T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection met...T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection methods face challenges owing to surface oxide scales,and conventional high-precision magnetic sensors are ineffective at high temperatures.To overcome these limitations,a small coil sensor was employed to measure the residual magnetism strength in oscillation traces,using metal magnetic memory and electromagnetic induction methods,which can carry out detection without an external excitation source.Using this technology,the proposed scheme successfully detects defects at high tempe-ratures(up to 670℃)without a cooling device.The key findings include the ability to detect both surface and near-surface defects,such as cracks and oscillation marks,with an enhanced signal-to-noise ratio(SNR)of 7.2 dB after signal processing.The method’s practicality was validated in a steel mill environment,where testing on casting slabs effectively detected defects,providing a foundation for improving industrial quality control.The proposed detection scheme offers a significant advancement in nondestructive testing(NDT)for high-temperature applications,contributing to more efficient and accurate monitoring of ferromagnetic material integrity.展开更多
Aging is a physiological and complex process produced by accumulative age-dependent cellular damage,which significantly impacts brain regions like the hippocampus,an essential region involved in memory and learning.A ...Aging is a physiological and complex process produced by accumulative age-dependent cellular damage,which significantly impacts brain regions like the hippocampus,an essential region involved in memory and learning.A crucial factor contributing to this decline is the dysfunction of mitochondria,particularly those located at synapses.Synaptic mitochondria are specialized organelles that produce the energy required for synaptic transmission but are also important for calcium homeostasis at these sites.In contrast,non-synaptic mitochondria primarily involve cellular metabolism and long-term energy supply.Both pools of mitochondria differ in their form,proteome,functionality,and cellular role.The proper functioning of synaptic mitochondria depends on processes such as mitochondrial dynamics,transport,and quality control.However,synaptic mitochondria are particularly vulnerable to age-associated damage,characterized by oxidative stress,impaired energy production,and calcium dysregulation.These changes compromise synaptic transmission,reducing synaptic activity and cognitive decline during aging.In the context of neurodegenerative diseases such as Alzheimer’s,Parkinson’s,and Huntington’s,the decline of synaptic mitochondrial function is even more pronounced.These diseases are marked by pathological protein accumulation,disrupted mitochondrial dynamics,and heightened oxidative stress,accelerating synaptic dysfunction and neuronal loss.Due to their specialized role and location,synaptic mitochondria are among the first organelles to exhibit dysfunction,underscoring their critical role in disease progression.This review delves into the main differences at structural and functional levels between synaptic and non-synaptic mitochondria,emphasizing the vulnerability of synaptic mitochondria to the aging process and neurodegeneration.These approaches highlight the potential of targeting synaptic mitochondria to mitigate age-associated cognitive impairment and synaptic degeneration.This review emphasizes the distinct vulnerabilities of hippocampal synaptic mitochondria,highlighting their essential role in sustaining brain function throughout life and their promise as therapeutic targets for safeguarding the cognitive capacities of people of advanced age.展开更多
Your hometown is often a special place.It can be big or small.And it usually has an important place in your life and heart.You may not live there anymore.But you remember it well.Maybe you have warm feelings for your ...Your hometown is often a special place.It can be big or small.And it usually has an important place in your life and heart.You may not live there anymore.But you remember it well.Maybe you have warm feelings for your school,your friends and your neighbors there.You remember special places and fun times.There may be interesting museums or great sports teams in your hometown.You can tell people about them.展开更多
After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,co...After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.展开更多
Alzheimer's disease is the primary cause of dementia and imposes a significant socioeconomic burden globally.Physical exercise,as an effective strategy for improving general health,has been largely reported for it...Alzheimer's disease is the primary cause of dementia and imposes a significant socioeconomic burden globally.Physical exercise,as an effective strategy for improving general health,has been largely reported for its effectiveness in slowing neurodegeneration and increasing brain functional plasticity,particularly in aging brains.However,the underlying mechanisms of exercise in cognitive aging remain largely unclear.Adiponectin,a cell-secreted protein hormone,has recently been found to regulate synaptic plasticity and mediate the antidepressant effects of physical exercise.Studies on the neuroprotective effects of adiponectin have revealed potential innovative treatments for Alzheimer's disease.Here,we reviewed the functions of adiponectin and its receptor in the brains of human and animal models of cognitive impairment.We summarized the role of adiponectin in Alzheimer's disease,focusing on its impact on energy metabolism,insulin resistance,and inflammation.We also discuss how exercise increases adiponectin secretion and its potential benefits for learning and memory.Finally,we highlight the latest research on chemical compounds that mimic exerciseenhanced secretion of adiponectin and its receptor in Alzheimer's disease.展开更多
The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a...The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a viable solution.Based on multiple light scattering technology,this paper collects the stability data as the training set of Triphala fruit juice over a three-month period and finds that the sediment amount reached 0.6 mg/mL,composed of ellagic acid and phlobaphene,with the solution's light transmittance fluctuating range(23%-76%)and the particle size(0.27-0.29μm)on day 75.The early warning model comprises a synergistic integration of long short-term memory and backpropagation neural network models.The model exhibits a mean absolute percentage error of 0.626%,an R^(2) of 0.911,and an accuracy of 85.71%.This model is capable of predicting key stability parameters,including sedimentation,transmittance,particle size,particle migration rate,and stability index,within a 90-day period in just 7 days,and thereby provide accurate early-stage stability alerts.展开更多
The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and e...The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and existence of uniform attractor under some suitable assumptions on the nonlinear term g(u),the nonlinear damping f(u_(t))and the external force h(x,t).Specifically,the asymptotic compactness of the semigroup is verified by the energy reconstruction method.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,th...The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.展开更多
Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationall...Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.展开更多
Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physic...Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.展开更多
Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban m...Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban memories.During the past decade,she has focused on creating freeform sculptures,and she has launched a global,cross-disciplinary-artist-collaboration project,which has involved the preservation of archives for contemporary artistic creation.展开更多
Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability...Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.展开更多
Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data ...Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.展开更多
With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows...With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.展开更多
The fabrication of a dynamic threshold-2T0C(DT-2T0C) DRAM cell incorporating a ZnO charge-trap layer in the write transistor has been successfully achieved, addressing the negative hold voltage(V_(HOLD)) issue of conv...The fabrication of a dynamic threshold-2T0C(DT-2T0C) DRAM cell incorporating a ZnO charge-trap layer in the write transistor has been successfully achieved, addressing the negative hold voltage(V_(HOLD)) issue of conventional 2T0C DRAM cells using oxide channel layers. The proposed device facilitates dynamic modulation of turn-on voltage(V_(ON)) through an additional SET operation, allowing V_(ON) to shift above 0 V. The retention time in SET operation was extended to 10^(4) s by optimizing the tunneling layer deposition conditions. The device characterization revealed a significant correlation between V_(ON) and both the WRITE speed and the retention properties of the DT-2T0C, verifying the trade-off between WRITE time and retention time. A long retention time over 1000 s was achieved, even under VHOLD of 0 V.展开更多
Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction pla...Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.展开更多
Alzheimer’s disease(AD)is the most common form of dementia characterized pathologically by the deposition of amyloid plaques and hyperphosphorylated tau containing neurofibrillary tangles.The disease presents clinica...Alzheimer’s disease(AD)is the most common form of dementia characterized pathologically by the deposition of amyloid plaques and hyperphosphorylated tau containing neurofibrillary tangles.The disease presents clinically with progressive memory loss and disruption of cognitive function.Currently,there is no cure for AD;recent advances in the therapeutics aimed at clearing the amyloid protein from the brain have led to potential disease stabilization,however,this does not prevent eventual disease progression(Cummings et al.,2024).展开更多
The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate cha...The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.展开更多
文摘T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection methods face challenges owing to surface oxide scales,and conventional high-precision magnetic sensors are ineffective at high temperatures.To overcome these limitations,a small coil sensor was employed to measure the residual magnetism strength in oscillation traces,using metal magnetic memory and electromagnetic induction methods,which can carry out detection without an external excitation source.Using this technology,the proposed scheme successfully detects defects at high tempe-ratures(up to 670℃)without a cooling device.The key findings include the ability to detect both surface and near-surface defects,such as cracks and oscillation marks,with an enhanced signal-to-noise ratio(SNR)of 7.2 dB after signal processing.The method’s practicality was validated in a steel mill environment,where testing on casting slabs effectively detected defects,providing a foundation for improving industrial quality control.The proposed detection scheme offers a significant advancement in nondestructive testing(NDT)for high-temperature applications,contributing to more efficient and accurate monitoring of ferromagnetic material integrity.
基金supported by ANID FONDECYT No.1221178Centro Ciencia&Vida,FB210008,Financiamiento Basal para Centros Científicos y Tecnológicos de Excelencia de ANID to CTR.
文摘Aging is a physiological and complex process produced by accumulative age-dependent cellular damage,which significantly impacts brain regions like the hippocampus,an essential region involved in memory and learning.A crucial factor contributing to this decline is the dysfunction of mitochondria,particularly those located at synapses.Synaptic mitochondria are specialized organelles that produce the energy required for synaptic transmission but are also important for calcium homeostasis at these sites.In contrast,non-synaptic mitochondria primarily involve cellular metabolism and long-term energy supply.Both pools of mitochondria differ in their form,proteome,functionality,and cellular role.The proper functioning of synaptic mitochondria depends on processes such as mitochondrial dynamics,transport,and quality control.However,synaptic mitochondria are particularly vulnerable to age-associated damage,characterized by oxidative stress,impaired energy production,and calcium dysregulation.These changes compromise synaptic transmission,reducing synaptic activity and cognitive decline during aging.In the context of neurodegenerative diseases such as Alzheimer’s,Parkinson’s,and Huntington’s,the decline of synaptic mitochondrial function is even more pronounced.These diseases are marked by pathological protein accumulation,disrupted mitochondrial dynamics,and heightened oxidative stress,accelerating synaptic dysfunction and neuronal loss.Due to their specialized role and location,synaptic mitochondria are among the first organelles to exhibit dysfunction,underscoring their critical role in disease progression.This review delves into the main differences at structural and functional levels between synaptic and non-synaptic mitochondria,emphasizing the vulnerability of synaptic mitochondria to the aging process and neurodegeneration.These approaches highlight the potential of targeting synaptic mitochondria to mitigate age-associated cognitive impairment and synaptic degeneration.This review emphasizes the distinct vulnerabilities of hippocampal synaptic mitochondria,highlighting their essential role in sustaining brain function throughout life and their promise as therapeutic targets for safeguarding the cognitive capacities of people of advanced age.
文摘Your hometown is often a special place.It can be big or small.And it usually has an important place in your life and heart.You may not live there anymore.But you remember it well.Maybe you have warm feelings for your school,your friends and your neighbors there.You remember special places and fun times.There may be interesting museums or great sports teams in your hometown.You can tell people about them.
文摘After billions of years of evolution,biological intelligence has converged on unrivalled energy efficiency and environmental adaptability.The human brain,for instance,is highly efficient in information transmission,consuming only about 20 W onaverage in a resting state[1,2].A key to this efficiency is that biological signal transduction and processing rely significantly on multi-ions as the signal carriers.Inspired by this paradigm.
基金supported by the National Natural Science Foundation of China,No.82072529(to HWHT)Key Laboratory of Guangdong Higher Education Institutes,No.2021KSYS009(to HWHT)the China Postdoctoral Science Foundation,No.2022M720907(to HHG)。
文摘Alzheimer's disease is the primary cause of dementia and imposes a significant socioeconomic burden globally.Physical exercise,as an effective strategy for improving general health,has been largely reported for its effectiveness in slowing neurodegeneration and increasing brain functional plasticity,particularly in aging brains.However,the underlying mechanisms of exercise in cognitive aging remain largely unclear.Adiponectin,a cell-secreted protein hormone,has recently been found to regulate synaptic plasticity and mediate the antidepressant effects of physical exercise.Studies on the neuroprotective effects of adiponectin have revealed potential innovative treatments for Alzheimer's disease.Here,we reviewed the functions of adiponectin and its receptor in the brains of human and animal models of cognitive impairment.We summarized the role of adiponectin in Alzheimer's disease,focusing on its impact on energy metabolism,insulin resistance,and inflammation.We also discuss how exercise increases adiponectin secretion and its potential benefits for learning and memory.Finally,we highlight the latest research on chemical compounds that mimic exerciseenhanced secretion of adiponectin and its receptor in Alzheimer's disease.
基金supported by grants from the National Natural Science Foundation of China(82304871)China Postdoctoral Science Foundation(2023MD734100)Sichuan Science and Technology Program(2024NSFSC1846),Sichuan Science and Technology Program(2024YFFKO185)。
文摘The stability of fruit juice has consistently been an important concern in the food processing industry,which can be time-consuming and costly.Therefore,developing accurate stability early-warning model may serve as a viable solution.Based on multiple light scattering technology,this paper collects the stability data as the training set of Triphala fruit juice over a three-month period and finds that the sediment amount reached 0.6 mg/mL,composed of ellagic acid and phlobaphene,with the solution's light transmittance fluctuating range(23%-76%)and the particle size(0.27-0.29μm)on day 75.The early warning model comprises a synergistic integration of long short-term memory and backpropagation neural network models.The model exhibits a mean absolute percentage error of 0.626%,an R^(2) of 0.911,and an accuracy of 85.71%.This model is capable of predicting key stability parameters,including sedimentation,transmittance,particle size,particle migration rate,and stability index,within a 90-day period in just 7 days,and thereby provide accurate early-stage stability alerts.
基金Supported by the National Natural Science Foundation of China(Grant Nos.11961059,1210502)the University Innovation Project of Gansu Province(Grant No.2023B-062)the Gansu Province Basic Research Innovation Group Project(Grant No.23JRRA684).
文摘The goal of this paper is to investigate the long-time dynamics of solutions to a Kirchhoff type suspension bridge equation with nonlinear damping and memory term.For this problem we establish the well-posedness and existence of uniform attractor under some suitable assumptions on the nonlinear term g(u),the nonlinear damping f(u_(t))and the external force h(x,t).Specifically,the asymptotic compactness of the semigroup is verified by the energy reconstruction method.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
基金supported by the National Natural Science Foundation of China(Grant No.62403486)。
文摘The highly dynamic nature,strong uncertainty,and coupled multiple safety constraints inherent in carrier aircraft recovery operations pose severe challenges for real-time decision-making.Addressing bolter scenarios,this study proposes an intelligent decision-making framework based on a deep long short-term memory Q-network.This framework transforms the real-time sequencing for bolter recovery problem into a partially observable Markov decision process.It employs a stacked long shortterm memory network to accurately capture the long-range temporal dependencies of bolter event chains and fuel consumption.Furthermore,it integrates a prioritized experience replay training mechanism to construct a safe and adaptive scheduling system capable of millisecond-level real-time decision-making.Experimental demonstrates that,within large-scale mass recovery scenarios,the framework achieves zero safety violations in static environments and maintains a fuel safety violation rate below 10%in dynamic scenarios,with single-step decision times at the millisecond level.The model exhibits strong generalization capability,effectively responding to unforeseen emergent situations—such as multiple bolters and fuel emergencies—without requiring retraining.This provides robust support for efficient carrier-based aircraft recovery operations.
基金supported and funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Recognizing human interactions in RGB videos is a critical task in computer vision,with applications in video surveillance.Existing deep learning-based architectures have achieved strong results,but are computationally intensive,sensitive to video resolution changes and often fail in crowded scenes.We propose a novel hybrid system that is computationally efficient,robust to degraded video quality and able to filter out irrelevant individuals,making it suitable for real-life use.The system leverages multi-modal handcrafted features for interaction representation and a deep learning classifier for capturing complex dependencies.Using Mask R-CNN and YOLO11-Pose,we extract grayscale silhouettes and keypoint coordinates of interacting individuals,while filtering out irrelevant individuals using a proposed algorithm.From these,we extract silhouette-based features(local ternary pattern and histogram of optical flow)and keypoint-based features(distances,angles and velocities)that capture distinct spatial and temporal information.A Bidirectional Long Short-Term Memory network(BiLSTM)then classifies the interactions.Extensive experiments on the UT Interaction,SBU Kinect Interaction and the ISR-UOL 3D social activity datasets demonstrate that our system achieves competitive accuracy.They also validate the effectiveness of the chosen features and classifier,along with the proposed system’s computational efficiency and robustness to occlusion.
基金funded by the National Natural Science Foundation of China(NSFC No.52322610)Hong Kong Research Grants Council Theme-based Research Scheme(T22-505/19-N)Furthermore,this research was undertaken with the assistance of computational resources from the National Computational Infrastructure(NCI Australia),an NCRISenabled capability supported by the Australian Government.
文摘Shrublands and grasslands,which constitute approximately 70%of Australia’s vegetation,play a critical role in global wildfire-prone regions.To advance the understanding of grass fire spread,a three-dimensional,physicsbased fire model provides valuable insights into fire dynamics.However,such models are computationally intensive and time-consuming.To address these challenges,we constructed an extensive numerical database comprising 64,000 high-fidelity wildfire simulation cases and implemented a Long Short-Term Memory neural network architecture.The model demonstrates strong predictive performance,achieving a coefficient of determination(R2)of 0.96 on training data,indicating excellent agreement with the physics-based simulation outputs.By utilizing coordinates from five reference points to predict fire front movement,this approach offers a novel method for analysing fire dynamics in homogeneous fuel beds with an average deviation of less than 2.5%.Combining the strengths of physics-based modelling and deep learning,our research enhances fire spread prediction accuracy of over 95%while significantly reducing computational demands.Future efforts will focus on refining the model,expanding the dataset,and incorporating additional variables to improve predictive capabilities and operational applicability.
文摘Chinese-German sculptor Zhou Xiaoping has been traveling between China and Germany,creating sculptures,since 2003.She has created more than 80 large,urban sculptures,each of which has become a vivid carrier of urban memories.During the past decade,she has focused on creating freeform sculptures,and she has launched a global,cross-disciplinary-artist-collaboration project,which has involved the preservation of archives for contemporary artistic creation.
基金supported by the National Natural Science Foundation of China(Nos.52473080,52403167 and 52173079)the Fundamental Research Funds for the Central Universities(Nos.xtr052023001 and xzy012023037)+1 种基金the Postdoctoral Research Project of Shaanxi Province(No.2024BSHSDZZ054)the Shaanxi Laboratory of Advanced Materials(No.2024ZY-JCYJ-04-12).
文摘Shape memory polymers used in 4D printing only had one permanent shape after molding,which limited their applications in requiring multiple reconstructions and multifunctional shapes.Furthermore,the inherent stability of the triazine ring structure within cyanate ester(CE)crosslinked networks after molding posed significant challenges for both recycling,repairing,and degradation of resin.To address these obstacles,dynamic thiocyanate ester(TCE)bonds and photocurable group were incorporated into CE,obtaining the recyclable and 3D printable CE covalent adaptable networks(CANs),denoted as PTCE1.5.This material exhibits a Young's modulus of 810 MPa and a tensile strength of 50.8 MPa.Notably,damaged printed PTCE1.5 objects can be readily repaired through reprinting and interface rejoining by thermal treatment.Leveraging the solid-state plasticity,PTCE1.5 also demonstrated attractive shape memory ability and permanent shape reconfigurability,enabling its reconfigurable 4D printing.The printed PTCE1.5 hinges and a main body were assembled into a deployable and retractable satellite model,validating its potential application as a controllable component in the aerospace field.Moreover,printed PTCE1.5 can be fully degraded into thiol-modified intermediate products.Overall,this material not only enriches the application range of CE resin,but also provides a reliable approach to addressing environmental issue.
基金supported by the National Natural Science Foundation of China(No.51977113)the Science and Technology Project of State Grid Zhejiang Electric Power Co.,Ltd.(No.5211JX240001).
文摘Accurate and reliable power system data are fundamental for critical operations such as gridmonitoring,fault diagnosis,and load forecasting,underpinned by increasing intelligentization and digitalization.However,data loss and anomalies frequently compromise data integrity in practical settings,significantly impacting system operational efficiency and security.Most existing data recovery methods require complete datasets for training,leading to substantial data and computational demands and limited generalization.To address these limitations,this study proposes a missing data imputation model based on an improved Generative Adversarial Network(BAC-GAN).Within the BAC-GAN framework,the generator utilizes Bidirectional Long Short-Term Memory(BiLSTM)networks and Multi-Head Attention mechanisms to capture temporal dependencies and complex relationships within power system data.The discriminator employs a Convolutional Neural Network(CNN)architecture to integrate local features with global structures,effectivelymitigating the generation of implausible imputations.Experimental results on two public datasets demonstrate that the BAC-GAN model achieves superior data recovery accuracy compared to five state-of-the-art and classical benchmarkmethods,with an average improvement of 17.7%in reconstruction accuracy.The proposedmethod significantly enhances the accuracy of grid fault diagnosis and provides reliable data support for the stable operation of smart grids,showing great potential for practical applications in power systems.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2025S1A5A2A01005171)by the BK21 programat Chungbuk National University(2025).
文摘With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.
基金supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2024-00334190)。
文摘The fabrication of a dynamic threshold-2T0C(DT-2T0C) DRAM cell incorporating a ZnO charge-trap layer in the write transistor has been successfully achieved, addressing the negative hold voltage(V_(HOLD)) issue of conventional 2T0C DRAM cells using oxide channel layers. The proposed device facilitates dynamic modulation of turn-on voltage(V_(ON)) through an additional SET operation, allowing V_(ON) to shift above 0 V. The retention time in SET operation was extended to 10^(4) s by optimizing the tunneling layer deposition conditions. The device characterization revealed a significant correlation between V_(ON) and both the WRITE speed and the retention properties of the DT-2T0C, verifying the trade-off between WRITE time and retention time. A long retention time over 1000 s was achieved, even under VHOLD of 0 V.
基金funded by scientific research projects under Grant JY2024B011.
文摘Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies,effectively reducing both the frequency of failures and associated costs.As a core component of PHM,RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making.However,deep learning models often falter when processing raw,noisy temporal signals,fail to quantify prediction uncertainty,and face challenges in effectively capturing the nonlinear dynamics of equipment degradation.To address these issues,this study proposes a novel deep learning framework.First,a newbidirectional long short-termmemory network integrated with an attention mechanism is designed to enhance temporal feature extraction with improved noise robustness.Second,a probabilistic prediction framework based on kernel density estimation is constructed,incorporating residual connections and stochastic regularization to achieve precise RUL estimation.Finally,extensive experiments on the C-MAPSS dataset demonstrate that our method achieves competitive performance in terms of RMSE and Score metrics compared to state-of-the-artmodels.More importantly,the probabilistic output provides a quantifiablemeasure of prediction confidence,which is crucial for risk-informed maintenance planning,enabling managers to optimize maintenance strategies based on a quantifiable understanding of failure risk.
基金funded by Wellcome 4ward North(Ref:216340/Z/19/Z)ARUK Yorkshire Network Centre Small Grant Scheme,ARUK Preparatory Clinical Fellowship scheme(Ref:ARUK-PCRF2016A-1)+3 种基金Academy of Medical Sciences Starter Grants for Clinical Lecturers Scheme(Ref:SGL028\1097),Parkinson’s UK(Ref:F1301)Michael J Fox Foundation(Ref:005021),Australian Research Council(CE200100012)European Union Seventh Framework Programme(Ref:FP7/2007-2013)under grant agreement no.601055the NIHR Sheffield Biomedical Research Centre award(NIHR 203321)(to SMB).
文摘Alzheimer’s disease(AD)is the most common form of dementia characterized pathologically by the deposition of amyloid plaques and hyperphosphorylated tau containing neurofibrillary tangles.The disease presents clinically with progressive memory loss and disruption of cognitive function.Currently,there is no cure for AD;recent advances in the therapeutics aimed at clearing the amyloid protein from the brain have led to potential disease stabilization,however,this does not prevent eventual disease progression(Cummings et al.,2024).
基金Supported by the National Key Research and Development Program of China(No.2023YFC3008202)the National Natural Science Foundation of China(No.42406019)the Scientific Research Fund of Zhejiang Provincial Education Department(No.Y202353066)。
文摘The prediction of sea surface partial pressure of carbon dioxide(pCO_(2))in the South China Sea is crucial for understanding the region’s contribution to the global carbon budget and its interactions with climate change.We applied the Spatiotemporal Convolutional Long Short-Term Memory(STConvLSTM)model,integrating key environmental factors including sea surface temperature(SST),sea surface salinity(SSS),and chlorophyll a(Chl a),to predict and analyze sea surface pCO_(2)in the South China Sea.The model demonstrated high accuracy in short-term predictions(1 month),with a mean absolute error(MAE)of 0.394,a root mean square error(RMSE)of 0.659,and a coefficient of determination(R^(2))of 0.998.For long-term predictions(12 months),the model maintained its predictive capability,with an MAE of 0.667,RMSE of 1.255,and R^(2)of 0.994.Feature importance analysis revealed that sea surface pCO_(2)and SST were the main drivers of the model’s predictions,whereas Chl a and SSS had relatively minor impacts.The model’s generalization ability was further validated in the northwest Pacific Ocean and tropical Pacific Ocean,where it successfully captured the spatiotemporal variation in pCO_(2)with small prediction errors.The ST-ConvLSTM model provides an efficient and accurate tool for forecasting and analyzing sea surface pCO_(2)in the South China Sea,offering new insights into global carbon cycling and climate change.This study demonstrates the potential of deep learning in marine science and provides a significant technical support for global changes and marine ecosystem research.
