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
Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overa...Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.展开更多
Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed ...Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.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
The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill...The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill incidents.Historically,several large oil spills have had long-term adverse effects on marine ecosystems and economic development,highlighting the importance of accurate-ly delineating and monitoring oil spill areas.In this study,graph neural network technology is introduced to implement semantic seg-mentation of SAR images,and two graph neural network models based on Graph-FCN and Graph-DeepLabV3+with the introduction of an attention mechanism are constructed and evaluated to improve the accuracy and efficiency of oil spill detection.By com-paring the Swin-Unet model,the Graph-DeepLabV3+model performs better in complex scenarios,especially in edge detail recognition.This not only provides strong technical support for marine oil spill monitoring but also provides an effective solution to deal with the potential risks brought by the increase of marine engineering activities,which is of great practical significance as it helps to safeguard the health and sustainable development of marine ecosystems and reduce the economic losses.展开更多
As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for...As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for multiple markers.While 3D pose estimation algorithms combined with ordinary cameras offer an alternative,their accuracy often deteriorates under significant body occlusion.To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance.Compared to traditional optical motion capture systems,this work achieves low-cost,high-precision motion parameter acquisition through the following innovations:(1)spatiotemporal dependency attention module is designed to establish dynamic spatiotemporal correlation graphs via cross-frame joint semantic matching,effectively resolving the feature fragmentation issue in existing methods.(2)noise-suppressed multi-scale temporal module is proposed,leveraging KL divergence-based information gain analysis for progressive feature filtering in long-range dependencies,reducing errors by 1.91 mm compared to conventional temporal convolutions.(3)text-pose contrastive learning paradigm is introduced for the first time,where BERT-generated action descriptions align semantic-geometric features via the BERT encoder,significantly enhancing robustness under severe occlusion(50%joint invisibility).On the Human3.6M dataset,the proposed method achieves an MPJPE of 56.21 mm under Protocol 1,outperforming the state-of-the-art baseline MHFormer by 3.3%.Extensive ablation studies on Human3.6M demonstrate the individual contributions of the core modules:the spatiotemporal dependency module and noise-suppressed multi-scale temporal module reduce MPJPE by 0.30 and 0.34 mm,respectively,while the multimodal training strategy further decreases MPJPE by 0.6 mm through text-skeleton contrastive learning.Comparative experiments involving 16 athletes show that the sagittal plane coupling angle measurements of hip-ankle joints differ by less than 1.2°from those obtained via traditional optical systems(two one-sided t-tests,p<0.05),validating real-world reliability.This study provides an AI-powered analytical solution for competitive sports training,serving as a viable alternative to specialized equipment.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
OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and ...OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.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
Non-Orthogonal Multiple Access(NOMA)assisted Unmanned Aerial Vehicle(UAV)communication is becoming a promising technique for future B5G/6G networks.However,the security of the NOMA-UAV networks remains critical challe...Non-Orthogonal Multiple Access(NOMA)assisted Unmanned Aerial Vehicle(UAV)communication is becoming a promising technique for future B5G/6G networks.However,the security of the NOMA-UAV networks remains critical challenges due to the shared wireless spectrum and Line-of-Sight(LoS)channel.This paper formulates a joint UAV trajectory design and power allocation problem with the aid of the ground jammer to maximize the sum secrecy rate.First,the joint optimization problem is modeled as a Markov Decision Process(MDP).Then,the Deep Reinforcement Learning(DRL)method is utilized to search the optimal policy from the continuous action space.In order to accelerate the sample accumulation,the Asynchronous Advantage Actor-Critic(A3C)scheme with multiple workers is proposed,which reformulates the action and reward to acquire complete update duration.Simulation results demonstrate that the A3C-based scheme outperforms the baseline schemes in term of the secrecy rate and stability.展开更多
Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitor...Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.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
The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model ...The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model for epoxy resin encapsulated nano HFTs, which aims to precisely predict the temperature distribution inside the transformer in combination with the finite element method(FEM). A magnetothermal bidirectional coupling 3DTN model is established by analyzing the thermal conduction between the core, windings, and epoxy resin, while also considering the convection and radiation heat transfer mechanisms on the surface of the epoxy resin. The model considers the impact of loss distribution in the core and windings on the temperature field and adopts a simplified 1/2 thermal network model to reduce computational complexity. Furthermore, the results of FEM are compared with experimental results to verify the accuracy of the 3DTN model in predicting the temperature rise of nano HFT. The results show that the 3DTN model reduces errors by an average of 5.25% over the traditional two-dimensional thermal network(2DTN) model, particularly for temperature distributions in the windings and core. This paper provides a temperature rise prediction method for the thermal design and offers a theoretical basis and engineering guidance for the optimization of their thermal management systems.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support from the National Natural Science Foundation of China(Grant Nos.42320104003,42177175,and 42077247)the Fundamental Research Funds for the Central Universities.
文摘Accurate prediction of hydraulic fracture propagation is vital for Enhanced Geothermal System(EGS)design.We study the first hydraulic fracturing job at the GR1 well in the Gonghe Basin using field data,where the overall direction of hydraulic fractures does not show a delineated shape parallel to the maximum principal stress orientation.A field-scale numerical model based on the distinct element method is set up to carry out a fully coupled hydromechanical simulation,with the explicit representation of natural fractures via the discrete fracture network(DFN)approach.The effects of injection parameters and in situ stress on hydraulic fracture patterns are then quantitatively assessed.The study reveals that shear-induced deformation primarily governs the fracturing morphology in the GR1 well,driven by smaller injection rates and viscosities that promote massive activation of natural fractures,ultimately dominating the direction of hydraulic fracturing.Furthermore,the increase of in situ differential stress may promote shear damage of natural fracture surfaces,with the exact influence pattern depending on the combination of specific discontinuity properties and in situ stress state.Finally,we provide recommendations for EGS fracturing based on the influence characteristics of multiple parameters.This study can serve as an effective basis and reference for the design and optimization of EGS in the Gonghe basin and other sites.
基金supported by the National Natural Science Foundation of China(No.62401597)Natural Science Foundation of Hunan Province,China(No.2024JJ6469)the Research Project of National University of Defense Technology,China(No.ZK22-02).
文摘Low Earth Orbit(LEO)mega-constellation networks,exemplified by Starlink,are poised to play a pivotal role in future mobile communication networks,due to their low latency and high capacity.With the massively deployed satellites,ground users now can be covered by multiple visible satellites,but also face complex handover issues with such massive high-mobility satellites in multi-layer.The end-to-end routing is also affected by the handover behavior.In this paper,we propose an intelligent handover strategy dedicated to multi-layer LEO mega-constellation networks.Firstly,an analytic model is utilized to rapidly estimate the end-to-end propagation latency as a key handover factor to construct a multi-objective optimization model.Subsequently,an intelligent handover strategy is proposed by employing the Dueling Double Deep Q Network(D3QN)-based deep reinforcement learning algorithm for single-layer constellations.Moreover,an optimal crosslayer handover scheme is proposed by predicting the latency-jitter and minimizing the cross-layer overhead.Simulation results demonstrate the superior performance of the proposed method in the multi-layer LEO mega-constellation,showcasing reductions of up to 8.2%and 59.5%in end-to-end latency and jitter respectively,when compared to the existing handover strategies.
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supported by the Natural Science Foun-dation of Shandong Province,China(No.ZR2024QF057)the Natural Science Foundation of Jiangsu Province,China(No.BK20240937)+1 种基金the Natural Science Foundation of China(No.42276215)the China University of Mining and Technology(CUMT)Open Sharing Fund for Large-Scale Instruments and Equipment(No.DYGX-2024-86).
文摘The increasing frequency of offshore engineering activities,particularly the expansion of offshore oil transport and the rise in the number of oil platforms,has greatly increased the potential risk of marine oil spill incidents.Historically,several large oil spills have had long-term adverse effects on marine ecosystems and economic development,highlighting the importance of accurate-ly delineating and monitoring oil spill areas.In this study,graph neural network technology is introduced to implement semantic seg-mentation of SAR images,and two graph neural network models based on Graph-FCN and Graph-DeepLabV3+with the introduction of an attention mechanism are constructed and evaluated to improve the accuracy and efficiency of oil spill detection.By com-paring the Swin-Unet model,the Graph-DeepLabV3+model performs better in complex scenarios,especially in edge detail recognition.This not only provides strong technical support for marine oil spill monitoring but also provides an effective solution to deal with the potential risks brought by the increase of marine engineering activities,which is of great practical significance as it helps to safeguard the health and sustainable development of marine ecosystems and reduce the economic losses.
基金supported by the Major Sports Research Projects of Jiangsu Provincial Sports Bureau in 2022(No.ST221101).
文摘As an essential tool for quantitative analysis of lower limb coordination,optical motion capture systems with marker-based encoding still suffer from inefficiency,high costs,spatial constraints,and the requirement for multiple markers.While 3D pose estimation algorithms combined with ordinary cameras offer an alternative,their accuracy often deteriorates under significant body occlusion.To address the challenge of insufficient 3D pose estimation precision in occluded scenarios—which hinders the quantitative analysis of athletes’lower-limb coordination—this paper proposes a multimodal training framework integrating spatiotemporal dependency networks with text-semantic guidance.Compared to traditional optical motion capture systems,this work achieves low-cost,high-precision motion parameter acquisition through the following innovations:(1)spatiotemporal dependency attention module is designed to establish dynamic spatiotemporal correlation graphs via cross-frame joint semantic matching,effectively resolving the feature fragmentation issue in existing methods.(2)noise-suppressed multi-scale temporal module is proposed,leveraging KL divergence-based information gain analysis for progressive feature filtering in long-range dependencies,reducing errors by 1.91 mm compared to conventional temporal convolutions.(3)text-pose contrastive learning paradigm is introduced for the first time,where BERT-generated action descriptions align semantic-geometric features via the BERT encoder,significantly enhancing robustness under severe occlusion(50%joint invisibility).On the Human3.6M dataset,the proposed method achieves an MPJPE of 56.21 mm under Protocol 1,outperforming the state-of-the-art baseline MHFormer by 3.3%.Extensive ablation studies on Human3.6M demonstrate the individual contributions of the core modules:the spatiotemporal dependency module and noise-suppressed multi-scale temporal module reduce MPJPE by 0.30 and 0.34 mm,respectively,while the multimodal training strategy further decreases MPJPE by 0.6 mm through text-skeleton contrastive learning.Comparative experiments involving 16 athletes show that the sagittal plane coupling angle measurements of hip-ankle joints differ by less than 1.2°from those obtained via traditional optical systems(two one-sided t-tests,p<0.05),validating real-world reliability.This study provides an AI-powered analytical solution for competitive sports training,serving as a viable alternative to specialized equipment.
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Shanghai Clinical Research Center for Chronic Musculoskeletal Diseases (20MC1920600)Shanghai Key Clinical Specialty "Traditional Chinese Medicine Orthopaedic Traumatology"(shslczdzk03901)+3 种基金The Second Round of Construction Project of National TCM Academic School Inheritance Studio "Shi's Trauma Department"[Letter of the People's Education of Traditional Chinese Medicine (2019) No.62]Shanghai High-level Local Universities "Chronic Muscle and Bone Damage Research and Transformation" Innovation Team [No.3 of Shanghai Education Commission (2022)]Program for Shanghai High-Level Local University Innovation Team (SZY20220315)Shanghai Shenkang Hospital Development Center Clinical Three-year Action Plan (SHDC2020CR3090B)。
文摘OBJECTIVE:To explore the potential molecular mechanism of Qigu capsule(芪骨胶囊,QGC) in the treatment of sarcopenia through network pharmacology and to verify it experimentally.METHODS:The active compounds of QGC and common targets between QGC and sarcopenia were screened from databases.Then the herbs-compounds-targets network,and protein-protein interaction(PPI) network was constructed.Gene ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis were performed by R software.Next,we used a dexamethasone-induced sarcopenia mouse model to evaluate the anti-sarcopenic mechanism of QGC.RESULTS:A total of 57 common targets of QGC and sarcopenia were obtained.Based on the enrichment analysis of GO and KEGG,we took the phosphatidylinositol 3-kinase(PI3K)/protein kinase B(Akt) signaling pathway as a key target to explore the mechanism of QGC on sarcopenia.Animal experiments showed that QGC could increase muscle strength and inhibit muscle fiber atrophy.In the model group,the expression of muscle ring finger-1 and Atrogin-1 were increased,while myosin heavy chain was decreased,QGC treatment reversed these changes.Moreover,compared with the model group,the expressions of pPI3K,p-Akt,p-mammalian target of rapamycin and pForkhead box O3 in the QGC group were all upregulated.CONCLUSION:QGC exerts an anti-sarcopenic effect by activating PI3K/Akt signaling pathway to regulate skeletal muscle protein metabolism.
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.
基金supported by the Fundamental Research Funds for the Central Universities,China(No.2024MS115).
文摘Non-Orthogonal Multiple Access(NOMA)assisted Unmanned Aerial Vehicle(UAV)communication is becoming a promising technique for future B5G/6G networks.However,the security of the NOMA-UAV networks remains critical challenges due to the shared wireless spectrum and Line-of-Sight(LoS)channel.This paper formulates a joint UAV trajectory design and power allocation problem with the aid of the ground jammer to maximize the sum secrecy rate.First,the joint optimization problem is modeled as a Markov Decision Process(MDP).Then,the Deep Reinforcement Learning(DRL)method is utilized to search the optimal policy from the continuous action space.In order to accelerate the sample accumulation,the Asynchronous Advantage Actor-Critic(A3C)scheme with multiple workers is proposed,which reformulates the action and reward to acquire complete update duration.Simulation results demonstrate that the A3C-based scheme outperforms the baseline schemes in term of the secrecy rate and stability.
文摘Reliable and efficient communication is essential for Unmanned Aerial Vehicle(UAV)networks,especially in dynamic and resource-constrained environments such as disaster management,surveillance,and environmental monitoring.Frequent topology changes,high mobility,and limited energy availability pose significant challenges to maintaining stable and high-performance routing.Traditional routing protocols,such as Ad hoc On-Demand Distance Vector(AODV),Load-Balanced Optimized Predictive Ad hoc Routing(LB-OPAR),and Destination-Sequenced Distance Vector(DSDV),often experience performance degradation under such conditions.To address these limitations,this study evaluates the effectiveness of Dynamic Adaptive Routing(DAR),a protocol designed to adapt routing decisions in real time based on network dynamics and resource constraints.The research utilizes the Network Simulator 3(NS-3)platform to conduct controlled simulations,measuring key performance indicators such as latency,Packet Delivery Ratio(PDR),energy consumption,and throughput.Comparative analysis reveals that DAR consistently outperforms conventional protocols,achieving a 20%-30% reduction in latency,a 25% decrease in energy consumption,and marked improvements in throughput and PDR.These results highlight DAR’s ability to maintain high communication reliability while optimizing resource usage in challenging operational scenarios.By providing empirical evidence of DAR’s advantages in highly dynamic UAV network environments,this study contributes to advancing adaptive routing strategies.The findings not only validate DAR’s robustness and scalability but also lay the groundwork for integrating artificial intelligence-driven decision-making and real-world UAV deployment.Future work will explore cross-layer optimization,multi-UAV coordination,and experimental validation in field trials,aiming to further enhance communication resilience and energy efficiency in next-generation aerial networks.
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supported by the Project of the National Key Research and Development Program of China under Grant 2022YFB2404100。
文摘The internal hotspot temperature rise prediction in nanocrystalline high-frequency transformers(nanoHFTs) is essential to ensure reliable operation. This paper presents a three-dimensional thermal network(3DTN) model for epoxy resin encapsulated nano HFTs, which aims to precisely predict the temperature distribution inside the transformer in combination with the finite element method(FEM). A magnetothermal bidirectional coupling 3DTN model is established by analyzing the thermal conduction between the core, windings, and epoxy resin, while also considering the convection and radiation heat transfer mechanisms on the surface of the epoxy resin. The model considers the impact of loss distribution in the core and windings on the temperature field and adopts a simplified 1/2 thermal network model to reduce computational complexity. Furthermore, the results of FEM are compared with experimental results to verify the accuracy of the 3DTN model in predicting the temperature rise of nano HFT. The results show that the 3DTN model reduces errors by an average of 5.25% over the traditional two-dimensional thermal network(2DTN) model, particularly for temperature distributions in the windings and core. This paper provides a temperature rise prediction method for the thermal design and offers a theoretical basis and engineering guidance for the optimization of their thermal management systems.
