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
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
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
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
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
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
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
Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as s...Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.展开更多
Electrocatalytic nitrate-to-ammonia conversion offers dual environmental and sustainable synthesis benefits,but achieving high efficiency with low-cost catalysts remains a major challenge.This review focuses on cobalt...Electrocatalytic nitrate-to-ammonia conversion offers dual environmental and sustainable synthesis benefits,but achieving high efficiency with low-cost catalysts remains a major challenge.This review focuses on cobalt-based electrocatalysts,emphasizing their structural engineering for enhanced the performance of electrocatalytic nitrate reduction reaction(NO3RR)through dimensional control,compositional tuning,and coordination microenvironment modulation.Notably,by critically analyzing metallic cobalt,cobalt alloys,cobalt compounds,cobalt single atom and molecular catalyst configurations,we firstly establish correlations between atomic-scale structural features and catalytic performance in a coordination environment perspective for NO3RR,including the dynamic reconstruction during operation and its impact on active site.Synergizing experimental breakthroughs with computational modeling,we decode mechanisms underlying competitive hydrogen evolution suppression,intermediate adsorption-energy optimization,and durability enhancement in complex aqueous environments.The development of cobalt-based catalysts was summarized and prospected,and the emerging opportunities of machine learning in accelerating the research and development of high-performance catalysts and the configuration of series reactors for scalable nitrate-to-ammonia systems were also introduced.Bridging surface science and applications,it outlines a framework for designing multifunctional electrocatalysts to restore nitrogen cycle balance sustainably.展开更多
Recent advances in geoscience have underscored the critical role of abiogenic processes in petroleum formation,especially the formation and polymerization of methane.However,whether a direct carbon-H_(2) reaction can ...Recent advances in geoscience have underscored the critical role of abiogenic processes in petroleum formation,especially the formation and polymerization of methane.However,whether a direct carbon-H_(2) reaction can produce C_(2+)hydrocarbons(e.g.,ethane and propane)beyond methane remains an open question.Here,we demonstrate the direct synthesis of ethane and propane via reactions between amorphous carbon and H_(2) under upper mantle conditions(2-10 GPa and 800-1200℃).A systematic investigation reveals that increasing structural disorder in carbon precursors,from graphite to glassy carbon-Ⅱ and carbon black,enhances the production of C_(2)-C_(3) hydrocarbons.Through integrated X-ray diffraction and reverse Monte Carlo simulations,we establish that the continuous random atomic network structures in amorphous carbon enable one-step synthesis of heavy hydrocarbons with H_(2).These models establish a direct link between atomic-scale carbon structures and the one-step synthesis of C_(2+)hydrocarbons under H2-rich,high-pressure,and high-temperature conditions—potentially revealing an efficient mechanism for the abiotic production of C_(2+)hydrocarbons in the upper mantle.展开更多
Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream...Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream of Red Mansions and All Men Are Brothers,two masterpieces of Chinese classical literature,have long been central to debates regarding the authorship of their later chapters.Previous studies,often based on word-frequency statistics,function word distributions,entropy measures,and complex network analyses,have provided valuable insights into stylistic differences;however,they remain limited in capturing cross-scale structural features.To address this gap,we apply a multi-scale structural complexity approach based on character-frequency time series to analyze the structural evolution of both novels under various segmentation strategies.Our results reveal significant differences in peak complexity positions,overall complexity levels,and intra-textual variations between the two works,which are closely linked to changes in authorship and stylistic patterns.This study not only provides new quantitative evidence for resolving authorship disputes in classical literature but also demonstrates,from the perspective of structural complexity,the profound depth and unique charm of Chinese literary expression,highlighting the richness of Chinese language and culture.Moreover,it emphasizes the potential of structural complexity analysis as a versatile tool for textual analysis and style attribution.展开更多
The failure mechanisms and structural damage of SiC MOSFETs induced by heavy ion irradiation were demonstrated.The findings reveal three degradation modes,depending on the drain voltage.At a relatively low voltage,the...The failure mechanisms and structural damage of SiC MOSFETs induced by heavy ion irradiation were demonstrated.The findings reveal three degradation modes,depending on the drain voltage.At a relatively low voltage,the damage is triggered by the formation and activation of gate latent damage(LDs),with damage concentrated in the gate oxide.The second degradation mode involves permanent leakage current degradation,with damage progressively transitioning from the oxide to the SiC material as the drain voltage escalates.Ultimately,the device undergoes catastrophic burnout above certain voltages,characterized by the lattice temperature reaching the sublimation point of SiC,resulting in surface cavity and complete structural destruction.This paper presents a comprehensive investigation of SiC MOSFETs under heavy ion exposure,providing radiation resistance methods of SiC-based devices for aerospace applications.展开更多
This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior...This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior.The specimens exhibit violent chemical reaction during the fracture process under the impact loading,and the size distribution of their residual debris follows Rosin-Rammler model.The dynamic fracture toughness is obtained by the fitting of debris length scale,approximately 1.87 MPa·m~(1/2).Microstructure observation on residual debris indicates that the failure process is determined by primary crack propagation under quasi-static compression,while it is affected by multiple cracks propagation in both particle and matrix in the case of dynamic impact.Impact test demonstrates that the novel energetic fragment performs brilliant penetration and combustion effect behind the front target,leading to the effective ignition of fuel tank.For the brittleness of as-cast W-ZrTi ESM,further study conducted bond-based peridynamic(BB-PD)C++computational code to simulate its fracture behavior during penetration.The BB-PD method successfully captured the fracture process and debris cloud formation of the energetic fragment.This paper explores a novel as-cast metallic ESM,and provides an available numerical avenue to the simulation of brittle energetic fragment.展开更多
An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The method...An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.展开更多
With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has ...With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.展开更多
AIM: To analyze the major constituents in Radix Scrophulariae(Scrophularia ningpoensis). METHOD: Radix Scrophulariae was analyzed by HPLC coupled with electrospray ionization quadrupole time-of-flight tandem mass spec...AIM: To analyze the major constituents in Radix Scrophulariae(Scrophularia ningpoensis). METHOD: Radix Scrophulariae was analyzed by HPLC coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry(ESI-Q-TOF MS/MS). Compounds were separated by HPLC using a C18 column and gradient elution of acetonitrile and 0.1 %(V/V) acetic acid-water. Negative ion mode was employed. RESULTS: A total of thirty-six compounds, including fourteen iridoid glycosides, nineteen phenylpropanoid glycosides, and three organic acids, were identified from Radix Scrophulariae based on the accurate mass measurement of precursor and product ions. Twenty-one of the constituents were identified by comparing their retention times(tR) and ESI-MS/MS data with those of reference standards and/or previous publications, while another fifteen compounds were tentatively identified or deduced according to their Q-TOF MS/MS data which afforded sufficient structural information. CONCLUSION: It is believed that this study is useful for the identification of constituents in Radix Scrophulariae, as well as related plants and complex prescriptions.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.
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 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.
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.
基金supported by Joint Funds of the National Natural Science Foundation of China(Grant No.U21A20228).
文摘Root rot is a prevalent soil-borne fungal disease in citrus.Citron C-05(Citrus medica)stands out as a germplasm within Citrus spp.due to its complete resistance to citrus canker and favorable characteristics such as single embryo and easy rooting.However,Citron C-05 was found to be highly susceptible to root rot during cultivation,with the specific pathogens previously unknown.In this study,four candidate fungal species were isolated from Citron C-05 roots.Sequence analysis of ITS,EF-1a,RPB1,and RPB2 identified two Fusarium solani strains,Rr-2 and Rr-4,as the candidates causing root rot in Citron C-05.Resistance tests showed these two pathogens increased root damage rate from 10.30%to 35.69%in Citron C-05,sour orange(Citrus aurantium),sweet orange(Citrus sinensis)and pummelo(Citrus grandis).F.solani exhibited the weak pathogenicity towards trifoliate orange(Poncirus trifoliata).DAB staining revealed none of reddish-brown precipitation in the four susceptible citrus germplasm after infection with F.solani,while trifoliate orange exhibited significant H2O2 accumulation.Trypan blue staining indicated increased cell death in the four susceptible citrus germplasm following infection with these two pathogens but not in trifoliate orange.These findings provide a comprehensive understanding of citrus root rot and support future research on the mechanisms of root rot resistance in citrus.
基金supported by the National Natural Science Foundation of China(Grant Nos.:21825201,52401244 and 52201227)Henan Province Key Research and Development and Promotion Program(Scientific and Technological Breakthrough Project:232102240088 and 252102230078)+3 种基金the Key Research&Development and Promotion of Special Project(Scientific Problem Tackling)of Henan Province(252102230078)Doctoral Research Startup Fund Project of Henan Open University(BSJH-2025-04)Zhejiang Provincial Natural Science Foundation of China(LQ24B020005,LQ23B030001)China Postdoctoral Science Foundation(2024M762442).
文摘Electrocatalytic nitrate-to-ammonia conversion offers dual environmental and sustainable synthesis benefits,but achieving high efficiency with low-cost catalysts remains a major challenge.This review focuses on cobalt-based electrocatalysts,emphasizing their structural engineering for enhanced the performance of electrocatalytic nitrate reduction reaction(NO3RR)through dimensional control,compositional tuning,and coordination microenvironment modulation.Notably,by critically analyzing metallic cobalt,cobalt alloys,cobalt compounds,cobalt single atom and molecular catalyst configurations,we firstly establish correlations between atomic-scale structural features and catalytic performance in a coordination environment perspective for NO3RR,including the dynamic reconstruction during operation and its impact on active site.Synergizing experimental breakthroughs with computational modeling,we decode mechanisms underlying competitive hydrogen evolution suppression,intermediate adsorption-energy optimization,and durability enhancement in complex aqueous environments.The development of cobalt-based catalysts was summarized and prospected,and the emerging opportunities of machine learning in accelerating the research and development of high-performance catalysts and the configuration of series reactors for scalable nitrate-to-ammonia systems were also introduced.Bridging surface science and applications,it outlines a framework for designing multifunctional electrocatalysts to restore nitrogen cycle balance sustainably.
基金supported by the Natural Science Foundation of China(Grant Nos.52288102,52090020,and 52372261)the Natural Science Foundation of Hebei Province(Grant No.E202403045)+2 种基金the S&T Program of Hebei(Grant No.225A1102D)the Ministry of Education Chang Jiang Scholar Professor Program(Grant No.T2022241)supported by the User Experiment Assist System at SSRF.
文摘Recent advances in geoscience have underscored the critical role of abiogenic processes in petroleum formation,especially the formation and polymerization of methane.However,whether a direct carbon-H_(2) reaction can produce C_(2+)hydrocarbons(e.g.,ethane and propane)beyond methane remains an open question.Here,we demonstrate the direct synthesis of ethane and propane via reactions between amorphous carbon and H_(2) under upper mantle conditions(2-10 GPa and 800-1200℃).A systematic investigation reveals that increasing structural disorder in carbon precursors,from graphite to glassy carbon-Ⅱ and carbon black,enhances the production of C_(2)-C_(3) hydrocarbons.Through integrated X-ray diffraction and reverse Monte Carlo simulations,we establish that the continuous random atomic network structures in amorphous carbon enable one-step synthesis of heavy hydrocarbons with H_(2).These models establish a direct link between atomic-scale carbon structures and the one-step synthesis of C_(2+)hydrocarbons under H2-rich,high-pressure,and high-temperature conditions—potentially revealing an efficient mechanism for the abiotic production of C_(2+)hydrocarbons in the upper mantle.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12275179,11875042,and12150410309)the Natural Science Foundation of Shanghai(Grant No.21ZR1443900)。
文摘Text,as a fundamental carrier of human language and culture,exhibits high structural and semantic complexity.Its systematic analysis is essential for understanding linguistic patterns and cultural transmission.A Dream of Red Mansions and All Men Are Brothers,two masterpieces of Chinese classical literature,have long been central to debates regarding the authorship of their later chapters.Previous studies,often based on word-frequency statistics,function word distributions,entropy measures,and complex network analyses,have provided valuable insights into stylistic differences;however,they remain limited in capturing cross-scale structural features.To address this gap,we apply a multi-scale structural complexity approach based on character-frequency time series to analyze the structural evolution of both novels under various segmentation strategies.Our results reveal significant differences in peak complexity positions,overall complexity levels,and intra-textual variations between the two works,which are closely linked to changes in authorship and stylistic patterns.This study not only provides new quantitative evidence for resolving authorship disputes in classical literature but also demonstrates,from the perspective of structural complexity,the profound depth and unique charm of Chinese literary expression,highlighting the richness of Chinese language and culture.Moreover,it emphasizes the potential of structural complexity analysis as a versatile tool for textual analysis and style attribution.
基金Project supported by the National Key Research and Development Program of China(Grant No.2023YFA1609000)the National Natural Science Foundation of China(Grant Nos.U2341222,U2441248,12275061,and 12075069)。
文摘The failure mechanisms and structural damage of SiC MOSFETs induced by heavy ion irradiation were demonstrated.The findings reveal three degradation modes,depending on the drain voltage.At a relatively low voltage,the damage is triggered by the formation and activation of gate latent damage(LDs),with damage concentrated in the gate oxide.The second degradation mode involves permanent leakage current degradation,with damage progressively transitioning from the oxide to the SiC material as the drain voltage escalates.Ultimately,the device undergoes catastrophic burnout above certain voltages,characterized by the lattice temperature reaching the sublimation point of SiC,resulting in surface cavity and complete structural destruction.This paper presents a comprehensive investigation of SiC MOSFETs under heavy ion exposure,providing radiation resistance methods of SiC-based devices for aerospace applications.
文摘This paper prepared a novel as-cast W-Zr-Ti metallic ESM using high-frequency vacuum induction melting technique.The above ESM performs a typical elastic-brittle material feature and strain rate strengthening behavior.The specimens exhibit violent chemical reaction during the fracture process under the impact loading,and the size distribution of their residual debris follows Rosin-Rammler model.The dynamic fracture toughness is obtained by the fitting of debris length scale,approximately 1.87 MPa·m~(1/2).Microstructure observation on residual debris indicates that the failure process is determined by primary crack propagation under quasi-static compression,while it is affected by multiple cracks propagation in both particle and matrix in the case of dynamic impact.Impact test demonstrates that the novel energetic fragment performs brilliant penetration and combustion effect behind the front target,leading to the effective ignition of fuel tank.For the brittleness of as-cast W-ZrTi ESM,further study conducted bond-based peridynamic(BB-PD)C++computational code to simulate its fracture behavior during penetration.The BB-PD method successfully captured the fracture process and debris cloud formation of the energetic fragment.This paper explores a novel as-cast metallic ESM,and provides an available numerical avenue to the simulation of brittle energetic fragment.
基金The National High Technology Research and Development Program of China(863Program)(No.2006AA04Z416)
文摘An output-only modal identification method by a combination use of the peak-picking method and the cross spectrum methods are presented. Meanwhile, a novel mode shape optimum method of the deck is proposed. The methods are applied to the operational modal identification system of the Runyang Suspension Bridge, which can be used to obtain the modal parameters of the bridge from out-only data sets collected by its structural health monitoring system （SHMS）. As an example, the vibration response data of the deck, cable and tower recorded during typhoon Matsa excitation are used to illustrate the program application. Some of the modal frequencies observed from deck vibration responses are also found in the vibration responses of the cable and the tower. The results show that some modal shapes of the deck are strongly coupled with the cable and the tower. By comparing the identification results from the operational modal system with those from field measurements, a good agreement between them is achieved, but some modal frequencies identified from the operational modal identification system （OMIS）, such as L1 and L2, obviously decrease compared with those from the field measurements.
基金supported by a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918 and 202103)。
文摘With the efficient and intelligent development of computer-based big data processing,applying machine learning methods to the processing and interpretation of logging data in the field of geophysical well logging has broad potential for improving production efficiency.Currently,the Jiyuan Oilfield in the Ordos Basin relies mainly on manual reprocessing and interpretation of old well logging data to identify different fluid types in low-contrast reservoirs,guiding subsequent production work.This study uses well logging data from the Chang 1 reservoir,partitioning the dataset based on individual wells for model training and testing.A deep learning model for intelligent reservoir fluid identification was constructed by incorporating the focal loss function.Comparative validations with five other models,including logistic regression(LR),naive Bayes(NB),gradient boosting decision trees(GBDT),random forest(RF),and support vector machine(SVM),show that this model demonstrates superior identification performance and significantly improves the accuracy of identifying oil-bearing fluids.Mutual information analysis reveals the model's differential dependency on various logging parameters for reservoir fluid identification.This model provides important references and a basis for conducting regional studies and revisiting old wells,demonstrating practical value that can be widely applied.
基金supported by the National Natural Science Foundation of China(Nos.30901956,30973965)2011’Program for Excellent Scientific and Technological Innovation Team of Jiangsu Higher Education and the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘AIM: To analyze the major constituents in Radix Scrophulariae(Scrophularia ningpoensis). METHOD: Radix Scrophulariae was analyzed by HPLC coupled with electrospray ionization quadrupole time-of-flight tandem mass spectrometry(ESI-Q-TOF MS/MS). Compounds were separated by HPLC using a C18 column and gradient elution of acetonitrile and 0.1 %(V/V) acetic acid-water. Negative ion mode was employed. RESULTS: A total of thirty-six compounds, including fourteen iridoid glycosides, nineteen phenylpropanoid glycosides, and three organic acids, were identified from Radix Scrophulariae based on the accurate mass measurement of precursor and product ions. Twenty-one of the constituents were identified by comparing their retention times(tR) and ESI-MS/MS data with those of reference standards and/or previous publications, while another fifteen compounds were tentatively identified or deduced according to their Q-TOF MS/MS data which afforded sufficient structural information. CONCLUSION: It is believed that this study is useful for the identification of constituents in Radix Scrophulariae, as well as related plants and complex prescriptions.
