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
The brain exhibits complex physiology characterized by unique features such as a brain-specific extracellular matrix, compartmentalized structure (white and grey matter), and an aligned axonal network. These physiolog...The brain exhibits complex physiology characterized by unique features such as a brain-specific extracellular matrix, compartmentalized structure (white and grey matter), and an aligned axonal network. These physiological characteristics underpin brain function and facilitate signal transduction similar to that in an electrical circuit. Therefore, investigating these features in vitro is crucial for understanding the interactions between neuronal signal transduction processes and the pathology of neurological diseases. Compared to neurons on patterned substrates, three-dimensional (3D) bioprinting-based neural models provide significant advantages in replicating axonal kinetics without physical limitations. This study proposes the development of a 3D bioprinted engineered neural network (BENN) model to replicate the physiological features of the brain, suggesting its application as a tool for studying neurodegenerative diseases. We employed 3D bioprinting to reconstruct the compartmentalized structure of the brain, and controlled the directionality of axonal growth by applying electrical stimuli to the printed neural structure for overcoming spatial constraints. The reconstructed axonal network demonstrated reliability as a neural analog, including the visualization of mature neuronal features and spontaneous calcium reactions. Furthermore, these brain-like neural network models have demonstrated usefulness for studying neurodegeneration by enabling the visualization of degenerative pathophysiology in alcohol-exposed neurons. The BENN facilitates the visualization of region-specific pathological markers in soma or axon populations, including amyloid-beta formation and axonal deformation. Overall, the BENN closely mimics brain physiology, offers insights into the dynamics of axonal networks, and can be applied to studying neurological diseases.展开更多
Scene graph prediction has emerged as a critical task in computer vision,focusing on transforming complex visual scenes into structured representations by identifying objects,their attributes,and the relationships amo...Scene graph prediction has emerged as a critical task in computer vision,focusing on transforming complex visual scenes into structured representations by identifying objects,their attributes,and the relationships among them.Extending this to 3D semantic scene graph(3DSSG)prediction introduces an additional layer of complexity because it requires the processing of point-cloud data to accurately capture the spatial and volumetric characteristics of a scene.A significant challenge in 3DSSG is the long-tailed distribution of object and relationship labels,causing certain classes to be severely underrepresented and suboptimal performance in these rare categories.To address this,we proposed a fusion prototypical network(FPN),which combines the strengths of conventional neural networks for 3DSSG with a Prototypical Network.The former are known for their ability to handle complex scene graph predictions while the latter excels in few-shot learning scenarios.By leveraging this fusion,our approach enhances the overall prediction accuracy and substantially improves the handling of underrepresented labels.Through extensive experiments using the 3DSSG dataset,we demonstrated that the FPN achieves state-of-the-art performance in 3D scene graph prediction as a single model and effectively mitigates the impact of the long-tailed distribution,providing a more balanced and comprehensive understanding of complex 3D environments.展开更多
Variable material screw-based material extrusion(S-MEX)3D printing technology provides a novel approach for fabricating composites with continuous material gradients.Nevertheless,achieving precise alignment between th...Variable material screw-based material extrusion(S-MEX)3D printing technology provides a novel approach for fabricating composites with continuous material gradients.Nevertheless,achieving precise alignment between the process parameters and material compositions is challenging because of fluctuations in the melt rheological state caused by material variations.In this study,an invertible extrusion prediction model for 0-40 wt% short carbon fiber reinforced polyether-ether-ketone(SCF/PEEK)in the S-MEX process was established using an invertible neural network(INN)that demonstrated the capabilities of forward flow rate prediction and inverse process optimization with accuracies of 0.852 and 0.877,respectively.Moreover,a strategy for adjusting the screw speeds using process parameters obtained from the INN was developed to maintain a consistent flow rate during the variable material printing process.Benefiting from uniform flow,the linewidth accuracy was improved by 77%,and the surface roughness was reduced by 51%.Adjusting the process parameters by using an INN offers significant potential for flow rate control and the enhancement of the overall performance of variable material 3D printing.展开更多
Naru Sanwei Pill,also known as Naru-3,a Mongolian medicine originating from Zhigao Pharmacopoeia,is a classic prescription used in the treatment of rheumatism.It is composed of Terminalia chebula,processed Aconitum ku...Naru Sanwei Pill,also known as Naru-3,a Mongolian medicine originating from Zhigao Pharmacopoeia,is a classic prescription used in the treatment of rheumatism.It is composed of Terminalia chebula,processed Aconitum kusnezoffii Reichb.,and Piper longum,and is known for its effects in eliminating“mucus,”relieving pain,and reducing swelling,with significant efficacy in treating joint effusion and lumbar pain.In recent years,researchers have summarized its chemical components and pharmacological effects,and employed network pharmacology methods based on the core theory of Traditional Chinese Medicine quality markers(Q-Markers)to analyze and predict its markers.The results identified potential Q-Markers for Naru-3,providing a scientific basis for quality control and further research.展开更多
In this work,we have developed a lignin-derived polymer electrolyte(LSELi),which demonstrates exceptional ionic conductivity of 1.6×10^(-3)S cm^(−1)and a high cation transference number of 0.57 at 25°C.Time ...In this work,we have developed a lignin-derived polymer electrolyte(LSELi),which demonstrates exceptional ionic conductivity of 1.6×10^(-3)S cm^(−1)and a high cation transference number of 0.57 at 25°C.Time of flight secondary ion mass spectrometry(TOF-SIMS)analysis shows that the large-size 1-ethyl-3-methylimidazolium cations(EMIM^(+))can induce the aggregation of the anionic segments in lignosulfonate to reconstruct the three-dimensional(3D)spatial structure of polyelectrolyte,thereby forming a fluent Li^(+)transport 3D network.Dielectric loss spectroscopy further reveals that within this transport network,Li^(+)transport is decoupled from the relaxation of lignosulfonate chain segments,exhibiting characteristics of rapid Li^(+)transport.Furthermore,in-situ distribution of relaxation times analysis indicates that a stable solid electrolyte interface layer is formed at the Li plating interface with LSELi,optimizing the Li plating interface and exhibiting low charge transfer impedance and stable Li plating and stripping.Thus,a substantially prolonged cycling stability and reversibility are obtained in the Li||LSELi||Li battery at 25°C(1800 h at 0.1 mA cm^(−2),0.1 mAh cm^(−2)).At 25°C,the Li||LSELi||LiFePO_(4)cell shows 132 mAh g^(−1)of capacity with 92.7%of retention over 120 cycles at 0.1 mA cm^(−2).展开更多
3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safe...3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.展开更多
Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials ...Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.展开更多
Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying t...Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying the actuator modeling and solving the difficulty of fault data collection.To solve the problem of real-time diagnosis of actuator faults in the 3-PR(P)S parallel robot,the model of 3-PR(P)S parallel robot and data-driven-based method for the fault diagnosis are presented.Firstly,only the input-output relationship of the actuator is considered for modeling actuator faults,reducing the complexity of fault modeling and reducing the time consumption of parameter identification,thereby meeting the requirements of real-time diagnosis.A Simulink model of the electromechanical actuator(EMA)was constructed to analyze actuator faults.Then the short-term analysis method was employed for collecting the sample data of the slider position on the test platform of the EMA system and feature extraction.Training samples for neural networks are obtained.Furthermore,we optimized the Back Propagation(BP)neural network using the Dung Beetle Optimization Algorithm(DBO),which effectively resolved the weights and thresholds of the BP neural network.Compared to BP and Particle Swarm Optimization(PSO)-BP,the DBO-BP has better convergence,convergence rate,and the best-classifying quality.So,the classification for the different actuator faults is obviously improved.Finally,a fault diagnosis system was designed for the actuator of the 3-PR(P)S parallel robot,and the experimental results demonstrate that this system can detect actuator faults within 0.1 seconds.This work also provides the technical support for the fault-tolerant control of the 3-PR(P)S Parallel robot.展开更多
Taking AuCu3-type sublattice system as an example, three discoveries have been presented: First, the third barrier hindering the progress in metal materials science is that researchers have got used to recognizing exp...Taking AuCu3-type sublattice system as an example, three discoveries have been presented: First, the third barrier hindering the progress in metal materials science is that researchers have got used to recognizing experimental phenomena of alloy phase transitions during extremely slow variation in temperature by equilibrium thinking mode and then taking erroneous knowledge of experimental phenomena as selected information for establishing Gibbs energy function and so-called equilibrium phase diagram. Second, the equilibrium holographic network phase diagrams of AuCu3-type sublattice system may be used to describe systematic correlativity of the composition?temperature-dependent alloy gene arranging structures and complete thermodynamic properties, and to be a standard for studying experimental subequilibrium order-disorder transition. Third, the equilibrium transition of each alloy is a homogeneous single-phase rather than a heterogeneous two-phase, and there exists a single-phase boundary curve without two-phase region of the ordered and disordered phases; the composition and temperature of the top point on the phase-boundary curve are far away from the ones of the critical point of the AuCu3 compound.展开更多
Taking Au3Cu-type sublattice system as an example, three discoveries have been presented. First, the fourth barrier to hinder the progress of metal materials science is that today’s researchers do not understand that...Taking Au3Cu-type sublattice system as an example, three discoveries have been presented. First, the fourth barrier to hinder the progress of metal materials science is that today’s researchers do not understand that the Gibbs energy function of an alloy phase should be derived from Gibbs energy partition function constructed of alloy gene sequence and their Gibbs energy sequence. Second, the six rules for establishing alloy gene Gibbs energy partition function have been discovered, and it has been specially proved that the probabilities of structure units occupied at the Gibbs energy levels in the degeneracy factor for calculating configuration entropy should be degenerated as ones of component atoms occupied at the lattice points. Third, the main characteristics unexpected by today’s researchers are as follows. There exists a single-phase boundary curve without two-phase region coexisting by the ordered and disordered phases. The composition and temperature of the top point on the phase-boundary curve are far away from those of the critical point of the Au3Cu compound; At 0 K, the composition of the lowest point on the composition-dependent Gibbs energy curve is notably deviated from that of the Au3Cu compounds. The theoretical limit composition range of long range ordered Au3Cu-type alloys is determined by the first jumping order degree.展开更多
An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power fallof...An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.展开更多
为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(sque...为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(squeeze and excitation networks,SENet)结构,使YOLO-v3算法更加关注与目标相关的特征通道,提升网络性能。结果表明,改进后算法的平均精度为84.42%,较原YOLO-v3算法提升了6.14%,检测时间减少了21 ms,改进后的YOLO-v3算法能较好地识别出风力机叶片表面损伤。展开更多
针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的...针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的层数以及采用K-means聚类算法选取初始先验框,提高了网络像素特征提取细粒度并加快了检测速度,进而结合人眼特征参数提取方法和眼行为识别算法,构建出了眼机交互模型并进行实验。实验结果表明,该模型对不同眼行为的识别率达91.30%,改进的YOLO-v3网络的平均检测准确率(mean average precision,mAP)为99.9%,识别速度达22.8 FPS,相比原YOLO-v3方法检测时间缩短了11.4%。展开更多
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 Korean Fund for Regenerative Medicine funded by Ministry of Science and ICT,and Ministry of Health and Welfare(22A0106L1,Republic of Korea)the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022M3C1A3081359).
文摘The brain exhibits complex physiology characterized by unique features such as a brain-specific extracellular matrix, compartmentalized structure (white and grey matter), and an aligned axonal network. These physiological characteristics underpin brain function and facilitate signal transduction similar to that in an electrical circuit. Therefore, investigating these features in vitro is crucial for understanding the interactions between neuronal signal transduction processes and the pathology of neurological diseases. Compared to neurons on patterned substrates, three-dimensional (3D) bioprinting-based neural models provide significant advantages in replicating axonal kinetics without physical limitations. This study proposes the development of a 3D bioprinted engineered neural network (BENN) model to replicate the physiological features of the brain, suggesting its application as a tool for studying neurodegenerative diseases. We employed 3D bioprinting to reconstruct the compartmentalized structure of the brain, and controlled the directionality of axonal growth by applying electrical stimuli to the printed neural structure for overcoming spatial constraints. The reconstructed axonal network demonstrated reliability as a neural analog, including the visualization of mature neuronal features and spontaneous calcium reactions. Furthermore, these brain-like neural network models have demonstrated usefulness for studying neurodegeneration by enabling the visualization of degenerative pathophysiology in alcohol-exposed neurons. The BENN facilitates the visualization of region-specific pathological markers in soma or axon populations, including amyloid-beta formation and axonal deformation. Overall, the BENN closely mimics brain physiology, offers insights into the dynamics of axonal networks, and can be applied to studying neurological diseases.
基金supported by the Glocal University 30 Project Fund of Gyeongsang National University in 2025.
文摘Scene graph prediction has emerged as a critical task in computer vision,focusing on transforming complex visual scenes into structured representations by identifying objects,their attributes,and the relationships among them.Extending this to 3D semantic scene graph(3DSSG)prediction introduces an additional layer of complexity because it requires the processing of point-cloud data to accurately capture the spatial and volumetric characteristics of a scene.A significant challenge in 3DSSG is the long-tailed distribution of object and relationship labels,causing certain classes to be severely underrepresented and suboptimal performance in these rare categories.To address this,we proposed a fusion prototypical network(FPN),which combines the strengths of conventional neural networks for 3DSSG with a Prototypical Network.The former are known for their ability to handle complex scene graph predictions while the latter excels in few-shot learning scenarios.By leveraging this fusion,our approach enhances the overall prediction accuracy and substantially improves the handling of underrepresented labels.Through extensive experiments using the 3DSSG dataset,we demonstrated that the FPN achieves state-of-the-art performance in 3D scene graph prediction as a single model and effectively mitigates the impact of the long-tailed distribution,providing a more balanced and comprehensive understanding of complex 3D environments.
基金supported by National Natural Science Foundation of China(Grant Nos.12202547,62461160259)Shaanxi Province Qingchuangyuan“Scientist and Engineering”Team Construction Project(Grant Nos.2022KXJ-102,2022KXJ-106)+1 种基金Fundamental Research Funds for the Central UniversitiesProgram for Innovation Team of Shaanxi Province(Grant No.2023-CX-TD-17).
文摘Variable material screw-based material extrusion(S-MEX)3D printing technology provides a novel approach for fabricating composites with continuous material gradients.Nevertheless,achieving precise alignment between the process parameters and material compositions is challenging because of fluctuations in the melt rheological state caused by material variations.In this study,an invertible extrusion prediction model for 0-40 wt% short carbon fiber reinforced polyether-ether-ketone(SCF/PEEK)in the S-MEX process was established using an invertible neural network(INN)that demonstrated the capabilities of forward flow rate prediction and inverse process optimization with accuracies of 0.852 and 0.877,respectively.Moreover,a strategy for adjusting the screw speeds using process parameters obtained from the INN was developed to maintain a consistent flow rate during the variable material printing process.Benefiting from uniform flow,the linewidth accuracy was improved by 77%,and the surface roughness was reduced by 51%.Adjusting the process parameters by using an INN offers significant potential for flow rate control and the enhancement of the overall performance of variable material 3D printing.
文摘Naru Sanwei Pill,also known as Naru-3,a Mongolian medicine originating from Zhigao Pharmacopoeia,is a classic prescription used in the treatment of rheumatism.It is composed of Terminalia chebula,processed Aconitum kusnezoffii Reichb.,and Piper longum,and is known for its effects in eliminating“mucus,”relieving pain,and reducing swelling,with significant efficacy in treating joint effusion and lumbar pain.In recent years,researchers have summarized its chemical components and pharmacological effects,and employed network pharmacology methods based on the core theory of Traditional Chinese Medicine quality markers(Q-Markers)to analyze and predict its markers.The results identified potential Q-Markers for Naru-3,providing a scientific basis for quality control and further research.
基金support from the National Natural Science Foundation of China(NSFC,22393901,22021001,22272143,22441030)the National Key Research and Development Program(2021YFA1502300)+1 种基金the Fundamental Research Funds for the Central Universities(20720220009)the Natural Science Foundation of Fujian Province,China(Grant No.2024J01213135)。
文摘In this work,we have developed a lignin-derived polymer electrolyte(LSELi),which demonstrates exceptional ionic conductivity of 1.6×10^(-3)S cm^(−1)and a high cation transference number of 0.57 at 25°C.Time of flight secondary ion mass spectrometry(TOF-SIMS)analysis shows that the large-size 1-ethyl-3-methylimidazolium cations(EMIM^(+))can induce the aggregation of the anionic segments in lignosulfonate to reconstruct the three-dimensional(3D)spatial structure of polyelectrolyte,thereby forming a fluent Li^(+)transport 3D network.Dielectric loss spectroscopy further reveals that within this transport network,Li^(+)transport is decoupled from the relaxation of lignosulfonate chain segments,exhibiting characteristics of rapid Li^(+)transport.Furthermore,in-situ distribution of relaxation times analysis indicates that a stable solid electrolyte interface layer is formed at the Li plating interface with LSELi,optimizing the Li plating interface and exhibiting low charge transfer impedance and stable Li plating and stripping.Thus,a substantially prolonged cycling stability and reversibility are obtained in the Li||LSELi||Li battery at 25°C(1800 h at 0.1 mA cm^(−2),0.1 mAh cm^(−2)).At 25°C,the Li||LSELi||LiFePO_(4)cell shows 132 mAh g^(−1)of capacity with 92.7%of retention over 120 cycles at 0.1 mA cm^(−2).
基金Supported by Sichuan Science and Technology Program(2023YFSY0026,2023YFH0004).
文摘3D medical image reconstruction has significantly enhanced diagnostic accuracy,yet the reliance on densely sampled projection data remains a major limitation in clinical practice.Sparse-angle X-ray imaging,though safer and faster,poses challenges for accurate volumetric reconstruction due to limited spatial information.This study proposes a 3D reconstruction neural network based on adaptive weight fusion(AdapFusionNet)to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images.To address the issue of spatial inconsistency in multi-angle image reconstruction,an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and perform weighted fusion,thereby improving the final reconstruction quality.The reconstruction network is built on an autoencoder(AE)framework and uses orthogonal-angle X-ray images(frontal and lateral projections)as inputs.The encoder extracts 2D features,which the decoder maps into 3D space.This study utilizes a lung CT dataset to obtain complete three-dimensional volumetric data,from which digitally reconstructed radiographs(DRR)are generated at various angles to simulate X-ray images.Since real-world clinical X-ray images rarely come with perfectly corresponding 3D“ground truth,”using CT scans as the three-dimensional reference effectively supports the training and evaluation of deep networks for sparse-angle X-ray 3D reconstruction.Experiments conducted on the LIDC-IDRI dataset with simulated X-ray images(DRR images)as training data demonstrate the superior performance of AdapFusionNet compared to other fusion methods.Quantitative results show that AdapFusionNet achieves SSIM,PSNR,and MAE values of 0.332,13.404,and 0.163,respectively,outperforming other methods(SingleViewNet:0.289,12.363,0.182;AvgFusionNet:0.306,13.384,0.159).Qualitative analysis further confirms that AdapFusionNet significantly enhances the reconstruction of lung and chest contours while effectively reducing noise during the reconstruction process.The findings demonstrate that AdapFusionNet offers significant advantages in 3D reconstruction of sparse-angle X-ray images.
基金financially supported by the National Key R&D Program of China(Grant No.2022YFE0207300)National Natural Science Foundation of China(Grant Nos.22179142 and 22075314)+1 种基金Jiangsu Funding Program for Excellent Postdoctoral Talent(Grant No.2024ZB051 and 2023ZB836)the technical support for Nano-X from Suzhou Institute of Nano-Tech and Nano-Bionics,Chinese Academy of Sciences(SINANO).
文摘Solid polymer electrolytes(SPEs)have attracted much attention for their safety,ease of packaging,costeffectiveness,excellent flexibility and stability.Poly-dioxolane(PDOL)is one of the most promising matrix materials of SPEs due to its remarkable compatibility with lithium metal anodes(LMAs)and suitability for in-situ polymerization.However,poor thermal stability,insufficient ionic conductivity and narrow electrochemical stability window(ESW)hinder its further application in lithium metal batteries(LMBs).To ameliorate these problems,we have successfully synthesized a polymerized-ionic-liquid(PIL)monomer named DIMTFSI by modifying DOL with imidazolium cation coupled with TFSI^(-)anion,which simultaneously inherits the lipophilicity of DOL,high ionic conductivity of imidazole,and excellent stability of PILs.Then the tridentate crosslinker trimethylolpropane tris[3-(2-methyl-1-aziridine)propionate](TTMAP)was introduced to regulate the excessive Li^(+)-O coordination and prepare a flame-retardant SPE(DT-SPE)with prominent thermal stability,wide ESW,high ionic conductivity and abundant Lit transference numbers(t_(Li+)).As a result,the LiFePO_(4)|DT-SPE|Li cell exhibits a high initial discharge specific capacity of 149.60 mAh g^(-1)at 0.2C and 30℃with a capacity retention rate of 98.68%after 500 cycles.This work provides new insights into the structural design of PIL-based electrolytes for long-cycling LMBs with high safety and stability.
文摘Any malfunctions of the actuators of the robots have the potential to destroy the robot’s normal motion,and most of the current actuator fault diagnosis methods are difficult to meet the requirements of simplifying the actuator modeling and solving the difficulty of fault data collection.To solve the problem of real-time diagnosis of actuator faults in the 3-PR(P)S parallel robot,the model of 3-PR(P)S parallel robot and data-driven-based method for the fault diagnosis are presented.Firstly,only the input-output relationship of the actuator is considered for modeling actuator faults,reducing the complexity of fault modeling and reducing the time consumption of parameter identification,thereby meeting the requirements of real-time diagnosis.A Simulink model of the electromechanical actuator(EMA)was constructed to analyze actuator faults.Then the short-term analysis method was employed for collecting the sample data of the slider position on the test platform of the EMA system and feature extraction.Training samples for neural networks are obtained.Furthermore,we optimized the Back Propagation(BP)neural network using the Dung Beetle Optimization Algorithm(DBO),which effectively resolved the weights and thresholds of the BP neural network.Compared to BP and Particle Swarm Optimization(PSO)-BP,the DBO-BP has better convergence,convergence rate,and the best-classifying quality.So,the classification for the different actuator faults is obviously improved.Finally,a fault diagnosis system was designed for the actuator of the 3-PR(P)S parallel robot,and the experimental results demonstrate that this system can detect actuator faults within 0.1 seconds.This work also provides the technical support for the fault-tolerant control of the 3-PR(P)S Parallel robot.
基金Project(51071181)supported by the National Natural Science Foundation of ChinaProject(2013FJ4043)supported by the Natural Science Foundation of Hunan Province,China
文摘Taking AuCu3-type sublattice system as an example, three discoveries have been presented: First, the third barrier hindering the progress in metal materials science is that researchers have got used to recognizing experimental phenomena of alloy phase transitions during extremely slow variation in temperature by equilibrium thinking mode and then taking erroneous knowledge of experimental phenomena as selected information for establishing Gibbs energy function and so-called equilibrium phase diagram. Second, the equilibrium holographic network phase diagrams of AuCu3-type sublattice system may be used to describe systematic correlativity of the composition?temperature-dependent alloy gene arranging structures and complete thermodynamic properties, and to be a standard for studying experimental subequilibrium order-disorder transition. Third, the equilibrium transition of each alloy is a homogeneous single-phase rather than a heterogeneous two-phase, and there exists a single-phase boundary curve without two-phase region of the ordered and disordered phases; the composition and temperature of the top point on the phase-boundary curve are far away from the ones of the critical point of the AuCu3 compound.
基金Project(51071181)supported by the National Natural Science Foundation of ChinaProject(2013FJ4043)supported by the Natural Science Foundation of Hunan Province,China
文摘Taking Au3Cu-type sublattice system as an example, three discoveries have been presented. First, the fourth barrier to hinder the progress of metal materials science is that today’s researchers do not understand that the Gibbs energy function of an alloy phase should be derived from Gibbs energy partition function constructed of alloy gene sequence and their Gibbs energy sequence. Second, the six rules for establishing alloy gene Gibbs energy partition function have been discovered, and it has been specially proved that the probabilities of structure units occupied at the Gibbs energy levels in the degeneracy factor for calculating configuration entropy should be degenerated as ones of component atoms occupied at the lattice points. Third, the main characteristics unexpected by today’s researchers are as follows. There exists a single-phase boundary curve without two-phase region coexisting by the ordered and disordered phases. The composition and temperature of the top point on the phase-boundary curve are far away from those of the critical point of the Au3Cu compound; At 0 K, the composition of the lowest point on the composition-dependent Gibbs energy curve is notably deviated from that of the Au3Cu compounds. The theoretical limit composition range of long range ordered Au3Cu-type alloys is determined by the first jumping order degree.
基金The National Natural Science Foundation of China(No.60872004)the Research Fund of National Mobile Communications Research Laboratory of Southeast University(No.2010A08)the Fundamental Research Funds for the Central Universities(No.2009B21814)
文摘An analytical approach to evaluate the performance of the 3G/ad hoc integrated network is presented. A channel model capturing both path loss and shadowing is applied to the analysis so as to characterize power falloff vs. distance. The 3G/ad hoc integrated network scenario model is introduced briefly. Based on this model, several performances of the 3G/ ad hoc integrated network in terms of outage probability, call dropping probability and new call blocking probability are evaluated. The corresponding performance formulae are deduced in accordance with the analytical models. Meanwhile, the formula of the 3G/ad hoc integrated network capacity is deduced on the basis of the formula of the outage probability. It is observed from extensive simulation and numerical analysis that the 3G/ad hoc integrated network remarkably outperforms the 3G network with regards to the network performance. This derived evaluation approach can be applied into planning and optimization of the 3G/ad hoc network.
文摘为对风力机叶片损伤状态进行有效检测,提出一种基于改进YOLO-v3算法的风力机叶片表面损伤检测识别技术。根据风力机叶片损伤区域特点,对网络中锚框(anchor)的尺度进行调整优化;在特征提取网络后引入基于注意力机制的挤压与激励网络(squeeze and excitation networks,SENet)结构,使YOLO-v3算法更加关注与目标相关的特征通道,提升网络性能。结果表明,改进后算法的平均精度为84.42%,较原YOLO-v3算法提升了6.14%,检测时间减少了21 ms,改进后的YOLO-v3算法能较好地识别出风力机叶片表面损伤。
文摘针对嵌入式眼-机交互技术中所采用的传统眼行为识别方法准确率低、速度慢等问题,并结合所研制眼机交互系统硬件特点及应用场景,提出一种基于改进YOLO-v3的眼机交互模型。该模型通过去除13×13特征分辨率的检测模块、增加浅层网络的层数以及采用K-means聚类算法选取初始先验框,提高了网络像素特征提取细粒度并加快了检测速度,进而结合人眼特征参数提取方法和眼行为识别算法,构建出了眼机交互模型并进行实验。实验结果表明,该模型对不同眼行为的识别率达91.30%,改进的YOLO-v3网络的平均检测准确率(mean average precision,mAP)为99.9%,识别速度达22.8 FPS,相比原YOLO-v3方法检测时间缩短了11.4%。
