Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon...Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.展开更多
[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis...[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.展开更多
Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly a...Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses.展开更多
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w...The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.展开更多
Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act ...Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.展开更多
Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other s...Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other seasons.The phenomenon significantly disrupts radio wave signals essential to communication and navigation systems.The national network of Global Navigation Satellite System(GNSS)receivers in Indonesia(>30°longitudinal range)provides an opportunity for detailed EPB studies.To explore this,we conducted preliminary 3D tomography of total electron content(TEC)data captured by GNSS receivers following a geomagnetic storm on December 3,2023,when at least four EPB clusters occurred in the Southeast Asian sector.TEC and extracted TEC depletion with a 120-minute running average were then used as inputs for a 3D tomography program.Their 2D spatial distribution consistently captured the four EPB clusters over time.These tomography results were validated through a classical checkerboard test and comparisons with other ionospheric data sources,such as the Global Ionospheric Map(GIM)and International Reference Ionosphere(IRI)profile.Validation of the results demonstrates the capability of the Indonesian GNSS network to measure peak ionospheric density.These findings highlight the potential for future three-dimensional research of plasma bubbles in low-latitude regions using existing GNSS networks,with extensive longitudinal coverage.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not...To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.展开更多
软件定义自组网(Software Defined Mobile Ad Hoc Network,SD-MANET)融合了软件定义网络(Software Defined Network,SDN)集中控制优势和移动自组网(Mobile Ad Hoc Network,MANET)的动态组网优势,但Mininet等现有仿真平台在动态拓扑与扩...软件定义自组网(Software Defined Mobile Ad Hoc Network,SD-MANET)融合了软件定义网络(Software Defined Network,SDN)集中控制优势和移动自组网(Mobile Ad Hoc Network,MANET)的动态组网优势,但Mininet等现有仿真平台在动态拓扑与扩展性等方面存在显著局限,严重制约了其关键算法的研究。针对这一问题,提出基于网络模拟器-3(Network Simulator-3,NS-3)的仿真平台NS3-SDMANET,具体工作包括:基于NS-3框架实现了SD-MANET网络层关键特性的仿真;基于远程过程调用对平台功能进行封装,并提供可编程接口。战术网的测试案例表明:NS3-SDMANET在SD-MANET网络层关键特性仿真方面表现优异,能够有效支撑SD-MANET网络层算法的高效验证与性能评估,为相关研究提供了高扩展性的基础仿真环境。展开更多
The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the tradit...The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.展开更多
Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep ...Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.展开更多
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.展开更多
Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised m...Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.展开更多
With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of arti...With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.展开更多
基金Supported by the National key research and development program in the 14th five year plan 2021YFA1200700)the National Natural Science Foundation of China(62535018,62431025,62561160113)the Natural Science Foundation of Shanghai(23ZR1473400).
文摘Near-infrared image sensors are widely used in fields such as material identification,machine vision,and autonomous driving.Lead sulfide colloidal quantum dot-based infrared photodiodes can be integrated with sil⁃icon-based readout circuits in a single step.Based on this,we propose a photodiode based on an n-i-p structure,which removes the buffer layer and further simplifies the manufacturing process of quantum dot image sensors,thus reducing manufacturing costs.Additionally,for the noise complexity in quantum dot image sensors when capturing images,traditional denoising and non-uniformity methods often do not achieve optimal denoising re⁃sults.For the noise and stripe-type non-uniformity commonly encountered in infrared quantum dot detector imag⁃es,a network architecture has been developed that incorporates multiple key modules.This network combines channel attention and spatial attention mechanisms,dynamically adjusting the importance of feature maps to en⁃hance the ability to distinguish between noise and details.Meanwhile,the residual dense feature fusion module further improves the network's ability to process complex image structures through hierarchical feature extraction and fusion.Furthermore,the pyramid pooling module effectively captures information at different scales,improv⁃ing the network's multi-scale feature representation ability.Through the collaborative effect of these modules,the network can better handle various mixed noise and image non-uniformity issues.Experimental results show that it outperforms the traditional U-Net network in denoising and image correction tasks.
文摘[Objective]Leaf diseases significantly affect both the yield and quality of tea throughout the year.To address the issue of inadequate segmentation finesse in the current tea spot segmentation models,a novel diagnosis of the severity of tea spots was proposed in this research,designated as MDC-U-Net3+,to enhance segmentation accuracy on the base framework of U-Net3+.[Methods]Multi-scale feature fusion module(MSFFM)was incorporated into the backbone network of U-Net3+to obtain feature information across multiple receptive fields of diseased spots,thereby reducing the loss of features within the encoder.Dual multi-scale attention(DMSA)was incorporated into the skip connection process to mitigate the segmentation boundary ambiguity issue.This integration facilitates the comprehensive fusion of fine-grained and coarse-grained semantic information at full scale.Furthermore,the segmented mask image was subjected to conditional random fields(CRF)to enhance the optimization of the segmentation results[Results and Discussions]The improved model MDC-U-Net3+achieved a mean pixel accuracy(mPA)of 94.92%,accompanied by a mean Intersection over Union(mIoU)ratio of 90.9%.When compared to the mPA and mIoU of U-Net3+,MDC-U-Net3+model showed improvements of 1.85 and 2.12 percentage points,respectively.These results illustrated a more effective segmentation performance than that achieved by other classical semantic segmentation models.[Conclusions]The methodology presented herein could provide data support for automated disease detection and precise medication,consequently reducing the losses associated with tea diseases.
基金supported by the National Key Research and Development Program Young Scientist Project(Grant No.2024YFC2911000)the National Natural Science Foundation of China(Grant No.52474103)the Major Basic Research Project of the Natural Science Foundation of Shandong Province(Grant No.ZR2024ZD22).
文摘Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses.
基金supported by the National Science and Technology Council,Taiwan,under Grants 113-2221-E-260-014-MY2 and 114-2119-M-033-001.
文摘The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.
基金supported by Interdisciplinary Innova-tion Project of“Bioarchaeology Laboratory”of Jilin University,China,and“MedicineþX”Interdisciplinary Innovation Team of Norman Bethune Health Science Center of Jilin University,China(Grant No.:2022JBGS05).
文摘Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)mutations are influenced by random and uncontrollable factors,and the risk of the next widespread epidemic remains.Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations.In this study,a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins.The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets.A multi-layer perceptron(MLP)was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution.Utilizing a conditional generative neural network,cG-SchNet,with 3D Euclidean group(E3)symmetries,the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated.The 1D probability,2D joint probability,and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area.Among the 201 generated molecules,42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol,demonstrating structure diversity along with strong dual-target affinities,good absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties,and ease of synthesis.Dual-target drugs are rare and difficult to find,and our“high-throughput docking-multi-conditional generation”workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
基金the National Institute of Information and Communication Technology International Exchange Program 2024−2025(No.2024−007)for their invaluable support in this research.3D tomography software is available at Prof.Kosuke Heki’s(Hokkaido University,Japan)personal homepage(https://www.ep.sci.hokudai.ac.jp/~heki/software.htm).support from the 2024 Japan Student Services Organization Research Follow-up Fellowship for a 90-day research visit at the Institute for Space−Earth Environmental Research,Nagoya University,Japan.PA also acknowledges the support received from Telkom University under the“Skema Penelitian Terapan Periode I Tahun Anggaran 2024”,and the Memorandum of Understanding for Research Collaboration on Regional Ionospheric Observation(No:092/SAM3/TE-DEK/2021).
文摘Equatorial Plasma Bubbles(EPBs)are ionospheric irregularities that take place near the magnetic equator.EPBs most commonly occur after sunset during the equinox months,although they can also be observed during other seasons.The phenomenon significantly disrupts radio wave signals essential to communication and navigation systems.The national network of Global Navigation Satellite System(GNSS)receivers in Indonesia(>30°longitudinal range)provides an opportunity for detailed EPB studies.To explore this,we conducted preliminary 3D tomography of total electron content(TEC)data captured by GNSS receivers following a geomagnetic storm on December 3,2023,when at least four EPB clusters occurred in the Southeast Asian sector.TEC and extracted TEC depletion with a 120-minute running average were then used as inputs for a 3D tomography program.Their 2D spatial distribution consistently captured the four EPB clusters over time.These tomography results were validated through a classical checkerboard test and comparisons with other ionospheric data sources,such as the Global Ionospheric Map(GIM)and International Reference Ionosphere(IRI)profile.Validation of the results demonstrates the capability of the Indonesian GNSS network to measure peak ionospheric density.These findings highlight the potential for future three-dimensional research of plasma bubbles in low-latitude regions using existing GNSS networks,with extensive longitudinal coverage.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金Supported by the China National Oil and Gas Major Project(2016ZX05010-003)PetroChina Science and Technology Major Project(2019B1210,2021DJ1201).
文摘To solve the problems of convolutional neural network–principal component analysis(CNN-PCA)in fine description and generalization of complex reservoir geological features,a 3D attention U-Net network was proposed not using a trained C3D video motion analysis model to extract the style of a 3D model,and applied to complement the details of geologic model lost in the dimension reduction of PCA method in this study.The 3D attention U-Net network was applied to a complex river channel sandstone reservoir to test its effects.The results show that compared with CNN-PCA method,the 3D attention U-Net network could better complement the details of geological model lost in the PCA dimension reduction,better reflect the fluid flow features in the original geologic model,and improve history matching results.
文摘软件定义自组网(Software Defined Mobile Ad Hoc Network,SD-MANET)融合了软件定义网络(Software Defined Network,SDN)集中控制优势和移动自组网(Mobile Ad Hoc Network,MANET)的动态组网优势,但Mininet等现有仿真平台在动态拓扑与扩展性等方面存在显著局限,严重制约了其关键算法的研究。针对这一问题,提出基于网络模拟器-3(Network Simulator-3,NS-3)的仿真平台NS3-SDMANET,具体工作包括:基于NS-3框架实现了SD-MANET网络层关键特性的仿真;基于远程过程调用对平台功能进行封装,并提供可编程接口。战术网的测试案例表明:NS3-SDMANET在SD-MANET网络层关键特性仿真方面表现优异,能够有效支撑SD-MANET网络层算法的高效验证与性能评估,为相关研究提供了高扩展性的基础仿真环境。
基金supported by National Natural Science Foundation of China(42364008,41804110)in part by Guizhou Provincial Basic Research Program(Natural Science)(ZK[2022]060)+1 种基金in part by China Postdoctoral Science Foundation(2022M723127)in part by Youth Innovation Team Project of Shandong Provincial Education Department(2022KJ141).
文摘The deep convolutional neural network U-net has been introduced into adaptive subtraction, which is a critical step in effectively suppressing seismic multiples. The U-net approach has higher precision than the traditional linear regression approach. However, the existing 2D U-net approach with 2D data windows can not deal with elaborate discrepancies between the actual and simulated multiples along the gather direction. It may lead to erroneous preservation of primaries or generate obvious vestigial multiples, especially in complex media. To further enhance the multiple suppression accuracy, we present an adaptive subtraction approach utilizing 3D U-net architecture, which can adaptively separate primaries and multiples utilizing 3D windows. The utilization of 3D windows allows for enhanced depiction of spatial continuity and anisotropy of seismic events along the gather direction in comparison to 2D windows. The 3D U-net approach with 3D windows can more effectively preserve the continuity of primaries and manage the complex disparities between the actual and simulated multiples. The proposed 3D U-net approach exhibits 1 dB improvement in the signal-to-noise ratio compared to the 2D U-net approach, as observed in the synthesis data section, and exhibits more outstanding performance in the preservation of primaries and removal of residual multiples in both synthesis and reality data sections. Moreover, to expedite network training in our proposed 3D U-net approach we employ the transfer learning (TL) strategy by utilizing the network parameters of 3D U-net estimated in the preceding data segment as the initial network parameters of 3D U-net for the subsequent data segment. In the reality data section, the 3D U-net approach incorporating TL reduces the computational expense by 70% compared to the one without TL.
文摘Accurate brain tumour segmentation is critical for diagnosis and treatment planning, yet challenging due to tumour complexity. Manual segmentation is time-consuming and variable, necessitating automated methods. Deep learning, particularly 3D U-Net architectures, has revolutionised medical image analysis by leveraging volumetric data to capture spatial context, enhancing segmentation accuracy. This paper reviews brain tumour segmentation methods, emphasising 3D U-Net advancements. We analyse contributions from the Brain Tumour Segmentation (BraTS) challenges (2014-2023), highlighting key improvements and persistent challenges, including tumour heterogeneity, limited annotated data, varied imaging protocols, computational constraints, and model generalisation. Unlike previous reviews, we synthesise these challenges, proposing targeted research directions: enhancing model robustness through domain adaptation and multi-institutional data sharing, developing lightweight architectures for clinical deployment, integrating multi-modal and clinical data, and incorporating explainability techniques to build clinician trust. By addressing these challenges, we aim to guide future research toward developing more robust, generalisable, and clinically applicable segmentation models, ultimately improving patient outcomes in neuro-oncology.
基金Project(2020YFC2008605)supported by the National Key Research and Development Project of ChinaProject(52072412)supported by the National Natural Science Foundation of ChinaProject(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
文摘Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific basis for governance and prevention efforts.In this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of data.Graph generative network(GGN)is used to process time-series meteorological data with complex structures.The graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the model.In addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval prediction.With the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale data.The PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.
基金Princess Nourah Bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R826),Princess Nourah Bint Abdulrahman University,Riyadh,Saudi ArabiaNorthern Border University,Saudi Arabia,for supporting this work through project number(NBU-CRP-2025-2933).
文摘Brain tumor segmentation from Magnetic Resonance Imaging(MRI)supports neurologists and radiologists in analyzing tumors and developing personalized treatment plans,making it a crucial yet challenging task.Supervised models such as 3D U-Net perform well in this domain,but their accuracy significantly improves with appropriate preprocessing.This paper demonstrates the effectiveness of preprocessing in brain tumor segmentation by applying a pre-segmentation step based on the Generalized Gaussian Mixture Model(GGMM)to T1 contrastenhanced MRI scans from the BraTS 2020 dataset.The Expectation-Maximization(EM)algorithm is employed to estimate parameters for four tissue classes,generating a new pre-segmented channel that enhances the training and performance of the 3DU-Net model.The proposed GGMM+3D U-Net framework achieved a Dice coefficient of 0.88 for whole tumor segmentation,outperforming both the standard multiscale 3D U-Net(0.84)and MMU-Net(0.85).It also delivered higher Intersection over Union(IoU)scores compared to models trained without preprocessing or with simpler GMM-based segmentation.These results,supported by qualitative visualizations,suggest that GGMM-based preprocessing should be integrated into brain tumor segmentation pipelines to optimize performance.
基金Guangdong Major Project of Basic and Applied Basic Research(2020B0301030004)。
文摘With the intensification of global warming,marine heatwaves(MHWs)have emerged as a significant extreme hazard,garnering widespread attention and creating a pressing need for accurate prediction.The development of artificial intelligence,particularly the application of deep learning to sea surface temperature(SST),has significantly improved the feasibility of predictions.This study utilizes SST and Outgoing Longwave Radiation(OLR)data to train a 3D U-Net model for predicting MHWs in the South China Sea(SCS)with lead times ranging from 1 to 7 days,based on the characteristics of intraseasonal weather processes.Analysis of MHWs occurrences from 1982 to 2023 reveals distinct seasonal patterns,with summer MHWs primarily concentrated in the northern and central SCS,and the highest temperature centers located in the Gulf of Tonkin and west of the Philippines.The 2023 MHW forecast results demonstrate that the 3D U-Net model achieves low error rates and high correlation coefficients with observational data.Incorporating OLR data enhances forecast accuracy compared to SST-only inputs,and training the model exclusively with summer data further improves prediction accuracy.These findings indicate that the proposed method can significantly enhance the accuracy of MHW forecasts.
