The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly comple...The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.展开更多
As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods ge...As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.展开更多
Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi...Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.展开更多
Given that the citrus psyllid is the primary vector of citrus Huanglongbing(HLB),there is an urgent need to control this pest to mitigate the spread of the disease.This paper reviews the current research on two predom...Given that the citrus psyllid is the primary vector of citrus Huanglongbing(HLB),there is an urgent need to control this pest to mitigate the spread of the disease.This paper reviews the current research on two predominant control strategies:chemical control and biological control agents,in managing the citrus psyllid.It emphasizes the mechanisms of action,efficacy,and application advancements of these control methods.Finally,the paper analyzes the principal challenges associated with the sustainable management of citrus psyllids and offers perspectives for future research.展开更多
Previous bidirectional quantum private comparison(BQPC)protocols cannot meet the requirements in some special application scenarios,where only one party needs to obtain the comparison results without a third party(TP)...Previous bidirectional quantum private comparison(BQPC)protocols cannot meet the requirements in some special application scenarios,where only one party needs to obtain the comparison results without a third party(TP),such as scenarios for authority surveys or healthcare data sharing.In addition to this,the BQPC protocol has the potential of information leakage in multiple comparisons.Therefore,we design a new unidirectional quantum private comparison(UQPC)protocol based on quantum private query(QPQ)protocols with ideal database security and zero failure probability(IDS-ZF),for the reason that they have excellent unidirectionality and security.Concretely,we design a UQPC protocol based on Wei et al.’s work[IEEE Transactions on Computers 672(2017)]and it includes an authentication process to increase the resistance to outside attacks.Moreover,we generalize the protocol and propose a general model that can transform a QPQ protocol with or without the IDS-ZF property into a secure UQPC protocol.Finally,our study shows that protocols using our model are secure,practical,and have the IDS-ZF property.展开更多
As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could ra...As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.展开更多
BACKGROUND Endoscopic retrograde cholangiopancreatography(ERCP)is an essential diagnostic and therapeutic procedure for pancreatobiliary disorders.However,few large-scale studies from South Asia have examined long-ter...BACKGROUND Endoscopic retrograde cholangiopancreatography(ERCP)is an essential diagnostic and therapeutic procedure for pancreatobiliary disorders.However,few large-scale studies from South Asia have examined long-term ERCP outcomes,particularly using established quality benchmarks.AIM To evaluate ERCP indications,success rates,complications,and quality performance at a high-volume tertiary care center in Pakistan over a 17-year period.METHODS This retrospective study analyzed 13215 ERCP procedures performed between 2006 and 2023.Data included demographics,indications,cannulation rates,complications,and pediatric cases.Findings were assessed against American Society of Gastroenterology/European Society of Gastrointestinal Endoscopy quality indicators.RESULTS Biliary ERCP accounted for 93.1%of procedures;choledocholithiasis was the most common indication(40%).Cannulation success was 93.9%for biliary and 94.2%for pancreatic ERCP.Pediatric ERCP comprised 4%of cases,mostly for stones and chronic pancreatitis.Bleeding(1.7%)and post-ERCP pancreatitis(2.3%)were the most frequent complications.Performance met or exceeded most American Society of Benchmarks.CONCLUSION This study offers insight into nearly two decades of ERCP practice within a public sector hospital.Our experience echoes the quality and efficiency of ERCP not previously available in Pakistan.As healthcare systems in resourcelimited sectors expand,our findings serve as a reference point.Continued training and quality improvement studies can further enhance ERCP effectiveness in the region and beyond.展开更多
Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective metho...Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.展开更多
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to vari...Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.展开更多
The NEutron Detector Array(NEDA)is designed to be coupled to gamma-ray spectrometers to enhance the sensitivity of the setup by enabling reaction channel selection through counting of the evaporated neutrons.This arti...The NEutron Detector Array(NEDA)is designed to be coupled to gamma-ray spectrometers to enhance the sensitivity of the setup by enabling reaction channel selection through counting of the evaporated neutrons.This article presents the implementation of a double trigger condition system for NEDA,which improves the acquisition of neutrons and reduces the number of gamma rays acquired.Two independent triggers are generated in the double trigger condition system:one based on charge comparison(CC)and the other on time-of-flight(TOF).These triggers can be combined using OR and AND logic,offering four distinct trigger modes.The developed firmware is added to the previous one in the Virtex 6 field programmable gate array(FPGA)present in the system,which also includes signal processing,baseline correction,and various trigger logic blocks.The performance of the trigger system is evaluated using data from the E703 experiment performed at GANIL.The four trigger modes are applied to the same data,and a subsequent offline analysis is performed.It is shown that most of the detected neutrons are preserved with the AND mode,and the total number of gamma rays is significantly reduced.Compared with the CC trigger mode,the OR trigger mode allows increasing the selection of neutrons.In addition,it is demonstrated that if the OR mode is selected,the online CC trigger threshold can be raised without losing neutrons.展开更多
Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the co...Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.展开更多
This paper focuses on the service management for digitally disadvantaged groups in Chinese and American public libraries.Utilizing a comparative analysis method,it explores the differences and dilemmas between the two...This paper focuses on the service management for digitally disadvantaged groups in Chinese and American public libraries.Utilizing a comparative analysis method,it explores the differences and dilemmas between the two countries in terms of policy support,resource allocation,service models,and librarian training.The research finds that the United States has established a relatively comprehensive legal guarantee system and diverse funding channels,but recent federal policy fluctuations pose risks to project sustainability.Although China has made significant progress in equalizing services,it still faces structural challenges such as insufficient institutional guarantees and uneven resource allocation.Based on the comparative analysis,this paper proposes strategic suggestions including establishing a four-dimensional support system of"Law-Resources-Technology-Personnel"and promoting a"Digital Inclusion Partnership"model,aiming to provide references for Chinese public libraries to improve services for digitally disadvantaged groups.展开更多
Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological...Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.展开更多
To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a compar...To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a comparison model.This method enhances the precision of system calibration by establishing a dataset that delineates the relationship between secondary mirror misalignment and wavefront aberration,subsequently inferring the misalignment from interferometric detection results during the calibration process.For the 2-m ring telescope,we develop a detection model using five local sub-apertures,enabling a root-mean-square detection accuracy of 0:0225λ(λ=632:8 nm)for full-aperture wavefront aberration.The calibration results for the 2-m Ring Solar Telescope system indicate that the root-mean-square value of sub-aperture wavefront aberration reaches 0.104λ,and the root-mean-square value of spliced full-aperture measurement yields reaches 0.112λ.This method offers a novel approach for calibrating small width-toradius ratio telescope systems and can be applied to the calibration of other irregular-aperture optical systems.展开更多
Mongolian pine (Pinus sylvestiris Linnaeus var. mongolica Litvinov) as a valuable conifer tree species has been broadly introduced to the sandy land areas in 揟hree North?regions (North, northwest and northeast of Chi...Mongolian pine (Pinus sylvestiris Linnaeus var. mongolica Litvinov) as a valuable conifer tree species has been broadly introduced to the sandy land areas in 揟hree North?regions (North, northwest and northeast of China), but many problems occurred in the earliest Mongolian pine plantations in Zhanggutai, Zhangwu County, Liaoning Province (ZZL). In order to clarify the reason, comprehensive investigations were carried out on differences in structure characteristics, growth processes and ecological factors between artificial stands (the first plantation established in ZZL in 1950s) and natural stands (the origin forests of the tree species in Honghuaerji, Inner Mongolia) on sandy land. The results showed that variation of diameter-class distributions in artificial stands and natural stands could be described by Weibull and Normal distribution models, respectively. Chapman-Richards growth model was employed to reconstruct the growth process of Mongolian pine based on the data from field investigation and stem analysis. The ages of maximum of relative growth rate and average growth rate of DBH, height, and volume of planted trees were 11, 22 years, 8, 15 years and 35, 59 years earlier than those of natural stand trees, respectively. In respect of the incremental acceleration of volume, the artificial and natural stands reached their maximum values at 14 years and 33 years respectively. The quantitative maturity ages of artificial stands and natural stands were 43 years and 102 years respectively. It was concluded that the life span of the Mongolian pine trees in natural stands was about 60 years longer than those in artificial stands. The differences mentioned above between artificial and natural Mongolian pine forests on sandy land were partially attributed to the drastic variations of ecological conditions such as latitude, temperature, precipitation, evaporation and height above sea level. Human beings' disturbances and higher density in plantation forest may be ascribed as additional reasons. Those results may be potentially useful for the management and afforestation of Mongolian pine plantations on sandy land in arid and semi-arid areas.展开更多
There are a lot of methods in city water consumption short-term forecasting both inside and outside the country. But among these methods there exist many advantages and shortcomings in model establishing, solving and ...There are a lot of methods in city water consumption short-term forecasting both inside and outside the country. But among these methods there exist many advantages and shortcomings in model establishing, solving and predicting accuracy, speed, applicability. This article draws lessons from other realm mature methods after many years′ study. It′s systematically studied and compared to predict the water consumption in accuracy, speed, effect and applicability among the time series triangle function method, artificial neural network method, gray system theories method, wavelet analytical method.展开更多
In order to explore the cultural value of waterfront in urban landscape,from the perspective of cross-cultural comparison psychology,the subjects from Britain,Japan and China have been surveyed to obtain their cogniti...In order to explore the cultural value of waterfront in urban landscape,from the perspective of cross-cultural comparison psychology,the subjects from Britain,Japan and China have been surveyed to obtain their cognitive structure and behavior on waterfront landscape.Based on the comparison of quantitative statistic results of life value,cognitive structure of waterfront space,and water-loving,a quantitative analysis has been conducted on the relevance between each factor by using Quantification Theory III.Then,it has analyzed the types and purpose of behavior in waterfront space,and the influence brought by cultural value difference.展开更多
The increasing scale and complexity of power systems require high performance and high reliability of power system protection.Protective relaying based on directional comparison with power line carrier or microwave ch...The increasing scale and complexity of power systems require high performance and high reliability of power system protection.Protective relaying based on directional comparison with power line carrier or microwave channels is the most suitable protection scheme for long distance EHV transmission lines and is widely used in power systems.The key element of such protection is a directional relay used to discriminate the fault direction.In order to overcome the disadvantages of conventional directional relays,the authors of this paper put forward the directional comparison carrier protection based on the artificial neural network(ANN).The protection is extensively tested using electromagnetic transient program (EMTP) under various electric power system operating and fault conditions.It is proved that the directional comparison carrier protection based on ANN,which can recognize various fault patterns of the protected transmission line(such as fault direction,fault phases etc.)correctly in any kind of operating and fault conditions and the whole process,is satisfactory for EHV transmission line protection.展开更多
Research of competitiveness of China and the United States is of great significance to enhancing China＇s economic competitiveness and achieving the objective of national rejuvenation. By creating a competitiveness fr...Research of competitiveness of China and the United States is of great significance to enhancing China＇s economic competitiveness and achieving the objective of national rejuvenation. By creating a competitiveness framework and a system of heterogeneous indicators, this paper investigates the competitiveness of China and the US in terms of current status, historic change and global environment. Our research led to the following findings： core factors determine the level of competitiveness for China and the US; the national competitiveness of both countries is evolving towards structural homogeneity; and China and the US lead most countries in many common areas. China has the potential to overtake the US in competitiveness in the future. We suggest that China increase its competitiveness by promoting its advantages, addressing its weaknesses and focusing on core areas.展开更多
基金financially supported by Guangdong Province Basic and Applied Basic Research Fund Project(Grant No.2022B1515250009)Liaoning Provincial Natural Science Foundation-Doctoral Research Start-up Fund Project(Grant No.2024-BSBA-05)+1 种基金Major Science and Technology Innovation Project in Shandong Province(Grant No.2024CXGC010803)the National Natural Science Foundation of China(Grant Nos.52271269 and 12302147).
文摘The umbilical,a key component in offshore energy extraction,plays a vital role in ensuring the stable operation of the entire production system.The extensive variety of cross-sectional components creates highly complex layout combinations.Furthermore,due to constraints in component quantity and geometry within the cross-sectional layout,filler bodies must be incorporated to maintain cross-section performance.Conventional design approaches based on manual experience suffer from inefficiency,high variability,and difficulties in quantification.This paper presents a multi-level automatic filling optimization design method for umbilical cross-sectional layouts to address these limitations.Initially,the research establishes a multi-objective optimization model that considers compactness,balance,and wear resistance of the cross-section,employing an enhanced genetic algorithm to achieve a near-optimal layout.Subsequently,the study implements an image processing-based vacancy detection technique to accurately identify cross-sectional gaps.To manage the variability and diversity of these vacant regions,the research introduces a multi-level filling method that strategically selects and places filler bodies of varying dimensions,overcoming the constraints of uniform-size fillers.Additionally,the method incorporates a hierarchical strategy that subdivides the complex cross-section into multiple layers,enabling layer-by-layer optimization and filling.This approach reduces manufac-turing equipment requirements while ensuring practical production process feasibility.The methodology is validated through a specific umbilical case study.The results demonstrate improvements in compactness,balance,and wear resistance compared with the initial cross-section,offering novel insights and valuable references for filler design in umbilical cross-sections.
基金National Natural Science Foundation of China(Nos.42301473,42271424,42171397)Chinese Postdoctoral Innovation Talents Support Program(No.BX20230299)+2 种基金China Postdoctoral Science Foundation(No.2023M742884)Natural Science Foundation of Sichuan Province(Nos.24NSFSC2264,2025ZNSFSC0322)Key Research and Development Project of Sichuan Province(No.24ZDYF0633).
文摘As a key node of modern transportation network,the informationization management of road tunnels is crucial to ensure the operation safety and traffic efficiency.However,the existing tunnel vehicle modeling methods generally have problems such as insufficient 3D scene description capability and low dynamic update efficiency,which are difficult to meet the demand of real-time accurate management.For this reason,this paper proposes a vehicle twin modeling method for road tunnels.This approach starts from the actual management needs,and supports multi-level dynamic modeling from vehicle type,size to color by constructing a vehicle model library that can be flexibly invoked;at the same time,semantic constraint rules with geometric layout,behavioral attributes,and spatial relationships are designed to ensure that the virtual model matches with the real model with a high degree of similarity;ultimately,the prototype system is constructed and the case region is selected for the case study,and the dynamic vehicle status in the tunnel is realized by integrating real-time monitoring data with semantic constraints for precise virtual-real mapping.Finally,the prototype system is constructed and case experiments are conducted in selected case areas,which are combined with real-time monitoring data to realize dynamic updating and three-dimensional visualization of vehicle states in tunnels.The experiments show that the proposed method can run smoothly with an average rendering efficiency of 17.70 ms while guaranteeing the modeling accuracy(composite similarity of 0.867),which significantly improves the real-time and intuitive tunnel management.The research results provide reliable technical support for intelligent operation and emergency response of road tunnels,and offer new ideas for digital twin modeling of complex scenes.
文摘Multi-firmware comparison techniques can improve efficiency when auditing firmwares in bulk.How-ever,the problem of matching functions between multiple firmwares has not been studied before.This paper proposes a multi-firmware comparison method based on evolutionary algorithms and trusted base points.We first model the multi-firmware comparison as a multi-sequence matching problem.Then,we propose an adaptation function and a population generation method based on trusted base points.Finally,we apply an evolutionary algorithm to find the optimal result.At the same time,we design the similarity of matching results as an evaluation metric to measure the effect of multi-firmware comparison.The experiments show that the proposed method outperforms Bindiff and the string-based method.Precisely,the similarity between the matching results of the proposed method and Bindiff matching results is 61%,and the similarity between the matching results of the proposed method and the string-based method is 62.8%.By sampling and manual verification,the accuracy of the matching results of the proposed method can be about 66.4%.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
基金Supported by National Undergraduate Training Programs for Innovation and Entrepreneurship(202510580009)Special Project for Promoting the Coordinated Development of Urban and Rural Areas and Regions by Introducing Scientific and Technological Achievements of Guangdong Province into Counties and Towns(2025B0202010051)Project of High-quality Development in Hundred Counties,Thousands Towns and Ten Thousand Villages of Guangdong Provincial Department of Science and Technology:Key Dispatch Project for Rural Science and Technology Commissioners(KTP20240704).
文摘Given that the citrus psyllid is the primary vector of citrus Huanglongbing(HLB),there is an urgent need to control this pest to mitigate the spread of the disease.This paper reviews the current research on two predominant control strategies:chemical control and biological control agents,in managing the citrus psyllid.It emphasizes the mechanisms of action,efficacy,and application advancements of these control methods.Finally,the paper analyzes the principal challenges associated with the sustainable management of citrus psyllids and offers perspectives for future research.
基金supported by the National Key Research and Development Program of China(Grant Nos.2024YFB2906504 and 2024YFB2906500)the National Natural Science Foundation of China(Grant Nos.62401067 and 62272051)the 111 Project(Grant No.B21049).
文摘Previous bidirectional quantum private comparison(BQPC)protocols cannot meet the requirements in some special application scenarios,where only one party needs to obtain the comparison results without a third party(TP),such as scenarios for authority surveys or healthcare data sharing.In addition to this,the BQPC protocol has the potential of information leakage in multiple comparisons.Therefore,we design a new unidirectional quantum private comparison(UQPC)protocol based on quantum private query(QPQ)protocols with ideal database security and zero failure probability(IDS-ZF),for the reason that they have excellent unidirectionality and security.Concretely,we design a UQPC protocol based on Wei et al.’s work[IEEE Transactions on Computers 672(2017)]and it includes an authentication process to increase the resistance to outside attacks.Moreover,we generalize the protocol and propose a general model that can transform a QPQ protocol with or without the IDS-ZF property into a secure UQPC protocol.Finally,our study shows that protocols using our model are secure,practical,and have the IDS-ZF property.
基金co-supported by the National Key Research and Development Program of China(No.2022YFF0503100)the Youth Innovation Project of Pandeng Program of National Space Science Center,Chinese Academy of Sciences(No.E3PD40012S).
文摘As we look ahead to future lunar exploration missions, such as crewed lunar exploration and establishing lunar scientific research stations, the lunar rovers will need to cover vast distances. These distances could range from kilometers to tens of kilometers, and even hundreds and thousands of kilometers. Therefore, it is crucial to develop effective long-range path planning for lunar rovers to meet the demands of lunar patrol exploration. This paper presents a hierarchical map model path planning method that utilizes the existing high-resolution images, digital elevation models and mineral abundance maps. The objective is to address the issue of the construction of lunar rover travel costs in the absence of large-scale, high-resolution digital elevation models. This method models the reference and semantic layers using the middle- and low-resolution remote sensing data. The multi-scale obstacles on the lunar surface are extracted by combining the deep learning algorithm on the high-resolution image, and the obstacle avoidance layer is modeled. A two-stage exploratory path planning decision is employed for long-distance driving path planning on a global–local scale. The proposed method analyzes the long-distance accessibility of various areas of scientific significance, such as Rima Bode. A high-precision digital elevation model is created using stereo images to validate the method. Based on the findings, it can be observed that the entire route spans a distance of 930.32 km. The route demonstrates an impressive ability to avoid meter-level impact craters and linear structures while maintaining an average slope of less than 8°. This paper explores scientific research by traversing at least seven basalt units, uncovering the secrets of lunar volcanic activities, and establishing ‘golden spike’ reference points for lunar stratigraphy. The final result of path planning can serve as a valuable reference for the design, mission demonstration, and subsequent project implementation of the new manned lunar rover.
文摘BACKGROUND Endoscopic retrograde cholangiopancreatography(ERCP)is an essential diagnostic and therapeutic procedure for pancreatobiliary disorders.However,few large-scale studies from South Asia have examined long-term ERCP outcomes,particularly using established quality benchmarks.AIM To evaluate ERCP indications,success rates,complications,and quality performance at a high-volume tertiary care center in Pakistan over a 17-year period.METHODS This retrospective study analyzed 13215 ERCP procedures performed between 2006 and 2023.Data included demographics,indications,cannulation rates,complications,and pediatric cases.Findings were assessed against American Society of Gastroenterology/European Society of Gastrointestinal Endoscopy quality indicators.RESULTS Biliary ERCP accounted for 93.1%of procedures;choledocholithiasis was the most common indication(40%).Cannulation success was 93.9%for biliary and 94.2%for pancreatic ERCP.Pediatric ERCP comprised 4%of cases,mostly for stones and chronic pancreatitis.Bleeding(1.7%)and post-ERCP pancreatitis(2.3%)were the most frequent complications.Performance met or exceeded most American Society of Benchmarks.CONCLUSION This study offers insight into nearly two decades of ERCP practice within a public sector hospital.Our experience echoes the quality and efficiency of ERCP not previously available in Pakistan.As healthcare systems in resourcelimited sectors expand,our findings serve as a reference point.Continued training and quality improvement studies can further enhance ERCP effectiveness in the region and beyond.
基金supported by the Fund of Key Laboratory of Biomedical Engineering of Hainan Province(No.BME20240001)the STI2030-Major Projects(No.2021ZD0200104)the National Natural Science Foundations of China under Grant 61771437.
文摘Deep learning networks are increasingly exploited in the field of neuronal soma segmentation.However,annotating dataset is also an expensive and time-consuming task.Unsupervised domain adaptation is an effective method to mitigate the problem,which is able to learn an adaptive segmentation model by transferring knowledge from a rich-labeled source domain.In this paper,we propose a multi-level distribution alignment-based unsupervised domain adaptation network(MDA-Net)for segmentation of 3D neuronal soma images.Distribution alignment is performed in both feature space and output space.In the feature space,features from different scales are adaptively fused to enhance the feature extraction capability for small target somata and con-strained to be domain invariant by adversarial adaptation strategy.In the output space,local discrepancy maps that can reveal the spatial structures of somata are constructed on the predicted segmentation results.Then thedistribution alignment is performed on the local discrepancies maps across domains to obtain a superior discrepancy map in the target domain,achieving refined segmentation performance of neuronal somata.Additionally,after a period of distribution align-ment procedure,a portion of target samples with high confident pseudo-labels are selected as training data,which assist in learning a more adaptive segmentation network.We verified the superiority of the proposed algorithm by comparing several domain adaptation networks on two 3D mouse brain neuronal somata datasets and one macaque brain neuronal soma dataset.
文摘Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and treatment.However,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound images.Although existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,etc.To address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule segmentation.The MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding methods.This transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the data.The approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the data.Furthermore,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation accuracy.Experimental results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)dataset.These findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
基金supported by MICIU MCIN/AEI/10.13039/501100011033Spain with Grant PID2020-118265GB-C42,-C44,PRTR-C17.I01Generalitat Valenciana,Spain with Grant CIPROM/2022/54,ASFAE/2022/031,CIAPOS/2021/114 and by the EU NextGenerationEU,ESF funds.This work was also supported by the National Science Centre(NCN),Poland(Grant No.2020/39/D/ST2/00466).
文摘The NEutron Detector Array(NEDA)is designed to be coupled to gamma-ray spectrometers to enhance the sensitivity of the setup by enabling reaction channel selection through counting of the evaporated neutrons.This article presents the implementation of a double trigger condition system for NEDA,which improves the acquisition of neutrons and reduces the number of gamma rays acquired.Two independent triggers are generated in the double trigger condition system:one based on charge comparison(CC)and the other on time-of-flight(TOF).These triggers can be combined using OR and AND logic,offering four distinct trigger modes.The developed firmware is added to the previous one in the Virtex 6 field programmable gate array(FPGA)present in the system,which also includes signal processing,baseline correction,and various trigger logic blocks.The performance of the trigger system is evaluated using data from the E703 experiment performed at GANIL.The four trigger modes are applied to the same data,and a subsequent offline analysis is performed.It is shown that most of the detected neutrons are preserved with the AND mode,and the total number of gamma rays is significantly reduced.Compared with the CC trigger mode,the OR trigger mode allows increasing the selection of neutrons.In addition,it is demonstrated that if the OR mode is selected,the online CC trigger threshold can be raised without losing neutrons.
基金financially supported by the Natural Science Foundation of Hunan Province,China(No.2024JJ2074)the National Natural Science Foundation of China(No.22376221)the Young Elite Scientists Sponsorship Program by CAST,China(No.2023QNRC001).
文摘Tailings produced by mining and ore smelting are a major source of soil pollution.Understanding the speciation of heavy metals(HMs)in tailings is essential for soil remediation and sustainable development.Given the complex and time-consuming nature of traditional sequential laboratory extraction methods for determining the forms of HMs in tailings,a rapid and precise identification approach is urgently required.To address this issue,a general empirical prediction method for HM occurrence was developed using machine learning(ML).The compositional information of the tailings,properties of the HMs,and sequential extraction steps were used as inputs to calculate the percentages of the seven forms of HMs.After the models were tuned and compared,extreme gradient boosting,gradient boosting decision tree,and categorical boosting methods were found to be the top three performing ML models,with the coefficient of determination(R^(2))values on the testing set exceeding 0.859.Feature importance analysis for these three optimal models indicated that electronegativity was the most important factor affecting the occurrence of HMs,with an average feature importance of 0.4522.The subsequent use of stacking as a model integration method enabled the ability of the ML models to predict HM occurrence forms to be further improved,and resulting in an increase of R^(2) to 0.879.Overall,this study developed a robust technique for predicting the occurrence forms in tailings and provides an important reference for the environmental assessment and recycling of tailings.
文摘This paper focuses on the service management for digitally disadvantaged groups in Chinese and American public libraries.Utilizing a comparative analysis method,it explores the differences and dilemmas between the two countries in terms of policy support,resource allocation,service models,and librarian training.The research finds that the United States has established a relatively comprehensive legal guarantee system and diverse funding channels,but recent federal policy fluctuations pose risks to project sustainability.Although China has made significant progress in equalizing services,it still faces structural challenges such as insufficient institutional guarantees and uneven resource allocation.Based on the comparative analysis,this paper proposes strategic suggestions including establishing a four-dimensional support system of"Law-Resources-Technology-Personnel"and promoting a"Digital Inclusion Partnership"model,aiming to provide references for Chinese public libraries to improve services for digitally disadvantaged groups.
基金Project(2024JJ2074) supported by the Natural Science Foundation of Hunan Province,ChinaProject(22376221) supported by the National Natural Science Foundation of ChinaProject(2023QNRC001) supported by the Young Elite Scientists Sponsorship Program by CAST,China。
文摘Driven by rapid technological advancements and economic growth,mineral extraction and metal refining have increased dramatically,generating huge volumes of tailings and mine waste(TMWs).Investigating the morphological fractions of heavy metals and metalloids(HMMs)in TMWs is key to evaluating their leaching potential into the environment;however,traditional experiments are time-consuming and labor-intensive.In this study,10 machine learning(ML)algorithms were used and compared for rapidly predicting the morphological fractions of HMMs in TMWs.A dataset comprising 2376 data points was used,with mineral composition,elemental properties,and total concentration used as inputs and concentration of morphological fraction used as output.After grid search optimization,the extra tree model performed the best,achieving coefficient of determination(R2)of 0.946 and 0.942 on the validation and test sets,respectively.Electronegativity was found to have the greatest impact on the morphological fraction.The models’performance was enhanced by applying an ensemble method to the top three optimal ML models,including gradient boosting decision tree,extra trees and categorical boosting.Overall,the proposed framework can accurately predict the concentrations of different morphological fractions of HMMs in TMWs.This approach can minimize detection time,aid in the safe management and recovery of TMWs.
基金supported by the Jiangsu Provincial Key Research and Development Program(BE2022072)the National Natural Science Foundation of China(12141304)the Natural Science Foundation of Jiangsu Province(BK20231134).
文摘To address the installation challenges of a 2-m ring Gregorian telescope system,and similar optical systems with a small width-to-radius ratio,we propose a detection method combining local interferometry with a comparison model.This method enhances the precision of system calibration by establishing a dataset that delineates the relationship between secondary mirror misalignment and wavefront aberration,subsequently inferring the misalignment from interferometric detection results during the calibration process.For the 2-m ring telescope,we develop a detection model using five local sub-apertures,enabling a root-mean-square detection accuracy of 0:0225λ(λ=632:8 nm)for full-aperture wavefront aberration.The calibration results for the 2-m Ring Solar Telescope system indicate that the root-mean-square value of sub-aperture wavefront aberration reaches 0.104λ,and the root-mean-square value of spliced full-aperture measurement yields reaches 0.112λ.This method offers a novel approach for calibrating small width-toradius ratio telescope systems and can be applied to the calibration of other irregular-aperture optical systems.
基金The research was supported by innovation research project of Chinese Academy of Sciences (KZCX3-SW-418) and by Nature Science Foundation of Liaoning Province (20021006).
文摘Mongolian pine (Pinus sylvestiris Linnaeus var. mongolica Litvinov) as a valuable conifer tree species has been broadly introduced to the sandy land areas in 揟hree North?regions (North, northwest and northeast of China), but many problems occurred in the earliest Mongolian pine plantations in Zhanggutai, Zhangwu County, Liaoning Province (ZZL). In order to clarify the reason, comprehensive investigations were carried out on differences in structure characteristics, growth processes and ecological factors between artificial stands (the first plantation established in ZZL in 1950s) and natural stands (the origin forests of the tree species in Honghuaerji, Inner Mongolia) on sandy land. The results showed that variation of diameter-class distributions in artificial stands and natural stands could be described by Weibull and Normal distribution models, respectively. Chapman-Richards growth model was employed to reconstruct the growth process of Mongolian pine based on the data from field investigation and stem analysis. The ages of maximum of relative growth rate and average growth rate of DBH, height, and volume of planted trees were 11, 22 years, 8, 15 years and 35, 59 years earlier than those of natural stand trees, respectively. In respect of the incremental acceleration of volume, the artificial and natural stands reached their maximum values at 14 years and 33 years respectively. The quantitative maturity ages of artificial stands and natural stands were 43 years and 102 years respectively. It was concluded that the life span of the Mongolian pine trees in natural stands was about 60 years longer than those in artificial stands. The differences mentioned above between artificial and natural Mongolian pine forests on sandy land were partially attributed to the drastic variations of ecological conditions such as latitude, temperature, precipitation, evaporation and height above sea level. Human beings' disturbances and higher density in plantation forest may be ascribed as additional reasons. Those results may be potentially useful for the management and afforestation of Mongolian pine plantations on sandy land in arid and semi-arid areas.
文摘There are a lot of methods in city water consumption short-term forecasting both inside and outside the country. But among these methods there exist many advantages and shortcomings in model establishing, solving and predicting accuracy, speed, applicability. This article draws lessons from other realm mature methods after many years′ study. It′s systematically studied and compared to predict the water consumption in accuracy, speed, effect and applicability among the time series triangle function method, artificial neural network method, gray system theories method, wavelet analytical method.
基金Supported by Independent Scientific Research Fund Project of Dalian Nationalities University(DC10030205)~~
文摘In order to explore the cultural value of waterfront in urban landscape,from the perspective of cross-cultural comparison psychology,the subjects from Britain,Japan and China have been surveyed to obtain their cognitive structure and behavior on waterfront landscape.Based on the comparison of quantitative statistic results of life value,cognitive structure of waterfront space,and water-loving,a quantitative analysis has been conducted on the relevance between each factor by using Quantification Theory III.Then,it has analyzed the types and purpose of behavior in waterfront space,and the influence brought by cultural value difference.
文摘The increasing scale and complexity of power systems require high performance and high reliability of power system protection.Protective relaying based on directional comparison with power line carrier or microwave channels is the most suitable protection scheme for long distance EHV transmission lines and is widely used in power systems.The key element of such protection is a directional relay used to discriminate the fault direction.In order to overcome the disadvantages of conventional directional relays,the authors of this paper put forward the directional comparison carrier protection based on the artificial neural network(ANN).The protection is extensively tested using electromagnetic transient program (EMTP) under various electric power system operating and fault conditions.It is proved that the directional comparison carrier protection based on ANN,which can recognize various fault patterns of the protected transmission line(such as fault direction,fault phases etc.)correctly in any kind of operating and fault conditions and the whole process,is satisfactory for EHV transmission line protection.
文摘Research of competitiveness of China and the United States is of great significance to enhancing China＇s economic competitiveness and achieving the objective of national rejuvenation. By creating a competitiveness framework and a system of heterogeneous indicators, this paper investigates the competitiveness of China and the US in terms of current status, historic change and global environment. Our research led to the following findings： core factors determine the level of competitiveness for China and the US; the national competitiveness of both countries is evolving towards structural homogeneity; and China and the US lead most countries in many common areas. China has the potential to overtake the US in competitiveness in the future. We suggest that China increase its competitiveness by promoting its advantages, addressing its weaknesses and focusing on core areas.
