This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi...This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.展开更多
The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions.Taking the proportion of ur...The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions.Taking the proportion of urbanization zones,agricultural development zones and ecological security zones as the basic parameter,this paper explores the spatial structures of major function zoning at different scales using spatial statistics,spatial modeling and landscape metrics methods.The results show:First,major function zones have spatial gradient structures,which are prominently represented by latitudinal and longitudinal gradients,a coastal distance gradient,and an eastern-central-western gradient.Second,the pole-axis system structure and core-periphery structure exist at provincial scales.The general principle of the pole-axis structure is that as one moves along the distance axis,the proportion of urbanization zones decreases and the proportion of ecological security zones increases.This also means that the proportion of different function zones has a ring-shaped spatial differentiation principle with distance from the core.Third,there is a spatial mosaic structure at the city and county scale.This spatial mosaic structure has features of both spatial heterogeneity,such as agglomeration and dispersion,as well as of mutual,adjacent topological correlation and spatial proximity.The results of this study contribute to scientific knowledge on major function zones and the principles of spatial organization,and it acts as an important reference for China’s integrated geographical zoning.展开更多
This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breac...This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.展开更多
Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete v...Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.展开更多
In the analysis of functionally graded materials （FGMs）, the uncoupled approach is used broadly, which is based on homogenized material property and ignores the effect Of local micro-structural interaction. The high...In the analysis of functionally graded materials （FGMs）, the uncoupled approach is used broadly, which is based on homogenized material property and ignores the effect Of local micro-structural interaction. The higher-order theory for FGMs （HOTFGM） is a coupled approach that explicitly takes the effect of micro-structural gradation and the local interaction of the spatially variable inclusion phase into account. Based on the HOTFGM, this article presents a quadrilateral element-based method for the calculation of multi-scale temperature field （QTF）. In this method, the discrete cells are quadrilateral including rectangular while the surface-averaged quantities are the primary variables which replace the coefficients employed in the temperature function. In contrast with the HOTFGM, this method improves the efficiency, eliminates the restriction of being rectangular cells and expands the solution scale. The presented results illustrate the efficiency of the QTF and its advantages in analyzing FGMs.展开更多
In this study,a multi-physics and multi-scale coupling program,Fluent/KMC-sub/NDK,was developed based on the user-defined functions(UDF)of Fluent,in which the KMC-sub-code is a sub-channel thermal-hydraulic code and t...In this study,a multi-physics and multi-scale coupling program,Fluent/KMC-sub/NDK,was developed based on the user-defined functions(UDF)of Fluent,in which the KMC-sub-code is a sub-channel thermal-hydraulic code and the NDK code is a neutron diffusion code.The coupling program framework adopts the"master-slave"mode,in which Fluent is the master program while NDK and KMC-sub are coupled internally and compiled into the dynamic link library(DLL)as slave codes.The domain decomposition method was adopted,in which the reactor core was simulated by NDK and KMC-sub,while the rest of the primary loop was simulated using Fluent.A simulation of the reactor shutdown process of M2LFR-1000 was carried out using the coupling program,and the code-to-code verification was performed with ATHLET,demonstrating a good agreement,with absolute deviation was smaller than 0.2%.The results show an obvious thermal stratification phenomenon during the shutdown process,which occurs 10 s after shutdown,and the change in thermal stratification phenomena is also captured by the coupling program.At the same time,the change in the neutron flux density distribution of the reactor was also obtained.展开更多
The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factor...The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.展开更多
Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD)...Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.展开更多
Determining how animals respond to resource availability across spatial and temporal extents is crucial to understand ecological processes underpinning habitat selection.Here,we used a multi-scale approach to study th...Determining how animals respond to resource availability across spatial and temporal extents is crucial to understand ecological processes underpinning habitat selection.Here,we used a multi-scale approach to study the year-round habitat selection of the Crested Tit(Lophophanes cristatus)in a semi-natural lowland woodland of northern Italy,analysing different habitat features at each scale.We performed Crested Tit censuses at three different spatial scales.At the macrohabitat scale,we used geolocalized observations of individuals to compute Manly's habitat selection index,based on a detailed land-use map of the study area.At the microhabitat scale,the trees features were compared between presence and absence locations.At the foraging habitat scale,individual foraging birds and their specific position on trees were recorded using focal animal sampling.Censuses were performed during both the breeding(March to May)and wintering(December to January)seasons.At the macrohabitat scale,the Crested Tits significantly selected pure and mixed pine forests and avoided woods of alien plant species,farmlands and urban areas.At the microhabitat scale,old pine woods with dense cover were selected,with no significant difference in the features of tree selection between the two phenological phases.At the foraging habitat scale,the species was observed spending more time foraging in the canopies than in the understorey,using mostly the portion of Scots Pine(Pinus sylvestris)canopies closer to the trunk in winter,while during the breeding period,the whole canopy was visited.Overall,breeding and wintering habitats largely overlapped in the Crested Tit.Based on our findings,lowland Crested Tits can be well defined as true habitat specialists:they are strictly related to some specific coniferous woodland features.Noteworthily,compared to other tit species,which normally show generalist habits during winter,the Crested Tit behaves as a habitat specialist also out of the breeding season.Our study stressed the importance of considering multi-scale(both spatial and phenological)habitat selection in birds.展开更多
Multi-agent reinforcement learning has recently been applied to solve pursuit problems.However,it suffers from a large number of time steps per training episode,thus always struggling to converge effectively,resulting...Multi-agent reinforcement learning has recently been applied to solve pursuit problems.However,it suffers from a large number of time steps per training episode,thus always struggling to converge effectively,resulting in low rewards and an inability for agents to learn strategies.This paper proposes a deep reinforcement learning(DRL)training method that employs an ensemble segmented multi-reward function design approach to address the convergence problem mentioned before.The ensemble reward function combines the advantages of two reward functions,which enhances the training effect of agents in long episode.Then,we eliminate the non-monotonic behavior in reward function introduced by the trigonometric functions in the traditional 2D polar coordinates observation representation.Experimental results demonstrate that this method outperforms the traditional single reward function mechanism in the pursuit scenario by enhancing agents’policy scores of the task.These ideas offer a solution to the convergence challenges faced by DRL models in long episode pursuit problems,leading to an improved model training performance.展开更多
An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generate...An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polyncmial phase signal component whose correlation with the current residue signal is largest. Simultaueously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation experiment demonstrated that this method can separate the camponents of the multi-component polynamial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its instantaneous frequency accurately.展开更多
Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low a...Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.展开更多
Background:Nonsuicidal self-injury(NSSI)in adolescents with depression disorders often exhibits addictive patterns,potentially linked to serum beta-endorphin levels and neural reward responsiveness.Beta-endorphin,invo...Background:Nonsuicidal self-injury(NSSI)in adolescents with depression disorders often exhibits addictive patterns,potentially linked to serum beta-endorphin levels and neural reward responsiveness.Beta-endorphin,involved in reward processing,alongside dysregulated neural reward pathways,may reinforce self-injurious behaviors,highlighting the need to explore these mechanisms.Methods:Adolescents(aged 12-17 years)with depression disorders were divided into an NSSI group(21 subjects)and a control group(11 subjects)according to inclusion criteria.Serum beta-endorphin concentration was measured using the enzyme-linked immunosorbent assay method.The Addiction Factor Scale was used to assess addiction levels.Statistical analyses were con-ducted using SPSS 25.0.The oxygenated hemoglobin response signal was detected using functional near-infrared spectroscopy.Analyses were performed using NIRS_KIT 2.0.Results:Compared with the control group,the NSSI group exhibited lower serum beta-endorphin concentration.Additionally,85.7%of those in the NSSI group displayed addictive behaviors,and serum beta-endorphin concentration was negatively correlated with the Addiction Factor Scale score.The reward task activated channels 17,20,and 21(corresponding to the dorsolateral prefrontal cortex[PFC]and frontopolar PFC)in the gain condition and channels 20 and 21 in the loss condition.The oxygenated hemoglobin concentration of the differential waveform(Δ[oxy-Hb])of channel 12(corresponding to the frontopolar PFC)correlated positively with the Addiction Factor Scale score and negatively with the serum beta-endorphin concentration.展开更多
Based on the generalized Hamilton's principle,the nonlinear governing equation of an axially functionally graded(AFG)pipe is established.The non-trivial equilibrium configuration is superposed by the modal functio...Based on the generalized Hamilton's principle,the nonlinear governing equation of an axially functionally graded(AFG)pipe is established.The non-trivial equilibrium configuration is superposed by the modal functions of a simply supported beam.Via the direct multi-scale method,the response and stability boundary to the pulsating fluid velocity are solved analytically and verified by the differential quadrature element method(DQEM).The influence of Young's modulus gradient on the parametric resonance is investigated in the subcritical and supercritical regions.In general,the pipe in the supercritical region is more sensitive to the pulsating excitation.The nonlinearity changes from hard to soft,and the non-trivial equilibrium configuration introduces more frequency components to the vibration.Besides,the increasing Young's modulus gradient improves the critical pulsating flow velocity of the parametric resonance,and further enhances the stability of the system.In addition,when the temperature increases along the axial direction,reducing the gradient parameter can enhance the response asymmetry.This work further complements the theoretical analysis of pipes conveying pulsating fluid.展开更多
Network marketing is a trading technique that provides companies with the opportunity to increase sales.With the increasing number of Internet-based purchases,several threats are increasingly observed in this field,su...Network marketing is a trading technique that provides companies with the opportunity to increase sales.With the increasing number of Internet-based purchases,several threats are increasingly observed in this field,such as user privacy violations,company owner(CO)fraud,the changing of sold products’information,and the scalability of selling networks.This study presents the concept of a blockchain-based market called ACR-MLM that functions based on the multi-level marketing(MLM)model,through which registered users receive anonymous and confidential rewards for their own and their subgroups’sales.Applying a public blockchain as the ACR-MLM framework’s infrastructure solves existing problems in MLM-based markets,such as CO fraud(against the government or its users),user privacy violations(obtaining their real names or subgroup users),and scalability(when vast numbers of users have been registered).To provide confidentiality and scalability to the ACR-MLM framework,hierarchical identity-based encryption(HIBE)was applied with a functional encryption(FE)scheme.Finally,the security of ACR-MLM is analyzed using the random oracle(RO)model and then evaluated.展开更多
基金funded by the Deanship of Research and Graduate Studies at King Khalid University through small group research under grant number RGP1/278/45.
文摘This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss function.The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance.Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable improvements.Specifically,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic accuracy.These findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.
基金National Natural Science Foundation of China,No.41630644Innovative Think-tank Foundation for Young Scientists of China Association for Science and Technology,No.DXB-ZKQN-2017-048。
文摘The spatial structures of China’s Major Function Zoning are important constraining indicators in all types of spatial planning and key parameters for accurately downscaling major functions.Taking the proportion of urbanization zones,agricultural development zones and ecological security zones as the basic parameter,this paper explores the spatial structures of major function zoning at different scales using spatial statistics,spatial modeling and landscape metrics methods.The results show:First,major function zones have spatial gradient structures,which are prominently represented by latitudinal and longitudinal gradients,a coastal distance gradient,and an eastern-central-western gradient.Second,the pole-axis system structure and core-periphery structure exist at provincial scales.The general principle of the pole-axis structure is that as one moves along the distance axis,the proportion of urbanization zones decreases and the proportion of ecological security zones increases.This also means that the proportion of different function zones has a ring-shaped spatial differentiation principle with distance from the core.Third,there is a spatial mosaic structure at the city and county scale.This spatial mosaic structure has features of both spatial heterogeneity,such as agglomeration and dispersion,as well as of mutual,adjacent topological correlation and spatial proximity.The results of this study contribute to scientific knowledge on major function zones and the principles of spatial organization,and it acts as an important reference for China’s integrated geographical zoning.
基金supported by National Key R&D Program of China:Gravitational Wave Detection Project(Grant Nos.2021YFC22026,2021YFC2202601,2021YFC2202603)National Natural Science Foundation of China(Grant Nos.12172288 and 12472046)。
文摘This paper investigates impulsive orbital attack-defense(AD)games under multiple constraints and victory conditions,involving three spacecraft:attacker,target,and defender.In the AD scenario,the attacker aims to breach the defender's interception to rendezvous with the target,while the defender seeks to protect the target by blocking or actively pursuing the attacker.Four different maneuvering constraints and five potential game outcomes are incorporated to more accurately model AD game problems and increase complexity,thereby reducing the effectiveness of traditional methods such as differential games and game-tree searches.To address these challenges,this study proposes a multiagent deep reinforcement learning solution with variable reward functions.Two attack strategies,Direct attack(DA)and Bypass attack(BA),are developed for the attacker,each focusing on different mission priorities.Similarly,two defense strategies,Direct interdiction(DI)and Collinear interdiction(CI),are designed for the defender,each optimizing specific defensive actions through tailored reward functions.Each reward function incorporates both process rewards(e.g.,distance and angle)and outcome rewards,derived from physical principles and validated via geometric analysis.Extensive simulations of four strategy confrontations demonstrate average defensive success rates of 75%for DI vs.DA,40%for DI vs.BA,80%for CI vs.DA,and 70%for CI vs.BA.Results indicate that CI outperforms DI for defenders,while BA outperforms DA for attackers.Moreover,defenders achieve their objectives more effectively under identical maneuvering capabilities.Trajectory evolution analyses further illustrate the effectiveness of the proposed variable reward function-driven strategies.These strategies and analyses offer valuable guidance for practical orbital defense scenarios and lay a foundation for future multi-agent game research.
基金the National Natural Science Foundation of China(No.62266025)。
文摘Segmentation of the retinal vessels in the fundus is crucial for diagnosing ocular diseases.Retinal vessel images often suffer from category imbalance and large scale variations.This ultimately results in incomplete vessel segmentation and poor continuity.In this study,we propose CT-MFENet to address the aforementioned issues.First,the use of context transformer(CT)allows for the integration of contextual feature information,which helps establish the connection between pixels and solve the problem of incomplete vessel continuity.Second,multi-scale dense residual networks are used instead of traditional CNN to address the issue of inadequate local feature extraction when the model encounters vessels at multiple scales.In the decoding stage,we introduce a local-global fusion module.It enhances the localization of vascular information and reduces the semantic gap between high-and low-level features.To address the class imbalance in retinal images,we propose a hybrid loss function that enhances the segmentation ability of the model for topological structures.We conducted experiments on the publicly available DRIVE,CHASEDB1,STARE,and IOSTAR datasets.The experimental results show that our CT-MFENet performs better than most existing methods,including the baseline U-Net.
基金National Natural Science Foundation of China (2009ZB52028,05C52013)Ph.D. Programs Foundation of Ministry of Education of China (20070287039)
文摘In the analysis of functionally graded materials （FGMs）, the uncoupled approach is used broadly, which is based on homogenized material property and ignores the effect Of local micro-structural interaction. The higher-order theory for FGMs （HOTFGM） is a coupled approach that explicitly takes the effect of micro-structural gradation and the local interaction of the spatially variable inclusion phase into account. Based on the HOTFGM, this article presents a quadrilateral element-based method for the calculation of multi-scale temperature field （QTF）. In this method, the discrete cells are quadrilateral including rectangular while the surface-averaged quantities are the primary variables which replace the coefficients employed in the temperature function. In contrast with the HOTFGM, this method improves the efficiency, eliminates the restriction of being rectangular cells and expands the solution scale. The presented results illustrate the efficiency of the QTF and its advantages in analyzing FGMs.
基金supported by Science and Technology on Reactor System Design Technology Laboratory,Chengdu,China(LRSDT2020106)
文摘In this study,a multi-physics and multi-scale coupling program,Fluent/KMC-sub/NDK,was developed based on the user-defined functions(UDF)of Fluent,in which the KMC-sub-code is a sub-channel thermal-hydraulic code and the NDK code is a neutron diffusion code.The coupling program framework adopts the"master-slave"mode,in which Fluent is the master program while NDK and KMC-sub are coupled internally and compiled into the dynamic link library(DLL)as slave codes.The domain decomposition method was adopted,in which the reactor core was simulated by NDK and KMC-sub,while the rest of the primary loop was simulated using Fluent.A simulation of the reactor shutdown process of M2LFR-1000 was carried out using the coupling program,and the code-to-code verification was performed with ATHLET,demonstrating a good agreement,with absolute deviation was smaller than 0.2%.The results show an obvious thermal stratification phenomenon during the shutdown process,which occurs 10 s after shutdown,and the change in thermal stratification phenomena is also captured by the coupling program.At the same time,the change in the neutron flux density distribution of the reactor was also obtained.
基金financially supported by the Research Project of Shanxi Scholarship Council of China (2017– 075)the Natural Science foundation for Young Scientists of Shanxi Province (201801D221103)the Innovation Grant of Shanxi Agricultural University (2017ZZ07)
文摘The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.
基金This work was supported by the National Natural Science Foundation of China(No.61906006).
文摘Whole brain functional connectivity(FC)patterns obtained from resting-state functional magnetic resonance imaging(rs-fMRI)have been widely used in the diagnosis of brain disorders such as autism spectrum disorder(ASD).Recently,an increasing number of studies have focused on employing deep learning techniques to analyze FC patterns for brain disease classification.However,the high dimensionality of the FC features and the interpretation of deep learning results are issues that need to be addressed in the FC-based brain disease classification.In this paper,we proposed a multi-scale attention-based deep neural network(MSA-DNN)model to classify FC patterns for the ASD diagnosis.The model was implemented by adding a flexible multi-scale attention(MSA)module to the auto-encoder based backbone DNN,which can extract multi-scale features of the FC patterns and change the level of attention for different FCs by continuous learning.Our model will reinforce the weights of important FC features while suppress the unimportant FCs to ensure the sparsity of the model weights and enhance the model interpretability.We performed systematic experiments on the large multi-sites ASD dataset with both ten-fold and leaveone-site-out cross-validations.Results showed that our model outperformed classical methods in brain disease classification and revealed robust intersite prediction performance.We also localized important FC features and brain regions associated with ASD classification.Overall,our study further promotes the biomarker detection and computer-aided classification for ASD diagnosis,and the proposed MSA module is flexible and easy to implement in other classification networks.
文摘Determining how animals respond to resource availability across spatial and temporal extents is crucial to understand ecological processes underpinning habitat selection.Here,we used a multi-scale approach to study the year-round habitat selection of the Crested Tit(Lophophanes cristatus)in a semi-natural lowland woodland of northern Italy,analysing different habitat features at each scale.We performed Crested Tit censuses at three different spatial scales.At the macrohabitat scale,we used geolocalized observations of individuals to compute Manly's habitat selection index,based on a detailed land-use map of the study area.At the microhabitat scale,the trees features were compared between presence and absence locations.At the foraging habitat scale,individual foraging birds and their specific position on trees were recorded using focal animal sampling.Censuses were performed during both the breeding(March to May)and wintering(December to January)seasons.At the macrohabitat scale,the Crested Tits significantly selected pure and mixed pine forests and avoided woods of alien plant species,farmlands and urban areas.At the microhabitat scale,old pine woods with dense cover were selected,with no significant difference in the features of tree selection between the two phenological phases.At the foraging habitat scale,the species was observed spending more time foraging in the canopies than in the understorey,using mostly the portion of Scots Pine(Pinus sylvestris)canopies closer to the trunk in winter,while during the breeding period,the whole canopy was visited.Overall,breeding and wintering habitats largely overlapped in the Crested Tit.Based on our findings,lowland Crested Tits can be well defined as true habitat specialists:they are strictly related to some specific coniferous woodland features.Noteworthily,compared to other tit species,which normally show generalist habits during winter,the Crested Tit behaves as a habitat specialist also out of the breeding season.Our study stressed the importance of considering multi-scale(both spatial and phenological)habitat selection in birds.
基金National Natural Science Foundation of China(Nos.61803260,61673262 and 61175028)。
文摘Multi-agent reinforcement learning has recently been applied to solve pursuit problems.However,it suffers from a large number of time steps per training episode,thus always struggling to converge effectively,resulting in low rewards and an inability for agents to learn strategies.This paper proposes a deep reinforcement learning(DRL)training method that employs an ensemble segmented multi-reward function design approach to address the convergence problem mentioned before.The ensemble reward function combines the advantages of two reward functions,which enhances the training effect of agents in long episode.Then,we eliminate the non-monotonic behavior in reward function introduced by the trigonometric functions in the traditional 2D polar coordinates observation representation.Experimental results demonstrate that this method outperforms the traditional single reward function mechanism in the pursuit scenario by enhancing agents’policy scores of the task.These ideas offer a solution to the convergence challenges faced by DRL models in long episode pursuit problems,leading to an improved model training performance.
基金supported by the National Science Foundation of China(No.50875078)
文摘An approach based on multi-scale ehirplet sparse signal decomposition is proposed to separate the malti-component polynomial phase signals, and estimate their instantaneous frequencies. In this paper, we have generated a family of multi-scale chirplet functions which provide good local correlations of chirps over shorter time interval. At every decomposition stage, we build the so-called family of chirplets and our idea is to use a structured algorithm which exploits information in the family to chain chirplets together adaptively as to form the polyncmial phase signal component whose correlation with the current residue signal is largest. Simultaueously, the polynomial instantaneous frequency is estimated by connecting the linear frequency of the chirplet functions adopted in the current separation. Simulation experiment demonstrated that this method can separate the camponents of the multi-component polynamial phase signals effectively even in the low signal-to-noise ratio condition, and estimate its instantaneous frequency accurately.
基金funded by the National Natural Foundation of China under Grant No.61172167the Science Fund Project of Heilongjiang Province(LH2020F035).
文摘Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.
基金supported by the National Natural Science Foundation of China(No.82260878)Guizhou Medical University Affiliated Hospital Doctoral Research Initiation Fund Project(gyfybsky-2021-44)+3 种基金Guizhou Provincial Science and Technology Plan Project(Qiankehe Achievements LC[2022]014)High-level Innovative Talents Cultivation Program of Guizhou Province(QianKeHe[2016]5679)Province Guiyang City Science and Technology Projects,Zhu Subjects Contract([2022]4-2-5)Guizhou Science and Technology Planning Project(QianKeHe[2020]4Y198).
文摘Background:Nonsuicidal self-injury(NSSI)in adolescents with depression disorders often exhibits addictive patterns,potentially linked to serum beta-endorphin levels and neural reward responsiveness.Beta-endorphin,involved in reward processing,alongside dysregulated neural reward pathways,may reinforce self-injurious behaviors,highlighting the need to explore these mechanisms.Methods:Adolescents(aged 12-17 years)with depression disorders were divided into an NSSI group(21 subjects)and a control group(11 subjects)according to inclusion criteria.Serum beta-endorphin concentration was measured using the enzyme-linked immunosorbent assay method.The Addiction Factor Scale was used to assess addiction levels.Statistical analyses were con-ducted using SPSS 25.0.The oxygenated hemoglobin response signal was detected using functional near-infrared spectroscopy.Analyses were performed using NIRS_KIT 2.0.Results:Compared with the control group,the NSSI group exhibited lower serum beta-endorphin concentration.Additionally,85.7%of those in the NSSI group displayed addictive behaviors,and serum beta-endorphin concentration was negatively correlated with the Addiction Factor Scale score.The reward task activated channels 17,20,and 21(corresponding to the dorsolateral prefrontal cortex[PFC]and frontopolar PFC)in the gain condition and channels 20 and 21 in the loss condition.The oxygenated hemoglobin concentration of the differential waveform(Δ[oxy-Hb])of channel 12(corresponding to the frontopolar PFC)correlated positively with the Addiction Factor Scale score and negatively with the serum beta-endorphin concentration.
基金Project supported by the National Natural Science Foundation of China (Nos.12002195 and 12372015)the National Science Fund for Distinguished Young Scholars of China (No.12025204)the Program of Shanghai Municipal Education Commission of China (No.2019-01-07-00-09-E00018)。
文摘Based on the generalized Hamilton's principle,the nonlinear governing equation of an axially functionally graded(AFG)pipe is established.The non-trivial equilibrium configuration is superposed by the modal functions of a simply supported beam.Via the direct multi-scale method,the response and stability boundary to the pulsating fluid velocity are solved analytically and verified by the differential quadrature element method(DQEM).The influence of Young's modulus gradient on the parametric resonance is investigated in the subcritical and supercritical regions.In general,the pipe in the supercritical region is more sensitive to the pulsating excitation.The nonlinearity changes from hard to soft,and the non-trivial equilibrium configuration introduces more frequency components to the vibration.Besides,the increasing Young's modulus gradient improves the critical pulsating flow velocity of the parametric resonance,and further enhances the stability of the system.In addition,when the temperature increases along the axial direction,reducing the gradient parameter can enhance the response asymmetry.This work further complements the theoretical analysis of pipes conveying pulsating fluid.
文摘Network marketing is a trading technique that provides companies with the opportunity to increase sales.With the increasing number of Internet-based purchases,several threats are increasingly observed in this field,such as user privacy violations,company owner(CO)fraud,the changing of sold products’information,and the scalability of selling networks.This study presents the concept of a blockchain-based market called ACR-MLM that functions based on the multi-level marketing(MLM)model,through which registered users receive anonymous and confidential rewards for their own and their subgroups’sales.Applying a public blockchain as the ACR-MLM framework’s infrastructure solves existing problems in MLM-based markets,such as CO fraud(against the government or its users),user privacy violations(obtaining their real names or subgroup users),and scalability(when vast numbers of users have been registered).To provide confidentiality and scalability to the ACR-MLM framework,hierarchical identity-based encryption(HIBE)was applied with a functional encryption(FE)scheme.Finally,the security of ACR-MLM is analyzed using the random oracle(RO)model and then evaluated.
