高压电缆长期过热可能导致绝缘热击穿,进而影响电网的稳定性。然而,当前研究主要集中在传统预测模型上,忽略了温度数据的复杂性和动态特征。为了解决此问题,提出一种基于多尺度Patch与卷积交互的电缆温度预测模型(MSP-CI)。首先,采用通...高压电缆长期过热可能导致绝缘热击穿,进而影响电网的稳定性。然而,当前研究主要集中在传统预测模型上,忽略了温度数据的复杂性和动态特征。为了解决此问题,提出一种基于多尺度Patch与卷积交互的电缆温度预测模型(MSP-CI)。首先,采用通道重组采样方法降低输入维度,并构建多尺度Patch分支结构,以实现复杂时间序列的解耦;其次,结合序列分解与卷积交互策略,分别提取粗粒度Patch的宏观信息与细粒度Patch的微观信息;最后,构建注意力融合模块,以动态平衡宏观与微观信息的权重,并得到最终的预测结果。在真实高压电缆温度数据集上的实验结果表明,MSP-CI相较于TimeMixer、PatchTST(Patch Time Series Transformer)和MSGNet(Multi-Scale interseries Graph Network)等基线模型,在均方误差(MSE)上下降了7.02%~34.87%,在平均绝对误差(MAE)上下降了5.15%~32.04%。可见,MSP-CI能有效提升电缆温度预测的准确率,为电力调度运行提供依据。展开更多
The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced met...The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.展开更多
光伏功率日前预测的准确性对电网的能源管理至关重要。针对光伏功率随机性及波动性大、预测精度不高的问题,文章提出一种基于PatchTST-STL模型的光伏发电功率日前预测方法。该方法通过引入时序块和季节趋势分解(seasonal and trend deco...光伏功率日前预测的准确性对电网的能源管理至关重要。针对光伏功率随机性及波动性大、预测精度不高的问题,文章提出一种基于PatchTST-STL模型的光伏发电功率日前预测方法。该方法通过引入时序块和季节趋势分解(seasonal and trend decomposition using loess,STL)改进Transformer的输入和架构,将每个时间步作为1个令牌(Token)改进为每个时序块(Patch)作为1个令牌,使得局部依赖关系被保留在1个令牌内,以提升局部模式捕捉能力,同时利用Transformer多头自注意力机制抽取序列长期依赖关系。考虑到光伏序列的模式复杂,采用STL对多变量光伏序列进行处理,分离出趋势、周期和残差部分,作为独立通道输入。使用灰狼优化算法(grey wolf optimizer,GWO)对模型超参数中时序块大小进行优化,以实现算法快速收敛。在澳大利亚沙漠知识太阳能中心数据集的实验结果表明,所提算法与Informer相比,平均绝对误差(mean absolute error,MAE)和均方根误差(root mean square error,RMSE)平均分别降低了36.7%和11.7%;与Transformer相比,MAE和RMSE平均分别降低了57.6%和30.9%。展开更多
Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,con...Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,conventional MNs employ long needles(≥600μm),leading to pain and skin irritation.Moreover,it is critical to stably apply MNs against complex skin surfaces for uniform nanoscale drug delivery.Herein,a dually amplified transdermal patch(MN@EV/SC)is developed as the stem cell-derived EV delivery platform by hierarchically integrating an octopusinspired suction cup(SC)with short MNs(≤300μm).While leveraging the suction effect to induce nanoscale deformation of the stratum corneum,MN@EV/SC minimizes skin damage and enhances the adhesion of MNs,allowing EV to penetrate deeper into the dermis.When MNs of various lengths are applied to mouse skin,the short MNs can elicit comparable corticosterone release to chemical adhesives,whereas long MNs induce a prompt stress response.MN@EV/SC can achieve a remarkable penetration depth(290μm)for EV,compared to that of MN alone(111μm).Consequently,MN@EV/SC facilitates the revitalization of fibroblasts and enhances collagen synthesis in middle-aged mice.Overall,MN@EV/SC exhibits the potential for skin regeneration by modulating the dermal microenvironment and ensuring patient comfort.展开更多
It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and externa...It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.展开更多
Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused signif...Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.展开更多
As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
Using abundant saline water for electrolysis,rather than limited freshwater,presents a promising technique for generating clean hydrogen energy.However,high concentration of corrosive chloride ions in saline water pos...Using abundant saline water for electrolysis,rather than limited freshwater,presents a promising technique for generating clean hydrogen energy.However,high concentration of corrosive chloride ions in saline water poses a great challenge in the stability of anode.In this study,we present a straightforward strategy to protect the anode from corrosion by patching the catalyst layer through a treatment of the anode with a sodium sulfide(Na2S) solution followed by electrochemical activation.The rapid sulfurization of the Ni electrode in Na2S results in the formation of a Na2S layer,which can subsequently be converted to NiOOH upon electrochemical activation,thereby shielding the inner Ni electrode from corrosion.The as-prepared electrode (P-NiFe-LDH/Ni) based on the strategy demonstrated stability over 3,500 h at an industrial current density of 0.5 A cm^(-2)in a 0.5 M NaCl and 1 M KOH solution.This study presents an effective strategy to significantly enhance the stability of anodes for saline water electrolysis by effectively patching the cracks in the catalyst layer.展开更多
Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each ch...Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each child)who were admitted to the hospital between January 2024 and June 2024 were selected and randomly divided into two groups using a random number table.The combined group received childlike nursing combined with Chinese herbal patching,while the control group received routine nursing.Symptom recovery time,treatment compliance,inflammatory factor levels,quality of life of the children,and family satisfaction were compared between the two groups.Results:The symptom recovery time in the combined group was shorter than that in the control group,treatment compliance was higher,inflammatory factor levels after intervention were lower,quality of life scores of the children were lower,and family satisfaction was higher(P<0.05).Conclusion:The implementation of childlike nursing combined with Chinese herbal patching for children with bronchopneumonia can shorten their symptom recovery time,significantly improve treatment compliance and quality of life,reduce inflammatory reactions,and achieve high satisfaction among family members.展开更多
Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribu...Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.展开更多
The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is drive...The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is driven by increases in patches number(NP),patch size(PS),or both.We based our predictions on two hypotheses:GE intensifies plant competition,and facilitation prevails near patches while competition prevails in interpatch spaces.We predicted that the NP would remain stable or decrease and PS would increase under GE treatment.To evaluate these predictions,we conducted a study in six lightly degraded alpine grasslands under free grazing(FG)conditions in Bangor County,Xizang Autonomous Region,China,with corresponding GE treatments using transects in 2017 and 2018.Results revealed that four sites in 2017 and five sites in 2018 had reduced NP and increased PS,with probabilities of 0.033(2017)and 0.004(2018),respectively,and a joint probability of 0.0001 under the null hypothesis that GE does not affect NP or PS.The NP reduction was solely due to the decrease in small patch sizes.An increase in PS was common across species,and a predominant tendency for NP reduction was observed among species across the sites.The overall changes in NP and PS were primarily driven by the three most abundant species(contributing more than 60%in both years),rather than by shifts in floristic composition.Our findings highlight that vegetation recovery in Bangor alpine steppes following GE relies solely on the expansion of existing patches rather than the recruitment of new ones in interpatch gaps.We recommend prioritizing growth-promoting measures,such as nutrient or water management,over seed addition when assisting with GE for restoring lightly degraded grasslands.展开更多
In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electros...In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electrospun Poly(lactic-co-glycolic acid)/Gelatin(PLGA/Gel)membrane incorporated with Vascular Endothelial Growth Fac-tor(VEGF)and hawthorn extract.Functionally,the DP supplies native Extracellular Matrix(ECM)components and mechanical support,while PANINPs provide conductivity.The electrospun PLGA/Gel layer mimics fibrous ECM.It incorporates bioactives,with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhanc-ing anticoagulant activity,as well as increasing surface hydrophilicity.The tissue adhesive ensures the interfacial integrity between the two layers.Decellularization efficiency was confirmed histologically using 4',6-diamidino-2-phenylindole(DAPI)and Hematoxylin-Eosin(H&E)staining.The DP exhibited a DNA content of 115.9±47.8 ng/mg DNA,compared to 982.88±395.42 ng/mg in Native Pericardium(NP).The PANINPs had an average par-ticle size of 104.94±13.7 nm.The conductivity of PANINPs-coated decellularized pericardium was measured to be 9.093±8.6×10-4 S/cm using the four-point probe method.PLGA/Gel membranes containing hawthorn extract(1%,5%,10%,and 15%w/v)and VEGF(0.1μg/mL,0.5μg/mL,and 1μg/mL)were fabricated by electrospinning,result-ing in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20μm.The anticoagulant efficiency of the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay(aPTT).Mechanical testing revealed a tensile strength of 22.70±6.33 MPa,an elongation of 53.58±10.63%,and Young's modulus of 0.67±0.10 MPa.The scaffold also exhibited over 91%cell viability and excellent cardiomyo-cyte adhesion.The hemolysis ratio was determined to be 0.421±0.191%,which confirms its blood compatibility.Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.展开更多
To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily tota...To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.展开更多
In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to ...In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.展开更多
By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution a...By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.展开更多
Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algor...Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.展开更多
Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fa...Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.展开更多
文摘高压电缆长期过热可能导致绝缘热击穿,进而影响电网的稳定性。然而,当前研究主要集中在传统预测模型上,忽略了温度数据的复杂性和动态特征。为了解决此问题,提出一种基于多尺度Patch与卷积交互的电缆温度预测模型(MSP-CI)。首先,采用通道重组采样方法降低输入维度,并构建多尺度Patch分支结构,以实现复杂时间序列的解耦;其次,结合序列分解与卷积交互策略,分别提取粗粒度Patch的宏观信息与细粒度Patch的微观信息;最后,构建注意力融合模块,以动态平衡宏观与微观信息的权重,并得到最终的预测结果。在真实高压电缆温度数据集上的实验结果表明,MSP-CI相较于TimeMixer、PatchTST(Patch Time Series Transformer)和MSGNet(Multi-Scale interseries Graph Network)等基线模型,在均方误差(MSE)上下降了7.02%~34.87%,在平均绝对误差(MAE)上下降了5.15%~32.04%。可见,MSP-CI能有效提升电缆温度预测的准确率,为电力调度运行提供依据。
文摘The rapid integration of Internet of Things(IoT)technologies is reshaping the global energy landscape by deploying smart meters that enable high-resolution consumption monitoring,two-way communication,and advanced metering infrastructure services.However,this digital transformation also exposes power system to evolving threats,ranging from cyber intrusions and electricity theft to device malfunctions,and the unpredictable nature of these anomalies,coupled with the scarcity of labeled fault data,makes realtime detection exceptionally challenging.To address these difficulties,a real-time decision support framework is presented for smart meter anomality detection that leverages rolling time windows and two self-supervised contrastive learning modules.The first module synthesizes diverse negative samples to overcome the lack of labeled anomalies,while the second captures intrinsic temporal patterns for enhanced contextual discrimination.The end-to-end framework continuously updates its model with rolling updated meter data to deliver timely identification of emerging abnormal behaviors in evolving grids.Extensive evaluations on eight publicly available smart meter datasets over seven diverse abnormal patterns testing demonstrate the effectiveness of the proposed full framework,achieving average recall and F1 score of more than 0.85.
文摘光伏功率日前预测的准确性对电网的能源管理至关重要。针对光伏功率随机性及波动性大、预测精度不高的问题,文章提出一种基于PatchTST-STL模型的光伏发电功率日前预测方法。该方法通过引入时序块和季节趋势分解(seasonal and trend decomposition using loess,STL)改进Transformer的输入和架构,将每个时间步作为1个令牌(Token)改进为每个时序块(Patch)作为1个令牌,使得局部依赖关系被保留在1个令牌内,以提升局部模式捕捉能力,同时利用Transformer多头自注意力机制抽取序列长期依赖关系。考虑到光伏序列的模式复杂,采用STL对多变量光伏序列进行处理,分离出趋势、周期和残差部分,作为独立通道输入。使用灰狼优化算法(grey wolf optimizer,GWO)对模型超参数中时序块大小进行优化,以实现算法快速收敛。在澳大利亚沙漠知识太阳能中心数据集的实验结果表明,所提算法与Informer相比,平均绝对误差(mean absolute error,MAE)和均方根误差(root mean square error,RMSE)平均分别降低了36.7%和11.7%;与Transformer相比,MAE和RMSE平均分别降低了57.6%和30.9%。
基金supported by National Research Foundation of Korea(NRF)grants funded by the Korean government(MSIT)(No.RS-2023-00256265,RS-2024-00352352,RS-2024-00405818)the Korean Fund for Regenerative Medicine(KFRM)grant funded by the Korea government(the Ministry of Science and ICT,the Ministry of Health&Welfare).(No.25A0102L1)support from the Market-led K-sensor technology program(RS-2022-00154781,Development of large-area wafer-level flexible/stretchable hybrid sensor platform technology for form factor-free highly integrated convergence sensor),funded By the Ministry of Trade,Industry&Energy(MOTIE,Korea).
文摘Microneedles(MNs)have been extensively investigated for transdermal delivery of large-sized drugs,including proteins,nucleic acids,and even extracellular vesicles(EVs).However,for their sufficient skin penetration,conventional MNs employ long needles(≥600μm),leading to pain and skin irritation.Moreover,it is critical to stably apply MNs against complex skin surfaces for uniform nanoscale drug delivery.Herein,a dually amplified transdermal patch(MN@EV/SC)is developed as the stem cell-derived EV delivery platform by hierarchically integrating an octopusinspired suction cup(SC)with short MNs(≤300μm).While leveraging the suction effect to induce nanoscale deformation of the stratum corneum,MN@EV/SC minimizes skin damage and enhances the adhesion of MNs,allowing EV to penetrate deeper into the dermis.When MNs of various lengths are applied to mouse skin,the short MNs can elicit comparable corticosterone release to chemical adhesives,whereas long MNs induce a prompt stress response.MN@EV/SC can achieve a remarkable penetration depth(290μm)for EV,compared to that of MN alone(111μm).Consequently,MN@EV/SC facilitates the revitalization of fibroblasts and enhances collagen synthesis in middle-aged mice.Overall,MN@EV/SC exhibits the potential for skin regeneration by modulating the dermal microenvironment and ensuring patient comfort.
基金National Key Research and Development Program of China,Grant/Award Number:2018YFB2101003National Natural Science Foundation of China,Grant/Award Numbers:51991395,U1806226,51778033,51822802,71901011,U1811463,51991391Science and Technology Major Project of Beijing,Grant/Award Number:Z191100002519012。
文摘It is crucial to predict future mechanical behaviors for the prevention of structural disasters.Especially for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex conditions.Given that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring data.Then,the proposed model was formalized on multiple time series of strain monitoring data.Also,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction model.As the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of structures.As a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.
基金The National Natural Science Foundation of China under contract No.42406188the Natural Science Foundation of Liaoning Province under contract No.2024-BS-022+1 种基金the Dalian High-Level Talent Innovation Program under contract No.2022RG02the Fundamental Research Funds for the Central Universities under contract No.3132025107.
文摘Red tide is an ecological disaster caused by the excessive proliferation of photosynthetic algae in the ocean.The frequent occurrences of red tide have brought serious harms to the marine aquaculture and caused significant economic losses to the marine industry.Red tide prediction can alleviate and even stop the long-term damages to marine ecosystems,which helps maintain the ecological balance of the ocean environment and contributes to the Sustainable Development Goal of“life below water”formulated by the United Nations.Aiming at red tide prediction using remote sensing technology,this study proposed a novel approach of red tide prediction using time-series hyperspectral observations,and examined the proposed method in the Xinghai Bay,China.Three spectral indices,namely the twoband ratio(TBR),the three-band spectral index(TBSI),and the fluorescence baseline height(FLH),were used to reduce the dimensionality of hyperspectral data and extract spectral features.Two machine learning models including the random forest(RF)and the support vector machine(SVM)were employed to predict whether red tide would occur on a target day based on the time-series spectral indices obtained in the previous days.By comparing and analyzing the prediction results of multiple machine learning models trained with different spectral indices and temporal lengths,it is found that both the RF and the SVM models can predict the red tide outbreaks at the accuracies over 0.9 using adequate temporal lengths of input data.When the temporal length of input data is limited,however,it is suggested to use the RF model,which accurately predicts red tide outbreaks using the temporal input of the 2-d TBSI.The proposed method is expected to provide oceanic and maritime agencies with early warnings on red tide outbreaks and ensure the safety of the coastal environment in large spatial scales using optical remote sensing technology.
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
基金financially supported by the National Key Research and Development Program(No.2023YFB4006100)the National Natural Science Foundation of China(No.52271232)+3 种基金Ningbo Youth Science and Technology Leading Talents Project(No.2023QL026)the Youth Innovation Promotion Association,CAS(No.2020300)the Natural Science Foundation of Zhejiang Province(Nos.LY21E020008 and LD21E020001)the“From 0 to 1”Innovative Program of CAS(No.ZDBS-LY-JSC021)
文摘Using abundant saline water for electrolysis,rather than limited freshwater,presents a promising technique for generating clean hydrogen energy.However,high concentration of corrosive chloride ions in saline water poses a great challenge in the stability of anode.In this study,we present a straightforward strategy to protect the anode from corrosion by patching the catalyst layer through a treatment of the anode with a sodium sulfide(Na2S) solution followed by electrochemical activation.The rapid sulfurization of the Ni electrode in Na2S results in the formation of a Na2S layer,which can subsequently be converted to NiOOH upon electrochemical activation,thereby shielding the inner Ni electrode from corrosion.The as-prepared electrode (P-NiFe-LDH/Ni) based on the strategy demonstrated stability over 3,500 h at an industrial current density of 0.5 A cm^(-2)in a 0.5 M NaCl and 1 M KOH solution.This study presents an effective strategy to significantly enhance the stability of anodes for saline water electrolysis by effectively patching the cracks in the catalyst layer.
文摘Objective:To evaluate the intervention effect of childlike nursing combined with Chinese herbal patching on pediatric bronchopneumonia.Methods:1036 children with bronchopneumonia(one family member included for each child)who were admitted to the hospital between January 2024 and June 2024 were selected and randomly divided into two groups using a random number table.The combined group received childlike nursing combined with Chinese herbal patching,while the control group received routine nursing.Symptom recovery time,treatment compliance,inflammatory factor levels,quality of life of the children,and family satisfaction were compared between the two groups.Results:The symptom recovery time in the combined group was shorter than that in the control group,treatment compliance was higher,inflammatory factor levels after intervention were lower,quality of life scores of the children were lower,and family satisfaction was higher(P<0.05).Conclusion:The implementation of childlike nursing combined with Chinese herbal patching for children with bronchopneumonia can shorten their symptom recovery time,significantly improve treatment compliance and quality of life,reduce inflammatory reactions,and achieve high satisfaction among family members.
基金Under the auspices of National Natural Science Foundation of China(No.42171407,42077242)Key Program of National Natural Science Foundation of China(No.42330607)。
文摘Recently,the outbreak and spread of larch caterpillar(Dendrolimus superans)pests have emerged as significant contributors to forest degradation in the Changbai Mountains,China.Understanding the spatiotemporal distribution patterns of these pests is crucial for effective management and protection of forest ecosystems.This study proposes a pest monitoring approach based on Sentinel imagery.Through time-series analysis,we extracted pest-sensitive features and developed a random forest classifier that integrated Sentinel-1,Sentinel-2,and field sampling data from 2019–2023 to monitor larch caterpillar pests in the Changbai Mountains National Nature Reserve(CMNNR),Northeast China.Our findings indicated that bands green(B3),near-infrared(B8),short wave infrared(B11 and B12)from Sentinel-2 remote sensing images exhibited notable discriminative capabilities for identifying larch caterpillar pests.Specifically,the Normalized Difference Vegetation Index(NDVI)at the end of the growing season emerged as the most valuable feature for pest extraction.Incorporating Synthetic Aperture Radar(SAR)features along with optical data marginally enhances model performance.Furthermore,our approach unveiled the outbreak of larch caterpillar pests,achieving classification map with overall accuracy exceeding 85%and Kappa coefficient surpassing 0.8 for five study years.The pest outbreak began in 2019 and progressively intensified over time.In September 2019,the affected area spanned 114.23 km^(2).The infested area exhibited a declining trend from 2020 to 2023.This study introduces a novel method for the high-precision identification of larch caterpillar pests,offering technical advancements and theoretical underpinnings to support forest management strategies.
基金financially supported by the Science and Technology Bureau of Ali Prefecture,project named“Assessing the Carbon Sequestration and Carbon Sink Enhancement Potential of Natural Ecosystems in Ali Region(QYXTZX-AL2022-05)”。
文摘The alpine grassland vegetation on the Qinghai-Tibet Plateau is composed of plant patches in varied sizes.It remains uncertain whether vegetation recovery following grazing exclusion(GE)in degraded grasslands is driven by increases in patches number(NP),patch size(PS),or both.We based our predictions on two hypotheses:GE intensifies plant competition,and facilitation prevails near patches while competition prevails in interpatch spaces.We predicted that the NP would remain stable or decrease and PS would increase under GE treatment.To evaluate these predictions,we conducted a study in six lightly degraded alpine grasslands under free grazing(FG)conditions in Bangor County,Xizang Autonomous Region,China,with corresponding GE treatments using transects in 2017 and 2018.Results revealed that four sites in 2017 and five sites in 2018 had reduced NP and increased PS,with probabilities of 0.033(2017)and 0.004(2018),respectively,and a joint probability of 0.0001 under the null hypothesis that GE does not affect NP or PS.The NP reduction was solely due to the decrease in small patch sizes.An increase in PS was common across species,and a predominant tendency for NP reduction was observed among species across the sites.The overall changes in NP and PS were primarily driven by the three most abundant species(contributing more than 60%in both years),rather than by shifts in floristic composition.Our findings highlight that vegetation recovery in Bangor alpine steppes following GE relies solely on the expansion of existing patches rather than the recruitment of new ones in interpatch gaps.We recommend prioritizing growth-promoting measures,such as nutrient or water management,over seed addition when assisting with GE for restoring lightly degraded grasslands.
文摘In this study,we developed a novel bilayered scaffold consisting of a bottom layer composed of the Decellularized Bovine Pericardium(DP)coated with Polyaniline Nanoparticles(PANINPs)and a top layer made of an electrospun Poly(lactic-co-glycolic acid)/Gelatin(PLGA/Gel)membrane incorporated with Vascular Endothelial Growth Fac-tor(VEGF)and hawthorn extract.Functionally,the DP supplies native Extracellular Matrix(ECM)components and mechanical support,while PANINPs provide conductivity.The electrospun PLGA/Gel layer mimics fibrous ECM.It incorporates bioactives,with VEGF promoting pro-angiogenic stimulation and hawthorn extract enhanc-ing anticoagulant activity,as well as increasing surface hydrophilicity.The tissue adhesive ensures the interfacial integrity between the two layers.Decellularization efficiency was confirmed histologically using 4',6-diamidino-2-phenylindole(DAPI)and Hematoxylin-Eosin(H&E)staining.The DP exhibited a DNA content of 115.9±47.8 ng/mg DNA,compared to 982.88±395.42 ng/mg in Native Pericardium(NP).The PANINPs had an average par-ticle size of 104.94±13.7 nm.The conductivity of PANINPs-coated decellularized pericardium was measured to be 9.093±8.6×10-4 S/cm using the four-point probe method.PLGA/Gel membranes containing hawthorn extract(1%,5%,10%,and 15%w/v)and VEGF(0.1μg/mL,0.5μg/mL,and 1μg/mL)were fabricated by electrospinning,result-ing in fiber diameters between 850 and 1200 nm and pore sizes between 14 and 20μm.The anticoagulant efficiency of the membranes containing hawthorn extract reached 430 s in the Activated Partial Thromboplastin Time Assay(aPTT).Mechanical testing revealed a tensile strength of 22.70±6.33 MPa,an elongation of 53.58±10.63%,and Young's modulus of 0.67±0.10 MPa.The scaffold also exhibited over 91%cell viability and excellent cardiomyo-cyte adhesion.The hemolysis ratio was determined to be 0.421±0.191%,which confirms its blood compatibility.Our results indicate that the proposed bilayered scaffold can be a promising candidate for cardiac patch applications.
文摘To investigate the association between temperature and daily mortality in Shanghai from June 1, 2000 to December 31, 2001. Methods Time-series approach was used to estimate the effect of temperature on daily total and cause-specific mortality. We fitted generalized additive Poisson regression using non-parametric smooth functions to control for long-term time trend, season and other variables. We also controlled for day of the week. Results A gently sloping V-like relationship between total mortality and temperature was found, with an optimum temperature (e.g. temperature with lowest mortality risk) value of 26.7癈 in Shanghai. For temperatures above the optimum value, total mortality increased by 0.73% for each degree Celsius increase; while for temperature below the optimum value, total mortality decreased by 1.21% for each degree Celsius increase. Conclusions Our findings indicate that temperature has an effect on daily mortality in Shanghai, and the time-series approach is a useful tool for studying the temperature-mortality association.
基金This work was funded by the National Key R&D Program of China(2019YFC1509205)the National Natural Science Foundation of China(Nos.42174023 and 41804015)+1 种基金the Postgraduate Scientific Research Innovation Project of Hunan Province(150110074)the Postgraduate Scientific Research Innovation Project of Central South University(212191010).
文摘In the past 30 years,the small baseline subset(SBAS)InSAR time-series technique has emerged as an essential tool for measuring slow surface displacement and estimating geophysical parameters.Because of its ability to monitor large-scale deformation with millimeter accuracy,the SBAS method has been widely used in various geodetic fields,such as ground subsidence,landslides,and seismic activity.The obtained long-term time-series cumulative deformation is vital for studying the deformation mecha-nism.This article reviews the algorithms,applications,and challenges of the SBAS method.First,we recall the fundamental principle and analyze the shortcomings of the traditional SBAS algorithm,which provides a basic framework for the following improved time series methods.Second,we classify the current improved SBAS techniques from different perspectives:solving the ill-posed equation,increasing the density of high-coherence points,improving the accuracy of monitoring deformation and measuring the multi-dimensional deformation.Third,we summarize the application of the SBAS method in monitoring ground subsidence,permafrost degradation,glacier movement,volcanic activity,landslides,and seismic activity.Finally,we discuss the difficulties faced by the SBAS method and explore its future development direction.
基金supported by the open research fund of the Key Laboratory of Agri-informatics,Ministry of Agriculture and the fund of Outstanding Agricultural Researcher,Ministry of Agriculture,China
文摘By employing the unique phenological feature of winter wheat extracted from peak before winter （PBW） and the advantages of moderate resolution imaging spectroradiometer （MODIS） data with high temporal resolution and intermediate spatial resolution, a remote sensing-based model for mapping winter wheat on the North China Plain was built through integration with Landsat images and land-use data. First, a phenological window, PBW was drawn from time-series MODIS data. Next, feature extraction was performed for the PBW to reduce feature dimension and enhance its information. Finally, a regression model was built to model the relationship of the phenological feature and the sample data. The amount of information of the PBW was evaluated and compared with that of the main peak （MP）. The relative precision of the mapping reached up to 92% in comparison to the Landsat sample data, and ranged between 87 and 96% in comparison to the statistical data. These results were sufficient to satisfy the accuracy requirements for winter wheat mapping at a large scale. Moreover, the proposed method has the ability to obtain the distribution information for winter wheat in an earlier period than previous studies. This study could throw light on the monitoring of winter wheat in China by using unique phenological feature of winter wheat.
文摘Clustering is used to gain an intuition of the struc tures in the data.Most of the current clustering algorithms pro duce a clustering structure even on data that do not possess such structure.In these cases,the algorithms force a structure in the data instead of discovering one.To avoid false structures in the relations of data,a novel clusterability assessment method called density-based clusterability measure is proposed in this paper.I measures the prominence of clustering structure in the data to evaluate whether a cluster analysis could produce a meaningfu insight to the relationships in the data.This is especially useful in time-series data since visualizing the structure in time-series data is hard.The performance of the clusterability measure is evalu ated against several synthetic data sets and time-series data sets which illustrate that the density-based clusterability measure can successfully indicate clustering structure of time-series data.
基金supported by the National Natural Science Foundation of China(62073140,62073141)the Shanghai Rising-Star Program(21QA1401800).
文摘Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.
