1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain bounda...1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].展开更多
In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the ...In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.展开更多
While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer ...While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.展开更多
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models...For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study...Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study aims to survey the level of radiation doses delivered to child patients during head exams in CT imaging to set up the Dosimetric Reference Levels (DRLs), a routine dose optimization tool, based on data acquired at the University Hospital of Angré (UHA), the University Hospital of Treichville (UHT) and the Polyclinic Hospital Farah (Farah) for optimizing procedures in Ivorian hospitals. Prospectively performed on 334 CT images of 186 child patients, this study was carried out on CT systems such as Hitachi Scenaria, Sinovision Insitum, and Philips Incisive used respectively at UHA, UHT and Farah. Children’s scan data were classified into four age bands: vol or dose-length product as DLP) value, whatever the hospital, increases with respect to the age of child patients. Based on the 75th percentile of the whole dose distributions, the DRLs of the CTDIvol is 54.37 mGy whatever the age groups and those of the DLP with respect to age bands are 1224.55 mGy∙cm, 1414.06 mGy∙cm, 1632.24 mGy.cm and 1544.57 mGy∙cm, respectively. The averaged values of CTDIvol and DLP smaller than the corresponding DRLs values suggest that practices in our three facilities are optimized. However, comparing our results with those from different international studies, we see that the CTDIvol and DLP values obtained in the present work are higher. These results suggest additional surveys to ensure our DRLs values and efforts from radiologists, imaging technicians and medical physicists to strengthen clinical procedures for the radiation protection of children undergoing CT scans in Côte d’Ivoire.展开更多
Metallogenic research on structural levels can reveal vertical patterns of mineralization and facilitate the deep exploration of economic minerals.However,research focusing on the correlation between structural levels...Metallogenic research on structural levels can reveal vertical patterns of mineralization and facilitate the deep exploration of economic minerals.However,research focusing on the correlation between structural levels and mineralization remains limited.In this study,we summarize the deformation patterns and associated mineral deposits observed at different crustal levels(i.e.,surface,shallow,middle,and deep structural levels,corresponding to depths of<2,2-8,8-15,and>15 km,respectively).Furthermore,we examine the genetic association between structural levels and metallogenesis,demonstrating that distinct structural levels are linked to specific types of mineralization.Key factors that vary across crustal levels include temperature,pressure,and fluid circulation.Ore-forming processes involve interactions between structures and fluids under varying temperatures and pressures.Structural levels influence mineralization by controlling the temperatures,pressures,and deformation mechanisms that drive the activation,migration,and enrichment of ore-forming materials.展开更多
BackgroundIt's crucial to study the effect of changes in thresholds(T)and most comfortable levels(M)on behavioral measurements in young children using cochlear implants.This would help the clinician with the optim...BackgroundIt's crucial to study the effect of changes in thresholds(T)and most comfortable levels(M)on behavioral measurements in young children using cochlear implants.This would help the clinician with the optimization and validation of programming parameters.ObjectiveThe study has attempted to describe the changes in behavioral responses with modification of T and M levels.MethodsTwenty-five participants in the age range 5 to 12 years using HR90K/HiFocus1J or HR90KAdvantage/HiFocus1J with Harmony speech processors participated in the study.A decrease in T levels,a rise in T levels,or a decrease in M levels in the everyday program were used to create experimental programs.Sound field thresholds and speech perception were measured at 50 dBHL for three experimental and everyday programs.ConclusionThe results indicated that only reductions of M levels resulted in significantly(p<0.01)poor aided thresholds and speech perception.On the other hand,variation in T levels did not have significant changes in either sound field thresholds or speech perception.The results highlight that M levels must be correctly established in order to prevent decreased speech perception and audibility.展开更多
The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, ...The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, including 10 radiologists working in the hospitals covered by the survey, to assess the practice of pediatric radiology and the degree of compliance with radiation protection principles for the safety of children and the environment. We collected radiophysical parameters to calculate entrance doses in pediatric radiology in radiology departments to determine the dosimetric level by comparison with the diagnostic reference levels of the International Commission on Radiological Protection (ICRP). All in all, we found that in Kongo Central in the DRC, many health personnel surveyed reported that more than 30% of requested radiological examinations are not justified. Also, after comparing the entrance doses produced in the surveyed departments with those of the International Commission on Radiological Protection (ICRP), a statistically significant difference was found in pediatric radiology between the average doses in five out of six surveyed departments and those of the ICRP. Therefore, almost all of the surveyed departments were found to be highly irradiating in children, while excessive X-ray irradiation in children can have significant effects due to their increased sensitivity to radiation. Among the risks are: increased cancer risks, damage to developing cells, potential genetic effects, and neurological effects. This is why support for implementing radiation protection principles is a necessity to promote the safety of patients and the environment against the harmful effects of X-rays in conventional radiology.展开更多
Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and e...Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.展开更多
The Thwaites Glacier in western Antarctica(Fig. 1) keeps glaciologists and climate scientists awake at night. The 120 kmwide glacier loses about 45 billion tonnes of ice each year, accounting for about 4% of global se...The Thwaites Glacier in western Antarctica(Fig. 1) keeps glaciologists and climate scientists awake at night. The 120 kmwide glacier loses about 45 billion tonnes of ice each year, accounting for about 4% of global sea level rise [1]. If it melted completely, sea levels would climb 65 cm, and follow-on effects could lead to a 3 m increase [2]. But if some scientists' vision becomes reality, in 10–15 years construction crews will sail into the Amundsen Sea off Antarctica to begin building an 80 km long underwater curtain that will shield the glacier from the warm currents that are accelerating its decline [3].展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.展开更多
Water level is the overriding control on carbon cycles in peatlands,which are important for global carbon cycles and ecosystem services.To date,our knowledge of the pattern of water level fluctuations in peatlands and...Water level is the overriding control on carbon cycles in peatlands,which are important for global carbon cycles and ecosystem services.To date,our knowledge of the pattern of water level fluctuations in peatlands and the influence of precipitation and air temperature on them in the subtropical remains poor.In this study,we conducted continuous high-resolution monitoring of water levels from 2014 to 2021 in the Dajiuhu peatland,a typically subtropical peatland in central China.Monitoring results showed that the water level had strong annual(370 days) and seasonal(130 days) oscillations in the Dajiuhu peatland.The annual oscillation is associated with both precipitation and temperature,while the seasonal oscillation is mainly controlled by precipitation.In addition,the depth of peat surface to the water table(DWT) has weak but significant correlations with precipitation and temperature on the daily and weekly scales(r = 0.1–0.21,p<0.01).Once replacing DWT with water table fluctuation cumulation,the correlation coefficients increase apparently(r = 0.47–0.69,p<0.01),especially on the monthly scale.These findings highlight a more important role of the fluctuation than the mean position of water level and have the potential to improve the interpretation of water-level related paleoenvironmental proxies and the understanding of the relationship between water level and biogeochemical processes.展开更多
Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness a...Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness and transparency,both correct and incorrect versions are displayed below.展开更多
To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance c...To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance controller based on multi⁃observers for flexible spacecraft is proposed.First,to prevent unwinding phenomenon in attitude description,the rotation matrix is used to represent the spacecraft’s attitude.Second,the flexible modes observer which can guarantee predefined⁃time convergence is designed,for the case where flexible vibrations are unmeasurable in practice.What’s more,the disturbance observer is applied to estimate and compensate the lumped disturbances to improve the robustness of attitude control.A predefined-time controller is proposed to satisfy the prescribed performance and stabilize the attitude tracking system via barrier Lyapunov function.Finally,through comparative numerical simulations,the proposed controller can achieve high-precision convergence compared with the existing finite-time attitude tracking controller.This paper provides certain references for the high-precision predefined-time prescribed performance attitude tracking of flexible spacecraft with multi-disturbance.展开更多
基金support by the National Natural Science Foundation of China(Grant Nos.U23A20546 and 52271010)the Chinese National Natural Science Fund for Distinguished Young Scholars(Grant No.52025015)the Natural Science Foundation of Tianjin City(No.21JCZDJC00510).
文摘1.Introduction The strength-ductility trade-offdilemma has long been a per-sistent challenge in Al matrix composites(AMCs)[1,2].This is-sue primarily arises from the agglomeration of reinforcements at the grain boundaries(GBs),which restricts local plastic flow dur-ing the plastic deformation and leads to stress concentration[3,4].Recently,the development of concepts aimed at achieving hetero-geneous grain has emerged as a promising approach for enhanc-ing comprehensive mechanical properties[5,6].
基金supported by the National Natural Science Foundation of China under Grant 52325402, 52274057, 52074340 and 51874335the National Key R&D Program of China under Grant 2023YFB4104200+1 种基金the Major Scientific and Technological Projects of CNOOC under Grant CCL2022RCPS0397RSN111 Project under Grant B08028。
文摘In the production of the sucker rod well, the dynamic liquid level is important for the production efficiency and safety in the lifting process. It is influenced by multi-source data which need to be combined for the dynamic liquid level real-time calculation. In this paper, the multi-source data are regarded as the different views including the load of the sucker rod and liquid in the wellbore, the image of the dynamometer card and production dynamics parameters. These views can be fused by the multi-branch neural network with special fusion layer. With this method, the features of different views can be extracted by considering the difference of the modality and physical meaning between them. Then, the extraction results which are selected by multinomial sampling can be the input of the fusion layer.During the fusion process, the availability under different views determines whether the views are fused in the fusion layer or not. In this way, not only the correlation between the views can be considered, but also the missing data can be processed automatically. The results have shown that the load and production features fusion(the method proposed in this paper) performs best with the lowest mean absolute error(MAE) 39.63 m, followed by the features concatenation with MAE 42.47 m. They both performed better than only a single view and the lower MAE of the features fusion indicates that its generalization ability is stronger. In contrast, the image feature as a single view contributes little to the accuracy improvement after fused with other views with the highest MAE. When there is data missing in some view, compared with the features concatenation, the multi-view features fusion will not result in the unavailability of a large number of samples. When the missing rate is 10%, 30%, 50% and 80%, the method proposed in this paper can reduce MAE by 5.8, 7, 9.3 and 20.3 m respectively. In general, the multi-view features fusion method proposed in this paper can improve the accuracy obviously and process the missing data effectively, which helps provide technical support for real-time monitoring of the dynamic liquid level in oil fields.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 72174121 and 71774111)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learningthe Project for the Natural Science Foundation of Shanghai, China (Grant No. 21ZR1444100)
文摘While the interaction between information and disease in static networks has been extensively investigated,many studies have ignored the characteristics of network evolution.In this study,we construct a new two-layer coupling model to explore the interactions between information and disease.The upper layer describes the diffusion of disease-related information,and the lower layer represents the disease transmission.We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation,revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network.Subsequently,we derive the disease outbreak threshold by using the microscopic Markov-chain approach(MMCA).Finally,we perform extensive Monte Carlo(MC)numerical simulations to verify the accuracy of our theoretical results.Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer.Moreover,reducing the damping factor can delay disease outbreaks and suppress disease transmission,while improving individual quarantine measures can contribute positively to disease control.This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.
基金Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金supported by the Beijing Natural Science Foundation(Grant No.L223013)。
文摘For the diagnostics and health management of lithium-ion batteries,numerous models have been developed to understand their degradation characteristics.These models typically fall into two categories:data-driven models and physical models,each offering unique advantages but also facing limitations.Physics-informed neural networks(PINNs)provide a robust framework to integrate data-driven models with physical principles,ensuring consistency with underlying physics while enabling generalization across diverse operational conditions.This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV)curves and estimate key ageing parameters at both the cell and electrode levels.These parameters include available capacity,electrode capacities,and lithium inventory capacity.The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs)and is validated using a public dataset.The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests,with errors in reconstructed OCV curves remaining within 15 mV.This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level,advancing the potential for precise and efficient battery health management.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
文摘Computed tomography is an indispensable X-ray imaging modality used to diagnose numerous pathologies, but it can also involve the delivery of high ionizing radiation doses harmful to the health of patients. This study aims to survey the level of radiation doses delivered to child patients during head exams in CT imaging to set up the Dosimetric Reference Levels (DRLs), a routine dose optimization tool, based on data acquired at the University Hospital of Angré (UHA), the University Hospital of Treichville (UHT) and the Polyclinic Hospital Farah (Farah) for optimizing procedures in Ivorian hospitals. Prospectively performed on 334 CT images of 186 child patients, this study was carried out on CT systems such as Hitachi Scenaria, Sinovision Insitum, and Philips Incisive used respectively at UHA, UHT and Farah. Children’s scan data were classified into four age bands: vol or dose-length product as DLP) value, whatever the hospital, increases with respect to the age of child patients. Based on the 75th percentile of the whole dose distributions, the DRLs of the CTDIvol is 54.37 mGy whatever the age groups and those of the DLP with respect to age bands are 1224.55 mGy∙cm, 1414.06 mGy∙cm, 1632.24 mGy.cm and 1544.57 mGy∙cm, respectively. The averaged values of CTDIvol and DLP smaller than the corresponding DRLs values suggest that practices in our three facilities are optimized. However, comparing our results with those from different international studies, we see that the CTDIvol and DLP values obtained in the present work are higher. These results suggest additional surveys to ensure our DRLs values and efforts from radiologists, imaging technicians and medical physicists to strengthen clinical procedures for the radiation protection of children undergoing CT scans in Côte d’Ivoire.
基金supported by National Key Research and Development Program of China(Grant Nos.2022YFF0800903 and 2024YFC2909905)the National Natural Science Foundation of China(NSFC)(Grant Nos.42261144669,42262026,and 42273073).
文摘Metallogenic research on structural levels can reveal vertical patterns of mineralization and facilitate the deep exploration of economic minerals.However,research focusing on the correlation between structural levels and mineralization remains limited.In this study,we summarize the deformation patterns and associated mineral deposits observed at different crustal levels(i.e.,surface,shallow,middle,and deep structural levels,corresponding to depths of<2,2-8,8-15,and>15 km,respectively).Furthermore,we examine the genetic association between structural levels and metallogenesis,demonstrating that distinct structural levels are linked to specific types of mineralization.Key factors that vary across crustal levels include temperature,pressure,and fluid circulation.Ore-forming processes involve interactions between structures and fluids under varying temperatures and pressures.Structural levels influence mineralization by controlling the temperatures,pressures,and deformation mechanisms that drive the activation,migration,and enrichment of ore-forming materials.
文摘BackgroundIt's crucial to study the effect of changes in thresholds(T)and most comfortable levels(M)on behavioral measurements in young children using cochlear implants.This would help the clinician with the optimization and validation of programming parameters.ObjectiveThe study has attempted to describe the changes in behavioral responses with modification of T and M levels.MethodsTwenty-five participants in the age range 5 to 12 years using HR90K/HiFocus1J or HR90KAdvantage/HiFocus1J with Harmony speech processors participated in the study.A decrease in T levels,a rise in T levels,or a decrease in M levels in the everyday program were used to create experimental programs.Sound field thresholds and speech perception were measured at 50 dBHL for three experimental and everyday programs.ConclusionThe results indicated that only reductions of M levels resulted in significantly(p<0.01)poor aided thresholds and speech perception.On the other hand,variation in T levels did not have significant changes in either sound field thresholds or speech perception.The results highlight that M levels must be correctly established in order to prevent decreased speech perception and audibility.
文摘The purpose of this research was to evaluate radiological safety in pediatric radiology in hospitals in the Kongo Central province of the DRC. To this end, we surveyed a convenience sample of 50 health professionals, including 10 radiologists working in the hospitals covered by the survey, to assess the practice of pediatric radiology and the degree of compliance with radiation protection principles for the safety of children and the environment. We collected radiophysical parameters to calculate entrance doses in pediatric radiology in radiology departments to determine the dosimetric level by comparison with the diagnostic reference levels of the International Commission on Radiological Protection (ICRP). All in all, we found that in Kongo Central in the DRC, many health personnel surveyed reported that more than 30% of requested radiological examinations are not justified. Also, after comparing the entrance doses produced in the surveyed departments with those of the International Commission on Radiological Protection (ICRP), a statistically significant difference was found in pediatric radiology between the average doses in five out of six surveyed departments and those of the ICRP. Therefore, almost all of the surveyed departments were found to be highly irradiating in children, while excessive X-ray irradiation in children can have significant effects due to their increased sensitivity to radiation. Among the risks are: increased cancer risks, damage to developing cells, potential genetic effects, and neurological effects. This is why support for implementing radiation protection principles is a necessity to promote the safety of patients and the environment against the harmful effects of X-rays in conventional radiology.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFE0134200)the National Natural Science Foundation of China(Grant No.12204214)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.GK202207012)QCYRCXM-2022-241。
文摘Multi-electron and multi-orbital effects play a crucial role in the interaction of strong laser fields with complex molecules.Here,multi-electron effects encompass not only electron-electron Coulomb interactions and exchangecorrelation effects but also the interference between the dynamics of different electron wave packets.
文摘The Thwaites Glacier in western Antarctica(Fig. 1) keeps glaciologists and climate scientists awake at night. The 120 kmwide glacier loses about 45 billion tonnes of ice each year, accounting for about 4% of global sea level rise [1]. If it melted completely, sea levels would climb 65 cm, and follow-on effects could lead to a 3 m increase [2]. But if some scientists' vision becomes reality, in 10–15 years construction crews will sail into the Amundsen Sea off Antarctica to begin building an 80 km long underwater curtain that will shield the glacier from the warm currents that are accelerating its decline [3].
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
基金supported by the National Natural Science Foundation of China(62302167,62477013)Natural Science Foundation of Shanghai(No.24ZR1456100)+1 种基金Science and Technology Commission of Shanghai Municipality(No.24DZ2305900)the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).
文摘Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feature representation.However,existing methods often rely on the single-scale deep feature,neglecting shallow and deeper layer features,which poses challenges when predicting objects of varying scales within the same image.Although some studies have explored multi-scale features,they rarely address the flow of information between scales or efficiently obtain class-specific precise representations for features at different scales.To address these issues,we propose a two-stage,three-branch Transformer-based framework.The first stage incorporates multi-scale image feature extraction and hierarchical scale attention.This design enables the model to consider objects at various scales while enhancing the flow of information across different feature scales,improving the model’s generalization to diverse object scales.The second stage includes a global feature enhancement module and a region selection module.The global feature enhancement module strengthens interconnections between different image regions,mitigating the issue of incomplete represen-tations,while the region selection module models the cross-modal relationships between image features and labels.Together,these components enable the efficient acquisition of class-specific precise feature representations.Extensive experiments on public datasets,including COCO2014,VOC2007,and VOC2012,demonstrate the effectiveness of our proposed method.Our approach achieves consistent performance gains of 0.3%,0.4%,and 0.2%over state-of-the-art methods on the three datasets,respectively.These results validate the reliability and superiority of our approach for multi-label image classification.
基金supported by the National Natural Science Foundation of China(No.U20A2094)。
文摘Water level is the overriding control on carbon cycles in peatlands,which are important for global carbon cycles and ecosystem services.To date,our knowledge of the pattern of water level fluctuations in peatlands and the influence of precipitation and air temperature on them in the subtropical remains poor.In this study,we conducted continuous high-resolution monitoring of water levels from 2014 to 2021 in the Dajiuhu peatland,a typically subtropical peatland in central China.Monitoring results showed that the water level had strong annual(370 days) and seasonal(130 days) oscillations in the Dajiuhu peatland.The annual oscillation is associated with both precipitation and temperature,while the seasonal oscillation is mainly controlled by precipitation.In addition,the depth of peat surface to the water table(DWT) has weak but significant correlations with precipitation and temperature on the daily and weekly scales(r = 0.1–0.21,p<0.01).Once replacing DWT with water table fluctuation cumulation,the correlation coefficients increase apparently(r = 0.47–0.69,p<0.01),especially on the monthly scale.These findings highlight a more important role of the fluctuation than the mean position of water level and have the potential to improve the interpretation of water-level related paleoenvironmental proxies and the understanding of the relationship between water level and biogeochemical processes.
文摘Correction to:Nuclear Science and Techniques(2025)36:100 https://doi.org/10.1007/s41365-025-01692-6 In this article,Fig.9 appeared incorrectly and have now been corrected in the original publication.For completeness and transparency,both correct and incorrect versions are displayed below.
基金supported by the National Natural Science Foundation of China(No.12472045)the Shanghai Aerospace Science and Technology Innovation Fund(No.SAST2022-036)。
文摘To overcome external environmental disturbances,inertial parameter uncertainties and vibration of flexible modes in the process of attitude tracking,a comprehensively effective predefined-time guaranteed performance controller based on multi⁃observers for flexible spacecraft is proposed.First,to prevent unwinding phenomenon in attitude description,the rotation matrix is used to represent the spacecraft’s attitude.Second,the flexible modes observer which can guarantee predefined⁃time convergence is designed,for the case where flexible vibrations are unmeasurable in practice.What’s more,the disturbance observer is applied to estimate and compensate the lumped disturbances to improve the robustness of attitude control.A predefined-time controller is proposed to satisfy the prescribed performance and stabilize the attitude tracking system via barrier Lyapunov function.Finally,through comparative numerical simulations,the proposed controller can achieve high-precision convergence compared with the existing finite-time attitude tracking controller.This paper provides certain references for the high-precision predefined-time prescribed performance attitude tracking of flexible spacecraft with multi-disturbance.
