Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,ther...Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.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
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
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited re...The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.展开更多
With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi...With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.展开更多
To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure ...To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.展开更多
Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the u...Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.展开更多
Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-see...Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.展开更多
Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul ar...Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.展开更多
Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior know...Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.展开更多
Firstly, a multilevel trust algorithm for MANET （mobile ad hoe networks） is presented in this paper and the trust level is defined as a three-tuple type in this multilevel trust algorithm. The paper introduces the m...Firstly, a multilevel trust algorithm for MANET （mobile ad hoe networks） is presented in this paper and the trust level is defined as a three-tuple type in this multilevel trust algorithm. The paper introduces the multilevel trust into MANET, thereby controlling restricted classified information flows among nodes that have different trust levels. Secondly, the infrastructure of MANET that suit to our multi-level trust is presented, Some conclusions are given at lastly.展开更多
Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly...Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.展开更多
Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisi...The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.展开更多
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
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
基金the funding support from the National Natural Science Foundation of China(Grant No.52308340)Chongqing Talent Innovation and Entrepreneurship Demonstration Team Project(Grant No.cstc2024ycjh-bgzxm0012)the Science and Technology Projects supported by China Coal Technology and Engineering Chongqing Design and Research Institute(Group)Co.,Ltd.(Grant No.H20230317).
文摘Accurate prediction of landslide displacement is crucial for effective early warning of landslide disasters.While most existing prediction methods focus on time-series forecasting for individual monitoring points,there is limited research on the spatiotemporal characteristics of landslide deformation.This paper proposes a novel Multi-Relation Spatiotemporal Graph Residual Network with Multi-Level Feature Attention(MFA-MRSTGRN)that effectively improves the prediction performance of landslide displacement through spatiotemporal fusion.This model integrates internal seepage factors as data feature enhancements with external triggering factors,allowing for accurate capture of the complex spatiotemporal characteristics of landslide displacement and the construction of a multi-source heterogeneous dataset.The MFA-MRSTGRN model incorporates dynamic graph theory and four key modules:multilevel feature attention,temporal-residual decomposition,spatial multi-relational graph convolution,and spatiotemporal fusion prediction.This comprehensive approach enables the efficient analyses of multi-source heterogeneous datasets,facilitating adaptive exploration of the evolving multi-relational,multi-dimensional spatiotemporal complexities in landslides.When applying this model to predict the displacement of the Liangshuijing landslide,we demonstrate that the MFA-MRSTGRN model surpasses traditional models,such as random forest(RF),long short-term memory(LSTM),and spatial temporal graph convolutional networks(ST-GCN)models in terms of various evaluation metrics including mean absolute error(MAE=1.27 mm),root mean square error(RMSE=1.49 mm),mean absolute percentage error(MAPE=0.026),and R-squared(R^(2)=0.88).Furthermore,feature ablation experiments indicate that incorporating internal seepage factors improves the predictive performance of landslide displacement models.This research provides an advanced and reliable method for landslide displacement prediction.
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 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 Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
文摘The most important problem in the security of wireless sensor network (WSN) is to distribute keys for the sensor nodes and to establish a secure channel in an insecure environment. Since the sensor node has limited resources, for instance, low battery life and low computational power, the key distribution scheme must be designed in an efficient manner. Recently many studies added a few high-level nodes into the network, called the heterogeneous sensor network (HSN). Most of these studies considered an application for two-level HSN instead of multi-level one. In this paper, we propose some definitions for multi-level HSN, and design a novel key management strategy based on the polynomial hash tree (PHT) method by using deployment knowledge. Our proposed strategy has lower computation and communication overheads but higher connectivity and resilience.
基金supported by the National Natural Science Foundation of China under Grant 52077146.
文摘With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.
基金Supported by the Shanxi Natural Science Foundation under contract number 20041070 and Natural Science Foundation of north u-niversity of China .
文摘To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.
文摘Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.
文摘Existing glass segmentation networks have high computational complexity and large memory occupation,leading to high hardware requirements and time overheads for model inference,which is not conducive to efficiency-seeking real-time tasks such as autonomous driving.The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers.These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers.We propose an efficient glass segmentation network(EGSNet)based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency.EGSNet divides the feature layers from different stages into low-level understanding,semantic-level understanding,and global understanding with boundary guidance.Based on the information differences among the different layers,we further propose the multi-angle collaborative enhancement(MCE)module,which extracts the detailed information from shallow features,and the large-scale contextual feature extraction(LCFE)module to understand semantic logic through deep features.The models are trained and evaluated on the glass segmentation datasets HSO(Home-Scene-Oriented)and Trans10k-stuff,respectively,and EGSNet achieves the best efficiency and performance compared to advanced methods.In the HSO test set results,the IoU,Fβ,MAE(Mean Absolute Error),and BER(Balance Error Rate)of EGSNet are 0.804,0.847,0.084,and 0.085,and the GFLOPs(Giga Floating Point Operations Per Second)are only 27.15.Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance.
文摘Tropospheric ozone concentrations, which are an important air pollutant, are modeled by the use of an artificial intelligence structure. Data obtained from air pollution measurement stations in the city of Istanbul are utilized in constituting the model. A supervised algorithm for the evaluation of ozone concentration using a genetically trained multi-level cellular neural network （ML-CNN） is introduced, developed, and applied to real data. A genetic algorithm is used in the optimization of CNN templates. The model results and the actual measurement results are compared and statistically evaluated. It is observed that seasonal changes in ozone concentrations are reflected effectively by the concentrations estimated by the multilevel-CNN model structure, with a correlation value of 0.57 ascertained between actual and model results. It is shown that the multilevel-CNN modeling technique is as satisfactory as other modeling techniques in associating the data in a complex medium in air pollution applications.
基金supported by the National Natural Science Foundation of China(Grant Nos.62005307 and 61975228).
文摘Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.
基金Supported by the National Natural Science Foun-dation of China (60372107) Doctoral Innovative Foundation ofJiangsu Province(46666001) .
文摘Firstly, a multilevel trust algorithm for MANET （mobile ad hoe networks） is presented in this paper and the trust level is defined as a three-tuple type in this multilevel trust algorithm. The paper introduces the multilevel trust into MANET, thereby controlling restricted classified information flows among nodes that have different trust levels. Secondly, the infrastructure of MANET that suit to our multi-level trust is presented, Some conclusions are given at lastly.
基金supported by the Sichuan Science and Technology Program (No.2019YJ0356).
文摘Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for funding this research through a project(NU/IFC/ENT/01/014)under the institutional funding committee at Najran University,Kingdom of Saudi Arabia.
文摘The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients worldwide.Even in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones.The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data.To over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images.The GAN network contains mainly two parts known as generator and discriminator.Commonly,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural network.In this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used.Our proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.
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.
