This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functio...This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functionally graded(FG)materials such as conventional,axial,bi-directional,and tri-directional,and the material distribution models like power-law,exponential,trigonometric,polynomial functions,etc.It also discusses the application of advanced size-dependent theories like Eringen’s nonlocal elasticity,nonlocal strain gradient,modified couple stress,and consistent couple stress theories,which are essential to predict the behavior of structures at small scales.The review covers the mechanical analysis of MDFG nanostructures in nanobeams,nanopipes,nanoplates,and nanoshells and their dynamic and static responses under different loading conditions.The effect of multi-directional material gradation on stiffness,stability and vibration is discussed.Moreover,the review highlights the need for more advanced analytical,semi-analytical,and numerical methods to solve the complex vibration problems ofMDFG nanostructures.It is evident that the continued development of these methods is crucial for the design,optimization,and real-world application of MDFG nanostructures in advanced engineering fields like aerospace,biomedicine,and micro/nanoelectromechanical systems(MEMS/NEMS).This study is a reference for researchers and engineers working in the domain of MDFG nanostructures.展开更多
The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ens...The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ensuring the seamless execution of the forum’s translation necessitated exceptionally high standards for simultaneous interpreters.This paper,through the lens of the Translation as Adaptation and Selection theory within Eco-Translatology,conducts an analytical study of the live simultaneous interpretation at the First Multidisciplinary Forum on COVID-19.It examines the interpreters’adaptations and selections across multiple dimensions-namely,linguistic,cultural,and communicative-with the aim of elucidating the guiding role and recommendations that Eco-Translatology can offer to simultaneous interpretation.Furthermore,it seeks to provide insights that may enhance the quality of interpreters’oral translations.展开更多
The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forgin...The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forging(MDF)experiments were carried out.The microstructure and mechanical properties of different regions(the center,middle and edge regions)in the MDFed alloys were systematically investigated,and the effect of LPSO phase on them was discussed.The results show that the alloys in different regions undergo significant grain refinement during the MDF process.Inhomogeneous microstructures with different degrees of dynamic recrystallization(DRX)are formed,resulting in microhardness heterogeneity.The alloy with the LPSO phase has higher microstructure homogeneity,a higher degree of recrystallization,and better comprehensive mechanical properties than the alloy without the LPSO phase.The furnace-cooled alloy after 18 passes of MDF has the best comprehensive mechanical properties,with an ultimate compressive strength of 488 MPa,yield strength of 258 MPa,and fracture strain of 21.2%.DRX behavior is closely related to the LPSO phase and deformation temperature.The kinked LPSO phase can act as a potential nucleation site for DRX grains,while the fragmented LPSO phase promotes DRX nucleation through the particle-stimulated nucleation mechanism.展开更多
The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl...The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.展开更多
The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitori...The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.展开更多
The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate t...The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate that after MDF at a temperature of 350℃and strain rates of 0.1 and 0.01 s^(−1)(1-MDFed and 2-MDFed),the superplasticity of the alloy can be significantly improved.The elongations of the MDFed alloys exceed 400%under the strain rate of 6.06×10^(−4)s^(−1)and temperatures of 350,375,and 400℃,and reach the maximum values of 766%(1-MDFed)and 693%(2-MDFed)at 375℃.The grain boundary sliding of the MDFed alloy is sufficient,and the energy barrier of deformation decreases.Theβphase limits the grain growth and promotes dynamic recrystallization,maintaining the stability of the fine-grained structure during superplastic deformation.Several Y-rich phases nucleate in the high-strain region(i.e.,the final fracture region)at high temperatures,accelerating the fracture of the specimen.展开更多
Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for ...Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.展开更多
Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-i...Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.展开更多
The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research ac...The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research activities.However,despite the extensive research carried out,we believe that a geophysical contribution in terms of magnetic properties and their repercussions on the structure of the Agadem block allowing the improvement of existing knowledge is essential.The present study aims to study the structural characteristics of the Agadem block associated with magnetic anomalies.For this,after data shaping,several filtering techniques were applied to the aeromagnetic data to identify and map deep geological structures.The reduction to the pole map shows large negative wavelength anomalies in the southeast half of the block and short positive wavelength anomalies in the northwest part embedded in a large positive anomaly occupying the lower northern half of the block.The maps of the total horizontal derivative and tilt angle show lineaments globally distributed along the NW-SE direction in accordance with the structural style of the study area.The resulting map highlights numerous lineaments that may be associated with faults hidden by the sedimentary cover.The calculation of the Euler deconvolution allowed us to locate and estimate the depths of magnetic sources at variable depths of up to 4000 m.The compilation of the results obtained allowed us to locate zones of high and low intensities which correspond respectively to horsts and grabens as major structures of the Agadem block.展开更多
In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accurac...In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.展开更多
The discrete fracture system of a rock mass plays a crucial role in controlling the stability of rock slopes.To fully account for the geometric shape and distribution characteristics of jointed rock masses,terrestrial...The discrete fracture system of a rock mass plays a crucial role in controlling the stability of rock slopes.To fully account for the geometric shape and distribution characteristics of jointed rock masses,terrestrial laser scanning(TLS)was employed to acquire high-resolution point-cloud data,and a developed automatic discontinuity-identification technology was coupled to automatically interpret and characterize geometric information such as orientation,trace length,spacing,and set number of the discontinuities.The discrete element method(DEM)was applied to study the influence of the geometric morphology and distribution characteristics of discontinuities on slope stability by generating a discrete fracture network(DFN)with the same statistical characteristics as the actual discontinuities.Based on slope data from the Yebatan Hydropower Station,a simulation was conducted to verify the applicability of the automatic discontinuity identification technology and the discrete fracture network-discrete element method(DFN-DEM).Various geological parameters,including trace length,persistence,and density,were examined to investigate the morphological evolution and response characteristics of rock slope excavation under different joint combination conditions through simulation.The simulation results indicate that joint parameters affect slope stability,with density having the most significant impact.The impact of joint parameters on stability is relatively small within a reasonable range but becomes significant beyond a certain threshold,further validating that the accuracy of field geological surveys is critical for simulation.This study provides a scientific basis for the construction of complex rock slope models,engineering assessments,and disaster prevention and mitigation,which is of great value in both theory and engineering applications.展开更多
Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Researc...Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.展开更多
Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in t...Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.展开更多
An analytical method for analyzing the thermal vibration of multi-directional functionally graded porous rectangular plates in fluid media with novel porosity patterns is developed in this study.Mechanical properties ...An analytical method for analyzing the thermal vibration of multi-directional functionally graded porous rectangular plates in fluid media with novel porosity patterns is developed in this study.Mechanical properties of MFG porous plates change according to the length,width,and thickness directions for various materials and the porosity distribution which can be widely applied in many fields of engineering and defence technology.Especially,new porous rules that depend on spatial coordinates and grading indexes are proposed in the present work.Applying Hamilton's principle and the refined higher-order shear deformation plate theory,the governing equation of motion of an MFG porous rectangular plate in a fluid medium(the fluid-plate system)is obtained.The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to compute the extra mass.The GalerkinVlasov solution is used to solve and give natural frequencies of MFG porous plates with various boundary conditions in a fluid medium.The validity and reliability of the suggested method are confirmed by comparing numerical results of the present work with those from available works in the literature.The effects of different parameters on the thermal vibration response of MFG porous rectangular plates are studied in detail.These findings demonstrate that the behavior of the structure within a liquid medium differs significantly from that within a vacuum medium.Thereby,they offer appropriate operational approaches for the structure when employed in various mediums.展开更多
With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural network...With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.展开更多
For departmental legal norms concerning citizens’basic rights,when multiple interpretations are possible based on individual case circumstances,interpreters representing public authority need to apply the method of c...For departmental legal norms concerning citizens’basic rights,when multiple interpretations are possible based on individual case circumstances,interpreters representing public authority need to apply the method of constitutional interpretation to screen out the interpretation conclusions that do not violate the Constitution.This means selecting interpretations at the constitutional level that do not overly restrict citizens’basic rights and understanding the specific connotations of legal norms with the principle of“not infringing on citizens’basic rights.”The Constitution,as a framework order,does not require interpreters to choose the most constitutionally aligned interpretation among various constitutional interpretations.If a legal norm does not have a constitutional interpretation conclusion in an individual case circumstance,it indicates that the application of that norm in the case is unconstitutional,and the interpreter should avoid applying the legal norm in that case.Regarding judgment standards,interpreters should apply the principle of proportionality to determine whether each legal interpretation conclusion concerning basic rights-related legal norms complies with the Constitution.Out of respect for the legislature,the application of the sub-principles of pro-portionality should consider the boundaries of interpretative actions.展开更多
Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classi...Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.展开更多
The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. T...The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.展开更多
During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval w...During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.展开更多
Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provide...Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.展开更多
文摘This review explores multi-directional functionally graded(MDFG)nanostructures,focusing on their material characteristics,modeling approaches,and mechanical behavior.It starts by classifying different types of functionally graded(FG)materials such as conventional,axial,bi-directional,and tri-directional,and the material distribution models like power-law,exponential,trigonometric,polynomial functions,etc.It also discusses the application of advanced size-dependent theories like Eringen’s nonlocal elasticity,nonlocal strain gradient,modified couple stress,and consistent couple stress theories,which are essential to predict the behavior of structures at small scales.The review covers the mechanical analysis of MDFG nanostructures in nanobeams,nanopipes,nanoplates,and nanoshells and their dynamic and static responses under different loading conditions.The effect of multi-directional material gradation on stiffness,stability and vibration is discussed.Moreover,the review highlights the need for more advanced analytical,semi-analytical,and numerical methods to solve the complex vibration problems ofMDFG nanostructures.It is evident that the continued development of these methods is crucial for the design,optimization,and real-world application of MDFG nanostructures in advanced engineering fields like aerospace,biomedicine,and micro/nanoelectromechanical systems(MEMS/NEMS).This study is a reference for researchers and engineers working in the domain of MDFG nanostructures.
文摘The“First Multidisciplinary Forum on COVID-19,”as one of the pivotal medical forums organized by China during the initial outbreak of the pandemic,garnered significant attention from numerous countries worldwide.Ensuring the seamless execution of the forum’s translation necessitated exceptionally high standards for simultaneous interpreters.This paper,through the lens of the Translation as Adaptation and Selection theory within Eco-Translatology,conducts an analytical study of the live simultaneous interpretation at the First Multidisciplinary Forum on COVID-19.It examines the interpreters’adaptations and selections across multiple dimensions-namely,linguistic,cultural,and communicative-with the aim of elucidating the guiding role and recommendations that Eco-Translatology can offer to simultaneous interpretation.Furthermore,it seeks to provide insights that may enhance the quality of interpreters’oral translations.
基金the financial supports from the Key Research and Development Program of Hunan Province,China(No.2023GK2020)。
文摘The homogenized Mg−5.6Gd−0.8Zn(wt.%)alloys were treated with water cooling and furnace cooling to obtain specimens without and with the 14H long-period stacking ordered(LPSO)phase.Subsequently,multi-directional forging(MDF)experiments were carried out.The microstructure and mechanical properties of different regions(the center,middle and edge regions)in the MDFed alloys were systematically investigated,and the effect of LPSO phase on them was discussed.The results show that the alloys in different regions undergo significant grain refinement during the MDF process.Inhomogeneous microstructures with different degrees of dynamic recrystallization(DRX)are formed,resulting in microhardness heterogeneity.The alloy with the LPSO phase has higher microstructure homogeneity,a higher degree of recrystallization,and better comprehensive mechanical properties than the alloy without the LPSO phase.The furnace-cooled alloy after 18 passes of MDF has the best comprehensive mechanical properties,with an ultimate compressive strength of 488 MPa,yield strength of 258 MPa,and fracture strain of 21.2%.DRX behavior is closely related to the LPSO phase and deformation temperature.The kinked LPSO phase can act as a potential nucleation site for DRX grains,while the fragmented LPSO phase promotes DRX nucleation through the particle-stimulated nucleation mechanism.
基金supported by the National Key Research and Development Program of China[Grant No.2022YFA1405300(PZ)]the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300700)。
文摘The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.
文摘The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.
基金supported by the National Natural Science Foundation of China(No.52127808)。
文摘The superplasticity of the Mg−8.59Gd−3.85Y−1.14Zn−0.49Zr alloy was investigated before and after multi-directional forging(MDF)and the mechanisms affecting superplastic deformation were analyzed.The results indicate that after MDF at a temperature of 350℃and strain rates of 0.1 and 0.01 s^(−1)(1-MDFed and 2-MDFed),the superplasticity of the alloy can be significantly improved.The elongations of the MDFed alloys exceed 400%under the strain rate of 6.06×10^(−4)s^(−1)and temperatures of 350,375,and 400℃,and reach the maximum values of 766%(1-MDFed)and 693%(2-MDFed)at 375℃.The grain boundary sliding of the MDFed alloy is sufficient,and the energy barrier of deformation decreases.Theβphase limits the grain growth and promotes dynamic recrystallization,maintaining the stability of the fine-grained structure during superplastic deformation.Several Y-rich phases nucleate in the high-strain region(i.e.,the final fracture region)at high temperatures,accelerating the fracture of the specimen.
基金supported by the National Natural Science Foundation of China(Nos.42177173,U23A20651 and 42130719)and the Outstanding Youth Science Fund Project of Sichuan Provincial Natural Science Foundation(No.2025NSFJQ0003)。
文摘Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.
基金partially supported by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office,Singapore,under the Land and Liveability National Innovation Challenge(L2 NIC)Research Program(Grant No.L2NICCFP2-2015-1)by the National Research Foundation(NRF)of Singapore,under the Virtual Singapore program(Grant No.NRF2019VSG-GMS-001).
文摘Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.
文摘The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research activities.However,despite the extensive research carried out,we believe that a geophysical contribution in terms of magnetic properties and their repercussions on the structure of the Agadem block allowing the improvement of existing knowledge is essential.The present study aims to study the structural characteristics of the Agadem block associated with magnetic anomalies.For this,after data shaping,several filtering techniques were applied to the aeromagnetic data to identify and map deep geological structures.The reduction to the pole map shows large negative wavelength anomalies in the southeast half of the block and short positive wavelength anomalies in the northwest part embedded in a large positive anomaly occupying the lower northern half of the block.The maps of the total horizontal derivative and tilt angle show lineaments globally distributed along the NW-SE direction in accordance with the structural style of the study area.The resulting map highlights numerous lineaments that may be associated with faults hidden by the sedimentary cover.The calculation of the Euler deconvolution allowed us to locate and estimate the depths of magnetic sources at variable depths of up to 4000 m.The compilation of the results obtained allowed us to locate zones of high and low intensities which correspond respectively to horsts and grabens as major structures of the Agadem block.
基金supported in part by the National Natural Science Foundation(Nos.62271248,62401256)in part by the Natural Science Foundation of Ji-angsu Province(Nos.BK20230090,BK20241384)in part by the Key Laboratory of Land Satellite Remote Sens-ing Application,Ministry of Natural Resources of China(No.KLSMNR-K202303)。
文摘In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.
基金We acknowledge the funding support from the National Key R&D Program of China(Grant No.2022YFC3080100)the National Natural Science Foundation of China(Grant No.42102316)the opening fund of State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University(Grant No.SKHL2306).
文摘The discrete fracture system of a rock mass plays a crucial role in controlling the stability of rock slopes.To fully account for the geometric shape and distribution characteristics of jointed rock masses,terrestrial laser scanning(TLS)was employed to acquire high-resolution point-cloud data,and a developed automatic discontinuity-identification technology was coupled to automatically interpret and characterize geometric information such as orientation,trace length,spacing,and set number of the discontinuities.The discrete element method(DEM)was applied to study the influence of the geometric morphology and distribution characteristics of discontinuities on slope stability by generating a discrete fracture network(DFN)with the same statistical characteristics as the actual discontinuities.Based on slope data from the Yebatan Hydropower Station,a simulation was conducted to verify the applicability of the automatic discontinuity identification technology and the discrete fracture network-discrete element method(DFN-DEM).Various geological parameters,including trace length,persistence,and density,were examined to investigate the morphological evolution and response characteristics of rock slope excavation under different joint combination conditions through simulation.The simulation results indicate that joint parameters affect slope stability,with density having the most significant impact.The impact of joint parameters on stability is relatively small within a reasonable range but becomes significant beyond a certain threshold,further validating that the accuracy of field geological surveys is critical for simulation.This study provides a scientific basis for the construction of complex rock slope models,engineering assessments,and disaster prevention and mitigation,which is of great value in both theory and engineering applications.
文摘Computer analysis of electrocardiograms(ECGs)was introduced more than 50 years ago,with the aim to improve efficiency and clinical workflow.[1,2]However,inaccuracies have been documented in the literature.[3,4]Research indicates that emergency department(ED)clinician interruptions occur every 4-10 min,which is significantly more common than in other specialties.[5]This increases the cognitive load and error rates and impacts patient care and clinical effi ciency.[1,2,5]De-prioritization protocols have been introduced in certain centers in the United Kingdom(UK),removing the need for clinician ECG interpretation where ECGs have been interpreted as normal by the machine.
文摘Reservoir heterogeneities play a crucial role in governing reservoir performance and management.Traditionally,detailed and inter-well heterogeneity analyses are commonly performed by mapping seismic facies change in the seismic data,which is a time-intensive task.Many researchers have utilized a robust Grey-level co-occurrence matrix(GLCM)-based texture attributes to map reservoir heterogeneity.However,these attributes take seismic data as input and might not be sensitive to lateral lithology variation.To incorporate the lithology information,we have developed an innovative impedance-based texture approach using GLCM workflow by integrating 3D acoustic impedance volume(a rock propertybased attribute)obtained from a deep convolution network-based impedance inversion.Our proposed workflow is anticipated to be more sensitive toward mapping lateral changes than the conventional amplitude-based texture approach,wherein seismic data is used as input.To evaluate the improvement,we applied the proposed workflow to the full-stack 3D seismic data from the Poseidon field,NW-shelf,Australia.This study demonstrates that a better demarcation of reservoir gas sands with improved lateral continuity is achievable with the presented approach compared to the conventional approach.In addition,we assess the implication of multi-stage faulting on facies distribution for effective reservoir characterization.This study also suggests a well-bounded potential reservoir facies distribution along the parallel fault lines.Thus,the proposed approach provides an efficient strategy by integrating the impedance information with texture attributes to improve the inference on reservoir heterogeneity,which can serve as a promising tool for identifying potential reservoir zones for both production benefits and fluid storage.
文摘An analytical method for analyzing the thermal vibration of multi-directional functionally graded porous rectangular plates in fluid media with novel porosity patterns is developed in this study.Mechanical properties of MFG porous plates change according to the length,width,and thickness directions for various materials and the porosity distribution which can be widely applied in many fields of engineering and defence technology.Especially,new porous rules that depend on spatial coordinates and grading indexes are proposed in the present work.Applying Hamilton's principle and the refined higher-order shear deformation plate theory,the governing equation of motion of an MFG porous rectangular plate in a fluid medium(the fluid-plate system)is obtained.The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to compute the extra mass.The GalerkinVlasov solution is used to solve and give natural frequencies of MFG porous plates with various boundary conditions in a fluid medium.The validity and reliability of the suggested method are confirmed by comparing numerical results of the present work with those from available works in the literature.The effects of different parameters on the thermal vibration response of MFG porous rectangular plates are studied in detail.These findings demonstrate that the behavior of the structure within a liquid medium differs significantly from that within a vacuum medium.Thereby,they offer appropriate operational approaches for the structure when employed in various mediums.
基金funded by the Fundamental Research Project of CNPC Geophysical Key Lab(2022DQ0604-4)the Strategic Cooperation Technology Projects of China National Petroleum Corporation and China University of Petroleum-Beijing(ZLZX 202003)。
文摘With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.
文摘For departmental legal norms concerning citizens’basic rights,when multiple interpretations are possible based on individual case circumstances,interpreters representing public authority need to apply the method of constitutional interpretation to screen out the interpretation conclusions that do not violate the Constitution.This means selecting interpretations at the constitutional level that do not overly restrict citizens’basic rights and understanding the specific connotations of legal norms with the principle of“not infringing on citizens’basic rights.”The Constitution,as a framework order,does not require interpreters to choose the most constitutionally aligned interpretation among various constitutional interpretations.If a legal norm does not have a constitutional interpretation conclusion in an individual case circumstance,it indicates that the application of that norm in the case is unconstitutional,and the interpreter should avoid applying the legal norm in that case.Regarding judgment standards,interpreters should apply the principle of proportionality to determine whether each legal interpretation conclusion concerning basic rights-related legal norms complies with the Constitution.Out of respect for the legislature,the application of the sub-principles of pro-portionality should consider the boundaries of interpretative actions.
基金supported by the National Natural Science Foundation of China[grant number 42071354]supported by the Fundamental Research Funds for the Central Universities[grant number 2042022dx0001]supported by the Fundamental Research Funds for the Central Universities[grant number WUT:223108001].
文摘Artificial Intelligence(AI)Machine Learning(ML)technologies,particularly Deep Learning(DL),have demonstrated significant potential in the interpretation of Remote Sensing(RS)imagery,covering tasks such as scene classification,object detection,land-cover/land-use classification,change detection,and multi-view stereo reconstruction.Large-scale training samples are essential for ML/DL models to achieve optimal performance.However,the current organization of training samples is ad-hoc and vendor-specific,lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks.This article proposes a solution to address these challenges by designing and implementing LuoJiaSET,a large-scale training sample database system for intelligent interpretation of RS imagery.LuoJiaSET accommodates over five million training samples,providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration.It overcomes challenges related to label semantic categories,structural heterogeneity in label representation,and interoperable data access.
基金supported by the National Natural Science Foundation of China(82171836)the 1·3·5 project for disciplines of excellence,West China Hospital,Sichuan University(ZYJC20002).
文摘The application of whole genome sequencing is expanding in clinical diagnostics across various genetic disorders, and the significance of non-coding variants in penetrant diseases is increasingly being demonstrated. Therefore, it is urgent to improve the diagnostic yield by exploring the pathogenic mechanisms of variants in non-coding regions. However, the interpretation of non-coding variants remains a significant challenge, due to the complex functional regulatory mechanisms of non-coding regions and the current limitations of available databases and tools. Hence, we develop the non-coding variant annotation database (NCAD, http://www.ncawdb.net/), encompassing comprehensive insights into 665,679,194 variants, regulatory elements, and element interaction details. Integrating data from 96 sources, spanning both GRCh37 and GRCh38 versions, NCAD v1.0 provides vital information to support the genetic diagnosis of non-coding variants, including allele frequencies of 12 diverse populations, with a particular focus on the population frequency information for 230,235,698 variants in 20,964 Chinese individuals. Moreover, it offers prediction scores for variant functionality, five categories of regulatory elements, and four types of non-coding RNAs. With its rich data and comprehensive coverage, NCAD serves as a valuable platform, empowering researchers and clinicians with profound insights into non-coding regulatory mechanisms while facilitating the interpretation of non-coding variants.
文摘During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.
基金financially supported by China Postdoctoral Science Foundation(Grant No.2023M730365)Natural Science Foundation of Hubei Province of China(Grant No.2023AFB232)。
文摘Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.
