Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in us...Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.展开更多
With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, ...With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.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
Increasing human disturbance and climate change have threatened ecological connectivity and structural stability,especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation.Large-sca...Increasing human disturbance and climate change have threatened ecological connectivity and structural stability,especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation.Large-scale ecological restoration,such as adding ecological sources or corridors,is difficult in such environments and often faces poor operability and high implementation costs in practice.Taking the southern slope of the Qilian Mountains in China as the study area and 2020 as the baseline,this study integrated weighted complex network theory into the"ecological source–resistance surface–corridor"framework to construct a heterogeneous ecological network(EN).Circuit theory was integrated with weighted betweenness to identify critical barrier points for locally differentiated restoration,followed by assessment of the network optimization effects.The results revealed that 494 ecological sources and 1308 ecological corridors were identified in the study area.Fifty-one barrier points with restoration potential were identified along key ecological corridors and locally restored.After optimization,the network gained 11 additional ecological corridors,and the total ecological corridor length increased by approximately 1143 km.Under simulated attacks,the decline rates of maximum connected subgraph(MCS)and network efficiency(Ne)slowed compared with pre-restoration conditions,indicating improved robustness.These findings demonstrate that targeted local restoration can enhance network connectivity and stability while minimizing disturbance to the overall landscape pattern,providing a practical pathway for ecological restoration and sustainable management in semi-arid mountain areas.展开更多
Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neur...Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.展开更多
Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing...Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.展开更多
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.展开更多
Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines A...Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.展开更多
The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle ap...The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.展开更多
This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using s...This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.展开更多
The present study employed network pharmacology to elucidate the molecular mechanism underlying the therapeutic effects of kuwanon G in diabetic encephalopathy.Utilizing the Pharmmapper databases,we identified potenti...The present study employed network pharmacology to elucidate the molecular mechanism underlying the therapeutic effects of kuwanon G in diabetic encephalopathy.Utilizing the Pharmmapper databases,we identified potential targets associated with kuwanon G.Simultaneously,targets related to diabetic encephalopathy were screened.The VENNY software facilitated the identification of 34 common target genes,forming the basis for constructing a protein-protein interaction network map via the STRING database.GO enrichment and KEGG pathway analyses were conducted using the David database,with Cytoscape software employed to pinpoint key target genes.Results revealed 101 potential targets for kuwanon G and 1058 for diabetic encephalopathy,with an overlap of 34 target genes.Notably,GSK3B,CASP3,MAKP14,ESR1,and PPARG emerged as pivotal genes in the therapeutic action of kuwanon G against diabetic encephalopathy.Pathway analysis of these key genes indicated that kuwanon G exerted its therapeutic effects through modulating pathways associated with lipid and atherosclerosis,fluid shear stress and atherosclerosis,IL-17 signaling,and the AGE-RAGE signaling pathway.This study offered valuable insights into the potential molecular mechanisms of kuwanon G in treating diabetic encephalopathy,presenting a novel framework for future research in this domain.展开更多
Objective:To analyze and validate how Jiawei Sanpian decoction treats migraines by integrating network pharmacology,molecular docking technology,and experimental studies.Method:Using network pharmacology,the chemical ...Objective:To analyze and validate how Jiawei Sanpian decoction treats migraines by integrating network pharmacology,molecular docking technology,and experimental studies.Method:Using network pharmacology,the chemical components and core target proteins of the Jiawei Sanpian decoction were analyzed.Key chemical components were docked with core targets using mo-lecular docking,and the results were visualized.Nitroglycerin was injected into the dorsal cervical region to establish a rat migraine model.Finally,experiments were conducted to verify the effects of Jiawei Sanpian on related pathways and targets.Results:Four notable chemical components were identified,namely,b-sitosterol,quercetin,mairin,and kaempferol.Five representative targets were identified,namely,insulin-like growth factor 1(IGF-1),matrix metallopeptidase 2(MMP-2),interleukin-2(IL-2),superoxide dismutase 2(SOD2),and inducible nitric oxide synthase(NOS2).Molecular docking results revealed that the minimum binding energies between the four chemical components and the five targets were below5 kcal/mol,indicating favor-able binding activity.Enzyme linked immunosorbent assay(ELISA)results demonstrated the efficacy of high-dose Jiawei Sanpian decoction in treating migraine by targeting IGF-1,IL-2,MMP-2,and SOD2(P<0.001).Real-time quantitative polymerase chain reaction(RT-qPCR)results demonstrated the effectiveness of high-dose Jiawei Sanpian decoction in treating migraine by targeting IGF-1,IL-2,MMP-2,and SOD2(P<0.001).After using erastin,the therapeutic effect of Jiawei Sanpian decoction declined.Conclusion:This study provides initial insights into the complex and multilayered therapeutic mecha-nisms of Jiawei Sanpian decoction in treating migraine,primarily through its diverse components,tar-gets,and pathways.These findings indicate that Jiawei Sanpian decoction may exert its effects mainly through processes linked to the mitochondrial inflammatory pathway,thereby providing therapeutic benefits for migraine.展开更多
Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is con...Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.展开更多
With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further impro...With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.展开更多
The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combin...The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combinatorial optimization.Recently,reinforcement learning approaches such as 2D Array Pointer Networks(2D-Ptr)have demonstrated remarkable speed in decision-making by modeling multiple agents’concurrent choices as a sequence of consecutive actions.However,these learning-based models often struggle with generalization,meaning they cannot seamlessly adapt to new scenarios with varying numbers of vehicles or customers without retraining.Inspired by the potential of multi-teacher knowledge distillation to harness diverse knowledge from multiple sources and craft a comprehensive student model,we propose to enhance the generalization capability of 2D-Ptr through Multiple Teacher-forcing Knowledge Distillation(MTKD).We initially train 12 unique 2D-Ptr models under various settings to serve as teacher models.Subsequently,we randomly sample a teacher model and a batch of problem instances,focusing on those where the chosen teacher performed best.This teacher model then solves these instances,generating high-reward action sequences to guide knowledge transfer to the student model.We conduct rigorous evaluations across four distinct datasets,each comprising four HCVRP instances of varying scales.Our empirical findings underscore the proposed method superiority over existing learning-based methods in terms of both computational efficiency and solution quality.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
Objective:To screen and identify the key active molecules,signaling pathways,and therapeutic targets of Shuxuening(SXN)injection for treating liver cirrhosis(LC)and to evaluate its therapeutic potential using a mouse ...Objective:To screen and identify the key active molecules,signaling pathways,and therapeutic targets of Shuxuening(SXN)injection for treating liver cirrhosis(LC)and to evaluate its therapeutic potential using a mouse model.Methods:Target genes of SXN and LC were retrieved from public databases,and enrichment analysis was performed.A proteineprotein interaction(PPI)network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING),and hub genes were identified using Molecular Complex Detection(MCODE).LC was induced in rats and mice via intraperitoneal injections of diethylnitrosamine and carbon tetrachloride(CCl4)for 12 weeks.Starting at week 7,SXN was administered intraperitoneally to the mice in the treatment group.Serum and liver tissues of the mice were collected for the detection of indicators,pathological staining,and expression analysis of hub targets using quantitative real-time polymerase chain reaction(qRT-PCR).Results:We identified 368 overlapping genes(OLGs)between SXN and LC targets.These OLGs were subsequently used to build a PPI network and to screen for hub genes.Enrichment analysis showed that these genes were associated with cancer-related pathways,including phosphoinositide-3-kinase/Akt and mitogen-activated protein kinase signaling and various cellular processes,such as responses to chemicals and metabolic regulation.In vivo experiments demonstrated that SXN treatment significantly improved liver function and pathology in CCl4-induced LC mice by reducing inflammation and collagen deposition.Furthermore,qRT-PCR demonstrated that SXN regulated the expression of MAPK8,AR and CASP3 in the livers of LC mice.Conclusion:This study highlighted the therapeutic effects of SXN in alleviating LC using both bioinformatics and experimental methods.The observed effect was associated with modulation of hub gene expression,particularly MAPK8,and CASP3.展开更多
Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient int...Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.展开更多
Background:Liver cancer(LC)remains a leading cause of cancer-related mortality worldwide,with current treatments often limited by suboptimal efficacy and adverse effects.Banxia Houpu Decoction(BHD),a traditional Chine...Background:Liver cancer(LC)remains a leading cause of cancer-related mortality worldwide,with current treatments often limited by suboptimal efficacy and adverse effects.Banxia Houpu Decoction(BHD),a traditional Chinese herbal formula,has demonstrated potential anti-tumor properties in clinical practice.However,its precise mechanisms against LC remain unclear.This study employs network pharmacology(NP)and molecular docking(MD)approaches to systematically identify BHD’s active components and their molecular targets,aiming to elucidate its anti-LC mechanisms and provide a scientific basis for further investigation.Methods:We utilized Liquid Chromatography-Mass Spectrometry alongside the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)to identify the constituents of BHD.We identified potential targets through the utilization of TCMSP,SwissTargetPrediction,Comparative Toxicogenomics Database,and SuperPred Database.Targets linked to LC were obtained from GeneCards,OMIM,the Therapeutic Target Database,and DrugBank.A Venn diagram illustrated the intersection between component and disease targets,while a protein-protein interaction(PPI)network was developed utilizing Cytoscape 3.9.1.Primary targets were discerned through the analysis of centrality metrics,including“Degree,”“Betweenness,”and“Closeness.”The study encompassed analyses of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways to clarify the biological roles and pathways associated with these proteins.The essential interactions between the active constituents of BHD and the principal LC targets were investigated through MD using AutoDock software.Results:We identified 34 active components in BHD.The PPI network revealed 212 interaction targets relevant to drug-disease correlations,emphasizing key proteins including Epidermal Growth Factor Receptor(EGFR),Signal Transducer and Activator of Transcription 3(STAT3),Steroid Receptor Coactivator(SRC),Heat Shock Protein 90 Alpha Family Class A Member 1(HSP90AA1),and AKT Serine/Threonine Protein Kinase 1(AKT1).The GO analysis revealed a total of 443 biological processes,94 cellular components,and 182 molecular functions.The KEGG analysis revealed a total of 169 pathways that are involved.The results from MD revealed that the majority of binding energies fell below−7 kcal/mol,indicating strong interactions between the active compounds and their target proteins.Conclusion:Evidence suggests that BHD effectively manages LC through a synergistic mechanism encompassing various components(Magnolol,Chrysoeriol,Cerevisterol,etc.),targets(EGFR,STAT3,SRC,HSP90AA1,AKT1,etc.),and pathways(PI3K-Akt,FoxO,and Ras signaling pathways,etc.).This analysis offers a comprehensive theoretical framework for further investigative and clinical exploration.展开更多
Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective meas...Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.展开更多
文摘Satellite edge computing has garnered significant attention from researchers;however,processing a large volume of tasks within multi-node satellite networks still poses considerable challenges.The sharp increase in user demand for latency-sensitive tasks has inevitably led to offloading bottlenecks and insufficient computational capacity on individual satellite edge servers,making it necessary to implement effective task offloading scheduling to enhance user experience.In this paper,we propose a priority-based task scheduling strategy based on a Software-Defined Network(SDN)framework for satellite-terrestrial integrated networks,which clarifies the execution order of tasks based on their priority.Subsequently,we apply a Dueling-Double Deep Q-Network(DDQN)algorithm enhanced with prioritized experience replay to derive a computation offloading strategy,improving the experience replay mechanism within the Dueling-DDQN framework.Next,we utilize the Deep Deterministic Policy Gradient(DDPG)algorithm to determine the optimal resource allocation strategy to reduce the processing latency of sub-tasks.Simulation results demonstrate that the proposed d3-DDPG algorithm outperforms other approaches,effectively reducing task processing latency and thus improving user experience and system efficiency.
基金supported by the National Natural Science Foundation of China(Nos.52373093 and 12072325)the Outstanding Youth Fund of Henan Province(No.242300421062)+1 种基金National Key R&D Program of China(No.2019YFA0706802)the 111 project(No.D18023).
文摘With the rapid development of wearable electronic skin technology, flexible strain sensors have shown great application prospects in the fields of human motion and physiological signal detection, medical diagnostics, and human-computer interaction owing to their outstanding sensing performance. This paper reports a strain sensor with synergistic conductive network, consisting of stable carbon nanotube dispersion (CNT) layer and brittle MXene layer by dip-coating and electrostatic self-assembly method, and breathable three-dimensional (3D) flexible substrate of thermoplastic polyurethane (TPU) fibrous membrane prepared through electrospinning technology. The MXene/CNT@PDA-TPU (MC@p-TPU) flexible strain sensor had excellent air permeability, wide operating range (0–450 %), high sensitivity (Gauge Factor, GFmax = 8089.7), ultra-low detection limit (0.05 %), rapid response and recovery times (40 ms/60 ms), and excellent cycle stability and durability (10,000 cycles). Given its superior strain sensing capabilities, this sensor can be applied in physiological signals detection, human motion pattern recognition, and driving exoskeleton robots. In addition, MC@p-TPU fibrous membrane also exhibited excellent photothermal conversion performance and can be used as a wearable photo-heater, which has far-reaching application potential in the photothermal therapy of human joint diseases.
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supported by the Sichuan Science and Technology Program(2022JDJQ0015)the Major Research and Development and Achievement Transformation Projects of Qinghai Province,China(2022-QY-224)the National Natural Science Foundation of China(42471225).
文摘Increasing human disturbance and climate change have threatened ecological connectivity and structural stability,especially in semi-arid mountain areas with sparse vegetation and weak hydrological regulation.Large-scale ecological restoration,such as adding ecological sources or corridors,is difficult in such environments and often faces poor operability and high implementation costs in practice.Taking the southern slope of the Qilian Mountains in China as the study area and 2020 as the baseline,this study integrated weighted complex network theory into the"ecological source–resistance surface–corridor"framework to construct a heterogeneous ecological network(EN).Circuit theory was integrated with weighted betweenness to identify critical barrier points for locally differentiated restoration,followed by assessment of the network optimization effects.The results revealed that 494 ecological sources and 1308 ecological corridors were identified in the study area.Fifty-one barrier points with restoration potential were identified along key ecological corridors and locally restored.After optimization,the network gained 11 additional ecological corridors,and the total ecological corridor length increased by approximately 1143 km.Under simulated attacks,the decline rates of maximum connected subgraph(MCS)and network efficiency(Ne)slowed compared with pre-restoration conditions,indicating improved robustness.These findings demonstrate that targeted local restoration can enhance network connectivity and stability while minimizing disturbance to the overall landscape pattern,providing a practical pathway for ecological restoration and sustainable management in semi-arid mountain areas.
基金supported by Projects 12325501,12047503,and 12247104 of the National Natural Science Foundation of ChinaProject ZDRW-XX-2022-3-02 of the Chinese Academy of Sciencessupported by the Innovation Program for Quantum Science and Technology project 2021ZD0301900。
文摘Computing free energy is a fundamental problem in statistical physics.Recently,two distinct methods have been developed and have demonstrated remarkable success:the tensor-network-based contraction method and the neural-network-based variational method.Tensor networks are accurate,but their application is often limited to low-dimensional systems due to the high computational complexity in high-dimensional systems.The neural network method applies to systems with general topology.However,as a variational method,it is not as accurate as tensor networks.In this work,we propose an integrated approach,tensor-network-based variational autoregressive networks(TNVAN),that leverages the strengths of both tensor networks and neural networks:combining the variational autoregressive neural network’s ability to compute an upper bound on free energy and perform unbiased sampling from the variational distribution with the tensor network’s power to accurately compute the partition function for small sub-systems,resulting in a robust method for precisely estimating free energy.To evaluate the proposed approach,we conducted numerical experiments on spin glass systems with various topologies,including two-dimensional lattices,fully connected graphs,and random graphs.Our numerical results demonstrate the superior accuracy of our method compared to existing approaches.In particular,it effectively handles systems with longrange interactions and leverages GPU efficiency without requiring singular value decomposition,indicating great potential in tackling statistical mechanics problems and simulating high-dimensional complex systems through both tensor networks and neural networks.
文摘Smart edge computing(SEC)is a novel paradigm for computing that could transfer cloud-based applications to the edge network,supporting computation-intensive services like face detection and natural language processing.A core feature of mobile edge computing,SEC improves user experience and device performance by offloading local activities to edge processors.In this framework,blockchain technology is utilized to ensure secure and trustworthy communication between edge devices and servers,protecting against potential security threats.Additionally,Deep Learning algorithms are employed to analyze resource availability and optimize computation offloading decisions dynamically.IoT applications that require significant resources can benefit from SEC,which has better coverage.Although access is constantly changing and network devices have heterogeneous resources,it is not easy to create consistent,dependable,and instantaneous communication between edge devices and their processors,specifically in 5G Heterogeneous Network(HN)situations.Thus,an Intelligent Management of Resources for Smart Edge Computing(IMRSEC)framework,which combines blockchain,edge computing,and Artificial Intelligence(AI)into 5G HNs,has been proposed in this paper.As a result,a unique dual schedule deep reinforcement learning(DS-DRL)technique has been developed,consisting of a rapid schedule learning process and a slow schedule learning process.The primary objective is to minimize overall unloading latency and system resource usage by optimizing computation offloading,resource allocation,and application caching.Simulation results demonstrate that the DS-DRL approach reduces task execution time by 32%,validating the method’s effectiveness within the IMRSEC framework.
文摘Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of placement.The experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
文摘Internet of things networks often suffer from early node failures and short lifespan due to energy limits.Traditional routing methods are not enough.This work proposes a new hybrid algorithm called ACOGA.It combines Ant Colony Optimization(ACO)and the Greedy Algorithm(GA).ACO finds smart paths while Greedy makes quick decisions.This improves energy use and performance.ACOGA outperforms Hybrid Energy-Efficient(HEE)and Adaptive Lossless Data Compression(ALDC)algorithms.After 500 rounds,only 5%of ACOGA’s nodes are dead,compared to 15%for HEE and 20%for ALDC.The network using ACOGA runs for 1200 rounds before the first nodes fail.HEE lasts 900 rounds and ALDC only 850.ACOGA saves at least 15%more energy by better distributing the load.It also achieves a 98%packet delivery rate.The method works well in mixed IoT networks like Smart Water Management Systems(SWMS).These systems have different power levels and communication ranges.The simulation of proposed model has been done in MATLAB simulator.The results show that that the proposed model outperform then the existing models.
文摘The integration of technologies like artificial intelligence,6G,and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications.However,these advancements also generate a surge in data processing requirements,necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles.Despite recent advancements,the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources,as well as privacy,remain a concern.In this paper,a lightweight offloading strategy that leverages ubiquitous connectivity through the Space Air Ground Integrated Vehicular Network architecture while ensuring privacy preservation is proposed.The Internet of Vehicles(IoV)environment is first modeled as a graph,with vehicles and base stations as nodes,and their communication links as edges.Secondly,vehicular applications are offloaded to suitable servers based on latency using an attention-based heterogeneous graph neural network(HetGNN)algorithm.Subsequently,a differential privacy stochastic gradient descent trainingmechanism is employed for privacypreserving of vehicles and offloading inference.Finally,the simulation results demonstrated that the proposedHetGNN method shows good performance with 0.321 s of inference time,which is 42.68%,63.93%,30.22%,and 76.04% less than baseline methods such as Deep Deterministic Policy Gradient,Deep Q Learning,Deep Neural Network,and Genetic Algorithm,respectively.
基金The financial support provided by the Project of the National Natural Science Foundation of China (22308314,U22A20415)the Natural Science Foundation of Zhejiang Province (LQ24B060001)+1 种基金the "Pioneer" and "Leading Goose" Research & Development Program of Zhejiang (2022C01SA442617)the SINOPEC Technology Development Project (224244)
文摘This study leverages machine learning to perform high-throughput computational screening of n-hexane cracking initiators.Artificial neural networks are applied to predict the chemical performance of initiators,using simulated pyrolysis data as the training dataset.Various feature extraction methods are utilized,and five neural network architectures are developed to predict the co-cracking product distribution based on molecular structures.High-throughput screening of 12946 molecules outside the training dataset identifies the top 10 initiators for each target product—ethylene,propylene,and butadiene.The relative error between predicted and simulated values is less than 7%.Additionally,reaction pathway analysis elucidates the mechanisms by which initiators influence the distribution of cracking products.The proposed framework provides a practical and efficient approach for the rapid identification and evaluation of high-performance cracking initiators.
基金financially supported by the Hebei Administration of Traditional Chinese Medicine(Grant No.2024040)。
文摘The present study employed network pharmacology to elucidate the molecular mechanism underlying the therapeutic effects of kuwanon G in diabetic encephalopathy.Utilizing the Pharmmapper databases,we identified potential targets associated with kuwanon G.Simultaneously,targets related to diabetic encephalopathy were screened.The VENNY software facilitated the identification of 34 common target genes,forming the basis for constructing a protein-protein interaction network map via the STRING database.GO enrichment and KEGG pathway analyses were conducted using the David database,with Cytoscape software employed to pinpoint key target genes.Results revealed 101 potential targets for kuwanon G and 1058 for diabetic encephalopathy,with an overlap of 34 target genes.Notably,GSK3B,CASP3,MAKP14,ESR1,and PPARG emerged as pivotal genes in the therapeutic action of kuwanon G against diabetic encephalopathy.Pathway analysis of these key genes indicated that kuwanon G exerted its therapeutic effects through modulating pathways associated with lipid and atherosclerosis,fluid shear stress and atherosclerosis,IL-17 signaling,and the AGE-RAGE signaling pathway.This study offered valuable insights into the potential molecular mechanisms of kuwanon G in treating diabetic encephalopathy,presenting a novel framework for future research in this domain.
基金funded by the National Natural Science Foundation of China(81873256)Natural Science Foundation of Xiamen(3502Z202371045).
文摘Objective:To analyze and validate how Jiawei Sanpian decoction treats migraines by integrating network pharmacology,molecular docking technology,and experimental studies.Method:Using network pharmacology,the chemical components and core target proteins of the Jiawei Sanpian decoction were analyzed.Key chemical components were docked with core targets using mo-lecular docking,and the results were visualized.Nitroglycerin was injected into the dorsal cervical region to establish a rat migraine model.Finally,experiments were conducted to verify the effects of Jiawei Sanpian on related pathways and targets.Results:Four notable chemical components were identified,namely,b-sitosterol,quercetin,mairin,and kaempferol.Five representative targets were identified,namely,insulin-like growth factor 1(IGF-1),matrix metallopeptidase 2(MMP-2),interleukin-2(IL-2),superoxide dismutase 2(SOD2),and inducible nitric oxide synthase(NOS2).Molecular docking results revealed that the minimum binding energies between the four chemical components and the five targets were below5 kcal/mol,indicating favor-able binding activity.Enzyme linked immunosorbent assay(ELISA)results demonstrated the efficacy of high-dose Jiawei Sanpian decoction in treating migraine by targeting IGF-1,IL-2,MMP-2,and SOD2(P<0.001).Real-time quantitative polymerase chain reaction(RT-qPCR)results demonstrated the effectiveness of high-dose Jiawei Sanpian decoction in treating migraine by targeting IGF-1,IL-2,MMP-2,and SOD2(P<0.001).After using erastin,the therapeutic effect of Jiawei Sanpian decoction declined.Conclusion:This study provides initial insights into the complex and multilayered therapeutic mecha-nisms of Jiawei Sanpian decoction in treating migraine,primarily through its diverse components,tar-gets,and pathways.These findings indicate that Jiawei Sanpian decoction may exert its effects mainly through processes linked to the mitochondrial inflammatory pathway,thereby providing therapeutic benefits for migraine.
基金supported in part by the Natural Science Foundation of China (62171110,U19B2028 and U20B2070)。
文摘Recently,the Fog-Radio Access Network(F-RAN)has gained considerable attention,because of its flexible architecture that allows rapid response to user requirements.In this paper,computational offloading in F-RAN is considered,where multiple User Equipments(UEs)offload their computational tasks to the F-RAN through fog nodes.Each UE can select one of the fog nodes to offload its task,and each fog node may serve multiple UEs.The tasks are computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link.In order to compute all UEs'tasks quickly,joint optimization of UE-Fog association,radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs.This min-max problem is formulated as a Mixed Integer Nonlinear Program(MINP).To tackle it,first,MINP is reformulated as a continuous optimization problem,and then the Majorization Minimization(MM)method is used to find a solution.The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM,thereby reducing the complexity of MM iteration.In addition,a cooperative offloading model is considered,where the fog nodes compress-and-forward their received signals to the cloud.Under this model,a similar min-max latency optimization problem is formulated and tackled by the inexact MM.Simulation results show that the proposed algorithms outperform some offloading strategies,and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.
基金supported in part by National Natural Science Foundation of China(No.62071393)Fundamental Research Funds for the Central Universities(2682023ZTPY058).
文摘With miscellaneous applications gener-ated in vehicular networks,the computing perfor-mance cannot be satisfied owing to vehicles’limited processing capabilities.Besides,the low-frequency(LF)band cannot further improve network perfor-mance due to its limited spectrum resources.High-frequency(HF)band has plentiful spectrum resources which is adopted as one of the operating bands in 5G.To achieve low latency and sustainable development,a task processing scheme is proposed in dual-band cooperation-based vehicular network where tasks are processed at local side,or at macro-cell base station or at road side unit through LF or HF band to achieve sta-ble and high-speed task offloading.Moreover,a utility function including latency and energy consumption is minimized by optimizing computing and spectrum re-sources,transmission power and task scheduling.Ow-ing to its non-convexity,an iterative optimization algo-rithm is proposed to solve it.Numerical results eval-uate the performance and superiority of the scheme,proving that it can achieve efficient edge computing in vehicular networks.
基金in part by the National Science Foundation of China under Grant No.62276238in part by the National Science Foundation for Distinguished Young Scholars of China under Grant No.62325602in part by the Natural Science Foundation of Henan,China under Grant No.232300421095.
文摘The Heterogeneous Capacitated Vehicle Routing Problem(HCVRP),which involves efficiently routing vehicles with diverse capacities to fulfill various customer demands at minimal cost,poses an NP-hard challenge in combinatorial optimization.Recently,reinforcement learning approaches such as 2D Array Pointer Networks(2D-Ptr)have demonstrated remarkable speed in decision-making by modeling multiple agents’concurrent choices as a sequence of consecutive actions.However,these learning-based models often struggle with generalization,meaning they cannot seamlessly adapt to new scenarios with varying numbers of vehicles or customers without retraining.Inspired by the potential of multi-teacher knowledge distillation to harness diverse knowledge from multiple sources and craft a comprehensive student model,we propose to enhance the generalization capability of 2D-Ptr through Multiple Teacher-forcing Knowledge Distillation(MTKD).We initially train 12 unique 2D-Ptr models under various settings to serve as teacher models.Subsequently,we randomly sample a teacher model and a batch of problem instances,focusing on those where the chosen teacher performed best.This teacher model then solves these instances,generating high-reward action sequences to guide knowledge transfer to the student model.We conduct rigorous evaluations across four distinct datasets,each comprising four HCVRP instances of varying scales.Our empirical findings underscore the proposed method superiority over existing learning-based methods in terms of both computational efficiency and solution quality.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金Our study was funded by the Clinical Research Special Fund of Wu Jieping Medical Foundation(320.6750.2022-25-8)the Fundamental Research Funds for the Cornell University(2024-JYBXJSJJ042 and 2024-JYB-JBZD-058).
文摘Objective:To screen and identify the key active molecules,signaling pathways,and therapeutic targets of Shuxuening(SXN)injection for treating liver cirrhosis(LC)and to evaluate its therapeutic potential using a mouse model.Methods:Target genes of SXN and LC were retrieved from public databases,and enrichment analysis was performed.A proteineprotein interaction(PPI)network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins(STRING),and hub genes were identified using Molecular Complex Detection(MCODE).LC was induced in rats and mice via intraperitoneal injections of diethylnitrosamine and carbon tetrachloride(CCl4)for 12 weeks.Starting at week 7,SXN was administered intraperitoneally to the mice in the treatment group.Serum and liver tissues of the mice were collected for the detection of indicators,pathological staining,and expression analysis of hub targets using quantitative real-time polymerase chain reaction(qRT-PCR).Results:We identified 368 overlapping genes(OLGs)between SXN and LC targets.These OLGs were subsequently used to build a PPI network and to screen for hub genes.Enrichment analysis showed that these genes were associated with cancer-related pathways,including phosphoinositide-3-kinase/Akt and mitogen-activated protein kinase signaling and various cellular processes,such as responses to chemicals and metabolic regulation.In vivo experiments demonstrated that SXN treatment significantly improved liver function and pathology in CCl4-induced LC mice by reducing inflammation and collagen deposition.Furthermore,qRT-PCR demonstrated that SXN regulated the expression of MAPK8,AR and CASP3 in the livers of LC mice.Conclusion:This study highlighted the therapeutic effects of SXN in alleviating LC using both bioinformatics and experimental methods.The observed effect was associated with modulation of hub gene expression,particularly MAPK8,and CASP3.
基金supported in part by Sub Project of National Key Research and Development plan in 2020 NO.2020YFC1511704Beijing Information Science and Technology University NO.2020KYNH212,NO.2021CGZH302+1 种基金Beijing Science and Technology Project(Grant No.Z211100004421009)in part by the National Natural Science Foundation of China(Grant No.62301058).
文摘Low Earth orbit(LEO)satellite networks have the advantages of low transmission delay and low deployment cost,playing an important role in providing reliable services to ground users.This paper studies an efficient inter-satellite cooperative computation offloading(ICCO)algorithm for LEO satellite networks.Specifically,an ICCO system model is constructed,which considers using neighboring satellites in the LEO satellite networks to collaboratively process tasks generated by ground user terminals,effectively improving resource utilization efficiency.Additionally,the optimization objective of minimizing the system task computation offloading delay and energy consumption is established,which is decoupled into two sub-problems.In terms of computational resource allocation,the convexity of the problem is proved through theoretical derivation,and the Lagrange multiplier method is used to obtain the optimal solution of computational resources.To deal with the task offloading decision,a dynamic sticky binary particle swarm optimization algorithm is designed to obtain the offloading decision by iteration.Simulation results show that the ICCO algorithm can effectively reduce the delay and energy consumption.
基金funded by the National Natural Science Foundation of China(No.82204250)the China Postdoctoral Science Foundation(No.2021M693961)+2 种基金the Young and Middle-Aged Talent Project of the Hubei Provincial Department of Education(No.Q20222808)High-level Talent Research Initiation Fund Project of Hunan University of Chinese Medicine(No.0004010)the Hunan Science Fund for Distinguished Young Scholars(No.2025JJ20098).
文摘Background:Liver cancer(LC)remains a leading cause of cancer-related mortality worldwide,with current treatments often limited by suboptimal efficacy and adverse effects.Banxia Houpu Decoction(BHD),a traditional Chinese herbal formula,has demonstrated potential anti-tumor properties in clinical practice.However,its precise mechanisms against LC remain unclear.This study employs network pharmacology(NP)and molecular docking(MD)approaches to systematically identify BHD’s active components and their molecular targets,aiming to elucidate its anti-LC mechanisms and provide a scientific basis for further investigation.Methods:We utilized Liquid Chromatography-Mass Spectrometry alongside the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform(TCMSP)to identify the constituents of BHD.We identified potential targets through the utilization of TCMSP,SwissTargetPrediction,Comparative Toxicogenomics Database,and SuperPred Database.Targets linked to LC were obtained from GeneCards,OMIM,the Therapeutic Target Database,and DrugBank.A Venn diagram illustrated the intersection between component and disease targets,while a protein-protein interaction(PPI)network was developed utilizing Cytoscape 3.9.1.Primary targets were discerned through the analysis of centrality metrics,including“Degree,”“Betweenness,”and“Closeness.”The study encompassed analyses of Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways to clarify the biological roles and pathways associated with these proteins.The essential interactions between the active constituents of BHD and the principal LC targets were investigated through MD using AutoDock software.Results:We identified 34 active components in BHD.The PPI network revealed 212 interaction targets relevant to drug-disease correlations,emphasizing key proteins including Epidermal Growth Factor Receptor(EGFR),Signal Transducer and Activator of Transcription 3(STAT3),Steroid Receptor Coactivator(SRC),Heat Shock Protein 90 Alpha Family Class A Member 1(HSP90AA1),and AKT Serine/Threonine Protein Kinase 1(AKT1).The GO analysis revealed a total of 443 biological processes,94 cellular components,and 182 molecular functions.The KEGG analysis revealed a total of 169 pathways that are involved.The results from MD revealed that the majority of binding energies fell below−7 kcal/mol,indicating strong interactions between the active compounds and their target proteins.Conclusion:Evidence suggests that BHD effectively manages LC through a synergistic mechanism encompassing various components(Magnolol,Chrysoeriol,Cerevisterol,etc.),targets(EGFR,STAT3,SRC,HSP90AA1,AKT1,etc.),and pathways(PI3K-Akt,FoxO,and Ras signaling pathways,etc.).This analysis offers a comprehensive theoretical framework for further investigative and clinical exploration.
基金supported by the National Natural Science Founion of China(U2241285).
文摘Accurate and efficient prediction of the distribution of surface loads on buildings subjected to explosive effects is crucial for rapidly calculating structural dynamic responses,establishing effective protective measures,and designing civil defense engineering solutions.Current state-of-the-art methods face several issues:Experimental research is difficult and costly to implement,theoretical research is limited to simple geometries and lacks precision,and direct simulations require substantial computational resources.To address these challenges,this paper presents a data-driven method for predicting blast loads on building surfaces.This approach increases both the accuracy and computational efficiency of load predictions when the geometry of the building changes while the explosive yield remains constant,significantly improving its applicability in complex scenarios.This study introduces an innovative encoder-decoder graph neural network model named BlastGraphNet,which uses a message-passing mechanism to predict the overpressure and impulse load distributions on buildings with conventional and complex geometries during explosive events.The model also facilitates related downstream applications,such as damage mode identification and rapid assessment of virtual city explosions.The calculation results indicate that the prediction error of the model for conventional building tests is less than 2%,and its inference speed is 3-4 orders of magnitude faster than that of state-of-the-art numerical methods.In extreme test cases involving buildings with complex geometries and building clusters,the method achieved high accuracy and excellent generalizability.The strong adaptability and generalizability of BlastGraphNet confirm that this novel method enables precise real-time prediction of blast loads and provides a new paradigm for damage assessment in protective engineering.
