The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical m...The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.展开更多
In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results i...In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.展开更多
BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probi...BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probiotics have shown good potential in regulating the endocrine system and improving the intestinal microecology,providing new ideas for the treatment of breast hyperplasia.OBJECTIVE:To explore the effects and mechanisms of traditional Chinese medicine compounds and fermented probiotic compounds on breast hyperplasia in mice,providing new theoretical and experimental bases for the clinical treatment and prevention of breast hyperplasia.METHODS:(1)Network pharmacology tools were used to predict the anti-breast-hyperplasia activity of Herba Gueldenstaedtiae(Euphorbia humifusa),as well as its potential targets and signaling pathways.The databases included:TCMSP,OMIM,GeneCards database,UniProt website,Venny2.1.0 website,Metascape,HERB website,and STRING database,all of which are open-access databases.Network pharmacology can predict and screen key information such as the targets corresponding to the active ingredients of traditional Chinese medicine,disease targets,and action pathways through network analysis and computer-system analysis.Therefore,it has been increasingly widely used in the research of traditional Chinese medicine.(2)A breast hyperplasia model was induced in mice by injecting estrogen and progesterone.Mice in the normal blank group were injected intraperitoneally with normal saline every day.Mice in the model group and drugadministration groups were injected intraperitoneally with estradiol benzoate injection at a concentration of 0.5 mg/kg every day for 25 days.From the 26th day,the injection of estradiol benzoate injection was stopped.Mice in the normal blank group were injected intramuscularly with normal saline every day,and mice in the model group and drug-administration groups were injected intramuscularly with progesterone injection at a concentration of 5 mg/kg for 5 days.After the model was established,each group was given drugs respectively.The normal blank group and the model group were gavaged with 0.2 mL/d of normal saline;the positive blank group(Xiaozheng Pill group)was gavaged with an aqueous solution of Xiaozheng Pill at 0.9 mg/g;the low-,medium-and high-dose groups of Compound Herba Gueldenstaedtiae were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively;the low-,medium-and high-dose groups of traditional Chinese medicine-bacteria fermentation were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively.The administration was continuous for 30 days.RESULTS AND CONCLUSION:(1)The results of network pharmacology research showed that the Compound Herba Gueldenstaedtiae(Euphorbia humifusa)contained 46 active ingredients,which were related to 1213 potential targets.After comparison with 588 known breast-hyperplasia targets,it was speculated that 50 of these targets might be related to the direct effect of the compound on breast hyperplasia.(2)After drug intervention,there was no significant change in the high-dose group of Compound Herba Gueldenstaedtiae compared with the normal blank group.The liver indicators of the other intervention groups all significantly decreased(P<0.05).(3)In terms of kidney and uterine indicators,the medium-dose group of Compound Herba Gueldenstaedtiae decreased significantly compared with the normal blank group(P<0.05).In terms of the uterine index,the model group increased significantly compared with the normal blank group(P<0.01).(4)After 1-month drug treatment,the number of lobules and acini in the breast tissue of the Xiaozheng Pill group,the low,medium,and high-dose group of Compound Herba Gueldenstaedtiae,the low,medium,and highdose groups of traditional Chinese medicine-bacteria fermentation decreased,and the duct openings narrowed.With the increase of drug dose,diffuse hyperplasia of breast tissue was significantly improved.(5)The ELISA results showed that compared with the model group,the estrogen level was lower in the medium-dose group of traditional Chinese medicine-bacteria fermentation after the intervention(P<0.05).In addition,the follicle-stimulating hormone level in the low-dose group of Compound Herba Gueldenstaedtiae was lower than that of the model group(P<0.05).(6)The intervention in the mouse model led to changes in the abundance of short chain fatty acids and intestinal flora in all groups.To conclude,the Compound Herba Gueldenstaedtiae and its probiotic fermentation products significantly improved mammary gland hyperplasia in mice by regulating hormone levels,improving the structure of the gut microbiota,and increasing the content of shortchain fatty acids,providing new ideas and potential sources of drugs for the treatment of breast hyperplasia.展开更多
Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to ...Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.展开更多
The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they ...The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence archite...Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-relate...Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY o...Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy...With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.展开更多
Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the ...Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the urgent need to explore new treatment strategies for epilepsy, recent research has highlighted the potential of targeting gliosis, metabolic disturbances, and neural circuit abnormalities as therapeutic strategies. Astrocytes, the largest group of nonneuronal cells in the central nervous system, play several crucial roles in maintaining ionic and energy metabolic homeostasis in neurons, regulating neurotransmitter levels, and modulating synaptic plasticity. This article briefly reviews the critical role of astrocytes in maintaining balance within the central nervous system. Building on previous research, we discuss how astrocyte dysfunction contributes to the onset and progression of epilepsy through four key aspects: the imbalance between excitatory and inhibitory neuronal signaling, dysregulation of metabolic homeostasis in the neuronal microenvironment, neuroinflammation, and the formation of abnormal neural circuits. We summarize relevant basic research conducted over the past 5 years that has focused on modulating astrocytes as a therapeutic approach for epilepsy. We categorize the therapeutic targets proposed by these studies into four areas: restoration of the excitation–inhibition balance, reestablishment of metabolic homeostasis, modulation of immune and inflammatory responses, and reconstruction of abnormal neural circuits. These targets correspond to the pathophysiological mechanisms by which astrocytes contribute to epilepsy. Additionally, we need to consider the potential challenges and limitations of translating these identified therapeutic targets into clinical treatments. These limitations arise from interspecies differences between humans and animal models, as well as the complex comorbidities associated with epilepsy in humans. We also highlight valuable future research directions worth exploring in the treatment of epilepsy and the regulation of astrocytes, such as gene therapy and imaging strategies. The findings presented in this review may help open new therapeutic avenues for patients with drugresistant epilepsy and for those suffering from other central nervous system disorders associated with astrocytic dysfunction.展开更多
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
Electronic devices capable of perceiving and responding to environmental changes are essential for applications in human-machine interaction,monitoring systems,and robotics.However,most existing devices struggle with ...Electronic devices capable of perceiving and responding to environmental changes are essential for applications in human-machine interaction,monitoring systems,and robotics.However,most existing devices struggle with the separation of sensing and actuation,resulting in complex integration and limited responsiveness.Here,inspired by the interplay between sensory and muscle cells in sea anemones,we present an intelligent thermoelectric device that seamlessly combines multimodal sensing with autonomous thermal actuation,achieving a closed-loop sensory-motor reflex.The device exhibits excellent temperature sensitivity(0.2℃)and pressure resolution(0.03 mm),attributable to its threedimensional(3D)architecture and hierarchical conductive network.Molecular dynamics simulations reveal that a dynamic hydrogen-bonding network enhances stress dissipation and interfacial adhesion,ensuring exceptional mechanical stability over 140,000 cycles.Notably,it also features thermal self-adaptation,actively triggering a protection mechanism to avoid high-temperature stimuli via thermoresponsive deformation,with an adjustable actuation threshold.This work advances intelligent electronics with real-time decision-making and environmental interaction.展开更多
基金National Natural Science Foundation of China(52175237)。
文摘The key parameters that characterize the morphological quality of multi-layer and multi-pass metal laser deposited parts are the surface roughness and the error between the actual printing height and the theoretical model height.The Taguchi method was employed to establish the correlations between process parameter combinations and multi-objective characterization of metal deposition morphology(height error and roughness).Results show that using the signal-to-noise ratio and grey relational analysis,the optimal parameter combination for multi-layer and multi-pass deposition is determined as follows:laser power of 800 W,powder feeding rate of 0.3 r/min,step distance of 1.6 mm,and scanning speed of 20 mm/s.Subsequently,a Genetic Bayesian-back propagation(GB-BP)network is constructed to predict multi-objective responses.Compared with the traditional back propagation network,the GB-back propagation network improves the prediction accuracy of height error and surface roughness by 43.14%and 71.43%,respectively.This network can accurately predict the multi-objective characterization of morphological quality of multi-layer and multi-pass metal deposited parts.
文摘In modern ZnO varistors,traditional aging mechanisms based on increased power consumption are no longer relevant due to reduced power consumption during DC aging.Prolonged exposure to both AC and DC voltages results in increased leakage current,decreased breakdown voltage,and lower nonlinearity,ultimately compromising their protective performance.To investigate the evolution in electrical properties during DC aging,this work developed a finite element model based on Voronoi networks and conducted accelerated aging tests on commercial varistors.Throughout the aging process,current-voltage characteristics and Schottky barrier parameters were measured and analyzed.The results indicate that when subjected to constant voltage,current flows through regions with larger grain sizes,forming discharge channels.As aging progresses,the current focus increases on these channels,leading to a decline in the varistor’s overall performance.Furthermore,analysis of the Schottky barrier parameters shows that the changes in electrical performance during aging are non-monotonic.These findings offer theoretical support for understanding the aging mechanisms and condition assessment of modern stable ZnO varistors.
文摘BACKGROUND:Breast hyperplasia is a common benign breast disease mainly caused by endocrine disorders,manifested as abnormal hyperplasia of breast tissue.In recent years,traditional Chinese medicine compounds and probiotics have shown good potential in regulating the endocrine system and improving the intestinal microecology,providing new ideas for the treatment of breast hyperplasia.OBJECTIVE:To explore the effects and mechanisms of traditional Chinese medicine compounds and fermented probiotic compounds on breast hyperplasia in mice,providing new theoretical and experimental bases for the clinical treatment and prevention of breast hyperplasia.METHODS:(1)Network pharmacology tools were used to predict the anti-breast-hyperplasia activity of Herba Gueldenstaedtiae(Euphorbia humifusa),as well as its potential targets and signaling pathways.The databases included:TCMSP,OMIM,GeneCards database,UniProt website,Venny2.1.0 website,Metascape,HERB website,and STRING database,all of which are open-access databases.Network pharmacology can predict and screen key information such as the targets corresponding to the active ingredients of traditional Chinese medicine,disease targets,and action pathways through network analysis and computer-system analysis.Therefore,it has been increasingly widely used in the research of traditional Chinese medicine.(2)A breast hyperplasia model was induced in mice by injecting estrogen and progesterone.Mice in the normal blank group were injected intraperitoneally with normal saline every day.Mice in the model group and drugadministration groups were injected intraperitoneally with estradiol benzoate injection at a concentration of 0.5 mg/kg every day for 25 days.From the 26th day,the injection of estradiol benzoate injection was stopped.Mice in the normal blank group were injected intramuscularly with normal saline every day,and mice in the model group and drug-administration groups were injected intramuscularly with progesterone injection at a concentration of 5 mg/kg for 5 days.After the model was established,each group was given drugs respectively.The normal blank group and the model group were gavaged with 0.2 mL/d of normal saline;the positive blank group(Xiaozheng Pill group)was gavaged with an aqueous solution of Xiaozheng Pill at 0.9 mg/g;the low-,medium-and high-dose groups of Compound Herba Gueldenstaedtiae were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively;the low-,medium-and high-dose groups of traditional Chinese medicine-bacteria fermentation were gavaged with an aqueous solution of the compound medicine at 0.75,1.5,and 3.0 mg/(g·d)respectively.The administration was continuous for 30 days.RESULTS AND CONCLUSION:(1)The results of network pharmacology research showed that the Compound Herba Gueldenstaedtiae(Euphorbia humifusa)contained 46 active ingredients,which were related to 1213 potential targets.After comparison with 588 known breast-hyperplasia targets,it was speculated that 50 of these targets might be related to the direct effect of the compound on breast hyperplasia.(2)After drug intervention,there was no significant change in the high-dose group of Compound Herba Gueldenstaedtiae compared with the normal blank group.The liver indicators of the other intervention groups all significantly decreased(P<0.05).(3)In terms of kidney and uterine indicators,the medium-dose group of Compound Herba Gueldenstaedtiae decreased significantly compared with the normal blank group(P<0.05).In terms of the uterine index,the model group increased significantly compared with the normal blank group(P<0.01).(4)After 1-month drug treatment,the number of lobules and acini in the breast tissue of the Xiaozheng Pill group,the low,medium,and high-dose group of Compound Herba Gueldenstaedtiae,the low,medium,and highdose groups of traditional Chinese medicine-bacteria fermentation decreased,and the duct openings narrowed.With the increase of drug dose,diffuse hyperplasia of breast tissue was significantly improved.(5)The ELISA results showed that compared with the model group,the estrogen level was lower in the medium-dose group of traditional Chinese medicine-bacteria fermentation after the intervention(P<0.05).In addition,the follicle-stimulating hormone level in the low-dose group of Compound Herba Gueldenstaedtiae was lower than that of the model group(P<0.05).(6)The intervention in the mouse model led to changes in the abundance of short chain fatty acids and intestinal flora in all groups.To conclude,the Compound Herba Gueldenstaedtiae and its probiotic fermentation products significantly improved mammary gland hyperplasia in mice by regulating hormone levels,improving the structure of the gut microbiota,and increasing the content of shortchain fatty acids,providing new ideas and potential sources of drugs for the treatment of breast hyperplasia.
基金supported by the MSIT(Ministry of Science and ICT),Republic of Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)support program(IITP-2025-RS-2023-00259497)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)and was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Republic of Korea government(MSIT)(No.IITP-2025-RS-2023-00254129+1 种基金Graduate School of Metaverse Convergence(Sungkyunkwan University))was supported by the Basic Science Research Program of the National Research Foundation(NRF)funded by the Republic of Korean government(MSIT)(No.RS-2024-00346737).
文摘Network attacks have become a critical issue in the internet security domain.Artificial intelligence technology-based detection methodologies have attracted attention;however,recent studies have struggled to adapt to changing attack patterns and complex network environments.In addition,it is difficult to explain the detection results logically using artificial intelligence.We propose a method for classifying network attacks using graph models to explain the detection results.First,we reconstruct the network packet data into a graphical structure.We then use a graph model to predict network attacks using edge classification.To explain the prediction results,we observed numerical changes by randomly masking and calculating the importance of neighbors,allowing us to extract significant subgraphs.Our experiments on six public datasets demonstrate superior performance with an average F1-score of 0.960 and accuracy of 0.964,outperforming traditional machine learning and other graph models.The visual representation of the extracted subgraphs highlights the neighboring nodes that have the greatest impact on the results,thus explaining detection.In conclusion,this study demonstrates that graph-based models are suitable for network attack detection in complex environments,and the importance of graph neighbors can be calculated to efficiently analyze the results.This approach can contribute to real-world network security analyses and provide a new direction in the field.
基金supported in part by the Rosetrees Trust(#CF-2023-I-2_113)by the Israel Ministry of Innovation,Science,and Technology(#7393)(to ES).
文摘The organization of biological neuronal networks into functional modules has intrigued scientists and inspired engineers to develop artificial systems.These networks are characterized by two key properties.First,they exhibit dense interconnectivity(Braitenburg and Schüz,1998;Campagnola et al.,2022).The strength and probability of connectivity depend on cell type,inter-neuronal distance,and species.Still,every cortical neuron receives input from thousands of other neurons while transmitting output to a similar number of neurons.Second,communication between neurons occurs primarily via chemical or electrical synapses.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金supported by the National Natural Science Foundation of China (Grant Nos.12192251,12334014,62335019,12134001,1230441812474378)+1 种基金the Quantum Science and Technology National Science and Technology Major Project(Grant No.2021ZD0301403)the Shanghai Municipal Science and Technology Major Project (Grant No.2019SHZDZX01)。
文摘Photonic neural networks(PNNs)of sufficiently large physical dimensions and high operation accuracies are envisaged as ideal candidates for breaking the major bottlenecks in the current artificial intelligence architectures in terms of latency,energy efficiency,and computational power.To achieve this vision,it is of vital importance to scale up the PNNs while simultaneously reducing the high demand on the dimensions required by them.The underlying cause of this strategy is the enormous gap between the scales of photonic and electronic integrated circuits.Here,we demonstrate monolithically integrated optical convolutional processors on thin film lithium niobate(TFLN)that harness inherent parallelism in photonics to enable large-scale programmable convolution kernels and,in turn,greatly reduce the dimensions required by subsequent fully connected layers.Experimental validation achieves high classification accuracies of 96%(86%)on the MNIST(Fashion-MNIST)dataset and 84.6%on the AG News dataset while dramatically reducing the required subsequent fully connected layer dimensions to 196×10(from 784×10)and 175×4(from 800×4),respectively.Furthermore,our devices can be driven by commercial field-programmable gate array systems;a unique advantage in addition to their scalable channel number and kernel size.Our architecture provides a solution to build practical machine learning photonic devices.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
基金supported by the Jilin Science and Technology Development Talent Special Project,Nos.20240601086RC,23JQ08(all to ZH)YDZJ202502CXJD077+1 种基金JLARS-2025-0802-09YDZJ202501ZYTS706.
文摘Population aging is one of the common challenges in the current world.As people age,the body’s tissues including cells,and molecules inevitably degrade,and their functions gradually decline,causing various age-related diseases like Alzheimer’s disease,osteoporosis,low immunity,glucose and lipid metabolism disorders,and cardiovascular diseases.With the continuous increase of the elderly population,the pressure on the medical industry is increasing.To lower the burden on the medical industry and increase the average age of the elderly,it is vital to explore effective anti-aging materials.Ginseng Radix et Rhizoma(Renshen),as a traditional and precious Chinese medicinal herb,is known as the“king of all herbs”.It is famous for its effects of“tonifying Qi,restoring pulse”(helping with the generation of Qi(the fundamental,vital energy that continuously flows within the body)and the circulation of blood)and strengthening the body,nourishing the spleen and lungs,generating fluids and nourishing blood,calming the mind and improving intelligence.Recently,its anti-aging effect has received increasing attention from modern scientific research.This study summarizes the pharmacological effects of the main active ingredients of Renshen(ginsenosides,polysaccharides,etc.)on resisting aging,including preventing neuroaging,suppressing skin aging,mitigating ovarian aging,inhibiting osteoporosis and arthritis,enhancing the immune system of the elderly,protecting the cardiovascular system,resisting aging-induced fatigue and exerting the anti-tumor effects.Through network pharmacology and molecular docking,the anti-aging active ingredients of Renshen were screened,and the key targets and pathways of anti-aging active ingredients in Renshen were determined.Using network pharmacology,totally 106 drug targets and 3,479 disease targets were screened,and 79 common targets between aging and Renshen were identified.Three core targets were identified in the PPI network,including TNF,AKT1,and IL-1β.Molecular docking was used to obtain further verification.This study emphasizes the potential of Renshen as a source of anti-aging activity,which can be developed into a novel drug for the treatment of age-related diseases.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by grants from the National Natural Science Foundation of China(82004252)the Project of Administration of Traditional Chinese Medicine of Guangdong Province(202405112017596500)the Basic and Applied Basic Research Foundation of Guangzhou Municipal Science and Technology Bureau(202102020533).
文摘Background:Wenqing Yin(WQY)is a classic prescription used to treat skin diseases like atopic dermatitis(AD)in China,and the aim of this study is to investigate the therapeutic effects and molecular mechanisms of WQY on AD.Methods:The DNFB-induced mouse models of AD were established to investigate the therapeutic effects of WQY on AD.The symptoms of AD in the ears and backs of the mice were assessed,while inflammatory factors in the ear were quantified using quantitative real-time-polymerase chain reaction(qRT-PCR),and the percentages of CD4^(+)and CD8^(+)cells in the spleen were analyzed through flow cytometry.The compounds in WQY were identified using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)analysis and the key targets and pathways of WQY to treat AD were predicted by network pharmacology.Subsequently,the key genes were tested and verified by qRT-PCR,and the potential active components and target proteins were verified by molecular docking.Results:WQY relieved the AD symptoms and histopathological injuries in the ear and back skin of mice with AD.Meanwhile,WQY significantly reduced the levels of inflammatory factors IL-6 and IL-1βin ear tissue,as well as the ratio of CD4^(+)/CD8^(+)cells in spleen.Additionally,a total of 142 compounds were identified from the water extract of WQY by UPLC-Orbitrap-MS/MS.39 key targets related to AD were screened out by network pharmacology methods.The KEGG analysis indicated that the effects of WQY were primarily mediated through pathways associated with Toll-like receptor signaling and T cell receptor signaling.Moreover,the results of qRT-PCR demonstrated that WQY significantly reduced the mRNA expressions of IL-4,IL-10,GATA3 and FOXP3,and molecular docking simulation verified that the active components of WQY had excellent binding abilities with IL-4,IL-10,GATA3 and FOXP3 proteins.Conclusion:The present study demonstrated that WQY effectively relieved AD symptoms in mice,decreased the inflammatory factors levels,regulated the balance of CD4^(+)and CD8^(+)cells,and the mechanism may be associated with the suppression of Th2 and Treg cell immune responses.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
文摘With the increasing complexity of vehicular networks and the proliferation of connected vehicles,Federated Learning(FL)has emerged as a critical framework for decentralized model training while preserving data privacy.However,efficient client selection and adaptive weight allocation in heterogeneous and non-IID environments remain challenging.To address these issues,we propose Federated Learning with Client Selection and Adaptive Weighting(FedCW),a novel algorithm that leverages adaptive client selection and dynamic weight allocation for optimizing model convergence in real-time vehicular networks.FedCW selects clients based on their Euclidean distance from the global model and dynamically adjusts aggregation weights to optimize both data diversity and model convergence.Experimental results show that FedCW significantly outperforms existing FL algorithms such as FedAvg,FedProx,and SCAFFOLD,particularly in non-IID settings,achieving faster convergence,higher accuracy,and reduced communication overhead.These findings demonstrate that FedCW provides an effective solution for enhancing the performance of FL in heterogeneous,edge-based computing environments.
基金supported by the National Key Research and Development Program of China,No. 2023YFF0714200 (to CW)the National Natural Science Foundation of China,Nos. 82472038 and 82202224 (both to CW)+3 种基金the Shanghai Rising-Star Program,No. 23QA1407700 (to CW)the Construction Project of Shanghai Key Laboratory of Molecular Imaging,No. 18DZ2260400 (to CW)the National Science Foundation for Distinguished Young Scholars,No. 82025019 (to CL)the Greater Bay Area Institute of Precision Medicine (Guangzhou)(to CW)。
文摘Epilepsy is a leading cause of disability and mortality worldwide. However, despite the availability of more than 20 antiseizure medications, more than one-third of patients continue to experience seizures. Given the urgent need to explore new treatment strategies for epilepsy, recent research has highlighted the potential of targeting gliosis, metabolic disturbances, and neural circuit abnormalities as therapeutic strategies. Astrocytes, the largest group of nonneuronal cells in the central nervous system, play several crucial roles in maintaining ionic and energy metabolic homeostasis in neurons, regulating neurotransmitter levels, and modulating synaptic plasticity. This article briefly reviews the critical role of astrocytes in maintaining balance within the central nervous system. Building on previous research, we discuss how astrocyte dysfunction contributes to the onset and progression of epilepsy through four key aspects: the imbalance between excitatory and inhibitory neuronal signaling, dysregulation of metabolic homeostasis in the neuronal microenvironment, neuroinflammation, and the formation of abnormal neural circuits. We summarize relevant basic research conducted over the past 5 years that has focused on modulating astrocytes as a therapeutic approach for epilepsy. We categorize the therapeutic targets proposed by these studies into four areas: restoration of the excitation–inhibition balance, reestablishment of metabolic homeostasis, modulation of immune and inflammatory responses, and reconstruction of abnormal neural circuits. These targets correspond to the pathophysiological mechanisms by which astrocytes contribute to epilepsy. Additionally, we need to consider the potential challenges and limitations of translating these identified therapeutic targets into clinical treatments. These limitations arise from interspecies differences between humans and animal models, as well as the complex comorbidities associated with epilepsy in humans. We also highlight valuable future research directions worth exploring in the treatment of epilepsy and the regulation of astrocytes, such as gene therapy and imaging strategies. The findings presented in this review may help open new therapeutic avenues for patients with drugresistant epilepsy and for those suffering from other central nervous system disorders associated with astrocytic dysfunction.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金supported by the National Natural Science Foundation of China(Nos.22175164,12232016,and 12172346)the Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB0450402)+2 种基金the Youth Innovation Promotion Association CAS(No.2022465)the Fundamental Research Funds for the Central Universities(No.WK2090000087)the University of Science and Technology of China(USTC)Tang Scholar.
文摘Electronic devices capable of perceiving and responding to environmental changes are essential for applications in human-machine interaction,monitoring systems,and robotics.However,most existing devices struggle with the separation of sensing and actuation,resulting in complex integration and limited responsiveness.Here,inspired by the interplay between sensory and muscle cells in sea anemones,we present an intelligent thermoelectric device that seamlessly combines multimodal sensing with autonomous thermal actuation,achieving a closed-loop sensory-motor reflex.The device exhibits excellent temperature sensitivity(0.2℃)and pressure resolution(0.03 mm),attributable to its threedimensional(3D)architecture and hierarchical conductive network.Molecular dynamics simulations reveal that a dynamic hydrogen-bonding network enhances stress dissipation and interfacial adhesion,ensuring exceptional mechanical stability over 140,000 cycles.Notably,it also features thermal self-adaptation,actively triggering a protection mechanism to avoid high-temperature stimuli via thermoresponsive deformation,with an adjustable actuation threshold.This work advances intelligent electronics with real-time decision-making and environmental interaction.
