We are far from knowing all species living on the planet.Understanding biodiversity is demanding and requires time and expertise.Most groups are understudied given problems of identifying and delimiting species.DNA ba...We are far from knowing all species living on the planet.Understanding biodiversity is demanding and requires time and expertise.Most groups are understudied given problems of identifying and delimiting species.DNA barcoding emerged to overcome some of the difficulties in identi-fying species.Its limitations derive from incomplete taxonomic knowledge and the lack of comprehensive DNA barcode libraries for so many taxonomic groups.Here,we evaluate how useful barcoding is for identifying arthropods from highly diverse leaf litter communities in the south-ern Appalachian Mountains(USA).We used 3 reference databases and several automated classification methods on a data set including several arthropod groups.Acari,Araneae,Collembola,Coleoptera,Diptera,and Hymenoptera were well represented,showing different performances across methods and databases.Spiders performed the best,with correct identification rates to species and genus levels of~50%across data-bases.Springtails performed poorly,no barcodes were identified to species or genus.Other groups showed poor to mediocre performance,from around 3%(mites)to 20%(beetles)correctly identified barcodes to species,but also with some false identifications.In general,BOLD-based identification offered the best identification results but,in all cases except spiders,performance is poor,with less than a fifth of specimens correctly identified to genus or species.Our results indicate that the soil arthropod fauna is still insufficiently documented,with many species unrepresented in DNA barcode libraries.More effort toward integrative taxonomic characterization is needed to complete our reference libraries before we can rely on DNAbarcoding as a universally applicable identification method.展开更多
Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vau...Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vaults for incorrect master password attempts, honey vaults force attackers with the vault’s storage fle to engage in online verifcation to distinguish the real vaults, thus thwarting ofine guessing attacks. However, sophisticated attackers can acquire additional information, such as personally identifable information (PII) and partial passwords contained within the vault from various data breaches. Since many users tend to incorporate PII in their passwords, attackers may utilize PII to distinguish the real vault. Furthermore, if attackers may learn partial passwords included in the real vault, it can exclude numerous decoy vaults without the need for online verifcation. Indeed, both leakages pose serious threats to the security of the existing honey vault schemes. In this paper, we explore two attack vari-antsof the inspired attack scenario, where the attacker gains access to the vault’s storage fle along with acquiring PII and partial passwords contained within the real vault, and design a new honey vault scheme. For security assurance, we prove that our scheme is secure against one of the aforementioned attack variants. Moreover, our experimental fndings suggest enhancements in security against the other attack. In particular, to evaluate the security in multiple leakage cases where both the vault’s storage fle and PII are leaked, we propose several new practical attacks (called PII-based attacks), building upon the existing practical attacks in the traditional single leakage case where only the vault’s storage fle is compromised. Our experimental results demonstrate that certain PII-based attacks achieve a 63–70% accuracy in distinguishing the real vault from decoys in the best-performing honey vault scheme (Cheng et al. in Incrementally updateable honey password vaults, pp 857–874, 2021). Our scheme reduces these metrics to 41–50%, closely approaching the ideal value of 50%.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
Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as ...Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as a primary source for developing novel antiviral drugs,making the rapid discovery and evaluation of marine antiviral agents particularly crucial.Thus,future research should place greater emphasis on the identification of novel antiviral targets through the combination of artificial intelligence(AI)and structural pharmacology,as well as expanding the marine resource and target databases.展开更多
Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent bioc...Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent biocompatibility in vivo and have significant advantages in the management of ischemic stroke.However,the uncertain distribution and rapid clearance of extracellular vesicles impede their delivery efficiency.By utilizing membrane decoration or by encapsulating therapeutic cargo within extracellular vesicles,their delivery efficacy may be greatly improved.Furthermore,previous studies have indicated that microvesicles,a subset of large-sized extracellular vesicles,can transport mitochondria to neighboring cells,thereby aiding in the restoration of mitochondrial function post-ischemic stroke.Small extracellular vesicles have also demonstrated the capability to transfer mitochondrial components,such as proteins or deoxyribonucleic acid,or their sub-components,for extracellular vesicle-based ischemic stroke therapy.In this review,we undertake a comparative analysis of the isolation techniques employed for extracellular vesicles and present an overview of the current dominant extracellular vesicle modification methodologies.Given the complex facets of treating ischemic stroke,we also delineate various extracellular vesicle modification approaches which are suited to different facets of the treatment process.Moreover,given the burgeoning interest in mitochondrial delivery,we delved into the feasibility and existing research findings on the transportation of mitochondrial fractions or intact mitochondria through small extracellular vesicles and microvesicles to offer a fresh perspective on ischemic stroke therapy.展开更多
To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.Howeve...To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.展开更多
Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the ...Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the present study,an innovative natural product virtual screening-interaction-phenotype(NP-VIP)strategy that integrates virtual screening,chemical proteomics,and metabolomics to identify and validate the bioactive targets of NPs.This approach reduces false positive results and enhances the efficiency of target identification.Salvia miltiorrhiza(SM),a herb with recognized therapeutic potential against ischemic stroke(IS),was used to illustrate the workflow.Utilizing virtual screening,chemical proteomics,and metabolomics,potential therapeutic targets for SM in the IS treatment were identified,totaling 29,100,and 78,respectively.Further analysis via the NP-VIP strategy highlighted five high-confidence targets,including poly[ADP-ribose]polymerase 1(PARP1),signal transducer and activator of transcription 3(STAT3),amyloid precursor protein(APP),glutamate-ammonia ligase(GLUL),and glutamate decarboxylase 67(GAD67).These targets were subsequently validated and found to play critical roles in the neuroprotective effects of SM.The study not only underscores the importance of SM in treating IS but also sets a precedent for NP research,proposing a comprehensive approach that could be adapted for broader pharmacological explorations.展开更多
Fifty agricultural soil samples collected from Fuzhou,southeast China,were first investigated for the occurrence,distribution,and potential risks of twelve organophosphate esters(OPEs).The total concentration of OPEs(...Fifty agricultural soil samples collected from Fuzhou,southeast China,were first investigated for the occurrence,distribution,and potential risks of twelve organophosphate esters(OPEs).The total concentration of OPEs(ΣOPEs)in soil ranged from 1.33 to 96.5 ng/g dry weight(dw),with an average value of 17.1 ng/g dw.Especially,halogenated-OPEs were the predominant group with amean level of 9.75 ng/g dw,and tris(1-chloro-2-propyl)phosphate(TCIPP)was the most abundant OPEs,accounting for 51.1%ofΣOPEs.The concentrations of TCIPP andΣOPEs were found to be significantly higher(P<0.05)in soils of urban areas than those in suburban areas.In addition,the use of agricultural plastic films and total organic carbon had a positive effect on the occurrence of OPE in this study.The positive matrix factorization model suggested complex sources of OPEs in agricultural soils from Fuzhou.The ecological risk assessment demonstrated that tricresyl phosphate presented a medium risk to land-based organisms(0.1≤risk quotient<1.0).Nevertheless,the carcinogenic and noncarcinogenic risks for human exposure to OPEs through soil ingestion and dermal absorption were negligible.These findings would facilitate further investigations into the pollution management and risk control of OPEs.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
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
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
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
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
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
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.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 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
Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)informati...Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.展开更多
基金funded by the U.S.National Science Foundation(Award DEB-1916263 to M.S.C.)the Clemson University Experiment Station(SC-1700596 to M.S.C.).
文摘We are far from knowing all species living on the planet.Understanding biodiversity is demanding and requires time and expertise.Most groups are understudied given problems of identifying and delimiting species.DNA barcoding emerged to overcome some of the difficulties in identi-fying species.Its limitations derive from incomplete taxonomic knowledge and the lack of comprehensive DNA barcode libraries for so many taxonomic groups.Here,we evaluate how useful barcoding is for identifying arthropods from highly diverse leaf litter communities in the south-ern Appalachian Mountains(USA).We used 3 reference databases and several automated classification methods on a data set including several arthropod groups.Acari,Araneae,Collembola,Coleoptera,Diptera,and Hymenoptera were well represented,showing different performances across methods and databases.Spiders performed the best,with correct identification rates to species and genus levels of~50%across data-bases.Springtails performed poorly,no barcodes were identified to species or genus.Other groups showed poor to mediocre performance,from around 3%(mites)to 20%(beetles)correctly identified barcodes to species,but also with some false identifications.In general,BOLD-based identification offered the best identification results but,in all cases except spiders,performance is poor,with less than a fifth of specimens correctly identified to genus or species.Our results indicate that the soil arthropod fauna is still insufficiently documented,with many species unrepresented in DNA barcode libraries.More effort toward integrative taxonomic characterization is needed to complete our reference libraries before we can rely on DNAbarcoding as a universally applicable identification method.
基金supported by the National Natural Science Foundation of China(Nos.62172404,62172411,61972094,62202458).
文摘Honey vaults are useful tools for password management. A vault usually contains usernames for each domain, and the corresponding passwords, encrypted with a master password chosen by the owner. By generating decoy vaults for incorrect master password attempts, honey vaults force attackers with the vault’s storage fle to engage in online verifcation to distinguish the real vaults, thus thwarting ofine guessing attacks. However, sophisticated attackers can acquire additional information, such as personally identifable information (PII) and partial passwords contained within the vault from various data breaches. Since many users tend to incorporate PII in their passwords, attackers may utilize PII to distinguish the real vault. Furthermore, if attackers may learn partial passwords included in the real vault, it can exclude numerous decoy vaults without the need for online verifcation. Indeed, both leakages pose serious threats to the security of the existing honey vault schemes. In this paper, we explore two attack vari-antsof the inspired attack scenario, where the attacker gains access to the vault’s storage fle along with acquiring PII and partial passwords contained within the real vault, and design a new honey vault scheme. For security assurance, we prove that our scheme is secure against one of the aforementioned attack variants. Moreover, our experimental fndings suggest enhancements in security against the other attack. In particular, to evaluate the security in multiple leakage cases where both the vault’s storage fle and PII are leaked, we propose several new practical attacks (called PII-based attacks), building upon the existing practical attacks in the traditional single leakage case where only the vault’s storage fle is compromised. Our experimental results demonstrate that certain PII-based attacks achieve a 63–70% accuracy in distinguishing the real vault from decoys in the best-performing honey vault scheme (Cheng et al. in Incrementally updateable honey password vaults, pp 857–874, 2021). Our scheme reduces these metrics to 41–50%, closely approaching the ideal value of 50%.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
文摘Owing to the emergence of drug resistance and high morbidity,the need for novel antiviral drugs with novel targets is highly sought after.Marine-derived compounds mostly possess potent antiviral activity and serve as a primary source for developing novel antiviral drugs,making the rapid discovery and evaluation of marine antiviral agents particularly crucial.Thus,future research should place greater emphasis on the identification of novel antiviral targets through the combination of artificial intelligence(AI)and structural pharmacology,as well as expanding the marine resource and target databases.
基金supported by the grants from University of Macao,China,Nos.MYRG2022-00221-ICMS(to YZ)and MYRG-CRG2022-00011-ICMS(to RW)the Natural Science Foundation of Guangdong Province,No.2023A1515010034(to YZ)。
文摘Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent biocompatibility in vivo and have significant advantages in the management of ischemic stroke.However,the uncertain distribution and rapid clearance of extracellular vesicles impede their delivery efficiency.By utilizing membrane decoration or by encapsulating therapeutic cargo within extracellular vesicles,their delivery efficacy may be greatly improved.Furthermore,previous studies have indicated that microvesicles,a subset of large-sized extracellular vesicles,can transport mitochondria to neighboring cells,thereby aiding in the restoration of mitochondrial function post-ischemic stroke.Small extracellular vesicles have also demonstrated the capability to transfer mitochondrial components,such as proteins or deoxyribonucleic acid,or their sub-components,for extracellular vesicle-based ischemic stroke therapy.In this review,we undertake a comparative analysis of the isolation techniques employed for extracellular vesicles and present an overview of the current dominant extracellular vesicle modification methodologies.Given the complex facets of treating ischemic stroke,we also delineate various extracellular vesicle modification approaches which are suited to different facets of the treatment process.Moreover,given the burgeoning interest in mitochondrial delivery,we delved into the feasibility and existing research findings on the transportation of mitochondrial fractions or intact mitochondria through small extracellular vesicles and microvesicles to offer a fresh perspective on ischemic stroke therapy.
基金supported by the National Natural Science Foundation of China(No.22276139)the Shanghai’s Municipal State-owned Assets Supervision and Administration Commission(No.2022028).
文摘To better understand the migration behavior of plastic fragments in the environment,development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary.However,most of the studies had focused only on colored plastic fragments,ignoring colorless plastic fragments and the effects of different environmental media(backgrounds),thus underestimating their abundance.To address this issue,the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis(PLS-DA),extreme gradient boost,support vector machine and random forest classifier.The effects of polymer color,type,thickness,and background on the plastic fragments classification were evaluated.PLS-DA presented the best and most stable outcome,with higher robustness and lower misclassification rate.All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm.A two-stage modeling method,which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background,was proposed.The method presented an accuracy higher than 99%in different backgrounds.In summary,this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.
基金supported by the National Natural Science Foundations of China(Grant No.:82204584)Liaoning Provincial Science and Technology Projects,China(Project No.:2021JH1/10400055).
文摘Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the present study,an innovative natural product virtual screening-interaction-phenotype(NP-VIP)strategy that integrates virtual screening,chemical proteomics,and metabolomics to identify and validate the bioactive targets of NPs.This approach reduces false positive results and enhances the efficiency of target identification.Salvia miltiorrhiza(SM),a herb with recognized therapeutic potential against ischemic stroke(IS),was used to illustrate the workflow.Utilizing virtual screening,chemical proteomics,and metabolomics,potential therapeutic targets for SM in the IS treatment were identified,totaling 29,100,and 78,respectively.Further analysis via the NP-VIP strategy highlighted five high-confidence targets,including poly[ADP-ribose]polymerase 1(PARP1),signal transducer and activator of transcription 3(STAT3),amyloid precursor protein(APP),glutamate-ammonia ligase(GLUL),and glutamate decarboxylase 67(GAD67).These targets were subsequently validated and found to play critical roles in the neuroprotective effects of SM.The study not only underscores the importance of SM in treating IS but also sets a precedent for NP research,proposing a comprehensive approach that could be adapted for broader pharmacological explorations.
基金supported by the Open Fund of the Laboratory for Earth Surface Processes,Ministry of Education,Peking University,Beijing,China,and the Cultivation Fund Program for Excellent Dissertation in Fujian Normal University,China(No.LWPYS202315)the Research Start-up Fund of Fujian Normal University,China(No.Y0720304X13).
文摘Fifty agricultural soil samples collected from Fuzhou,southeast China,were first investigated for the occurrence,distribution,and potential risks of twelve organophosphate esters(OPEs).The total concentration of OPEs(ΣOPEs)in soil ranged from 1.33 to 96.5 ng/g dry weight(dw),with an average value of 17.1 ng/g dw.Especially,halogenated-OPEs were the predominant group with amean level of 9.75 ng/g dw,and tris(1-chloro-2-propyl)phosphate(TCIPP)was the most abundant OPEs,accounting for 51.1%ofΣOPEs.The concentrations of TCIPP andΣOPEs were found to be significantly higher(P<0.05)in soils of urban areas than those in suburban areas.In addition,the use of agricultural plastic films and total organic carbon had a positive effect on the occurrence of OPE in this study.The positive matrix factorization model suggested complex sources of OPEs in agricultural soils from Fuzhou.The ecological risk assessment demonstrated that tricresyl phosphate presented a medium risk to land-based organisms(0.1≤risk quotient<1.0).Nevertheless,the carcinogenic and noncarcinogenic risks for human exposure to OPEs through soil ingestion and dermal absorption were negligible.These findings would facilitate further investigations into the pollution management and risk control of OPEs.
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 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.
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 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the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
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.
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.
基金supported by the National Natural Science Foundation of China(Nos.U2441250,62301380,and 62231027)Natural Science Basic Research Program of Shaanxi,China(2024JC-JCQN-63)+3 种基金the Key Research and Development Program of Shaanxi,China(No.2023-YBGY-249)the Guangxi Key Research and Development Program,China(No.2022AB46002)the China Postdoctoral Science Foundation(No.2022M722504 and 2024T170696)the Innovation Capability Support Program of Shaanxi,China(No.2024RS-CXTD-01).
文摘Automatic Dependent Surveillance-Broadcast(ADS-B)technology,with its open signal sharing,faces substantial security risks from false signals and spoofing attacks when broadcasting Unmanned Aerial Vehicle(UAV)information.This paper proposes a security position verification technique based on Multilateration(MLAT)to detect false signals,ensuring UAV safety and reliable airspace operations.First,the proposed method estimates the current position of the UAV by calculating the Time Difference of Arrival(TDOA),Time Sum of Arrival(TSOA),and Angle of Arrival(AOA)information.Then,this estimated position is compared with the ADS-B message to eliminate false UAV signals.Furthermore,a localization model based on TDOA/TSOA/AOA is established by utilizing reliable reference sources for base station time synchronization.Additionally,an improved Chan-Taylor algorithm is developed,incorporating the Constrained Weighted Least Squares(CWLS)method to initialize UAV position calculations.Finally,a false signal detection method is proposed to distinguish between true and false positioning targets.Numerical simulation results indicate that,at a positioning error threshold of 150 m,the improved Chan-Taylor algorithm based on TDOA/TSOA/AOA achieves 100%accuracy coverage,significantly enhancing localization precision.And the proposed false signal detection method achieves a detection accuracy rate of at least 90%within a 50-meter error range.
