An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction...An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
Natural gas hydrate(NGH)has a bright future as a clean energy source with huge reserves.Coring is one of the most direct methods for NGH exploration and research.Preserving the in-situ properties of the core as much a...Natural gas hydrate(NGH)has a bright future as a clean energy source with huge reserves.Coring is one of the most direct methods for NGH exploration and research.Preserving the in-situ properties of the core as much as possible during the coring process is crucial for the assessment of NGH resources.However,most existing NGH coring techniques cannot preserve the in-situ temperature of NGH,leading to distortion of the physical properties of the obtained core,which makes it difficult to effectively guide NGH exploration and development.To overcome this limitation,this study introduces an innovative active temperature-preserved coring method for NGH utilizing phase change materials(PCM).An active temperature-preserved corer(ATPC)is designed and developed,and an indoor experimental system is established to investigate the heat transfer during the coring process.Based on the experimental results under different environment temperatures,a heat transfer model for the entire ATPC coring process has been established.The indoor experimental results are consistent with the theoretical predictions of the heat transfer model,confirming its validity.This model has reconstructed the temperature changes of the NGH core during the coring process,demonstrating that compared to the traditional coring method with only passive temperature-preserved measures,ATPC can effectively reduce the core temperature by more than 5.25℃.With ATPC,at environment temperatures of 15,20,25,and 30℃,the duration of low-temperature state for the NGH core is 53.85,32.87,20.32,and 11.83 min,respectively.These findings provide new perspectives on temperature-preserving core sampling in NGH and provide technical support for exploration and development in NGH.展开更多
Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by ...Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.展开更多
The accurate identification of microporosity is crucial for the characterization of hydrocarbon reservoir permeability and production.Scanning electron microscopy(SEM)is among the limited number of methods available t...The accurate identification of microporosity is crucial for the characterization of hydrocarbon reservoir permeability and production.Scanning electron microscopy(SEM)is among the limited number of methods available to directly observe the microscopic structure of the hydrocarbon reservoir rocks.Nevertheless,precise segmentation of microscopic pores at different depths in SEM images remains an unsolved challenge,known as the‘depth-related resolution loss'problem.Therefore,in this study,a 3D reconstruction technique for regions of interest(ROI)was developed for in-depth pixel analysis and differentiation among various depths of SEM images.The processed SEM images,together with the processing outcomes of this technique,were used as the input database to train a stochastic depth with multi-channel residual pathways(SdstMcrp)deep learning model programmed in Python to develop a tool for segmenting the microscopic pore spaces in SEM images obtained from the Beibuwan Basin.The more accurate segmentation helped to detect an average of 1.2 times more microporosity in SEM images,accounting for about 1.6 times more pixels and 1.2 times more pore surface area.Finally,the impact of the accurate segmentation on the calculation of permeability,a significant reservoir production property,was investigated using fractal geometry models and sensitivity analysis.The results showed that the obtained permeability values would vary by a factor of 6,which represents a considerable difference.These findings demonstrate that the proposed models can effectively identify features across a wide range of grayscale values in SEM images.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug rese...Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.展开更多
Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide ...Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the chall...Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the challenge of being computationally overpowered.This study proposes an efficient fracturing simulator to analyze fracture morphology during hydraulic fracturing processes in deep shale gas reservoirs.The simulator integrates the boundary element displacement discontinuity method and the finite volume method to model the fluid-solid coupling process by employing a pseudo-3D fracture model to calculate the fracture height.In particular,the Broyden iteration method was introduced to improve the computational efficiency and model robustness;it achieved a 46.6%reduction in computation time compared to the Newton-Raphson method.The influences of horizontal stress differences,natural fracture density,and natural fracture angle on the modified zone of the reservoir were simulated,and the following results were observed.(1)High stress difference reservoirs have smaller stimulated reservoir area than low stress difference reservoirs.(2)A higher natural fracture angle resulted in larger modification zones at low stress differences,while the effect of a natural fracture angle at high stress differences was not significant.(3)High-density and long natural fracture zones played a significant role in enhancing the stimulated reservoir area.These findings are critical for comprehending the impact of geological parameters on deep shale reservoirs.展开更多
Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these cha...Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.展开更多
Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbule...Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.展开更多
Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,...Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.展开更多
Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in ...Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.展开更多
The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of dril...The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of drill string.Due to the super slenderness ratio of drill string,strong nonlinearity implied in dynamic analysis and the complex load environment,dynamic simulation of drill string faces great challenges.At present,many simulation methods have been developed to analyze drill string dynamics,and node iteration method is one of them.The node iteration method has a unique advantage in dealing with the contact characteristics between drill string and borehole wall,but its drawback is that the calculation consumes a considerable amount of time.This paper presents a dynamic simulation method of drilling string in extra-deep well based on successive over-relaxation node iterative method(SOR node iteration method).Through theoretical analysis and numerical examples,the correctness and validity of this method were verified,and the dynamics characteristics of drill string in extra-deep wells were calculated and analyzed.The results demonstrate that,in contrast to the conventional node iteration method,the SOR node iteration method can increase the computational efficiency by 48.2%while achieving comparable results.And the whirl trajectory of the extra-deep well drill string is extremely complicated,the maximum rotational speed downhole is approximately twice the rotational speed on the ground.The dynamic torque increases rapidly at the position of the bottom stabilizer,and the lateral vibration in the middle and lower parts of drill string is relatively intense.展开更多
Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learnin...Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.展开更多
Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-sc...Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.展开更多
High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by t...High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope,many without any known counterpart at other wavelengths or clear identification of the source.Deep learning algorithms have been successfully applied to a variety of problems in astronomy.In this paper,I give some typical examples for classifying Fermi sources with deep learning methods,to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.展开更多
Reaction crystallization method is a common cocrystal synthesis approach attributed to the advantage of avoiding individual crystallization of insoluble components,but faces the defects of soluble components precipita...Reaction crystallization method is a common cocrystal synthesis approach attributed to the advantage of avoiding individual crystallization of insoluble components,but faces the defects of soluble components precipitated due to organic solvent volatilization and the formation of unwanted solvates.Our group recently proposed a slurry method based on deep eutectic solvents(DESs)for cocrystal synthesis,which is green,safe and can avoid solvate formation.However,some reactions only produce insoluble raw materials rather than cocrystals due to insufficient activity of the soluble cocrystal co-formers in DESs.Herein,combining the dual benefits of the two methods,a novel reaction crystallization method based on DESs was proposed and employed for cocrystal synthesis of nicotinamide,carbamazepine and theophylline,which can prevent individual crystallization,unwanted solvate formation,and soluble component precipitation,providing a promising alternative for green and efficient synthesis of cocrystals.展开更多
Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Insp...Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.展开更多
基金financially supported by the National Science and Technology Major Project——Deep Earth Probe and Mineral Resources Exploration(No.2024ZD1003701)the National Key R&D Program of China(No.2022YFC2905004)。
文摘An image processing and deep learning method for identifying different types of rock images was proposed.Preprocessing,such as rock image acquisition,gray scaling,Gaussian blurring,and feature dimensionality reduction,was conducted to extract useful feature information and recognize and classify rock images using Tensor Flow-based convolutional neural network(CNN)and Py Qt5.A rock image dataset was established and separated into workouts,confirmation sets,and test sets.The framework was subsequently compiled and trained.The categorization approach was evaluated using image data from the validation and test datasets,and key metrics,such as accuracy,precision,and recall,were analyzed.Finally,the classification model conducted a probabilistic analysis of the measured data to determine the equivalent lithological type for each image.The experimental results indicated that the method combining deep learning,Tensor Flow-based CNN,and Py Qt5 to recognize and classify rock images has an accuracy rate of up to 98.8%,and can be successfully utilized for rock image recognition.The system can be extended to geological exploration,mine engineering,and other rock and mineral resource development to more efficiently and accurately recognize rock samples.Moreover,it can match them with the intelligent support design system to effectively improve the reliability and economy of the support scheme.The system can serve as a reference for supporting the design of other mining and underground space projects.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
基金financially supported by Shenzhen Science and Technology Program(Nos.JSGG20220831105002005 and KJZD20231025152759002)the National Natural Science Foundation of China(Nos.52274133 and 523B2101).
文摘Natural gas hydrate(NGH)has a bright future as a clean energy source with huge reserves.Coring is one of the most direct methods for NGH exploration and research.Preserving the in-situ properties of the core as much as possible during the coring process is crucial for the assessment of NGH resources.However,most existing NGH coring techniques cannot preserve the in-situ temperature of NGH,leading to distortion of the physical properties of the obtained core,which makes it difficult to effectively guide NGH exploration and development.To overcome this limitation,this study introduces an innovative active temperature-preserved coring method for NGH utilizing phase change materials(PCM).An active temperature-preserved corer(ATPC)is designed and developed,and an indoor experimental system is established to investigate the heat transfer during the coring process.Based on the experimental results under different environment temperatures,a heat transfer model for the entire ATPC coring process has been established.The indoor experimental results are consistent with the theoretical predictions of the heat transfer model,confirming its validity.This model has reconstructed the temperature changes of the NGH core during the coring process,demonstrating that compared to the traditional coring method with only passive temperature-preserved measures,ATPC can effectively reduce the core temperature by more than 5.25℃.With ATPC,at environment temperatures of 15,20,25,and 30℃,the duration of low-temperature state for the NGH core is 53.85,32.87,20.32,and 11.83 min,respectively.These findings provide new perspectives on temperature-preserving core sampling in NGH and provide technical support for exploration and development in NGH.
文摘Deep learning-based object detection has revolutionized various fields,including agriculture.This paper presents a systematic review based on the PRISMA 2020 approach for object detection techniques in agriculture by exploring the evolution of different methods and applications over the past three years,highlighting the shift from conventional computer vision to deep learning-based methodologies owing to their enhanced efficacy in real time.The review emphasizes the integration of advanced models,such as You Only Look Once(YOLO)v9,v10,EfficientDet,Transformer-based models,and hybrid frameworks that improve the precision,accuracy,and scalability for crop monitoring and disease detection.The review also highlights benchmark datasets and evaluation metrics.It addresses limitations,like domain adaptation challenges,dataset heterogeneity,and occlusion,while offering insights into prospective research avenues,such as multimodal learning,explainable AI,and federated learning.Furthermore,the main aim of this paper is to serve as a thorough resource guide for scientists,researchers,and stakeholders for implementing deep learning-based object detection methods for the development of intelligent,robust,and sustainable agricultural systems.
基金the Natural Science Foundation of Shandong Province of China(Nos.ZR2022QD080 , ZR2025MS575)the National Natural Science Foundation of China(Nos.W25322063,42250410333,52250410357)+1 种基金the Fundamental Research Funds for the Central Universities,CHD(No.300102263103)the Young Talent Fund of Association for Science and Technology in Shaanxi,China(No.20230703)。
文摘The accurate identification of microporosity is crucial for the characterization of hydrocarbon reservoir permeability and production.Scanning electron microscopy(SEM)is among the limited number of methods available to directly observe the microscopic structure of the hydrocarbon reservoir rocks.Nevertheless,precise segmentation of microscopic pores at different depths in SEM images remains an unsolved challenge,known as the‘depth-related resolution loss'problem.Therefore,in this study,a 3D reconstruction technique for regions of interest(ROI)was developed for in-depth pixel analysis and differentiation among various depths of SEM images.The processed SEM images,together with the processing outcomes of this technique,were used as the input database to train a stochastic depth with multi-channel residual pathways(SdstMcrp)deep learning model programmed in Python to develop a tool for segmenting the microscopic pore spaces in SEM images obtained from the Beibuwan Basin.The more accurate segmentation helped to detect an average of 1.2 times more microporosity in SEM images,accounting for about 1.6 times more pixels and 1.2 times more pore surface area.Finally,the impact of the accurate segmentation on the calculation of permeability,a significant reservoir production property,was investigated using fractal geometry models and sensitivity analysis.The results showed that the obtained permeability values would vary by a factor of 6,which represents a considerable difference.These findings demonstrate that the proposed models can effectively identify features across a wide range of grayscale values in SEM images.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金upported by the National Key Research and Development Program of China(Grant No.:2023YFF1204904)the National Natural Science Foundation of China(Grant Nos.:U23A20530 and 82173746)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2024-00460621,Developing BCI-Based Digital Health Technologies for Mental Illness and Pain Management).
文摘Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare system.Analysis of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical experts.For Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes worse.The researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of ALL.The systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this field.Different image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,Science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were presented.Convolutional Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the field.Finally,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
文摘Hydraulic fracturing plays a critical role in enhancing shale gas production in deep shale reservoirs.Conventional hydraulic fracturing simulation methods rely on prefabricated grids,which can be hindered by the challenge of being computationally overpowered.This study proposes an efficient fracturing simulator to analyze fracture morphology during hydraulic fracturing processes in deep shale gas reservoirs.The simulator integrates the boundary element displacement discontinuity method and the finite volume method to model the fluid-solid coupling process by employing a pseudo-3D fracture model to calculate the fracture height.In particular,the Broyden iteration method was introduced to improve the computational efficiency and model robustness;it achieved a 46.6%reduction in computation time compared to the Newton-Raphson method.The influences of horizontal stress differences,natural fracture density,and natural fracture angle on the modified zone of the reservoir were simulated,and the following results were observed.(1)High stress difference reservoirs have smaller stimulated reservoir area than low stress difference reservoirs.(2)A higher natural fracture angle resulted in larger modification zones at low stress differences,while the effect of a natural fracture angle at high stress differences was not significant.(3)High-density and long natural fracture zones played a significant role in enhancing the stimulated reservoir area.These findings are critical for comprehending the impact of geological parameters on deep shale reservoirs.
基金Science & Technology Specific Project of Jiangsu Province (No. BZ2024047)Key R&D Program of Ningbo (No. 2024H013)the National Natural Science Foundation of China (No. W2412092)。
文摘Implementing the conventional total focus method(TFM)for visualizing internal damage in reinforced con-crete(RC)is beset with computational challenges and a high dependence on physical principles.To overcome these challenges,an efficient total focus imaging method based on deep learning is proposed.This method deals with array ultrasonic time-domain signals from cracked RC beams.A deep neural network(DNN)employing a feature extraction+multilevel feature fusion+matrix construction architec-ture was developed;this architecture enabled the DNN to learn the underlying physical principles of the TFM.The ar-chitecture effectively transformed ultrasonic time-domain signals into a B-scan matrix.Training,validation,and test-ing data were collected by measuring eight RC beams with preset artificial cracks using a low-frequency shear wave ar-ray ultrasonic system.The results demonstrated that the re-constructed B-scan matrices had a peak signal-to-noise ratio of 26.94 dB and a structural similarity index of 0.978.Fur-thermore,the proposed method required 42%fewer floating-point operations compared with physics-based cal-culations,achieving total focus imaging with lower compu-tational cost.The study facilitates the advancement of ultra-sonic total focus imaging of RC structures from physics-based methods to data-driven methods without re-quiring prior physical knowledge,thereby providing robust support for further nondestructive evaluation and quantita-tive analysis.
基金supported by the National Natural Science Foundation of China(No.12104141).
文摘Aiming at the problem that the bit error rate(BER)of asymmetrically clipped optical orthogonal frequency division multiplexing(ACO-OFDM)space optical communication system is significantly affected by different turbulence intensities,the deep learning technique is proposed to the polarization code decoding in ACO-OFDM space optical communication system.Moreover,this system realizes the polarization code decoding and signal demodulation without frequency conduction with superior performance and robustness compared with the performance of traditional decoder.Simulations under different turbulence intensities as well as different mapping orders show that the convolutional neural network(CNN)decoder trained under weak-medium-strong turbulence atmospheric channels achieves a performance improvement of about 10^(2)compared to the conventional decoder at 4-quadrature amplitude modulation(4QAM),and the BERs for both 16QAM and 64QAM are in between those of the conventional decoder.
基金funded by the Norwegian Research Council(IKTPLUSS-IKT og digital innovasjon,project no.332901).
文摘Foraminifera are shell-bearing microorganisms that are commonly found in marine deposits on the seabed.They are important indicators in many analyses,are used in climate change research,monitoring marine environments,evolutionary studies,and are also frequently used in the oil and gas industry.Although some research has focused on automating the classification of foraminifera images,few have addressed the uncertainty in these classifications.Although foraminifera classification is not a safety-critical task,estimating uncertainty is crucial to avoid misclassifications that could overlook rare and ecologically significant species that are informative indicators of the environment in which they lived.Uncertainty estimation in deep learning has gained significant attention and many methods have been developed.However,evaluating the performance of these methods in practical settings remains a challenge.To create a benchmark for uncertainty estimation in the classification of foraminifera,we administered a multiple choice questionnaire containing classification tasks to four senior geologists.By analyzing their responses,we generated human-derived uncertainty estimates for a test set of 260 images of foraminifera and sediment grains.These uncertainty estimates served as a baseline for comparison when training neural networks in classification.We then trained multiple deep neural networks using a range of uncertainty quantification methods to classify and state the uncertainty about the classifications.The results of the deep learning uncertainty quantification methods were then analyzed and compared with the human benchmark,to see how the methods performed individually and how the methods aligned with humans.Our results show that human-level performance can be achieved with deep learning and that test-time data augmentation and ensembling can help improve both uncertainty estimation and classification performance.Our results also show that human uncertainty estimates are helpful indicators for detecting classification errors and that deep learning-based uncertainty estimates can improve calibration and classification accuracy.
基金supported by the National Natural Science Foundation of China(T2222002,22032004)the Fundamental Research Funds for the Central Universities(Xiamen University:No.20720240053)State Key Laboratory of Vaccines for Infectious Diseases,Xiang An Biomedicine Laboratory(2023XAKJ0103074).
文摘Text mining has emerged as a powerful strategy for extracting domain knowledge structure from large amounts of text data.To date,most text mining methods are restricted to specific literature information,resulting in incomplete knowledge graphs.Here,we report a method that combines citation analysis with topic modeling to describe the hidden development patterns in the history of science.Leveraging this method,we construct a knowledge graph in the field of Raman spectroscopy.The traditional Latent DirichletAllocation model is chosen as the baseline model for comparison to validate the performance of our model.Our method improves the topic coherence with a minimum growth rate of 100%compared to the traditional text mining method.It outperforms the traditional text mining method on the diversity,and its growth rate ranges from 0 to 126%.The results show the effectiveness of rule-based tokenizer we designed in solving the word tokenizer problem caused by entity naming rules in the field of chemistry.It is versatile in revealing the distribution of topics,establishing the similarity and inheritance relationships,and identifying the important moments in the history of Raman spectroscopy.Our work provides a comprehensive tool for the science of science research and promises to offer new insights into the historical survey and development forecast of a research field.
基金supported by the National Natural Science Foundation of China(52174003,52374008).
文摘The complex vibration directly affects the dynamic safety of drill string in ultra-deep wells and extra-deep wells.It is important to understand the dynamic characteristics of drill string to ensure the safety of drill string.Due to the super slenderness ratio of drill string,strong nonlinearity implied in dynamic analysis and the complex load environment,dynamic simulation of drill string faces great challenges.At present,many simulation methods have been developed to analyze drill string dynamics,and node iteration method is one of them.The node iteration method has a unique advantage in dealing with the contact characteristics between drill string and borehole wall,but its drawback is that the calculation consumes a considerable amount of time.This paper presents a dynamic simulation method of drilling string in extra-deep well based on successive over-relaxation node iterative method(SOR node iteration method).Through theoretical analysis and numerical examples,the correctness and validity of this method were verified,and the dynamics characteristics of drill string in extra-deep wells were calculated and analyzed.The results demonstrate that,in contrast to the conventional node iteration method,the SOR node iteration method can increase the computational efficiency by 48.2%while achieving comparable results.And the whirl trajectory of the extra-deep well drill string is extremely complicated,the maximum rotational speed downhole is approximately twice the rotational speed on the ground.The dynamic torque increases rapidly at the position of the bottom stabilizer,and the lateral vibration in the middle and lower parts of drill string is relatively intense.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)Postdoctoral Fellowship Program of CPSF(GZC20232598)+1 种基金China Postdoctoral Science Foundation(2024M753168)National Key Scientific and Technological Infrastructure Project“Earth System Numerical Simulation Facility”(EarthLab)。
文摘Seasonal prediction of summer rainfall in China plays a crucial role in decision-making,environmental protection,and socio-economic development,while it currently has a low prediction skill.We developed a deep learning-based seasonal prediction bias correction method for summer rainfall in China.Based on prediction fields from the flexible Global Ocean-Atmosphere-Land System Model finite volume version 2(FGOALS-f2),we optimized the loss function of U-Net,trained with different hyperparameters,and selected the optimum model.U-Net model can extract multi-scale feature information and preserve spatial information,making it suitable for processing meteorological data.With this endto-end model,the precipitation distribution can be obtained directly without using the traditional method of data dimensionality reduction(e.g.,Empirical Orthogonal Function),which could maximize the retention of spatio-temporal information of the input data.Optimization of the loss function enhances the prediction results and mitigates model overfitting.The independent prediction shows a significant skill improvement measured by the anomalous correlation coefficient score.The skill has an average value of 0.679 in China(0°–63°N,73°–133°E)and 0.691 in the region of the Chinese mainland,which significantly improves the dynamical prediction skill by 1357%and 4836%.This study suggests that the deep learning(U-Net)-based seasonal prediction bias correction method is a promising approach for improving rainfall prediction of the dynamical model.
文摘Manufacturers must identify and classify various defects in automotive sealing rings to ensure product quality.Deep learning algorithms show promise in this field,but challenges remain,especially in detecting small-scale defects under harsh industrial conditions with multimodal data.This paper proposes an enhanced version of You Only Look Once(YOLO)v8 for improved defect detection in automotive sealing rings.We introduce the Multi-scale Adaptive Feature Extraction(MAFE)module,which integrates Deformable ConvolutionalNetwork(DCN)and Spaceto-Depth(SPD)operations.This module effectively captures long-range dependencies,enhances spatial aggregation,and minimizes information loss of small objects during feature extraction.Furthermore,we introduce the Blur-Aware Wasserstein Distance(BAWD)loss function,which improves regression accuracy and detection capabilities for small object anchor boxes,particularly in scenarios involving defocus blur.Additionally,we have constructed a high-quality dataset of automotive sealing ring defects,providing a valuable resource for evaluating defect detection methods.Experimental results demonstrate our method’s high performance,achieving 98.30% precision,96.62% recall,and an inference speed of 20.3 ms.
基金supported by the Natural Science Foundation of China(12393853)。
文摘High-energy gamma-ray astronomy,at frequencies of 100 MeV to 100 GeV,yields insights into the fields of compact objects,extreme processes,and particle propagation.Thousands of gamma-ray sources have been detected by the Fermi Gamma-ray Space Telescope,many without any known counterpart at other wavelengths or clear identification of the source.Deep learning algorithms have been successfully applied to a variety of problems in astronomy.In this paper,I give some typical examples for classifying Fermi sources with deep learning methods,to show how such techniques can improve capability to unveil the nature of high-energy gamma-ray sources.
基金financially supported by National Natural Science Foundation of China(Nos.22101204 and 22271220)the project grant from the Innovation and Strengthening Project of Guangdong Pharmaceutical University-Special Project of the Guangdong Education Commission(No.2020KZDZX1128)+1 种基金the Research Projects of the Chinese Medicine Council of Guangdong Province(No.20231209)the Key Laboratory of Tropical Medicinal Resource Chemistry of the Ministry of Education at Hainan Normal University(No.RDZH2023001)。
文摘Reaction crystallization method is a common cocrystal synthesis approach attributed to the advantage of avoiding individual crystallization of insoluble components,but faces the defects of soluble components precipitated due to organic solvent volatilization and the formation of unwanted solvates.Our group recently proposed a slurry method based on deep eutectic solvents(DESs)for cocrystal synthesis,which is green,safe and can avoid solvate formation.However,some reactions only produce insoluble raw materials rather than cocrystals due to insufficient activity of the soluble cocrystal co-formers in DESs.Herein,combining the dual benefits of the two methods,a novel reaction crystallization method based on DESs was proposed and employed for cocrystal synthesis of nicotinamide,carbamazepine and theophylline,which can prevent individual crystallization,unwanted solvate formation,and soluble component precipitation,providing a promising alternative for green and efficient synthesis of cocrystals.
基金supported by the National Natural Science Foundation of China(No.12104396)the National Key R&D Program of China(Nos.2021YFF1200404 and 2021YFA1201200)+2 种基金the National Independent Innovation Demonstration Zone Shanghai Zhangjiang Major Projects(No.ZJZX2020014)the Starry Night Science Fund at Shanghai Institute for Advanced Study of Zhejiang University(No.SN-ZJU-SIAS-003)the Shanghai Artificial Intelligence Lab(No.P22KN00272),China.
文摘Antibodies currently comprise the predominant treatment modality for a variety of diseases;therefore,optimizing their properties rapidly and efficiently is an indispensable step in antibody-based drug development.Inspired by the great success of artificial intelligence-based algorithms,especially deep learning-based methods in the field of biology,various computational methods have been introduced into antibody optimization to reduce costs and increase the success rate of lead candidate generation and optimization.Herein,we briefly review recent progress in deep learning-based antibody optimization,focusing on the available datasets and algorithm input data types that are crucial for constructing appropriate deep learning models.Furthermore,we discuss the current challenges and potential solutions for the future development of general-purpose deep learning algorithms in antibody optimization.
