Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly a...Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses.展开更多
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
Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network...Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.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 development of gradient lubrication materials is critical for numerous biomedical applications,particularly in magnifying mechanical properties and service longevity.Herein,we present an innovative approach to fab...The development of gradient lubrication materials is critical for numerous biomedical applications,particularly in magnifying mechanical properties and service longevity.Herein,we present an innovative approach to fabricate biomimetic gradient lubrication hydrogel through the synergistic integration of three-dimensional(3D)printed metal-organic frameworks(MOFs)nanoparticle network hydrogel skeletons with bioinspired lubrication design.Specifically,robust hydrogel skeletons were engineered through single or multi-material 3D printing,followed by the in situ growth of MOFs nanoparticles within this hydrogel network to create a reinforced,load-bearing architecture.Subsequently,biomimetic lubrication capability was enabled by mechanically coupling another lubricating hydrogel within 3D-printed MOFs nanoparticle network hydrogel skeleton.The superficial layer is highly lubricious to ensure low coefficient of friction(~0.1141)and wear resistance(40,000 cycles),while the deeper layer is stiffer to afford the obligatory mechanical support(fracture strength~2.50 MPa).Furthermore,the gradient architecture stiffness of the hydrogel can be modulated by manipulating the spatial distribution of MOFs within the 3D-printed hydrogel skeleton.As a proof-of-concept,biomimetic gradient hydrogel meniscus structures with C-and O-shaped configurations were constructed by leveraging multi-material 3D printing,demonstrating exceptional lubrication performance.This innovative biomimetic design opens new avenues for creating implantable biomedical gradient lubricating materials with reinforced mechanical and lubrication performance.展开更多
Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imagin...Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.展开更多
基金supported by the National Key Research and Development Program Young Scientist Project(Grant No.2024YFC2911000)the National Natural Science Foundation of China(Grant No.52474103)the Major Basic Research Project of the Natural Science Foundation of Shandong Province(Grant No.ZR2024ZD22).
文摘Internal structural defects in engineering rock masses vary in size,exhibit complex shapes,and are unevenly distributed.Dominant fractures within a rock mass often play a critical to its mechanical behavior,directly affecting the macromechanical properties and failure modes.These fractures affect the instability and failure of the surrounding rock,significantlyimpacting the overall stability of engineering structures.Herein,sand-powder three-dimensional(3D)printing technology was used to prepare rock-like specimens with internal fracture networks.Triaxial compression testing,post-failure fracture mapping,and fractal dimension analysis of the fracture surfaces were conducted to investigate the effects of dominant fracture angles on the strength and deformation of rocks with internal fracture networks under triaxial stress.The results indicate that the dominant fracture angle has a pronounced effect on the mechanical behavior of rock.With increasing angle,both compressive strength and elastic modulus exhibit an initial decline followed by an increase.Moreover,higher confiningpressure significantlyimproves the compressive strength of fractured rock.This enhancement weakens as the confiningpressure further increases.Moreover,with increasing confiningpressure,the differences between the maximum and minimum values of elastic moduli and lateral strain ratios in fractured rock gradually decrease.Thus,the impact of the dominant fracture angle on rock mass deformation decreases with increasing confiningpressure.This research elucidates the effects of dominant fracture angles on the mechanical and failure properties of complex fractured rock masses and the influenceof the confiningpressure on these relationships.It provides valuable theoretical insights and practical guidance for stability analyses in engineering rock masses.
基金supported by 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.
基金supported by the National Natural Science Foundation of China(No.22202033)National College Students Innovation and Entrepreneurship Training Program(No.202310347048)Zhejiang Provincial Training Programs of Innovation and Entrepreneurship for Undergraduates(No.S202310347032)。
文摘Photocatalytic H_(2) evolution from wastewater exhibits fascinating prospects in environment and energy fields.Here,we propose a novel 3D cross-linked g-C_(3)N_(4) network(SCN)assembling with 1D nanowires.This network structure endows SCN with abundant carbon defects,creating a defect energy level and shallow charge trapping centres,which significantly prolongs the photocarrier lifetime,suppresses their recombination and facilitates the mass transfer process during the dye photodegradation.Consequently,in photocatalytic H_(2) evolution coupled with Rhodamine B(RhB)photodegradation under visible light,the H_(2) production rate of SCN is 283μmol h^(-1)g^(-1),accompanying by 97%RhB photodegradation efficiency,much higher than UCN's 31μmol h^(-1)g^(-1)and 64%.In particular,AQY of SCN for H_(2) evolution from RhB solution reaches 23.7%at 380 nm.Furthermore,the calculated transition states demonstrate that the N1 site connected to the defect in SCN has a minimum Gibbs free energy ΔG(H^(*)),indicating that H~+undergoes an H^(+)→H^(*)→H_(2) evolution process.
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support from the National Key Research and Development Program of China(2022YFB4600101)the National Natural Science Foundation of China(52505231 and 52175201)+5 种基金the Key R&D Program of Shandong Province(2024CXPT035)the Research Program of Science and Technology Department of Gansu Province(24JRRA059,24JRRA044 and 24ZDGA014)the Science Fund of Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai(AMGM2024F12)the Innovation and Entrepreneurship Team Prject of YEDA(2021TD007)the Special Supporting Project for Provincial Leading Talents of Yantai,the Major Program(ZYFZFX-2)the Fundamental Research Special Zone Project of the Lanzhou Institute of Chemical Physics,CAS,the Special Research Assistant Project of the Chinese Academy of Sciences,and the Taishan Scholars Program.
文摘The development of gradient lubrication materials is critical for numerous biomedical applications,particularly in magnifying mechanical properties and service longevity.Herein,we present an innovative approach to fabricate biomimetic gradient lubrication hydrogel through the synergistic integration of three-dimensional(3D)printed metal-organic frameworks(MOFs)nanoparticle network hydrogel skeletons with bioinspired lubrication design.Specifically,robust hydrogel skeletons were engineered through single or multi-material 3D printing,followed by the in situ growth of MOFs nanoparticles within this hydrogel network to create a reinforced,load-bearing architecture.Subsequently,biomimetic lubrication capability was enabled by mechanically coupling another lubricating hydrogel within 3D-printed MOFs nanoparticle network hydrogel skeleton.The superficial layer is highly lubricious to ensure low coefficient of friction(~0.1141)and wear resistance(40,000 cycles),while the deeper layer is stiffer to afford the obligatory mechanical support(fracture strength~2.50 MPa).Furthermore,the gradient architecture stiffness of the hydrogel can be modulated by manipulating the spatial distribution of MOFs within the 3D-printed hydrogel skeleton.As a proof-of-concept,biomimetic gradient hydrogel meniscus structures with C-and O-shaped configurations were constructed by leveraging multi-material 3D printing,demonstrating exceptional lubrication performance.This innovative biomimetic design opens new avenues for creating implantable biomedical gradient lubricating materials with reinforced mechanical and lubrication performance.
文摘Optical coherence tomography(OCT),particularly Swept-Source OCT,is widely employed in medical diagnostics and industrial inspections owing to its high-resolution imaging capabilities.However,Swept-Source OCT 3D imaging often suffers from stripe artifacts caused by unstable light sources,system noise,and environmental interference,posing challenges to real-time processing of large-scale datasets.To address this issue,this study introduces a real-time reconstruction system that integrates stripe-artifact suppression and parallel computing using a graphics processing unit.This approach employs a frequency-domain filtering algorithm with adaptive anti-suppression parameters,dynamically adjusted through an image quality evaluation function and optimized using a convolutional neural network for complex frequency-domain feature learning.Additionally,a graphics processing unit integrated 3D reconstruction framework is developed,enhancing data processing throughput and real-time performance via a dual-queue decoupling mechanism.Experimental results demonstrate significant improvements in structural similarity(0.92),peak signal-to-noise ratio(31.62 dB),and stripe suppression ratio(15.73 dB)compared with existing methods.On the RTX 4090 platform,the proposed system achieved an end-to-end delay of 94.36 milliseconds,a frame rate of 10.3 frames per second,and a throughput of 121.5 million voxels per second,effectively suppressing artifacts while preserving image details and enhancing real-time 3D reconstruction performance.
