Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms d...Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.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
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
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
Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at diff...Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.展开更多
To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is deve...To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.展开更多
Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-graine...Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.展开更多
As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and...As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.展开更多
Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimo...Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.展开更多
Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on ...Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.展开更多
In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant ...In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.展开更多
Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.Howev...Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.展开更多
Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from b...Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.展开更多
It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in...It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.展开更多
The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor...The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.展开更多
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has...In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.展开更多
Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,a...Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.展开更多
Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoo...Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.展开更多
In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hi...In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.展开更多
In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology bas...In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.展开更多
基金supported by the National Natural Science Foundation of China(32171777)the Natural Science Foundation of Heilongjiang for Distinguished Young Scientists(JQ2023F002)the Fundamental Research Funds for Central Universities(2572023CT16).
文摘Climate change is a global phenomenon that has profound impacts on ecological dynamics and biodiversity,shaping the interactions between species and their environment.To gain a deeper understanding of the mechanisms driving climate change,phenological monitoring is essential.Traditional methods of defining phenological phases often rely on fixed thresholds.However,with the development of technology,deep learning-based classification models are now able to more accurately delineate phenological phases from images,enabling phenological monitoring.Despite the significant advancements these models have made in phenological monitoring,they still face challenges in fully capturing the complexity of biotic-environmental interactions,which can limit the fine-grained accuracy of phenological phase identification.To address this,we propose a novel deep learning model,RESformer,designed to monitor tree phenology at a fine-grained level using PhenoCam images.RESformer features a lightweight structure,making it suitable for deployment in resource-constrained environments.It incorporates a dual-branch routing mechanism that considers both global and local information,thereby improving the accuracy of phenological monitoring.To validate the effectiveness of RESformer,we conducted a case study involving 82,118 images taken over two years from four different locations in Wisconsin,focusing on the phenology of Acer.The images were classified into seven distinct phenological stages,with RESformer achieving an overall monitoring accuracy of 96.02%.Furthermore,we compared RESformer with a phenological monitoring approach based on the Green Chromatic Coordinate(GCC)index and ten popular classification models.The results showed that RESformer excelled in fine-grained monitoring,effectively capturing and identifying changes in phenological stages.This finding not only provides strong support for monitoring the phenology of Acer species but also offers valuable insights for understanding ecological trends and developing more effective ecosystem conservation and management strategies.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported 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 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(42030102,42371321).
文摘Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications.However,existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples.This approach fails to adequately support the fixed-resolution image interpretation requirements in real-world scenarios.To address this limitation,we introduce the million-scale fine-grained geospatial scene classification dataset(MEET),which contains over 1.03 million zoom-free remote sensing scene samples,manually annotated into 80 fine-grained categories.In MEET,each scene sample follows a scene-in-scene layout,where the central scene serves as the reference,and auxiliary scenes provide crucial spatial context for fine-grained classification.Moreover,to tackle the emerging challenge of scene-in-scene classification,we present the context-aware transformer(CAT),a model specifically designed for this task,which adaptively fuses spatial context to accurately classify the scene samples.CAT adaptively fuses spatial context to accurately classify the scene samples by learning attentional features that capture the relationships between the center and auxiliary scenes.Based on MEET,we establish a comprehensive benchmark for fine-grained geospatial scene classification,evaluating CAT against 11 competitive baselines.The results demonstrate that CAT significantly outperforms these baselines,achieving a 1.88%higher balanced accuracy(BA)with the Swin-Large backbone,and a notable 7.87%improvement with the Swin-Huge backbone.Further experiments validate the effectiveness of each module in CAT and show the practical applicability of CAT in the urban functional zone mapping.The source code and dataset will be publicly available at https://jerrywyn.github.io/project/MEET.html.
基金The National Natural Science Foundation of China(No.52338011,52378291)Young Elite Scientists Sponsorship Program by CAST(No.2022-2024QNRC0101).
文摘To overcome the limitations of low efficiency and reliance on manual processes in the measurement of geometric parameters for bridge prefabricated components,a method based on deep learning and computer vision is developed to identify the geometric parameters.The study utilizes a common precast element for highway bridges as the research subject.First,edge feature points of the bridge component section are extracted from images of the precast component cross-sections by combining the Canny operator with mathematical morphology.Subsequently,a deep learning model is developed to identify the geometric parameters of the precast components using the extracted edge coordinates from the images as input and the predefined control parameters of the bridge section as output.A dataset is generated by varying the control parameters and noise levels for model training.Finally,field measurements are conducted to validate the accuracy of the developed method.The results indicate that the developed method effectively identifies the geometric parameters of bridge precast components,with an error rate maintained within 5%.
基金supported by National Natural Science Foundation of China(Grant Nos.42072126,42372139)the Natural Science Foundation of Sichuan Province(Grant Nos.2022NSFSC0990).
文摘Fine-grained sediments are widely distributed and constitute the most abundant component in sedi-mentary systems,thus the research on their genesis and distribution is of great significance.In recent years,fine-grained sediment gravity-flows(FGSGF)have been recognized as an important transportation and depositional mechanism for accumulating thick successions of fine-grained sediments.Through a comprehensive review and synthesis of global research on FGSGF deposition,the characteristics,depositional mechanisms,and distribution patterns of fine-grained sediment gravity-flow deposits(FGSGFD)are discussed,and future research prospects are clarified.In addition to the traditionally recognized low-density turbidity current and muddy debris flow,wave-enhanced gravity flow,low-density muddy hyperpycnal flow,and hypopycnal plumes can all form widely distributed FGSGFD.At the same time,the evolution of FGSGF during transportation can result in transitional and hybrid gravity-flow deposits.The combination of multiple triggering mechanisms promotes the widespread develop-ment of FGSGFD,without temporal and spatial limitations.Different types and concentrations of clay minerals,organic matters,and organo-clay complexes are the keys to controlling the flow transformation of FGSGF from low-concentration turbidity currents to high-concentration muddy debris flows.Further study is needed on the interaction mechanism of FGSGF caused by different initiations,the evolution of FGSGF with the effect of organic-inorganic synergy,and the controlling factors of the distribution pat-terns of FGSGFD.The study of FGSGFD can shed some new light on the formation of widely developed thin-bedded siltstones within shales.At the same time,these insights may broaden the exploration scope of shale oil and gas,which have important geological significances for unconventional shale oil and gas.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of Sciences(CAS)Natural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360)。
文摘As artificial intelligence(AI)technology has continued to develop,its efficient data processing and pattern recognition capabilities have significantly improved the precision and speed of decision-making processes,and it has been widely applied across various fields.In the field of astronomy,AI techniques have demonstrated unique advantages,particularly in the identification of pulsars and their candidates.AI is able to address the challenges of pulsar celestial body identification and classification because of its accuracy and efficiency.This paper systematically surveys commonly used AI models for pulsar candidate identification,analyzing and discussing the typical applications of machine learning,artificial neural networks,convolutional neural networks,and generative adversarial networks in candidate identification.Furthermore,it explores how th.e introduction of AI techniques not only enhances the efficiency and accuracy of pulsar identification but also provides new perspectives and tools for pulsar survey data processing,thus playing a significant role in advancing pulsar research and the field of astronomy.
基金supported by the Science and Technology Project of Henan Province(No.222102210081).
文摘Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal features.Given this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for JMASA.First,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect terms.Then,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature learning.Thereby,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and enhanced.Experiments on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.
基金support from the National Natural Science Foundation of China(Grant Nos:52379103 and 52279103)the Natural Science Foundation of Shandong Province(Grant No:ZR2023YQ049).
文摘Bayesian-optimized lithology identification has important basic geological research significance and engineering application value,and this paper proposes a Bayesian-optimized lithology identification method based on machine learning of rock visible and near-infrared spectral data.First,the rock spectral data are preprocessed using Savitzky-Golay(SG)smoothing to remove the noise of the spectral data;then,the preprocessed rock spectral data are downscaled using Principal Component Analysis(PCA)to reduce the redundancy of the data,optimize the effective discriminative information,and obtain the rock spectral features;finally,a Bayesian-optimized lithology identification model is established based on rock spectral features,optimize the model hyperparameters using Bayesian optimization(BO)algorithm to avoid the combination of hyperparameters falling into the local optimal solution,and output the predicted type of rock,so as to realize the Bayesian-optimized lithology identification.In addition,this paper conducts comparative analysis on models based on Artificial Neural Network(ANN)/Random Forest(RF),dimensionality reduction/full band,and optimization algorithms.It uses the confusion matrix,accuracy,Precison(P),Recall(R)and F_(1)values(F_(1))as the evaluation indexes of model accuracy.The results indicate that the lithology identification model optimized by the BO-ANN after dimensionality reduction achieves an accuracy of up to 99.80%,up to 99.79%and up to 99.79%.Compared with the BO-RF model,it has higher identification accuracy and better stability for each type of rock identification.The experiments and reliability analysis show that the Bayesian-optimized lithology identification method proposed in this paper has good robustness and generalization performance,which is of great significance for realizing fast,accurate and Bayesian-optimized lithology identification in tunnel site.
基金supported by the National Defense Fundamental Research Project(No.JCKY2022404C005)the Nuclear Energy Development Project(No.23ZG6106)+1 种基金the Sichuan Scientific and Technological Achievements Transfer and Transformation Demonstration Project(No.2023ZHCG0026)the Mianyang Applied Technology Research and Development Project(No.2021ZYZF1005)。
文摘In scenarios such as vehicle radiation monitoring and unmanned aerial vehicle radiation detection,rapid measurements using a NaI(Tl)detector often result in low photon counts,weak characteristic peaks,and significant statistical fluctuations.These issues can lead to potential failures in peak-searching-based identification methods.To address the low precision associated with short-duration measurements of radionuclides,this paper proposes an identification algorithm that leverages heterogeneous spectral transfer to develop a low-count energy spectral identification model.Comparative experiments demonstrated that transferring samples from 26 classes of simulated heterogeneous gamma spectra aids in creating a reliable model for measured gamma spectra.With only 10%of target domain samples used for training,the accuracy on real low-count spectral samples was 95.56%.This performance shows a significant improvement over widely employed full-spectrum analysis methods trained on target domain samples.The proposed method also exhibits strong generalization capabilities,effectively mitigating overfitting issues in low-count energy spectral classification under short-duration measurements.
基金Supported by the National Natural Science Foundation of China(Nos.52222904 and 52309117)China Postdoctoral Science Foundation(Nos.2022TQ0168 and 2023M731895).
文摘Ocean energy has progressively gained considerable interest due to its sufficient potential to meet the world’s energy demand,and the blade is the core component in electricity generation from the ocean current.However,the widened hydraulic excitation frequency may satisfy the blade resonance due to the time variation in the velocity and angle of attack of the ocean current,even resulting in blade fatigue and destructively interfering with grid stability.A key parameter that determines the resonance amplitude of the blade is the hydrodynamic damping ratio(HDR).However,HDR is difficult to obtain due to the complex fluid-structure interaction(FSI).Therefore,a literature review was conducted on the hydrodynamic damping characteristics of blade-like structures.The experimental and simulation methods used to identify and obtain the HDR quantitatively were described,placing emphasis on the experimental processes and simulation setups.Moreover,the accuracy and efficiency of different simulation methods were compared,and the modal work approach was recommended.The effects of key typical parameters,including flow velocity,angle of attack,gap,rotational speed,and cavitation,on the HDR were then summarized,and the suggestions on operating conditions were presented from the perspective of increasing the HDR.Subsequently,considering multiple flow parameters,several theoretical derivations and semi-empirical prediction formulas for HDR were introduced,and the accuracy and application were discussed.Based on the shortcomings of the existing research,the direction of future research was finally determined.The current work offers a clear understanding of the HDR of blade-like structures,which could improve the evaluation accuracy of flow-induced vibration in the design stage.
基金support by the National Natural Science Foundation of China(Grant No.52402520)。
文摘Actuator faults can be critical in turbofan engines as they can lead to stall,surge,loss of thrust and failure of speed control.Thus,fault diagnosis of gas turbine actuators has attracted considerable attention,from both academia and industry.However,the extensive literature that exists on this topic does not address identifying the severity of actuator faults and focuses mainly on actuator fault detection and isolation.In addition,previous studies of actuator fault identification have not dealt with multiple concurrent faults in real time,especially when these are accompanied by sudden failures under dynamic conditions.This study develops component-level models for fault identification in four typical actuators used in high-bypass ratio turbofan engines under both dynamic and steady-state conditions and these are then integrated with the engine performance model developed by the authors.The research results reported here present a novel method of quantifying actuator faults using dynamic effect compensation.The maximum error for each actuator is less than0.06%and 0.07%,with average computational time of less than 0.0058 s and 0.0086 s for steady-state and transient cases,respectively.These results confirm that the proposed method can accurately and efficiently identify concurrent actuator fault for an engine operating under either transient or steady-state conditions,even in the case of a sudden malfunction.The research results emonstrate the potential benefit to emergency response capabilities by introducing this method of monitoring the health of aero engines.
基金supported by the National Natural Science Foundation of China(42122017,41821002)the Independent Innovation Research Program of China University of Petroleum(East China)(21CX06001A).
文摘It is of great significance to accurately and rapidly identify shale lithofacies in relation to the evaluation and prediction of sweet spots for shale oil and gas reservoirs.To address the problem of low resolution in logging curves,this study establishes a grayscale-phase model based on high-resolution grayscale curves using clustering analysis algorithms for shale lithofacies identification,working with the Shahejie For-mation,Bohai Bay Basin,China.The grayscale phase is defined as the sum of absolute grayscale and relative amplitude as well as their features.The absolute grayscale is the absolute magnitude of the gray values and is utilized for evaluating the material composition(mineral composition+total organic carbon)of shale,while the relative amplitude is the difference between adjacent gray values and is used to identify the shale structure type.The research results show that the grayscale phase model can identify shale lithofacies well,and the accuracy and applicability of this model were verified by the fitting relationship between absolute grayscale and shale mineral composition,as well as corresponding re-lationships between relative amplitudes and laminae development in shales.Four lithofacies are iden-tified in the target layer of the study area:massive mixed shale,laminated mixed shale,massive calcareous shale and laminated calcareous shale.This method can not only effectively characterize the material composition of shale,but also numerically characterize the development degree of shale laminae,and solve the problem that difficult to identify millimeter-scale laminae based on logging curves,which can provide technical support for shale lithofacies identification,sweet spot evaluation and prediction of complex continental lacustrine basins.
文摘The precise identification of quartz minerals is crucial in mineralogy and geology due to their widespread occurrence and industrial significance.Traditional methods of quartz identification in thin sections are labor-intensive and require significant expertise,often complicated by the coexistence of other minerals.This study presents a novel approach leveraging deep learning techniques combined with hyperspectral imaging to automate the identification process of quartz minerals.The utilizied four advanced deep learning models—PSPNet,U-Net,FPN,and LinkNet—has significant advancements in efficiency and accuracy.Among these models,PSPNet exhibited superior performance,achieving the highest intersection over union(IoU)scores and demonstrating exceptional reliability in segmenting quartz minerals,even in complex scenarios.The study involved a comprehensive dataset of 120 thin sections,encompassing 2470 hyperspectral images prepared from 20 rock samples.Expert-reviewed masks were used for model training,ensuring robust segmentation results.This automated approach not only expedites the recognition process but also enhances reliability,providing a valuable tool for geologists and advancing the field of mineralogical analysis.
文摘In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.
基金Supported by the CNPC Major Science and Technology Project(2021DJ1806).
文摘Based on recent advancements in shale oil exploration within the Ordos Basin,this study presents a comprehensive investigation of the paleoenvironment,lithofacies assemblages and distribution,depositional mechanisms,and reservoir characteristics of shale oil of fine-grained sediment deposition in continental freshwater lacustrine basins,with a focus on the Chang 7_(3) sub-member of Triassic Yanchang Formation.The research integrates a variety of exploration data,including field outcrops,drilling,logging,core samples,geochemical analyses,and flume simulation.The study indicates that:(1)The paleoenvironment of the Chang 7_(3) deposition is characterized by a warm and humid climate,frequent monsoon events,and a large water depth of freshwater lacustrine basin.The paleogeomorphology exhibits an asymmetrical pattern,with steep slopes in the southwest and gentle slopes in the northeast,which can be subdivided into microgeomorphological units,including depressions and ridges in lakebed,as well as ancient channels.(2)The Chang 7_(3) sub-member is characterized by a diverse array of fine-grained sediments,including very fine sandstone,siltstone,mudstone and tuff.These sediments are primarily distributed in thin interbedded and laminated arrangements vertically.The overall grain size of the sandstone predominantly falls below 62.5μm,with individual layer thicknesses of 0.05–0.64 m.The deposits contain intact plant fragments and display various sedimentary structure,such as wavy bedding,inverse-to-normal grading sequence,and climbing ripple bedding,which indicating a depositional origin associated with density flows.(3)Flume simulation experiments have successfully replicated the transport processes and sedimentary characteristics associated with density flows.The initial phase is characterized by a density-velocity differential,resulting in a thicker,coarser sediment layer at the flow front,while the upper layers are thinner and finer in grain size.During the mid-phase,sliding water effects cause the fluid front to rise and facilitate rapid forward transport.This process generates multiple“new fronts”,enabling the long-distance transport of fine-grained sandstones,such as siltstone and argillaceous siltstone,into the center of the lake basin.(4)A sedimentary model primarily controlled by hyperpynal flows was established for the southwestern part of the basin,highlighting that the frequent occurrence of flood events and the steep slope topography in this area are primary controlling factors for the development of hyperpynal flows.(5)Sandstone and mudstone in the Chang 7_(3) sub-member exhibit micro-and nano-scale pore-throat systems,shale oil is present in various lithologies,while the content of movable oil varies considerably,with sandstone exhibiting the highest content of movable oil.(6)The fine-grained sediment complexes formed by multiple episodes of sandstones and mudstones associated with density flow in the Chang 7_(3) formation exhibit characteristics of“overall oil-bearing with differential storage capacity”.The combination of mudstone with low total organic carbon content(TOC)and siltstone is identified as the most favorable exploration target at present.
基金supported by the National Key Research and Development Program of China(No.2022YFB4300902)。
文摘Radio Frequency Fingerprint Identification(RFFI)technology provides a means of identifying spurious signals.This technology has been widely used in solving Automatic Dependent Surveillance–Broadcast(ADS-B)signal spoofing problems.However,the effects of circuit changes over time often lead to a decline in identification accuracy within open-time set.This paper proposes an ADS-B transmitter identification method to solve the degradation of identification accuracy.First,a real-time data processing system is established to receive and store ADS-B signals to meet the conditions for open-time set.The system possesses the following functionalities:data collection,data parsing,feature extraction,and identity recognition.Subsequently,a two-dimensional TimeFrequency Feature Diagram(TFFD)is proposed as a signal pre-processing method.The TFFD is constructed from the received ADS-B signal and the reconstructed signal for input to the recognition model.Finally,incorporating a frequency offset layer into the Swin Transformer architecture,a novel recognition network framework is proposed.This integration can enhance the network recognition accuracy and robustness by tailoring to the specific characteristics of ADSB signals.Experimental results indicate that the proposed recognition architecture achieves recognition accuracy of 95.86%in closed-time set and 84.33%in open-time set,surpassing other algorithms.
基金Supported by the National Natural Science Foundation of China(61601176)。
文摘In this paper,we propose hierarchical attention dual network(DNet)for fine-grained image classification.The DNet can randomly select pairs of inputs from the dataset and compare the differences between them through hierarchical attention feature learning,which are used simultaneously to remove noise and retain salient features.In the loss function,it considers the losses of difference in paired images according to the intra-variance and inter-variance.In addition,we also collect the disaster scene dataset from remote sensing images and apply the proposed method to disaster scene classification,which contains complex scenes and multiple types of disasters.Compared to other methods,experimental results show that the DNet with hierarchical attention is robust to different datasets and performs better.
基金financially supported by National Key R&D Program(2021YFF0701905)。
文摘In order to save manpower and time costs,and to achieve simultaneous detection of multiple animal-derived components in meat and meat products,this study used multiple nucleotide polymorphism(MNP)marker technology based on the principle of high-throughput sequencing,and established a multi-locus 10 animalderived components identification method of cattle,goat,sheep,donkey,horse,chicken,duck,goose,pigeon,quail in meat and meat products.The specific loci of each species could be detected and the species could be accurately identified,including 5 loci for cattle and duck,3 loci for sheep,9 loci for chicken and horse,10 loci for goose and pigeon,6 loci for quail and 1 locus for donkey and goat,and an adulteration model was established to simulate commercially available samples.The results showed that the method established in this study had high throughput,good repeatability and accuracy,and was able to identify 10 animalderived components simultaneously with 100%repeatability accuracy.The detection limit was 0.1%(m/m)in simulated samples of chicken,duck and horse.Using the method established in this study to test commercially available samples,4 samples from 14 commercially available samples were detected to be inconsistent with the labels,of which 2 did not contain the target ingredient and 2 were adulterated with small amounts of other ingredients.
