Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculat...In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculation after the grains are crushed,and reduce the producing time of specimen.In this work,several one-dimensional compressing simulations are carried out to investigate the effect of particle crushing on mechanical properties of granular materials under a wide range of stress.The results show that the crushing process of granular materials can be divided into four different stages according to er-logσy curves.At the end of the second stage,there exists a yield point,after which the physical and mechanical properties of specimens will change significantly.Under extremely high stress,particle crushing will wipe some initial information of specimens,and specimens with different initial gradings and void ratios present some similar characteristics.Particle crushing has great influence on grading,lateral pressure coefficient and compressibility of granular materials,and introduce extra irreversible volume deformation,which is necessary to be considered in modelling of granular materials in wide stress range.展开更多
Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes...Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes highly desirable.Here,dense SnO_(2) nanoparticles are uniformly grown on three-dimensional(3D)fiber network of carbon cloth(CC)by a facile dip-coating and calcination method.Importantly,Zn modification strategy is employed to restrain the growth of long-range order of SnO_(2) lattices and to produce rich grain boundaries.The hybrid architecture can act as a flexible electrode for CO_(2)-to-formate conversion,which delivers a high partial current of 18.8 m A cm-2 with a formate selectivity of 80%at a moderate cathodic potential of-0.947 V vs.RHE.The electrode exhibits remarkable stability over a 16 h continuous operation.The superior performance is attributed to the synergistic effect of ultrafine SnO_(2) nanoparticles with abundant active sites and 3D fiber network of the electrode for efficient mass transport and electron transfer.The sizeable electrodes hold promise for industrial applications.展开更多
Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate resul...Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.展开更多
Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seri...Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network(DDFN).Since the Femtocell Access Points(FAPs) are randomly deployed by their customers,the interference cannot be predicted in advance.Meanwhile,new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse(FFR).Aiming for the downlink interference coordination in the DDFN,in this paper,we propose a User-oriented Graph based Frequency Allocation(UGFA)algorithm.Firstly,we construct the interference graph for users in the network.Secondly,we study the conventional graph based resources allocation algorithm.Then an improved two steps graph based frequency allocation mechanism is proposed.Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.展开更多
The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing i...The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing in a DSC system for uplink of the DSC system. To further improve the uplink performance, the high-rank codebook is designed based on singular value decomposition （SVD） due to the unnecessary preservation of strict constant modulus in the DSC system. And according to the simulation result, the proposed codebook leads to significant sum-rate gain and appreciable block error rate （BLER） performance improvement in the DSC system.展开更多
This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, s...This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, such as the lack of green space and. open space, ecological disturbance in some areas, poor landscape quality, this paper focused on the ecological space optimization in the process of urban renewal Firstly, theories related to this field were analyzed, and a comprehensive ecological efficiency evaluation sjrstem was established based on disciplines such as urban ecology, landscape ecology, urban sociology, behavioral psychology, biology, urban planning and design. Secondly, this system was used to judge the ecological efficiency of typical blocks on GIS platform and to find out the key spatial nodes that need to be updated. Thirdly, in different cases, space optimization projects witii different theories were designed, and the spatial model of influence was used to comprehensively evaluate their ecological efficiency. Finally, the parameters under different conditions were corrected to get a systematic system for evaluating the green space system in densely built-up areas＊ Due to the lack of undetstanding of the ecological fonction of green space in the past, die environmental condition of densely built-up areas is not good. Therefore, the most important task of urban oigauic renewal is ecological restoiation. In this paper, die exploiation is based on the reservation for built-up afeas to avoid repeated reconstruction and interference. Authors of this paper tried to find out a way to rebuild green space system that performed more complex functions with limited spatial resources. The application of “micfo-ttansfotmation” of green space system in densely built-up areas turns out to improve the quality of landscape while reducing the construction costrds展开更多
To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates ...To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.展开更多
Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only cha...Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.展开更多
Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has...Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has shown great application potential as a sensor material.However,its durability and performance consistency in practical applications still require enhancement.In this study,magnetic graphene fibers(MGFs)are prepared via wet spinning,using graphene oxide(GO),doped with Fe_(3)O_(4)nanoparticles.The resulting MGFs exhibit a high tensile strength of 58.6 MPa,a strain of 5.3%and an electrical conductivity of 1.7×10^(4)S/m.These MGFs are utilised to construct a multilayer fabric for fabrication of flexible pressure sensors.The confinement within the spinning channel facilitates an ordered arrangement of GO sheets,resulting in MGFs with superior electrical and mechanical properties.The issuing MGFs pressure sensors demonstrate a wide detection range(0-120 kPa),high sensitivity(0.233 kPa^(−1),0-40 kPa)and rapid response/recovery times(121 ms/158 ms).In addition,it exhibits a remarkable durability,maintaining performance over 1300 cycles,during continuous operation,with negligible degradation.This sensor shows excellent capability in monitoring human physiological activities,indicating its substantial application potential in wearable devices.展开更多
Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse ...Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.展开更多
In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although ...In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.展开更多
The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network sele...The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.展开更多
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and inten...The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and intensity(MMI)IX hit the city of Haicheng,Liaoning,China.Although deep seismic profiling was previously conducted along the Haichenghe fault,the limited horizontal resolution in the shallow part prevented the recognition of kilometer-scale anomalies.The velocity structure characteristics of the Haichenghe fault and its NE-trending conjugate faults remain unclear.Using the extended range phase shift method,the high-resolution S-wave velocity structures are obtained by deploying a long,dense linear array of 55 short-period seismometers across the fault and NE-trending conjugate faults.The array length was 32 km and inter-station spacing was approximately 600 m,facilitating the collection of approximately 22 days of continuous waveform data.Employing the Extended Range Phase Shift(ERPS)method enabled the extraction of broadband 0.2–5 s Rayleigh wave phase velocity dispersion curves.The broadband dispersion data were used for inversion of the high-resolution S-wave velocity structure to a depth of 8 km from the surface.The velocity structure characteristics and seismicity of the Haichenghe fault and NE-trending conjugate faults were analyzed and compared with nearby fault gas measurements.Results show(1)shallow S-wave velocities show a low-high-low horizontal distribution,corresponding to basin-uplift-basin topography;(2)significant velocity contrasts occur across the Haichenghe fault:its SW segment(0–17 km)exhibits high velocities consistent with Paleoproterozoic crystalline basement(Pt_(1)),while the NE segment(17–32 km)shows low velocities related to Yanshanian intrusions(γ_(5))and Quaternary sediments.NE-trending conjugate faults display sharp velocity gradients marking fracture locations,with all faults being near-vertical to~8 km depth;(3)seismicity at 1–6 km depth mainly clusters in high-velocity zones;at 6–8 km depth,it concentrates beneath the Haichenghe fault in low-velocity areas and along NE-trending faults;(4)the seismic activity characteristics and fault zone width of the Haicheng he fault reflected by velocity imaging results are basically consistent with those obtained by the fault gas measurement method.展开更多
Aqueous zinc-ion batteries(AZIBs) have advantages including low economic cost and high safety.Nevertheless,the serious hydrogen evolution reactions(HER) and rampant growth of Zn dendrite hinder their further developme...Aqueous zinc-ion batteries(AZIBs) have advantages including low economic cost and high safety.Nevertheless,the serious hydrogen evolution reactions(HER) and rampant growth of Zn dendrite hinder their further development.Herein,potassium acetate(KAc) additive with cation/anion synergy effect is added into the ZnSO_(4) electrolyte to effectively promote the oriented uniform Zn deposition and suppress side reactions.According to density functional theory calculation and experimental results,CH_(3)COO^(-)(Ac^(-))anions are capable of forming stronger hydrogen bonds with H_(2)O molecules,leading to an expanded electrochemical stability window,reduced the reactivity of H_(2)O,and hence suppressing HER.Meanwhile,Ac-anions can also preferentially adsorb onto the Zn anode,promoting dense deposition towards the(100) crystal plane.Besides,dissociated K^(+) ions serve as electrostatic shielding cations,which significantly promote uniform Zn deposition and prevent dendrite formation.Thus,the Zn||Zn symmetric cell demonstrates an impressive cycle lifespan of 3000 h at 1.0 m A/cm^(2).Furthermore,the Zn||MnO_(2) full battery exhibits superior stability with a capacity retention of 86.95 % at 2.0 A/g after 4000 cycles.Therefore,the cation/anion synergy effect in KAc additive offers a viable solution to address HER and hinder dendrite growth at the interface of Zn anodes.展开更多
Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without...Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.展开更多
The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications...The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications,both in research and industry.We report the two-dimensional dense packing patterns of bending and assembled rods,which are non-convexly deformed from simple objects and modeled as entangled particles.Monte Carlo simulations and further analytical constructions are carried out to explore possible densely packed structures.Two typical densely packed structures of C-bending rods are found,and their packing densities are identified as being functions of the aspect ratio and central angle.Six shapes of assembled rods,representing the combined deformations of rods,are employed in simulations with the packing structures classified into three types.The dense packing density of each packing pattern is derived as a function of different shape parameters.In contrast with the case of disordered packings,both the shape and order are verified to affect the packing density.展开更多
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金The authors wish to thank the National Natural Science Foundation of China(No.11772117)the Fundamental Research Funds for the Central Universities(No.2015B37414)+1 种基金Henan Scientific and Technical Project under Grant(No.192102310480)Key Scientific Research Project of Colleges and Universities in Henan Province(CN)(21B560015)for financial support.
文摘In this paper,the densely arrayed bonded particle model is proposed for simulation of granular materials with discrete element method(DEM)considering particle crushing.This model can solve the problem of pore calculation after the grains are crushed,and reduce the producing time of specimen.In this work,several one-dimensional compressing simulations are carried out to investigate the effect of particle crushing on mechanical properties of granular materials under a wide range of stress.The results show that the crushing process of granular materials can be divided into four different stages according to er-logσy curves.At the end of the second stage,there exists a yield point,after which the physical and mechanical properties of specimens will change significantly.Under extremely high stress,particle crushing will wipe some initial information of specimens,and specimens with different initial gradings and void ratios present some similar characteristics.Particle crushing has great influence on grading,lateral pressure coefficient and compressibility of granular materials,and introduce extra irreversible volume deformation,which is necessary to be considered in modelling of granular materials in wide stress range.
基金supported by the National Natural Science Foundation of China(51902204,22003041,21975163)Bureau of Industry and Information Technology of Shenzhen(201901171518)Shenzhen Science and Technology Program(KQTD20190929173914967)。
文摘Electrochemical reduction of CO_(2) to fuels and chemicals is a viable strategy for CO_(2) utilization and renewable energy storage.Developing free-standing electrodes from robust and scalable electrocatalysts becomes highly desirable.Here,dense SnO_(2) nanoparticles are uniformly grown on three-dimensional(3D)fiber network of carbon cloth(CC)by a facile dip-coating and calcination method.Importantly,Zn modification strategy is employed to restrain the growth of long-range order of SnO_(2) lattices and to produce rich grain boundaries.The hybrid architecture can act as a flexible electrode for CO_(2)-to-formate conversion,which delivers a high partial current of 18.8 m A cm-2 with a formate selectivity of 80%at a moderate cathodic potential of-0.947 V vs.RHE.The electrode exhibits remarkable stability over a 16 h continuous operation.The superior performance is attributed to the synergistic effect of ultrafine SnO_(2) nanoparticles with abundant active sites and 3D fiber network of the electrode for efficient mass transport and electron transfer.The sizeable electrodes hold promise for industrial applications.
基金This research is partially supported by grant from the National Natural Science Foundation of China(No.72071019)grant from the Natural Science Foundation of Chongqing(No.cstc2021jcyj-msxmX0185)grant from the Chongqing Graduate Education and Teaching Reform Research Project(No.yjg193096).
文摘Bone age assessment(BAA)helps doctors determine how a child’s bones grow and develop in clinical medicine.Traditional BAA methods rely on clinician expertise,leading to time-consuming predictions and inaccurate results.Most deep learning-based BAA methods feed the extracted critical points of images into the network by providing additional annotations.This operation is costly and subjective.To address these problems,we propose a multi-scale attentional densely connected network(MSADCN)in this paper.MSADCN constructs a multi-scale dense connectivity mechanism,which can avoid overfitting,obtain the local features effectively and prevent gradient vanishing even in limited training data.First,MSADCN designs multi-scale structures in the densely connected network to extract fine-grained features at different scales.Then,coordinate attention is embedded to focus on critical features and automatically locate the regions of interest(ROI)without additional annotation.In addition,to improve the model’s generalization,transfer learning is applied to train the proposed MSADCN on the public dataset IMDB-WIKI,and the obtained pre-trained weights are loaded onto the Radiological Society of North America(RSNA)dataset.Finally,label distribution learning(LDL)and expectation regression techniques are introduced into our model to exploit the correlation between hand bone images of different ages,which can obtain stable age estimates.Extensive experiments confirm that our model can converge more efficiently and obtain a mean absolute error(MAE)of 4.64 months,outperforming some state-of-the-art BAA methods.
基金supported by the National Natural Science Foundation of China under Grant No.61372092the China National Science and Technology Major Projects on New Generation Broadband Wireless Mobile Communications Network under Grants No.2011ZX03005-004,No.2012ZX03001029-003,No.2012ZX03001008-003
文摘Femtocell is a promising technology for improving indoor coverage and offloading the macrocell.Femtocells tend to be densely deployed in populated areas such as the dormitories.However,the inter-tier interference seriously exists in the co-channel Densely Deployed Femtocell Network(DDFN).Since the Femtocell Access Points(FAPs) are randomly deployed by their customers,the interference cannot be predicted in advance.Meanwhile,new characteristics such as the short radius of femtocell and the small number of users lead to the inefficiency of the traditional frequency reuse algorithms such as Fractional Frequency Reuse(FFR).Aiming for the downlink interference coordination in the DDFN,in this paper,we propose a User-oriented Graph based Frequency Allocation(UGFA)algorithm.Firstly,we construct the interference graph for users in the network.Secondly,we study the conventional graph based resources allocation algorithm.Then an improved two steps graph based frequency allocation mechanism is proposed.Simulation results show that UGFA has a high frequency reuse ratio mean while guarantees a better throughput.
基金supported by the National High-tech Research and Development Program of China(863 Program)(2012AA111902)the Shanghai Natural Science Foundation(12ZR1433900)
文摘The shrinking of cell-size brings significant changes to the wireless uplink of densely small cells （DSCs）. A codebook design is proposed that utilizes the strong line of sight （LOS） chan- nel component existing in a DSC system for uplink of the DSC system. To further improve the uplink performance, the high-rank codebook is designed based on singular value decomposition （SVD） due to the unnecessary preservation of strict constant modulus in the DSC system. And according to the simulation result, the proposed codebook leads to significant sum-rate gain and appreciable block error rate （BLER） performance improvement in the DSC system.
基金Sponsored by National Natural Science Fund of China(51578454)
文摘This paper is aimed at studying the environmental degradation of densely built-up areas in the process of urbanization in Qiina. In consideration of the severe environmental conditions of die densely built-up afeas, such as the lack of green space and. open space, ecological disturbance in some areas, poor landscape quality, this paper focused on the ecological space optimization in the process of urban renewal Firstly, theories related to this field were analyzed, and a comprehensive ecological efficiency evaluation sjrstem was established based on disciplines such as urban ecology, landscape ecology, urban sociology, behavioral psychology, biology, urban planning and design. Secondly, this system was used to judge the ecological efficiency of typical blocks on GIS platform and to find out the key spatial nodes that need to be updated. Thirdly, in different cases, space optimization projects witii different theories were designed, and the spatial model of influence was used to comprehensively evaluate their ecological efficiency. Finally, the parameters under different conditions were corrected to get a systematic system for evaluating the green space system in densely built-up areas＊ Due to the lack of undetstanding of the ecological fonction of green space in the past, die environmental condition of densely built-up areas is not good. Therefore, the most important task of urban oigauic renewal is ecological restoiation. In this paper, die exploiation is based on the reservation for built-up afeas to avoid repeated reconstruction and interference. Authors of this paper tried to find out a way to rebuild green space system that performed more complex functions with limited spatial resources. The application of “micfo-ttansfotmation” of green space system in densely built-up areas turns out to improve the quality of landscape while reducing the construction costrds
基金the National Natural Science Foundation of China(No.81830052)the Shanghai Natural Science Foundation of China(No.20ZR1438300)the Shanghai Science and Technology Support Project(No.18441900500),China。
文摘To overcome the computational burden of processing three-dimensional(3 D)medical scans and the lack of spatial information in two-dimensional(2 D)medical scans,a novel segmentation method was proposed that integrates the segmentation results of three densely connected 2 D convolutional neural networks(2 D-CNNs).In order to combine the lowlevel features and high-level features,we added densely connected blocks in the network structure design so that the low-level features will not be missed as the network layer increases during the learning process.Further,in order to resolve the problems of the blurred boundary of the glioma edema area,we superimposed and fused the T2-weighted fluid-attenuated inversion recovery(FLAIR)modal image and the T2-weighted(T2)modal image to enhance the edema section.For the loss function of network training,we improved the cross-entropy loss function to effectively avoid network over-fitting.On the Multimodal Brain Tumor Image Segmentation Challenge(BraTS)datasets,our method achieves dice similarity coefficient values of 0.84,0.82,and 0.83 on the BraTS2018 training;0.82,0.85,and 0.83 on the BraTS2018 validation;and 0.81,0.78,and 0.83 on the BraTS2013 testing in terms of whole tumors,tumor cores,and enhancing cores,respectively.Experimental results showed that the proposed method achieved promising accuracy and fast processing,demonstrating good potential for clinical medicine.
基金This work was supported in part by the Natural Science Foundation of China under Grant 62063004 and 61762033in part by the Hainan Provincial Natural Science Foundation of China under Grant 2019RC018 and 619QN246by the Postdoctoral Science Foundation under Grant 2020TQ0293.
文摘Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented performance.However,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance further.The present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial information.Specifically,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep network.This structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature learning.The qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.
基金supported by Zhejiang Natural Science Foundation Technology Project(LGG22E030015)National Key Research and Development Program of China support by Ministry of Science and Technology(2022YFB3704504)+5 种基金Jiaxing Science and Technology Plan Project(2023AY11004)Jiaxing Project of Science and Technology(2023AY11014)Jiaxing University General Cultivation Project(00321056AL)Qinshen Youth Backbone Training Program of Jiaxing University(CD70623038)Jiaxing City Public Welfare Project(Young Talent Special Project)(2023AY40027)Key Laboratory of Yarn Materials Forming and Composite Processing Technology,Zhejiang Province(MTC-2022-06).
文摘Piezoresistive pressure sensors have received considerable attention because of their simple structure,high sensitivity and low cost.Graphene,which is known for its outstanding mechanical and electrical properties,has shown great application potential as a sensor material.However,its durability and performance consistency in practical applications still require enhancement.In this study,magnetic graphene fibers(MGFs)are prepared via wet spinning,using graphene oxide(GO),doped with Fe_(3)O_(4)nanoparticles.The resulting MGFs exhibit a high tensile strength of 58.6 MPa,a strain of 5.3%and an electrical conductivity of 1.7×10^(4)S/m.These MGFs are utilised to construct a multilayer fabric for fabrication of flexible pressure sensors.The confinement within the spinning channel facilitates an ordered arrangement of GO sheets,resulting in MGFs with superior electrical and mechanical properties.The issuing MGFs pressure sensors demonstrate a wide detection range(0-120 kPa),high sensitivity(0.233 kPa^(−1),0-40 kPa)and rapid response/recovery times(121 ms/158 ms).In addition,it exhibits a remarkable durability,maintaining performance over 1300 cycles,during continuous operation,with negligible degradation.This sensor shows excellent capability in monitoring human physiological activities,indicating its substantial application potential in wearable devices.
基金partially supported by the Center for Advanced Systems Understanding (CASUS), financed by Germany’s Federal Ministry of Education and Research and the Saxon State Government out of the State Budget approved by the Saxon State Parliamentthe European Union’s Just Transition Fund (JTF) within the project Röntgenlaser Optimierung der Laserfusion (ROLF), Contract No. 5086999001, co-financed by the Saxon State Government out of the State Budget approved by the Saxon State Parliament+3 种基金the European Research Council (ERC) under the European Union’s Horizon 2022 Research and Innovation Programme (Grant Agreement No. 101076233, “PREXTREME”)Computations were performed on a Bull Cluster at the Center for Information Services and High-Performance Computing (ZIH) at Technische Universität Dresden and at the Norddeutscher Verbund für Hoch- und Höchstleistungsrechnen (HLRN) under Grant No. mvp00024support by the National Natural Science Foundation of China under Grant No. 12274171support by the Advanced Materials–National Science and Technology Major Project (Grant No. 2024ZD0606900)
文摘Understanding the properties of warm dense hydrogen is of key importance for the modeling of compact astrophysical objects and to understand and further optimize inertial confinement fusion applications.The workhorse of warm dense matter theory is thermal density functional theory(DFT),which,however,suffers from two limitations:(i)its accuracy can depend on the utilized exchange-correlation functional,which has to be approximated,and(ii)it is generally limited to single-electron properties such as the density distribution.Here,we present a new ansatz combining time-dependent DFT results for the dynamic structure factor S_(ee)(q,ω)with static DFT results for the density response.This allows us to estimate the electron-electron static structure factor S_(ee)(q)of warm dense hydrogen with high accuracy over a broad range of densities and temperatures.In addition to its value for the study of warm dense matter,our work opens up new avenues for the future study of electronic correlations exclusively within the framework of DFT for a host of applications.
基金funded by the National Natural Science Foundation of China under Grant 62306128the Leading Innovation Project of Changzhou Science and Technology Bureau underGrant CQ20230072+2 种基金the Basic Science Research Project of Jiangsu Provincial Department of Education under Grant 23KJD520003the Science and Technology Development Plan Project of Jilin Provinceunder Grant 20240101382JCthe National KeyR esearch and Development Program of China under Grant 2023YFF1105102.
文摘In fire rescue scenarios,traditional manual operations are highly dangerous,as dense smoke,low visibility,extreme heat,and toxic gases not only hinder rescue efficiency but also endanger firefighters’safety.Although intelligent rescue robots can enter hazardous environments in place of humans,smoke poses major challenges for human detection algorithms.These challenges include the attenuation of visible and infrared signals,complex thermal fields,and interference frombackground objects,all ofwhichmake it difficult to accurately identify trapped individuals.To address this problem,we propose VIF-YOLO,a visible–infrared fusion model for real-time human detection in dense smoke environments.The framework introduces a lightweight multimodal fusion(LMF)module based on learnable low-rank representation blocks to end-to-end integrate visible and infrared images,preserving fine details while enhancing salient features.In addition,an efficient multiscale attention(EMA)mechanism is incorporated into the YOLOv10n backbone to improve feature representation under low-light conditions.Extensive experiments on our newly constructedmultimodal smoke human detection(MSHD)dataset demonstrate thatVIF-YOLOachievesmAP50 of 99.5%,precision of 99.2%,and recall of 99.3%,outperforming YOLOv10n by a clear margin.Furthermore,when deployed on the NVIDIA Jetson Xavier NX,VIF-YOLO attains 40.6 FPS with an average inference latency of 24.6 ms,validating its real-time capability on edge-computing platforms.These results confirm that VIF-YOLO provides accurate,robust,and fast detection across complex backgrounds and diverse smoke conditions,ensuring reliable and rapid localization of individuals in need of rescue.
基金National Key Research and Development Program of China(No.2022YFB3903404,2024YFC3015403)National Natural Science Foundation of China(NSFC No.42271431,42271425)Hubei Province Major Science and Technology Innovation Program(2024BAA011)。
文摘The dense heterogeneous network provides standardized connectivity and access guarantees for 5G communication services.However,the complex network environment and high level of dynamism pose challenges to network selection decisions.Existing vertical handover algorithms often overlook the dynamic nature of user mobility and network condition,resulting in problems such as handover failure and frequent handover,ultimately impacting the quality of the user communication service.To address these problems,we propose an intelligent switching method,iMALSTM-DQN,which integrates an improved Multi-level Associative Long Short-Term Memory model(iMALSTM)with Deep Reinforcement Learning(DRL).The algorithm leverages iMALSTM to predict the global network state in the next moment based on the global user movement trajectory and historical network status information within a region,thereby enhancing the prediction accuracy of network states.Subsequently,based on the predicted network state,we employ the Deep Q Network(DON)model to make handover decisions,adaptively determining the optimal switching and network selection strategy through interaction with the environment.Experimental results demonstrate that the proposed algorithm enhances decision timeliness,significantly reduces the number of switch failures,and alleviates the problem of frequent handovers resulting from network dynamics.
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
基金supported by the Special Fund of the Institute of Geophysics,China Earthquake Administration,(No.DQJB21B34).
文摘The Haicheng region,Liaoning,China,likely hosts a conjugate fault system comprising the NW-trending Haichenghe fault and NE-trending secondary faults.On February 4,1975,at 19:36 CST,an earthquake of M_(S)7.3 and intensity(MMI)IX hit the city of Haicheng,Liaoning,China.Although deep seismic profiling was previously conducted along the Haichenghe fault,the limited horizontal resolution in the shallow part prevented the recognition of kilometer-scale anomalies.The velocity structure characteristics of the Haichenghe fault and its NE-trending conjugate faults remain unclear.Using the extended range phase shift method,the high-resolution S-wave velocity structures are obtained by deploying a long,dense linear array of 55 short-period seismometers across the fault and NE-trending conjugate faults.The array length was 32 km and inter-station spacing was approximately 600 m,facilitating the collection of approximately 22 days of continuous waveform data.Employing the Extended Range Phase Shift(ERPS)method enabled the extraction of broadband 0.2–5 s Rayleigh wave phase velocity dispersion curves.The broadband dispersion data were used for inversion of the high-resolution S-wave velocity structure to a depth of 8 km from the surface.The velocity structure characteristics and seismicity of the Haichenghe fault and NE-trending conjugate faults were analyzed and compared with nearby fault gas measurements.Results show(1)shallow S-wave velocities show a low-high-low horizontal distribution,corresponding to basin-uplift-basin topography;(2)significant velocity contrasts occur across the Haichenghe fault:its SW segment(0–17 km)exhibits high velocities consistent with Paleoproterozoic crystalline basement(Pt_(1)),while the NE segment(17–32 km)shows low velocities related to Yanshanian intrusions(γ_(5))and Quaternary sediments.NE-trending conjugate faults display sharp velocity gradients marking fracture locations,with all faults being near-vertical to~8 km depth;(3)seismicity at 1–6 km depth mainly clusters in high-velocity zones;at 6–8 km depth,it concentrates beneath the Haichenghe fault in low-velocity areas and along NE-trending faults;(4)the seismic activity characteristics and fault zone width of the Haicheng he fault reflected by velocity imaging results are basically consistent with those obtained by the fault gas measurement method.
基金financially supported by the National Natural Science Foundation of China (No.52372188)the 111 Project (No.D17007)2023 Introduction of studying abroad talent program。
文摘Aqueous zinc-ion batteries(AZIBs) have advantages including low economic cost and high safety.Nevertheless,the serious hydrogen evolution reactions(HER) and rampant growth of Zn dendrite hinder their further development.Herein,potassium acetate(KAc) additive with cation/anion synergy effect is added into the ZnSO_(4) electrolyte to effectively promote the oriented uniform Zn deposition and suppress side reactions.According to density functional theory calculation and experimental results,CH_(3)COO^(-)(Ac^(-))anions are capable of forming stronger hydrogen bonds with H_(2)O molecules,leading to an expanded electrochemical stability window,reduced the reactivity of H_(2)O,and hence suppressing HER.Meanwhile,Ac-anions can also preferentially adsorb onto the Zn anode,promoting dense deposition towards the(100) crystal plane.Besides,dissociated K^(+) ions serve as electrostatic shielding cations,which significantly promote uniform Zn deposition and prevent dendrite formation.Thus,the Zn||Zn symmetric cell demonstrates an impressive cycle lifespan of 3000 h at 1.0 m A/cm^(2).Furthermore,the Zn||MnO_(2) full battery exhibits superior stability with a capacity retention of 86.95 % at 2.0 A/g after 4000 cycles.Therefore,the cation/anion synergy effect in KAc additive offers a viable solution to address HER and hinder dendrite growth at the interface of Zn anodes.
文摘Many networks are designed to stack a large number of residual blocks,deepen the network and improve network performance through short residual connec-tion,long residual connection,and dense connection.However,without consider-ing different contributions of different depth features to the network,these de-signs have the problem of evaluating the importance of different depth features.To solve this problem,this paper proposes an adaptive densely residual net-work(ADRNet)for the single image super resolution.ADRN realizes the evalua-tion of distributions of different depth features and learns more representative features.An adaptive densely residual block(ADRB)was designed,combining 3 residual blocks(RB)and dense connection was added.It learned the attention score of each dense connection through adaptive dense connections,and the at-tention score reflected the importance of the features of each RB.To further en-hance the performance of ADRB,a multi-direction attention block(MDAB)was introduced to obtain multidirectional context information.Through comparative experiments,it is proved that theproposed ADRNet is superior to the existing methods.Through ablation experiments,it is proved that evaluating features of different depths helps to improve network performance.
基金the National Natural Science Foundation of China(Nos.11602088,11672110 and 11472110)Natural Science Foundation of Guangdong Province(No.2017A030313014)+1 种基金the opening project of the State Key Lab-oratory for Strength and Vibration of Mechanical Structures(Xi'an Jiaotong University)(Nos.SV2018-KF-33 and SV2017-KF-04)Fundamental Research Funds for the Central Universities(Nos.2017BQ094 and 2018PY21).
文摘The investigation of the problem of particle packing has provided basic insights into the structure,symmetry,and physical properties of condensed matter.Dense packings of non-spherical particles have many applications,both in research and industry.We report the two-dimensional dense packing patterns of bending and assembled rods,which are non-convexly deformed from simple objects and modeled as entangled particles.Monte Carlo simulations and further analytical constructions are carried out to explore possible densely packed structures.Two typical densely packed structures of C-bending rods are found,and their packing densities are identified as being functions of the aspect ratio and central angle.Six shapes of assembled rods,representing the combined deformations of rods,are employed in simulations with the packing structures classified into three types.The dense packing density of each packing pattern is derived as a function of different shape parameters.In contrast with the case of disordered packings,both the shape and order are verified to affect the packing density.
