quartz,and apatite.This study contributes to advancing our understanding of inorganic pore characteristics in shale.
基金financially supported by the Youth Innovation Promotion Association CAS(No.2021325)the National Natural Science Foundation of China(Nos.52179117,U21A20159)the Research project of Panzhihua Iron and Steel Group Mining Co.,Ltd.(No.2021-P6-D2-05)。
文摘Source identification and deformation analysis of disaster bodies are the main contents of high-steep slope risk assessment,the establishment of high-precision model and the quantification of the fine geometric features of the slope are the prerequisites for the above work.In this study,based on the UAV remote sensing technology in acquiring refined model and quantitative parameters,a semi-automatic dangerous rock identification method based on multi-source data is proposed.In terms of the periodicity UAV-based deformation monitoring,the monitoring accuracy is defined according to the relative accuracy of multi-temporal point cloud.Taking a high-steep slope as research object,the UAV equipped with special sensors was used to obtain multi-source and multitemporal data,including high-precision DOM and multi-temporal 3D point clouds.The geometric features of the outcrop were extracted and superimposed with DOM images to carry out semi-automatic identification of dangerous rock mass,realizes the closed-loop of identification and accuracy verification;changing detection of multi-temporal 3D point clouds was conducted to capture deformation of slope with centimeter accuracy.The results show that the multi-source data-based semiautomatic dangerous rock identification method can complement each other to improve the efficiency and accuracy of identification,and the UAV-based multi-temporal monitoring can reveal the near real-time deformation state of slopes.
基金supported by the Fundamental Research Funds for the Public Research Institutes of Chinese Academy of Inspection and Quarantine(2022JK04).
文摘Sesame is one of the eight major allergens that cause food allergies.Study of the epitopes of sesame allergens is important for understanding their sensitization mechanisms.Currently,less information is available on the epitope studies of sesame allergens.In this study,we analyzed the molecular characteristics,structure and homology of Ses i 3,one of the important sesame allergens.We predicted the B-cell linear epitopes of Ses i 3 using bioinformatics tools and characterized them by slot blot immuno-microarrays technology.Eight peptides as B-cell linear epitopes of Ses i 3 were identified,in addition,key amino acids in these epitopes were predicted and leucine 422 was identified as a key amino acid.The present work will contribute to further understanding of the sesame allergen and provide some help in the prevention and treatment of sesame allergy.
基金supported by the National Natural Science Foundation of China(22078132,22108103,and U22A20413)the Open Funding Project of the National Key Labora-tory of Biochemical Engineering(2021KF-02)+3 种基金China Postdoctoral Science Foundation(2021M691301)Jiangsu Key Research and Development Program(BE2022356)the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(CPSF)(GZ20230989)Jiangsu Agricultural Independent Innovation Fund Project(CX(21)3079).
文摘Expanding the specific surface area of substrates and carrying out precise surface engineering of imprinted nanocavities are crucial methods for enhancing the identification efficiency of molecularly imprinted polymers(MIPs).To implement this synergistic strategy,bioinspired surface engineering was used to incorporate dual covalent receptors via precise post-imprinting modifications(PIMs)onto mesoporous silica nanosheets.The prepared sorbents(denoted as‘‘D-PMIPs”)were utilized to improve the specific identification of adenosine 5-monophosphate(AMP).Significantly,the mesoporous silica nanosheets possess a high surface area of approximately 498.73 m^(2)·g^(-1),which facilitates the formation of abundant specific recognition sites in the D-PMIPs.The dual covalent receptors are valuable for estab-lishing the spatial orientation and arrangement of AMP through multiple cooperative interactions.PIMs enable precise site-specific functionalization within the imprinted cavities,leading to the tailor-made formation of complementary binding sites.The maximum number of high-affinity binding sites(Nmax)of the D-PMIPs is 39.99 lmol·g^(-1),which is significantly higher than that of imprinted sorbents with a sin-gle receptor(i.e.,S-BMIPs or S-PMIPs).The kinetic data of the D-PMIPs can be effectively described by a pseudo-second-order model,indicating that the main binding mechanism involves synergistic chemisorption from boronate affinity and the pyrimidine base.This study suggests that using dual cova-lent receptors and PIMs is a reliable approach for creating imprinted sorbents with high selectivity,allow-ing for the controlled engineering of imprinted sites.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
