AIM:To report and analyze cases of sterile intraocular inflammation(IOI)following intravitreal faricimab injections in patients treated for neovascular age-related macular degeneration(nAMD)and diabetic macular edema(...AIM:To report and analyze cases of sterile intraocular inflammation(IOI)following intravitreal faricimab injections in patients treated for neovascular age-related macular degeneration(nAMD)and diabetic macular edema(DME).METHODS:This double-center case series included nine eyes of six patients who developed uveitis after faricimab therapy.Comprehensive clinical evaluation was performed,including slit-lamp examination,intraocular pressure(IOP)measurement,fluorescein and indocyanine green angiography(ICGA),and laboratory tests.Inflammatory responses were treated with topical or systemic corticosteroids,and patients were monitored for visual acuity and inflammatory activity.RESULTS:The incidence of IOI was 0.8%per patient(Innsbruck)and 0.23%(Czechia),with inflammation typically occurring between the third and sixth injection(mean interval:10d post-injection).Inflammator y presentations ranged from anterior uveitis to posterior segment involvement.One notable case demonstrated novel choroidal hypofluorescent lesions on angiography,suggesting deeper ocular involvement.The mean patient age was 76y;five of six affected patients were female.All cases responded to local and systemic corticosteroids,with full recovery of initial visual acuity.CONCLUSION:Sterile IOI after faricimab appears to be a rare but relevant adverse event.Although the incidence falls within expected ranges for anti-vascular endothelial growth factor(anti-VEGF)agents,the observed choroidal involvement represents a potentially new safety signal.Prompt diagnosis and corticosteroid therapy are effective in all cases.Our findings support the need for vigilant post-marketing surveillance and further studies to better understand the underlying mechanisms and risk factors of faricimab-associated inflammation.展开更多
Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charg...Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.展开更多
To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations ...Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.展开更多
Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies ha...Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.展开更多
OsMAPK6 plays a critical role in regulating rice growth,development,and stress responses.However,the embryonic lethality associated with loss-of-function mutations prevents the generation of homozygous mutant seeds,si...OsMAPK6 plays a critical role in regulating rice growth,development,and stress responses.However,the embryonic lethality associated with loss-of-function mutations prevents the generation of homozygous mutant seeds,significantly hindering functional studies of this gene.Although the weak mutant dsg1 has offered valuable insights into OsMAPK6 function,its extremely low seed-setting rate limits its use for detailed genetic analysis.Here,we employed prime editing to perform precise multi-site modifications at the C-terminus of OsMAPK6,generating a series of osmapk6 mutants with truncated proteins of varying lengths.Among these,the osmapk6(379)and osmapk6(383)mutants exhibited phenotypic defects similar to dsg1,while osmapk6(386)showed a significantly improved seed-setting rate despite persistent developmental defects.Through phenotypic characterization and protein functional analysis,we further clarified how different C-terminal deletion lengths affect plant growth,development,stress responses,and OsMAPK6 protein function.In summary,this study elucidates the importance of the OsMAPK6 C-terminus in rice biology and provides a fertile weak mutant with enhanced seed production,offering a valuable genetic resource for future research on OsMAPK6.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider...Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS.展开更多
Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extrac...Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.展开更多
Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Hom...Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing th...Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.展开更多
Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the origin...Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.展开更多
文摘AIM:To report and analyze cases of sterile intraocular inflammation(IOI)following intravitreal faricimab injections in patients treated for neovascular age-related macular degeneration(nAMD)and diabetic macular edema(DME).METHODS:This double-center case series included nine eyes of six patients who developed uveitis after faricimab therapy.Comprehensive clinical evaluation was performed,including slit-lamp examination,intraocular pressure(IOP)measurement,fluorescein and indocyanine green angiography(ICGA),and laboratory tests.Inflammatory responses were treated with topical or systemic corticosteroids,and patients were monitored for visual acuity and inflammatory activity.RESULTS:The incidence of IOI was 0.8%per patient(Innsbruck)and 0.23%(Czechia),with inflammation typically occurring between the third and sixth injection(mean interval:10d post-injection).Inflammator y presentations ranged from anterior uveitis to posterior segment involvement.One notable case demonstrated novel choroidal hypofluorescent lesions on angiography,suggesting deeper ocular involvement.The mean patient age was 76y;five of six affected patients were female.All cases responded to local and systemic corticosteroids,with full recovery of initial visual acuity.CONCLUSION:Sterile IOI after faricimab appears to be a rare but relevant adverse event.Although the incidence falls within expected ranges for anti-vascular endothelial growth factor(anti-VEGF)agents,the observed choroidal involvement represents a potentially new safety signal.Prompt diagnosis and corticosteroid therapy are effective in all cases.Our findings support the need for vigilant post-marketing surveillance and further studies to better understand the underlying mechanisms and risk factors of faricimab-associated inflammation.
基金supported in part by the National Natural Science Foundation of China(62173048,62373065,61873304,62106023)the Key Science and Technology Projects of Jilin Province,China(20230204081YY)the Research and Innovation Team of Anhui Province(2024AH010023)。
文摘Dear Editor,This letter presents a model predictive control(MPC)scheme for human-robot interaction(HRI)in a multi-joint exoskeleton robot(ER)driven by series elastic actuator(SEA).The proposed scheme in robot-in-charge(RIC)mode facilitates the ER driven by SEA to provide the required assistance and support for the subject.
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
文摘Multivariate time series forecasting plays a crucial role in decision-making for systems like energy grids and transportation networks,where temporal patterns emerge across diverse scales from short-term fluctuations to long-term trends.However,existing Transformer-based methods often process data at a single resolution or handle multiple scales independently,overlooking critical cross-scale interactions that influence prediction accuracy.To address this gap,we introduce the Hierarchical Attention Transformer(HAT),which enables direct information exchange between temporal hierarchies through a novel cross-scale attention mechanism.HAT extracts multi-scale features using hierarchical convolutional-recurrent blocks,fuses them via temperature-controlled mechanisms,and optimizes gradient flow with residual connections for stable training.Evaluations on eight benchmark datasets show HAT outperforming state-of-the-art baselines,with average reductions of 8.2%in MSE and 7.5%in MAE across horizons,while achieving a 6.1×training speedup over patch-based methods.These advancements highlight HAT’s potential for applications requiring multi-resolution temporal modeling.
基金supported by the Tianjin Manufacturing High Quality Development Special Foundation(No.20232185)the Roycom Foundation(No.70306901).
文摘Hard disk drives(HDDs)serve as the primary storage devices in modern data centers.Once a failure occurs,it often leads to severe data loss,significantly degrading the reliability of storage systems.Numerous studies have proposed machine learning-based HDD failure prediction models.However,the Self-Monitoring,Analysis,and Reporting Technology(SMART)attributes differ across HDD manufacturers.We define hard drives of the same brand and model as homogeneous HDD groups,and those from different brands or models as heterogeneous HDD groups.In practical engineering scenarios,a data center is often composed of a heterogeneous population of HDDs,spanning multiple vendors and models.Existing research predominantly focuses on homogeneous datasets,ignoring the model’s generalization capability across heterogeneous HDDs.As a result,HDD models with limited samples often suffer from poor training effectiveness and prediction performance.To address this issue,we investigate generalizable SMART predictors across heterogeneous HDD groups.By extracting time-series features within a fixed sliding time window,we propose a Heterogeneous Disk Failure Prediction Method based on Time Series Features(HDFPM)framework.This method is adaptable to HDD models with limited sample sizes,thereby enhancing its applicability and robustness across diverse drive populations.Experimental results show that the proposed model achieves an F1-score of 0.9518 when applied to two different Seagate HDD models,while maintaining the False Positive Rate(FPR)below 1%.After incorporating the Complexity-Ratio Dynamic Time Warping(CDTW)based feature enhancement method,the best prediction model achieves a True Positive Rate(TPR)of up to 0.93 between the two models.For next-day failure prediction across various Seagate models,the model achieves an F1-score of up to 0.8792.Moreover,the experimental results also show that within the same brand,the higher the proportion of shared SMART attributes across different models,the better the prediction performance.In addition,HDFPMdemonstrates the best stability andmost significant performance in heterogeneous environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.32441024,32572315,and U25A20674)the Youth Innovation Promotion Association of Chinese Academy of Sciences(Grant No.2021229)+1 种基金the Heilongjiang Key Research and Development Program,China(Grant No.2025ZX04B02)the Young Scientist Group Project of Northeast Institute of Geography and Agroecology,Chinese Academy of Sciences(Grant No.2023QNXZ02).
文摘OsMAPK6 plays a critical role in regulating rice growth,development,and stress responses.However,the embryonic lethality associated with loss-of-function mutations prevents the generation of homozygous mutant seeds,significantly hindering functional studies of this gene.Although the weak mutant dsg1 has offered valuable insights into OsMAPK6 function,its extremely low seed-setting rate limits its use for detailed genetic analysis.Here,we employed prime editing to perform precise multi-site modifications at the C-terminus of OsMAPK6,generating a series of osmapk6 mutants with truncated proteins of varying lengths.Among these,the osmapk6(379)and osmapk6(383)mutants exhibited phenotypic defects similar to dsg1,while osmapk6(386)showed a significantly improved seed-setting rate despite persistent developmental defects.Through phenotypic characterization and protein functional analysis,we further clarified how different C-terminal deletion lengths affect plant growth,development,stress responses,and OsMAPK6 protein function.In summary,this study elucidates the importance of the OsMAPK6 C-terminus in rice biology and provides a fertile weak mutant with enhanced seed production,offering a valuable genetic resource for future research on OsMAPK6.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the Young Scientists Fund of the National Natural Science Foundation of China(62303491)the Major Program of Xiangjiang Laboratory(22XJ01005)+1 种基金the Science and Technology Innovation Program of Hunan Province(2024RC1007)the Natural Science Foundation of Hunan Province(2025JJ10007)。
文摘Temporal alignment of multisensor time series(MTS)is a critical prerequisite for accurate modeling and optimal control in subsequent data-driven applications.Nevertheless,many approaches frequently neglect to consider the complex interdependencies between different sensors in MTS,and temporal alignment in many methods is typically treated as an isolated task disconnected from the downstream objectives,leading to unsatisfactory performances in follow-up applications.To address these challenges,this paper proposes a novel knowledge graph(KG)-guided iterative-updating graph neural network(GNN)for time-delay estimation(TDE)in MTS.Initially,a domain-specific KG is constructed from domain mechanism knowledge,providing a foundation for GNN's initialization.Next,capitalizing on the inherent structure of the graph topology,a GNN-based TDE method is developed.Then,a customized loss function is constructed,which synthesizes both the performances of downstream tasks and graph-based constraints.Moreover,an innovative algorithm for GNN structure learning and iterative-updating is proposed to renovate the graph structure further.Finally,experimental results across various regression and classification tasks on numerical simulation,public datasets,and the real blast furnace ironmaking dataset demonstrate that the proposed method can achieve accurate temporal alignment of MTS.
文摘Objective To identify patterns of hand, foot and mouth disease （HFMD） incidence in China during declining incidence periods of 2008, 2009, and 2010. Methods Reported HFMD cases over a period of 25 months were extracted from the National Disease Reporting System （NDRS） and analyzed. An interrupted time series （ITS） technique was used to detect changes in HFMD incidence rates in terms of level and slope between declining incidence periods of the three years. Results Over 3.58 million HFMD cases younger than 5 years were reported to the NDRS between May 1, 2008, and May 31, 2011. Males comprised 63.4% of the cases. ITS analyses demonstrated a significant increase in incidence rate level （P〈0.0001） when comparing the current period with the previous period. There were significant changes in declining slopes when comparing 2010 to 2009, and 2010 to 2008 （all P〈O.O05）, but not 2009 to 2008. Conclusion Incremental changes in incidence rate level during the declining incidence periods of 2009 and 2010 can potentially be attributed to a few factors. The more steeply declining slope in 2010 compared with previous years could be ascribed to the implementation of more effective interventions and preventive strategies in 2010. Further investigation is required to examine this possibility.
基金supported by the National Basic Research Program of China 2009CB421401 and 2006CB400503
文摘Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects in the results.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
基金funded by the Ministry-level Scientific and Technological Key Programs of Ministry of Natural Resources and Environment of Viet Nam "Application of thermal infrared remote sensing and GIS for mapping underground coal fires in Quang Ninh coal basin" (Grant No. TNMT.2017.08.06)
文摘Underground coal fires are one of the most common and serious geohazards in most coal producing countries in the world. Monitoring their spatio-temporal changes plays an important role in controlling and preventing the effects of coal fires, and their environmental impact. In this study, the spatio-temporal changes of underground coal fires in Khanh Hoa coal field(North-East of Viet Nam) were analyzed using Landsat time-series data during the 2008-2016 period. Based on land surface temperatures retrieved from Landsat thermal data, underground coal fires related to thermal anomalies were identified using the MEDIAN+1.5×IQR(IQR: Interquartile range) threshold technique. The locations of underground coal fires were validated using a coal fire map produced by the field survey data and cross-validated using the daytime ASTER thermal infrared imagery. Based on the fires extracted from seven Landsat thermal imageries, the spatiotemporal changes of underground coal fire areas were analyzed. The results showed that the thermalanomalous zones have been correlated with known coal fires. Cross-validation of coal fires using ASTER TIR data showed a high consistency of 79.3%. The largest coal fire area of 184.6 hectares was detected in 2010, followed by 2014(181.1 hectares) and 2016(178.5 hectares). The smaller coal fire areas were extracted with areas of 133.6 and 152.5 hectares in 2011 and 2009 respectively. Underground coal fires were mainly detected in the northern and southern part, and tend to spread to north-west of the coal field.
基金supported in part by the Interdisciplinary Project of Dalian University(DLUXK-2023-ZD-001).
文摘Multivariate time series forecasting iswidely used in traffic planning,weather forecasting,and energy consumption.Series decomposition algorithms can help models better understand the underlying patterns of the original series to improve the forecasting accuracy of multivariate time series.However,the decomposition kernel of previous decomposition-based models is fixed,and these models have not considered the differences in frequency fluctuations between components.These problems make it difficult to analyze the intricate temporal variations of real-world time series.In this paper,we propose a series decomposition-based Mamba model,DecMamba,to obtain the intricate temporal dependencies and the dependencies among different variables of multivariate time series.A variable-level adaptive kernel combination search module is designed to interact with information on different trends and periods between variables.Two backbone structures are proposed to emphasize the differences in frequency fluctuations of seasonal and trend components.Mamba with superior performance is used instead of a Transformer in backbone structures to capture the dependencies among different variables.A new embedding block is designed to capture the temporal features better,especially for the high-frequency seasonal component whose semantic information is difficult to acquire.A gating mechanism is introduced to the decoder in the seasonal backbone to improve the prediction accuracy.A comparison with ten state-of-the-art models on seven real-world datasets demonstrates that DecMamba can better model the temporal dependencies and the dependencies among different variables,guaranteeing better prediction performance for multivariate time series.
