Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters accordi...Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.展开更多
Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NP...Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.展开更多
The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-a...The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-aided design(TCAD)simulation.Comparative analysis of the heavy-ion irradiation effects on three device structures reveals distinct susceptibility characteristics.The gate oxide region is identified as the most sensitive position in planar devices,while trench and doubletrench structures exhibit no localized sensitive regions.Furthermore,the single-event susceptibility demonstrates strong depth dependence across all three structures,with enhanced vulnerability observed at greater ion penetration depths.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
To deepen understanding of the evolution of coal char microstructural properties of coal char during the co-pyrolysis of coking coal with additives,this study incorporated two typical additives,coal tar pitch(CTP)and ...To deepen understanding of the evolution of coal char microstructural properties of coal char during the co-pyrolysis of coking coal with additives,this study incorporated two typical additives,coal tar pitch(CTP)and waste plastic(HDPE),into a blended coal sample and carried out pyrolysis experiments.The pyrolysis process and the microstructure of char were systematically characterized using various analytical techniques,including thermogravimetric analysis(TGA),X-ray diffraction(XRD)and Raman spectroscopy.Data correlation analysis was performed to reveal the mechanism of carbon structural ordering evolution within the critical temperature range(350−600℃)from colloidal layer formation to semi-coke conversion in coking coal,and to elucidate the regulatory effects of different additives on coal pyrolysis pathways.The results indicate that HDPE releases free radicals during high-temperature pyrolysis,accelerating the pyrolysis reaction and increase the yield of volatile components.Conversely,CTP facilitates pyrolysis at low temperatures through its light components,thereby delaying high-temperature reactions due to the colloidal layer’s effect.XRD results indicate that during the process of pyrolysis,there is a progressive decrease in the interlayer spacing of aromatic layers(d002),while the aromatic ring stacking height(L_(c))and lateral size(L_(a))undergo significant of carbon skeleton ordering.Further comparative reveals that CTP partially suppresses structural ordering at low temperatures,whereas HDPE promotes the condensation and alignment of aromatic clusters via a free radical mechanism.Raman spectroscopy reveals a two-stage reorganization mechanism in the microstructure of the coal char:the decrease in the I_(D)/I_(G)ratio between 350 and 550℃is primarily attributed to the cleavage of aliphatic side chains and cross-linking bonds,leading to a reduction in defective structures;whereas the increase in ID/IG between 550 and 600℃is closely associated with enhanced condensation reactions of aromatic structures.Correlation analysis further demonstrates progressive graphitization during pyrolysis,with a significant positive correlation(R^(2)>0.85)observed between d002 and the full width at half maximum of the G-band(FWHM-G).展开更多
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.展开更多
This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the correspo...This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the corresponding ester[Fe_(2)(CO)_(6)(μ‑tedt)](2),where tedt=SCH_(2)CH(CH_(2)OOC(5‑C_(3)HNSCH_(3)))S.Further reactions of complex 2 with tri(ptolyl)phosphine(tp)or tris(4‑fluorophenyl)phosphine(fp)gave the phosphine‑substituted derivatives[Fe_(2)(CO)_(5)(tp)(μ‑tedt)](3)and[Fe_(2)(CO)_(5)(fp)(μ‑tedt)](4).The structures of the newly prepared complexes were elucidated by elemental analysis,NMR,IR,and X‑ray photoelectron spectroscopy.Moreover,single‑crystal X‑ray diffraction analysis confirmed their molecular structures,showing that they contain a di‑iron core ligated by a bridged dithiolate bearing a thiazole moiety and terminal carbonyls.The electrochemical and electrocatalytic proton reduction were probed by cyclic voltammetry,revealing that three complexes can catalyze the reduction of protons to H_(2) under the electrochemical conditions.For comparison,complex 4 possessed the best efficiency with a turnover frequency of 23.5 s^(-1)at 10 mmol·L^(-1)HOAc concentration.In addition,the fungicidal activity of these complexes was also investigated in this study.CCDC:2477511,2;2477512,3;2477513,4.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously...Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.展开更多
Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associa...Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associated with structural degradation and side reactions during electrochemical cycling.Herein,we demonstrate the possibility of preparing NRCs,typically Li Ni_(0.9)Co_(0.05)Mn_(0.05)O_(2)(NCM9055),with much-improved mechanical and chemical stability based on the surface coating of the hydroxide precursors.Specifically,a conformal nanoshell containing both Al^(3+)and W^(6+)was first deposited around the precursor particles,and the following high-temperature lithiation produced the targeted NCM9055 with favorable structural features,where Al3+existed as a bulk dopant to enhance the structural stability while the high-valent W^(6+)promoted the microstructural evolution into radially-architectured elongated primary particles.Such a structural engineering benefiting from the Al^(3+)/W^(6+)co-modification endowed the prepared NCM9055 cathode(NCM9055-Al W)with much-improved cycling stability,as revealed by a high-capacity retention of 98.0%after 100 cycles(tested at 0.5 C,4.3 V)as compared to only 79.0%for the pristine cathode without Al^(3+)/W^(6+).The NCM9055-15Al W cathode also showed a high-rate capability with extraordinary structural stability against mechanical failure.Our study highlighted the enormous potential of precursor multi-element treatment as an effective tool in structural refinement of NRCs to circumvent their stability challenge for their applications in high-energy LIBs.展开更多
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digesti...In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.展开更多
From cracking the code of viruses to mentoring the next generation of scientists,the former president of Nankai University has contributed a lot to turning microscopic discoveries into monumental shields for global he...From cracking the code of viruses to mentoring the next generation of scientists,the former president of Nankai University has contributed a lot to turning microscopic discoveries into monumental shields for global health.OVER the past 40 years,one man has distinguished himself through a deep commitment to researching protein structures of high pathogenic viruses,and published numerous significant works in top international scientific journals.展开更多
High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP g...High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.展开更多
Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes canno...Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes cannot achieve high active material loading and efficient ion/electron transport simultaneously.By contrast,three-dimensional(3D)structures have attracted increasing interest because of their capacity to enhance active material utilization,shorten ion and electron transport pathways,reduce interfacial impedance,and provide spatial accommodation for volume expansion.Additive manufacturing(AM)technology effectively fabricates energy-storage materials with 3D structures by accurately constructing complex 3D structures via layer-by-layer deposition.Recent studies have employed AM to construct ordered 3D electrodes that can optimize ion/electron transport,regulate electric field distribution,or improve the electrode-electrolyte interface,thereby contributing to enhanced kinetic performance and cycling stability.This review systematically summarizes the applications of several AM technologies in the fabrication of energy storage materials and analyzes their respective advantages and limitations.Subsequently,the advantages of AM technology in the fabrication of energy storage materials and several major optimization strategies are comprehensively discussed.Finally,the major challenges and potential applications of AM technology in energy storage material optimization are discussed.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.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
This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loo...This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.展开更多
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to conce...Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to concentration quenching has become a key research focus.In this work,we successfully synthesized KBi(MoO_(4))_(2):x Tb^(3+)(x=0-100 at%)(denoted as KBM:x Tb^(3+))phosphors via a high-temperature solid-state reaction.Remarkably,no concentration quenching was observed across the entire doping range.This anti-quenching behavior originates from the large Tb^(3+)-Tb^(3+)interionic distance(>5Å)inherent to the quasi-layered crystal structure,which effectively suppresses multipole-interaction-mediated energy migration.At full Tb^(3+)substitution(x=100 at%),the material undergoes a structural phase transition from the monoclinic KBM phase to the triclinicα-KTb(MoO_(4))_(2)(α-KTM)phase.Theα-KTM phosphor exhibits excellent thermal stability(activation energy=0.6129 eV)and a single-exponential decay profile,whereas KBM:x Tb^(3+)(x<100%)display double-exponential decay behaviors,attributed to dual energy transfer pathways.These findings provide new insights into the luminescence mechanisms of high-concentration rare-earth-doped systems and offer guidance for designing nextgeneration anti-quenching phosphors.展开更多
基金supported by the Innovation Foundation of Provincial Education Department of Gansu(2024B-005)the Gansu Province National Science Foundation(22YF7GA182)the Fundamental Research Funds for the Central Universities(No.lzujbky2022-kb01)。
文摘Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.
基金National Natural Science Foundation of China under Grant Nos.52208191 and 51908397Shanxi Province Science Foundation for Youths under Grant No.201901D211025China Postdoctoral Science Foundation under Grant No.2020M670695。
文摘Seismic fragility analysis(SFA)is known as an effective probabilistic-based approach used to evaluate seismic fragility.There are various sources of uncertainties associated with this approach.A nuclear power plant(NPP)system is an extremely important infrastructure and contains many structural uncertainties due to construction issues or structural deterioration during service.Simulation of structural uncertainties effects is a costly and time-consuming endeavor.A novel approach to SFA for the NPP considering structural uncertainties based on the damage state is proposed and examined.The results suggest that considering the structural uncertainties is essential in assessing the fragility of the NPP structure,and the impact of structural uncertainties tends to increase with the state of damage.Subsequently,machine learning(ML)is found to be superior in high-precision damage state identification of the NPP for reducing the time of nonlinear time-history analysis(NLTHA)and could be applied in the damage state-based SFA.Also,the impact of various sources of uncertainties is investigated through sensitivity analysis.The Sobol and Shapley additive explanations(SHAP)method can be complementary to each other and able to solve the problem of quantifying seismic and structural uncertainties simultaneously and the interaction effect of each parameter.
基金National Key Research and Development Program of China(2023YFA1609000)National Natural Science Foundation of China(62474190,U22B2043,U2267210)。
文摘The single-event susceptibility of three silicon carbide(SiC)metal-oxide-semiconductor field-effect transistor(MOSFET)power devices structures(planar,trench and double trench)is researched by the technology computer-aided design(TCAD)simulation.Comparative analysis of the heavy-ion irradiation effects on three device structures reveals distinct susceptibility characteristics.The gate oxide region is identified as the most sensitive position in planar devices,while trench and doubletrench structures exhibit no localized sensitive regions.Furthermore,the single-event susceptibility demonstrates strong depth dependence across all three structures,with enhanced vulnerability observed at greater ion penetration depths.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金Supported by National Natural Science Foundation of China(22378180,22078141)Education Department Foundation of Liaoning Province(JYTMS20230960)。
文摘To deepen understanding of the evolution of coal char microstructural properties of coal char during the co-pyrolysis of coking coal with additives,this study incorporated two typical additives,coal tar pitch(CTP)and waste plastic(HDPE),into a blended coal sample and carried out pyrolysis experiments.The pyrolysis process and the microstructure of char were systematically characterized using various analytical techniques,including thermogravimetric analysis(TGA),X-ray diffraction(XRD)and Raman spectroscopy.Data correlation analysis was performed to reveal the mechanism of carbon structural ordering evolution within the critical temperature range(350−600℃)from colloidal layer formation to semi-coke conversion in coking coal,and to elucidate the regulatory effects of different additives on coal pyrolysis pathways.The results indicate that HDPE releases free radicals during high-temperature pyrolysis,accelerating the pyrolysis reaction and increase the yield of volatile components.Conversely,CTP facilitates pyrolysis at low temperatures through its light components,thereby delaying high-temperature reactions due to the colloidal layer’s effect.XRD results indicate that during the process of pyrolysis,there is a progressive decrease in the interlayer spacing of aromatic layers(d002),while the aromatic ring stacking height(L_(c))and lateral size(L_(a))undergo significant of carbon skeleton ordering.Further comparative reveals that CTP partially suppresses structural ordering at low temperatures,whereas HDPE promotes the condensation and alignment of aromatic clusters via a free radical mechanism.Raman spectroscopy reveals a two-stage reorganization mechanism in the microstructure of the coal char:the decrease in the I_(D)/I_(G)ratio between 350 and 550℃is primarily attributed to the cleavage of aliphatic side chains and cross-linking bonds,leading to a reduction in defective structures;whereas the increase in ID/IG between 550 and 600℃is closely associated with enhanced condensation reactions of aromatic structures.Correlation analysis further demonstrates progressive graphitization during pyrolysis,with a significant positive correlation(R^(2)>0.85)observed between d002 and the full width at half maximum of the G-band(FWHM-G).
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
文摘This paper reports the preparation of three di‑iron complexes containing a thiazole moiety.Esterification of complex[Fe_(2)(CO)_(6)(μ‑SCH_(2)CH(CH_(2)OH)S)](1)with 4‑methylthiazole‑5‑carboxylic acid gave the corresponding ester[Fe_(2)(CO)_(6)(μ‑tedt)](2),where tedt=SCH_(2)CH(CH_(2)OOC(5‑C_(3)HNSCH_(3)))S.Further reactions of complex 2 with tri(ptolyl)phosphine(tp)or tris(4‑fluorophenyl)phosphine(fp)gave the phosphine‑substituted derivatives[Fe_(2)(CO)_(5)(tp)(μ‑tedt)](3)and[Fe_(2)(CO)_(5)(fp)(μ‑tedt)](4).The structures of the newly prepared complexes were elucidated by elemental analysis,NMR,IR,and X‑ray photoelectron spectroscopy.Moreover,single‑crystal X‑ray diffraction analysis confirmed their molecular structures,showing that they contain a di‑iron core ligated by a bridged dithiolate bearing a thiazole moiety and terminal carbonyls.The electrochemical and electrocatalytic proton reduction were probed by cyclic voltammetry,revealing that three complexes can catalyze the reduction of protons to H_(2) under the electrochemical conditions.For comparison,complex 4 possessed the best efficiency with a turnover frequency of 23.5 s^(-1)at 10 mmol·L^(-1)HOAc concentration.In addition,the fungicidal activity of these complexes was also investigated in this study.CCDC:2477511,2;2477512,3;2477513,4.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported in part by the National Natural Science Foundation of China (62431018)in part by the Guangzhou Municipal Science and Technology Bureau (SL2023A04J00435)in part by the One Hundred Youth Project of Guangdong University of Technology (263113873)。
文摘Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.
基金supported by the National Key R&D Program of China(Grant No.2022YFB2404402)the National Natural Science Foundation of China(Grant Nos.22025507,22421001,and 22409200)+1 种基金the Strategic Priority Research Program of the Chinese Academy of SciencesGrant No.XDB 1040200。
文摘Nickel-rich cathodes(NRCs)hold great promise for next-generation high-energy lithium-ion batteries(LIBs)due to high specific energy and low cost.However,the higher Ni content exacerbates the instability issues associated with structural degradation and side reactions during electrochemical cycling.Herein,we demonstrate the possibility of preparing NRCs,typically Li Ni_(0.9)Co_(0.05)Mn_(0.05)O_(2)(NCM9055),with much-improved mechanical and chemical stability based on the surface coating of the hydroxide precursors.Specifically,a conformal nanoshell containing both Al^(3+)and W^(6+)was first deposited around the precursor particles,and the following high-temperature lithiation produced the targeted NCM9055 with favorable structural features,where Al3+existed as a bulk dopant to enhance the structural stability while the high-valent W^(6+)promoted the microstructural evolution into radially-architectured elongated primary particles.Such a structural engineering benefiting from the Al^(3+)/W^(6+)co-modification endowed the prepared NCM9055 cathode(NCM9055-Al W)with much-improved cycling stability,as revealed by a high-capacity retention of 98.0%after 100 cycles(tested at 0.5 C,4.3 V)as compared to only 79.0%for the pristine cathode without Al^(3+)/W^(6+).The NCM9055-15Al W cathode also showed a high-rate capability with extraordinary structural stability against mechanical failure.Our study highlighted the enormous potential of precursor multi-element treatment as an effective tool in structural refinement of NRCs to circumvent their stability challenge for their applications in high-energy LIBs.
文摘In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
文摘From cracking the code of viruses to mentoring the next generation of scientists,the former president of Nankai University has contributed a lot to turning microscopic discoveries into monumental shields for global health.OVER the past 40 years,one man has distinguished himself through a deep commitment to researching protein structures of high pathogenic viruses,and published numerous significant works in top international scientific journals.
基金supported by the Science and Technology Talent Support Project of Hunan Province,China (2022TJ-N15)the Hunan Agricultural Science and Technology Innovation Fund,China (2024CX90 and 2024CX65)the Science and Technology Innovation Program of Hunan Province,China (2021NK1006)。
文摘High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.
基金support of the National Natural Science Foundation of China(No.52574411)Beijing Natural Science Foundation(No.2242043).
文摘Achieving high energy and power densities is currently a core challenge in the fabrication of energy storage materials.Although numerous high-capacity materials have been developed,conventional planar electrodes cannot achieve high active material loading and efficient ion/electron transport simultaneously.By contrast,three-dimensional(3D)structures have attracted increasing interest because of their capacity to enhance active material utilization,shorten ion and electron transport pathways,reduce interfacial impedance,and provide spatial accommodation for volume expansion.Additive manufacturing(AM)technology effectively fabricates energy-storage materials with 3D structures by accurately constructing complex 3D structures via layer-by-layer deposition.Recent studies have employed AM to construct ordered 3D electrodes that can optimize ion/electron transport,regulate electric field distribution,or improve the electrode-electrolyte interface,thereby contributing to enhanced kinetic performance and cycling stability.This review systematically summarizes the applications of several AM technologies in the fabrication of energy storage materials and analyzes their respective advantages and limitations.Subsequently,the advantages of AM technology in the fabrication of energy storage materials and several major optimization strategies are comprehensively discussed.Finally,the major challenges and potential applications of AM technology in energy storage material optimization are discussed.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
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.
文摘This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
基金supported by the Natural Science Research Project of Anhui Province Education Department for Excellent Young Scholars(Grant No.2024AH030007)the National Natural Science Foundation of China(Grant No.52202001)。
文摘Conventional Tb^(3+)-doped phosphors typically suffer from concentration quenching once the doping level exceeds a critical threshold.Consequently,the development of Tb^(3+)phosphors with intrinsic resistance to concentration quenching has become a key research focus.In this work,we successfully synthesized KBi(MoO_(4))_(2):x Tb^(3+)(x=0-100 at%)(denoted as KBM:x Tb^(3+))phosphors via a high-temperature solid-state reaction.Remarkably,no concentration quenching was observed across the entire doping range.This anti-quenching behavior originates from the large Tb^(3+)-Tb^(3+)interionic distance(>5Å)inherent to the quasi-layered crystal structure,which effectively suppresses multipole-interaction-mediated energy migration.At full Tb^(3+)substitution(x=100 at%),the material undergoes a structural phase transition from the monoclinic KBM phase to the triclinicα-KTb(MoO_(4))_(2)(α-KTM)phase.Theα-KTM phosphor exhibits excellent thermal stability(activation energy=0.6129 eV)and a single-exponential decay profile,whereas KBM:x Tb^(3+)(x<100%)display double-exponential decay behaviors,attributed to dual energy transfer pathways.These findings provide new insights into the luminescence mechanisms of high-concentration rare-earth-doped systems and offer guidance for designing nextgeneration anti-quenching phosphors.
