A Level考试素有“英国高考”之称。其制度演进大致经历了发轫与探索、扩张与调适、回归与重塑三个阶段,其主要变革内容包括四个方面:组织形式从年终末考的线性考试模式发展为一年多考的模块化考试,再回归线性考试模式;考试评价从常模...A Level考试素有“英国高考”之称。其制度演进大致经历了发轫与探索、扩张与调适、回归与重塑三个阶段,其主要变革内容包括四个方面:组织形式从年终末考的线性考试模式发展为一年多考的模块化考试,再回归线性考试模式;考试评价从常模参照转变为标准参照,评价手段逐步优化;考试要求从注重学科深度转变为强调知识广度,再发展为追求广度和深度并重;考试内容从偏重学术性转变为普职并重,再发展为职普融通和强调基础学科。变革的动因既有来自外部的国际竞争加剧和国内政党轮替,也有来自内部的文化价值观驱动和考试选才效度追求。A Level考试制度对我国高考改革有一定启发,我国可结合国情,以基础学科为支点、职普融通为路径、多样化的考试选择为依托、预测效度为导向,开展本土化探索。展开更多
Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propo...Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.展开更多
The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the orig...The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.展开更多
Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread...Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread application remains constrained by intrinsic mechanical limitations,fundamentally rooted in the nature of crystallographic defects.Atomic-scale modeling techniques are transforming our ability to unravel the structures,energetics,and dynamics of these defects and to explore their complex interactions,thereby guiding defect engineering in Mg alloys.However,the growing body of available data can make it difficult for researchers to identify critical knowledge gaps and promising areas for further exploration.To address this challenge,we highlight key research domains with significant potential for impactful advancements,aiming to illuminate these areas while inspiring innovative approaches and encouraging deeper exploration of pivotal topics that may shape the future of Mg alloy development.This review presents a comprehensive overview of the state-of-the-art in atomic-scale modeling of defects in Mg and its alloys.We introduce key simulation methodologies,including density functional theory and atomistic simulations,and highlight their applications to defect distribution,defect dynamics,and defect-defect interactions.By bridging fundamental insights in defects with alloy design strategies,this review aims to support and inspire the broader Mg research community and to underscore the growing impact of atomic-scale modeling in the accelerated development of high-performance Mg alloys.展开更多
With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluat...Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure,defective mechanical seals are often replaced empirically,which not only contributes to economic losses but also poses risks to reactor safety.To reveal the mechanism by which surface defects affect sealing performance,this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals.A defect characterization model was established by extracting key features of the observed defects.The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model.Changes in sealing gap,leakage rates,and film stiffness with respect to defect size,location,and other characteristics are discussed.This work contributes to a deeper understanding of defect failure mechanisms.These results can serve as a reference for evaluating defective seals.展开更多
The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the...The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fati...This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.展开更多
Dear Editor,The Cay2.1 channel,also known as the P/Q-type Ca^(2+) channel,is a particular type of voltage-gated Ca^(2+) channel primarily expressed on the presynaptic membrane in the brain[1].It serves as an essential...Dear Editor,The Cay2.1 channel,also known as the P/Q-type Ca^(2+) channel,is a particular type of voltage-gated Ca^(2+) channel primarily expressed on the presynaptic membrane in the brain[1].It serves as an essential part of the precisely orchestrated neurotransmitter release machinery.展开更多
Due to their chiral structure,carbon nanosprings possess unique properties that are promising for nanotechnology applications.The structural transformations of carbon nanosprings in the form of spiral macromolecules d...Due to their chiral structure,carbon nanosprings possess unique properties that are promising for nanotechnology applications.The structural transformations of carbon nanosprings in the form of spiral macromolecules derived from planar coronene and kekulene molecules(graphene helicoids and spiral nanoribbons)are analyzed using molecular dynamics simulations.The interatomic interactions are described by a force field including valence bonds,bond angles,torsional and dihedral angles,as well as van derWaals interactions.While the tension/compression of such nanosprings has been analyzed in the literature,this study investigates other modes of deformation,including bending and twisting.Depending on the geometric characteristics of the carbon nanosprings,the formation of structural and helix reversal topological defects is described.During these structural transformations of the nanosprings,only van der Waals bonds break and recover,but breaking or recovery of covalent bonds does not take place.It is found that nanosprings demonstrate a significantly higher coefficient of axial thermal expansion than many metals and alloys.Under axial compression,Euler instability leads to lateral bending with continuous deformation of the nanospring axis at relatively low compression,while at high compression,bending kinks form.Various types of topological defects form on the instantly released nanospring during its relaxation from a highly stretched configuration.These results are useful for the development of nanosensors operating over a wide temperature range.展开更多
The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which ...The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.展开更多
Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pi...Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pits,or uneven surfaces,where localized stress concentrations are readily induced.This study conducted a series of laboratory desiccation tests on slurry samples to investigate the initiation and propagation of desiccation cracks in the presence of varying types and quantities of surface defects.Digital image correlation(DIC)technology was employed to monitor the strain and displacement fields on the soil surface during the desiccation process.The results reveal that strain and displacement data derived from DIC can precisely predict the initiation sites and propagation directions of desiccation cracks.In samples with internal defects,cracks predominantly propagate through the defect,whereas external defects tend to initiate cracks along their edges.In samples with multiple defects,Y-shaped crack patterns generally form initially,followed by T-shaped and straight cracks,driven by the evolving stress field.The dynamic interplay between crack formation and tensile stress redistribution governs the initiation and propagation of desiccation cracks.展开更多
The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab b...The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab ballastless track structure.This study sought to enhance technical standards for evaluating interfacial bonding properties by suggesting the use of the splitting tensile strength to evaluate the impact of bubble defects.Specimens were fabricated through on-site experiment.The percent of each area of 6 cm^(2)or more bubble defect was 0 in most of specimens.When the cumulative area of all bub-ble defects reached 12%,the splitting tensile strength value was 0.67 MPa,which exceeded the minimum required value of 0.5 MPa for ensuring bonding interface adhesion.Furthermore,when the cumulative area of all bubble defects reached 8%,the splitting tensile strength value was 0.85 MPa,which exceeded the minimum required value of 0.8 MPa,thereby over-coming the negative impact of each area of 10 cm^(2) or more bubble defect.Additionally,keeping the cumulative area of each area of 6 cm^(2) or more bubble defect below 6%ensured adequate bonding strength and reduced the occurrence of specimens with lower splitting tensile strength values.展开更多
Hainan Province in south China is the country’s second-largest island and the largest free trade port by area.It has entered a historic phase in China’s drive to promote high-level institutional opening up.On 18 Dec...Hainan Province in south China is the country’s second-largest island and the largest free trade port by area.It has entered a historic phase in China’s drive to promote high-level institutional opening up.On 18 December 2025,Hainan officially launched island-wide special customs operations,commonly referred to as“customs closure.”From that date,goods entering or leaving the island,except those traded with the Chinese mainland,are subject to simplified customs procedures and potentially reduced or zero tariffs.展开更多
During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once ag...During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once again,3,000 exhibitors from across the globe placed their trust in the industry’s central platform in Frankfurt,presenting current collections,materials and textile solutions for holistic interior design to approximately 47,000 buyers.Under the motto“Lead the Change”,Heimtextil brought evolving market dynamics,Artificial Intelligence(AI)and new business opportunities to life.The focus was on progressive design approaches,visionary talents,functional textiles and new hospitality concepts shaping the future of interior design.A tangible sense of confidence and a clear commitment to Heimtextil as a strong industry partner resonated throughout the exhibition halls.展开更多
Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi...Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.展开更多
In order to reduce deep level defects, the theory and process design of 4H-SiC homoepitaxial layer implanted by carbon ion are studied. With the Monte Carlo simulator TRIM, the ion implantation range, location of peak...In order to reduce deep level defects, the theory and process design of 4H-SiC homoepitaxial layer implanted by carbon ion are studied. With the Monte Carlo simulator TRIM, the ion implantation range, location of peak concentration and longitudinal straggling of carbon are calculated. The process for improving deep energy level in undoped 4H-SiC homoepitaxial layer by three times carbon ion-implantation is proposed, including implantation energy, dose, the SiO2 resist mask, annealing temperature, annealing time and annealing protection. The deep energy level in 4H-SiC material can be significantly improved by implantation of carbon atoms into a shallow surface layer. The damage of crystal lattice can be repaired well, and the carbon ions are effectively activated after 1 600 ℃ annealing, meanwhile, deep level defects are decreased.展开更多
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘Manual inspection of onba earing casting defects is not realistic and unreliable,particularly in the case of some micro-level anomalies which lead to major defects on a large scale.To address these challenges,we propose BearFusionNet,an attention-based deep learning architecture with multi-stream,which merges both DenseNet201 and MobileNetV2 for feature extraction with a classification head inspired by VGG19.This hybrid design,figuratively beaming from one layer to another,extracts the enormity of representations on different scales,backed by a prepreprocessing pipeline that brings defect saliency to the fore through contrast adjustment,denoising,and edge detection.The use of multi-head self-attention enhances feature fusion,enabling the model to capture both large and small spatial features.BearFusionNet achieves an accuracy of 99.66%and Cohen’s kappa score of 0.9929 in Kaggle’s Real-life Industrial Casting Defects dataset.Both McNemar’s and Wilcoxon signed-rank statistical tests,as well as fivefold cross-validation,are employed to assess the robustness of our proposed model.To interpret the model,we adopt Grad-Cam visualizations,which are the state of the art standard.Furthermore,we deploy BearFusionNet as a webbased system for near real-time inference(5-6 s per prediction),which enables the quickest yet accurate detection with visual explanations.Overall,BearFusionNet is an interpretable,accurate,and deployable solution that can automatically detect casting defects,leading to significant advances in the innovative industrial environment.
文摘The original online version of this article was revised:The layout update for Article 758 has impacted the page range in the published issue,but did not affect the scholarly content.To ensure consistency with the originally assigned pages(2595-2614),we will need to publish an erratum to correct the article and restore the original page range.The original article has been corrected.
基金support by the Deutsche Forschungsgemeinschaft(DFG)-Projektnummer 505716422the French National Research Agency(ANR)grants ANR22-CE92-0058-01(SILA)and ANR-21-CE08-0001(ATOUUM)+2 种基金support by the DFG through the projects A05 of the SFB1394 StructuralChemical Atomic Complexity-From Defect Phase Diagrams to Material Properties,project ID 409476157support funded by the DFG-Projektnummer 562592407 and 555365333.
文摘Magnesium(Mg)and its alloys,known for their low density and high specific strength,are increasingly explored as lightweight structural materials across a broad range of industrial applications.However,their widespread application remains constrained by intrinsic mechanical limitations,fundamentally rooted in the nature of crystallographic defects.Atomic-scale modeling techniques are transforming our ability to unravel the structures,energetics,and dynamics of these defects and to explore their complex interactions,thereby guiding defect engineering in Mg alloys.However,the growing body of available data can make it difficult for researchers to identify critical knowledge gaps and promising areas for further exploration.To address this challenge,we highlight key research domains with significant potential for impactful advancements,aiming to illuminate these areas while inspiring innovative approaches and encouraging deeper exploration of pivotal topics that may shape the future of Mg alloy development.This review presents a comprehensive overview of the state-of-the-art in atomic-scale modeling of defects in Mg and its alloys.We introduce key simulation methodologies,including density functional theory and atomistic simulations,and highlight their applications to defect distribution,defect dynamics,and defect-defect interactions.By bridging fundamental insights in defects with alloy design strategies,this review aims to support and inspire the broader Mg research community and to underscore the growing impact of atomic-scale modeling in the accelerated development of high-performance Mg alloys.
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金Supported by National Natural Science Foundation of China(Grant No.51975315)National Science and Technology Major Project of China(Grant No.2019-IV-0020-0088).
文摘Nuclear reactor coolant pumps require frequent maintenance to ensure operational safety.One critical aspect of this maintenance is verifying the integrity of the mechanical sealing system.Due to the lack of an evaluation criteria and an incomplete understanding of how end-face defects lead to failure,defective mechanical seals are often replaced empirically,which not only contributes to economic losses but also poses risks to reactor safety.To reveal the mechanism by which surface defects affect sealing performance,this study proposes a classification method for end-face defects based on the analysis of approximately one hundred used mechanical seals.A defect characterization model was established by extracting key features of the observed defects.The influence of these defects on sealing performance was analyzed using a liquid-thermal-solid coupling model.Changes in sealing gap,leakage rates,and film stiffness with respect to defect size,location,and other characteristics are discussed.This work contributes to a deeper understanding of defect failure mechanisms.These results can serve as a reference for evaluating defective seals.
基金supported by the China National Science Foundation(No.42130506,42071031)the Special Technology Innovation Fund of Carbon Peak and Carbon Neutrality in Jiangsu Province(BK20231515)+1 种基金the Spanish Government grant PID2022-140808NB-I00 funded by MICIU/AEI/https://doi.org/10.13039/501100011033the Catalan Government grants SGR 2021-1333 and AGAUR2023 CLIMA 00118.
文摘The root-to-shoot(R/S)ratio is a critical indicator of the balance between root biomass and shoot biomass,representing the ecological strategies and adaptive responses of plants to environmental conditions.However,the patterns of change in community R/S ratios during forest succession and their response to moisture levels across broad geographic gradients remains unclear.Based on forest biomass data from a national field inventory of 5,825 plots conducted across China between 2011 and 2015,this study looked into allocating biomass shoots and roots at the early,middle,and late stages of growth in plantations and succession in natural forests,and evaluated how moisture availability influences this allocation.The results revealed a significant decline in R/S ratios from early to late stages for both plantations and natural forests.Shoot and root biomass in plantations grew isometrically during the early and middle succession stages but shifted to allometric growth in the late stage,with the slope of the log-transformed shoot-root biomass relationship differing significantly across growth stages.Natural forests,in contrast,maintained isometric growth across successional stages,showing no significant variation in the slope of the log-transformed shoot-root biomass relationship.Environmental factors,particularly moisture levels,strongly influenced R/S ratios.Moisture levels significantly affected size-corrected R/S ratios,particularly in the middle stage of plantations and the early and middle stages of natural forests,supporting the hypothesis of optimal allocation.These findings suggest that in water-limited regions,forest management should prioritize drought-tolerant,deep-rooted native species,encourage mixed-species planting in the early stage,and reduce logging intensity in mature plantations.Conserving natural forests to maintain successional dynamics is essential for long-term ecological resilience.These findings emphasize the importance of balancing productivity with ecological sustainability by adapting practices to specific environments and forest types under climate change.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金supported by National Natural Science Foundation of China[NO.11932013].
文摘This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.
基金supported by the National Natural Science Foundation of China(32100773 and U20A6005)the National Science and Technology Innovation 2030-Major Project of China(2021ZD0202500)+4 种基金Shenzhen Medical Research Fund(B2402024)China Postdoctoral Science Foundation(2021M693296)Shenzhen Science and Technology Program(JCYJ20230807093815032)Guangdong High-level Hospital Construction Fund(ynkt2021-zz33 and LCYJ2022093)the Natural Science Foundation of Guangdong Province,China(2022A1515010297).
文摘Dear Editor,The Cay2.1 channel,also known as the P/Q-type Ca^(2+) channel,is a particular type of voltage-gated Ca^(2+) channel primarily expressed on the presynaptic membrane in the brain[1].It serves as an essential part of the precisely orchestrated neurotransmitter release machinery.
基金funded by the Russian Science Foundation(RSF),grant No.25-73-20038(conceptualization,methodology,manuscript writing).
文摘Due to their chiral structure,carbon nanosprings possess unique properties that are promising for nanotechnology applications.The structural transformations of carbon nanosprings in the form of spiral macromolecules derived from planar coronene and kekulene molecules(graphene helicoids and spiral nanoribbons)are analyzed using molecular dynamics simulations.The interatomic interactions are described by a force field including valence bonds,bond angles,torsional and dihedral angles,as well as van derWaals interactions.While the tension/compression of such nanosprings has been analyzed in the literature,this study investigates other modes of deformation,including bending and twisting.Depending on the geometric characteristics of the carbon nanosprings,the formation of structural and helix reversal topological defects is described.During these structural transformations of the nanosprings,only van der Waals bonds break and recover,but breaking or recovery of covalent bonds does not take place.It is found that nanosprings demonstrate a significantly higher coefficient of axial thermal expansion than many metals and alloys.Under axial compression,Euler instability leads to lateral bending with continuous deformation of the nanospring axis at relatively low compression,while at high compression,bending kinks form.Various types of topological defects form on the instantly released nanospring during its relaxation from a highly stretched configuration.These results are useful for the development of nanosensors operating over a wide temperature range.
基金supported in part by the National Natural Science Foundation of China(No.42271446)in part by the Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-Temporary Big Data Technology,China(No.TKL2024B13)in part by the Science and Technology Program of Tianjin,China(No.24YFYSHZ00080)。
文摘The selection of a suitable navigation area is pivotal in aircraft scene matching guidance technology.This study addresses the challenge of identifying suitable reference image ranges for precise scene matching,which is crucial for enhancing aircraft positioning accuracy.Traditional methods for image matchability analysis are often limited by their reliance on manual feature parameter design and threshold-based filtering,resulting in suboptimal accuracy and efficiency.This paper proposes a novel network architecture for selecting suitable navigation areas using image Matching Level Segmentation(MLSNet).The approach involves two key innovations:a method for generating segmentation labels that quantify matchability levels and an end-to-end network architecture for rapid and precise prediction of reference image matchability segmentation maps.The network includes two core modules:the saliency analysis module uses multi-layer convolutional networks to accurately detect image saliency features across various levels and scales;the multidimensional attention module utilizes attention mechanisms to focus on feature channels and spatial neighborhood scenes to assess the image’s matchability.Our method was rigorously tested on an extensive collection of remote sensing images,where it was benchmarked against a range of both traditional and cutting-edge deep learning methods.The findings indicate that MLSNet is significantly superior to traditional methods in accuracy and efficiency of matchability analysis,and is also relatively ahead of state-of-the-art deep learning models.
基金supported by the National Natural Science Foundation of China(Grant Nos.42525201,42230710,42407521).
文摘Soil desiccation cracking is a prevalent natural phenomenon that poses significant geotechnical and geoenvironmental challenges.Cracks typically initiate at surface defects such as air bubbles,large aggregates,tiny pits,or uneven surfaces,where localized stress concentrations are readily induced.This study conducted a series of laboratory desiccation tests on slurry samples to investigate the initiation and propagation of desiccation cracks in the presence of varying types and quantities of surface defects.Digital image correlation(DIC)technology was employed to monitor the strain and displacement fields on the soil surface during the desiccation process.The results reveal that strain and displacement data derived from DIC can precisely predict the initiation sites and propagation directions of desiccation cracks.In samples with internal defects,cracks predominantly propagate through the defect,whereas external defects tend to initiate cracks along their edges.In samples with multiple defects,Y-shaped crack patterns generally form initially,followed by T-shaped and straight cracks,driven by the evolving stress field.The dynamic interplay between crack formation and tensile stress redistribution governs the initiation and propagation of desiccation cracks.
基金supported by a grant from China railway corporation science and technology research and development plan project(Grant No.2017G005-B)funding support by Wuyi University’s Hong Kong and Macao Joint Research and Development Fund(Grants No.2021WGALH15)funding support by the Innovation and Technology Commission of Hong Kong SAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center(Grant No.K-BBY1).
文摘The current technical standards primarily relied on experience to judge the interfacial bonding properties between the self-compacting concrete filling layer and the steam-cured concrete precast slab in CRTS Ⅲ slab ballastless track structure.This study sought to enhance technical standards for evaluating interfacial bonding properties by suggesting the use of the splitting tensile strength to evaluate the impact of bubble defects.Specimens were fabricated through on-site experiment.The percent of each area of 6 cm^(2)or more bubble defect was 0 in most of specimens.When the cumulative area of all bub-ble defects reached 12%,the splitting tensile strength value was 0.67 MPa,which exceeded the minimum required value of 0.5 MPa for ensuring bonding interface adhesion.Furthermore,when the cumulative area of all bubble defects reached 8%,the splitting tensile strength value was 0.85 MPa,which exceeded the minimum required value of 0.8 MPa,thereby over-coming the negative impact of each area of 10 cm^(2) or more bubble defect.Additionally,keeping the cumulative area of each area of 6 cm^(2) or more bubble defect below 6%ensured adequate bonding strength and reduced the occurrence of specimens with lower splitting tensile strength values.
文摘Hainan Province in south China is the country’s second-largest island and the largest free trade port by area.It has entered a historic phase in China’s drive to promote high-level institutional opening up.On 18 December 2025,Hainan officially launched island-wide special customs operations,commonly referred to as“customs closure.”From that date,goods entering or leaving the island,except those traded with the Chinese mainland,are subject to simplified customs procedures and potentially reduced or zero tariffs.
文摘During 13 to 16 January 2026,with 148 participating nations,rising global relevance and a marked increase in visitor quality,Heimtextil 2026 stood for stability and reliability in a volatile market environment.Once again,3,000 exhibitors from across the globe placed their trust in the industry’s central platform in Frankfurt,presenting current collections,materials and textile solutions for holistic interior design to approximately 47,000 buyers.Under the motto“Lead the Change”,Heimtextil brought evolving market dynamics,Artificial Intelligence(AI)and new business opportunities to life.The focus was on progressive design approaches,visionary talents,functional textiles and new hospitality concepts shaping the future of interior design.A tangible sense of confidence and a clear commitment to Heimtextil as a strong industry partner resonated throughout the exhibition halls.
基金Under the auspices of the National Key Research and Development Program of China(No.2024YFC3713102)。
文摘Groundwater level(GWL)is a key indicator used to accurately assess groundwater resources and form the foundation for ef-fective groundwater management.This paper integrates a Gate Recurrent Unit(GRU)model with a Multi-head Self-attention mechan-ism(MSAM-GRU)to simulate GWLs in both confined and unconfined aquifers simultaneously.The model innovatively captures the lag times between GWLs in the unconfined aquifer and precipitation,as well as between GWLs in the confined aquifer and the upper aquifer.We have assessed the effectiveness of the proposed model using a case study in the Beijing Plain,China from January 2005 to December 2020.With the consideration of lag times,the results indicated that the MSAM-GRU model exhibits a maximum 67%and 73%reduction in RMSE compared to the Attention mechanism-GRU(AM-GRU)and GRU model,respectively.MSAM-GRU model exhibited a 31%reduction in RMSE and a 0.12 increase in R^(2) compared to the same model that do not account for lag time.In Region I,the shortest lag time of GWL in the unconfined aquifer was two months,while that in the confined aquifer was three months,indicating a longer delayed response in the confined aquifer.MSAM-GRU model considering lag time,was then applied to simulate the GWLs in the unconfined aquifer under different scenarios and to analyze whether GWL fluctuations affect subway operations.The simulation res-ults showed that under the scenario 1,the GWL in the unconfined aquifer would rise above the depth of subway station floor,threaten-ing the operation of subways.This study can provide reliable technical support for the accurate simulation of GWLs in multi-aquifer systems.
基金Supported by the National Natural Science Foundation of China (No. 61006008)Xi'an Applied Materials Innovation Fund (No. XA-AM-200607)
文摘In order to reduce deep level defects, the theory and process design of 4H-SiC homoepitaxial layer implanted by carbon ion are studied. With the Monte Carlo simulator TRIM, the ion implantation range, location of peak concentration and longitudinal straggling of carbon are calculated. The process for improving deep energy level in undoped 4H-SiC homoepitaxial layer by three times carbon ion-implantation is proposed, including implantation energy, dose, the SiO2 resist mask, annealing temperature, annealing time and annealing protection. The deep energy level in 4H-SiC material can be significantly improved by implantation of carbon atoms into a shallow surface layer. The damage of crystal lattice can be repaired well, and the carbon ions are effectively activated after 1 600 ℃ annealing, meanwhile, deep level defects are decreased.
