This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposit...This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.展开更多
In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investm...In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.展开更多
In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal perio...In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.展开更多
Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecas...Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.展开更多
Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The sy...Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The symposium was organized by the Institute of Earthquake Forecasting,China Earthquake Administration(CEA),the State Key Laboratory of Earthquake Dynamics and Forecasting,and the China Seismic Experimental Site(CSES),in collaboration with the International Association of Seismology and Physics of the Earth’s Interior(IASPEI),the APEC Cooperation for Earthquake Science(ACES).展开更多
Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed...Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.展开更多
Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier resear...Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.展开更多
This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.展开更多
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest fre...While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.展开更多
Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning ...Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.展开更多
Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature...Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and,in conjunction with the ocean forecasting system LICOM Forecast System(LFS),constructed a hybrid Fusion model using Wasserstein-Distance optimization.The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS.Overall,the Fusion model takes advantage of LFS and ConvLSTM,providing superior forecasts for both the duration and intensity of MHWs in the southern SCS.LFS(ConvLSTM)overestimates(underestimates)the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS.The Fusion model's superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs.This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.展开更多
With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyz...With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.展开更多
Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a referen...Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.展开更多
This research evaluates the performance of an eddy-resolving forecast system(LFS)in simulating mesoscale eddies over the South China Sea(SCs)through a comparative analysis with satellite observations and the reanalysi...This research evaluates the performance of an eddy-resolving forecast system(LFS)in simulating mesoscale eddies over the South China Sea(SCs)through a comparative analysis with satellite observations and the reanalysis dataset from the Global Ocean Physics Reanalysis product(CMEMS).The findings indicate that the spatial characteristics of eddy kinetic energy,number,and amplitude of coherent mesoscale eddies simulated by LFS exhibit a reasonable agreement with satellite observations.The reproduced seasonal variations are also comparable to outputs from the CMEMS reanalysis dataset.Nevertheless,certain systematic biases have also been identified.In the SCS,LFS generates approximately 17%fewer eddies than observed.Such biases are also evident in the CMEMS reanalysis dataset.Similar to the statistics shown in the CMEMS reanalysis dataset,both cyclonic and anticyclonic eddies are significantly weaker in LFS compared to the observations.Additionally,the composite three-dimensional structures of mesoscale eddies simulated by LFS exhibit a remarkable similarity to those identified in the CMEMS reanalysis datasets.This work lays the foundation for further studies using LFS to investigate the predictability of mesoscale eddies and enhance the accuracy of simulations.展开更多
Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the c...Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longit...AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.展开更多
Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction...Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction in patients with subarachnoid hemorrhage.However,there is notable absence of biological markers to predict long-term prognosis in this patient population.Given the aging-like neurocognitive phenomena associated with subarachnoid hemorrhage,this study postulates that telomere length,a recognized biomarker for aging,could be used as a prognostic indicator for subarachnoid hemorrhage.A left internal carotid artery intravascular puncture mouse model was used to simulate subarachnoid hemorrhage.Comprehensive neurological test scores were obtained through neurobehavioral assessments conducted at one-month intervals.Concurrently,the relative telomere length was analyzed by quantitative polymerase chain reaction,which was performed using DNA extracted from ear notch and brain tissue after each assessment.Furthermore,proteomic analysis was employed to investigate differential protein expression in hippocampal tissue.Subarachnoid hemorrhage mice exhibited persistent neurocognitive impairment over a prolonged period of time.There was a significant positive correlation between telomere length and neurological test scores,confirming the usefulness of telomere length as a prognostic indicator in subarachnoid hemorrhage.Hippocampal tissue from subarachnoid hemorrhage mice showed reduced expression of acetyl-coenzyme A synthetase-2 and abnormalities in the expression of proteins related to ribosomes,energy metabolism,and cellular signal transduction.This study confirmed telomere shortening in the brain and metabolic disturbances in the hippocampi of subarachnoid hemorrhage mice.Thus,telomere length is a predictive marker for long-term impairment of cognitive function in mice following experimental subarachnoid hemorrhage.展开更多
基金the program of the Institute of Water Problems and Hydro Power of National Academy of Sciences of the Kyrgyz Republic
文摘This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.
文摘In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.
基金supported by COMPETE:POCI-01-0247-FEDER-039719 and FCT-Fundação para a Ciência e Tecnologia within the Project Scope:UIDB/00127/2020.
文摘In this study,we address a demanding time series forecasting problem that deals simultaneously with the following:(1)intermittent time series,(2)multi-step ahead forecasting,(3)time series with multiple seasonal periods,and(4)performance measures for model selection across multiple time series.Current literature deals with these types of problems separately,and no study has dealt with all these characteristics simultaneously.To fill this knowledge gap,we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem.Several adaptions and innovations have been conducted,which are marked as contributions to the literature.Specifically,we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance.To gather strong evidence that our ensemble model works in practice,we undertook a large-scale study across 98 time series,rigorously assessed with unbiased performance measures,where a week seasonal naïve was set as a benchmark.The results demonstrate that the proposed ensemble model achieves eyecatching forecasting accuracy.
基金funded by National Natural Science Foundation of China,grant number 62071491.
文摘Long-termpetroleum production forecasting is essential for the effective development andmanagement of oilfields.Due to its ability to extract complex patterns,deep learning has gained popularity for production forecasting.However,existing deep learning models frequently overlook the selective utilization of information from other production wells,resulting in suboptimal performance in long-term production forecasting across multiple wells.To achieve accurate long-term petroleum production forecast,we propose a spatial-geological perception graph convolutional neural network(SGP-GCN)that accounts for the temporal,spatial,and geological dependencies inherent in petroleum production.Utilizing the attention mechanism,the SGP-GCN effectively captures intricate correlations within production and geological data,forming the representations of each production well.Based on the spatial distances and geological feature correlations,we construct a spatial-geological matrix as the weight matrix to enable differential utilization of information from other wells.Additionally,a matrix sparsification algorithm based on production clustering(SPC)is also proposed to optimize the weight distribution within the spatial-geological matrix,thereby enhancing long-term forecasting performance.Empirical evaluations have shown that the SGP-GCN outperforms existing deep learning models,such as CNN-LSTM-SA,in long-term petroleum production forecasting.This demonstrates the potential of the SGP-GCN as a valuable tool for long-term petroleum production forecasting across multiple wells.
基金supported by the National Natural Science Foundation of China (No.U2039207)
文摘Symposium overview The International Symposium on Earthquake Forecasting to Commemorate the 50th Anniversary of the 1975 Haicheng M7.3 Earthquake,Liaoning,China,was held in Shenyang,China,from 8 to 11 July 2025.The symposium was organized by the Institute of Earthquake Forecasting,China Earthquake Administration(CEA),the State Key Laboratory of Earthquake Dynamics and Forecasting,and the China Seismic Experimental Site(CSES),in collaboration with the International Association of Seismology and Physics of the Earth’s Interior(IASPEI),the APEC Cooperation for Earthquake Science(ACES).
基金Science and Technology Development Program of the“Taihu Light”(K20231023)CMA Numerical Weather Prediction R&D Project(TCYF2024QH007)+1 种基金“Qing Lan”Project of Jiangsu Province for C.H.LUWuxi University Research Start-up Fund for Introduced Talents(2023r037)。
文摘Since the initiation of the subseasonal-to-seasonal prediction project by the World Meteorological Organization,the accuracy of model forecasts has improved notably.However,substantial discrepancies have been observed among forecast results produced by different ensemble members when applied to South China.To enhance the accuracy of sub-seasonal forecasts in this region,it is essential to develop new methods that can effectively leverage multiple predictive models.This study introduces a weighted ensemble forecasting method based on online learning to improve forecast accuracy.We utilized ensemble forecasts from three models:the Integrated Forecasting System model from the European Centre for Medium-Range Weather Forecasts,the Climate Forecast System Version 2 model from the National Centers for Environmental Prediction,and the Beijing Climate Center-Climate Prediction System version 3 model from the China Meteorological Administration.The ensemble weights are trained using an online learning approach.The results indicate that the forecasts obtained through online learning outperform those of the original dynamical models.Compared to the simple ensemble results of the three models,the weighted ensemble model showed a stronger capability to capture temperature and precipitation patterns in South China.Therefore,this method has the potential to improve the accuracy of sub-seasonal forecasts in this region.
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
基金financially supported by the China Atomic Energy Authority(CAEA)through the Geological Disposal Programthe National Natural Science Foundation of China(No.42307258)the China National Nuclear Corporation Fundamental Research Project(No.CNNC-JCYJ-202307)。
文摘Investigations into the long-term creep behavior of Beishan granite in uniaxial compression were conducted.Four levels of axial stress(60,70,87,and 95 MPa)were applied to rock specimens.Contrasting with earlier research,the long-term creep data in this work present a substantial advancement in the time dimension.Except for the sample subjected to 60 MPa axial loading,which did not fail after a loading duration of 1650 d,the specimens under the other three stresses all failed after sustained constant loading durations of 1204,1023,and 839 d,respectively.A lower envelope of driving stress-ratio for crystalline rocks was obtained,tending towards approximately 0.45 over an infinite time scale.According to the experimental results,as axial stress increases,both the axial strain accumulated in the transient creep process and the strain rate associated with steady-state creep deformation increase exponentially;however,the share of steady-state creep strain remains nearly constant at about82.53%.A novel damage-based creep model was put forward.It provides an enhanced depiction of the comprehensive creep process in rocks,notably improving the accuracy in forecasting the accelerated creep phase,which significantly impacts the long-term stability of engineering structures.
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
基金Supported by the National Natural Science Foundation of China(No.43277051)the Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education(No.B230203006).
文摘While oceanic and coastal acidification has gained increased attention,long-term pH trends and their drivers in large freshwater systems remain poorly understood.The Laurentian Great Lakes are the world’s largest freshwater system,and in many ways resemble marine ecosystems.However,unlike the open ocean and coastal waters where pH has declined due to rising atmospheric CO_(2),no significant pH trends have been observed in the Laurentian Great Lakes,despite significant ecosystem changes driven partly by the invasion of dreissenid mussels.This study examined 41 years of field observations from Lake Michigan to investigate the long-term carbonate chemistry dynamics.Observational results revealed substantial declines in both total alkalinity(TA)and dissolved inorganic carbon(DIC)over the four decades.Mussel shell calcification emerged as the primary mechanism behind these declines,accounting for 97%and 47%of the observed changes in TA and DIC,respectively,lowering water column pH by 0.24 units.Elevated carbon accumulation in soft mussel tissues,coupled with long-term changes in the air-water pCO_(2)gradient during summer,significantly contributed to long-term DIC variations,explaining 18%and 28%of the lake-wide DIC loss.These two mechanisms also resulted in an overall pH increase of 0.09 and 0.12 units,largely offsetting the calcification-driven pH decrease.These findings bridge a gap in acidification research for large freshwater systems and provide valuable insights for comprehensive lake-wide management strategies.
文摘Accurate short-term electricity price forecasts are essential for market participants to optimize bidding strategies,hedge risk and plan generation schedules.By leveraging advanced data analytics and machine learning methods,accurate and reliable price forecasts can be achieved.This study forecasts day-ahead prices in Türkiye’s electricity market using eXtreme Gradient Boosting(XGBoost).We benchmark XGBoost against four alternatives—Support Vector Machines(SVM),Long Short-Term Memory(LSTM),Random Forest(RF),and Gradient Boosting(GBM)—using 8760 hourly observations from 2023 provided by Energy Exchange Istanbul(EXIST).All models were trained on an identical chronological 80/20 train–test split,with hyperparameters tuned via 5-fold cross-validation on the training set.XGBoost achieved the best performance(Mean Absolute Error(MAE)=144.8 TRY/MWh,Root Mean Square Error(RMSE)=201.8 TRY/MWh,coefficient of determination(R^(2))=0.923)while training in 94 s.To enhance interpretability and identify key drivers,we employed Shapley Additive Explanations(SHAP),which highlighted a strong association between higher prices and increased natural-gas-based generation.The results provide a clear performance benchmark and practical guidance for selecting forecasting approaches in day-ahead electricity markets.
基金supported by the National Natural Science Foundation of China [grant numbers 42375168 and 42205035]a Shanghai Science and Technology Commission Project [grant number 23DZ1204704]。
文摘Marine heatwaves(MHWs)in the South China Sea(SCS)significantly impact marine ecosystems and socioeconomic development,yet accurately forecasting MHWs remains a challenge.This study developed an upper-ocean temperature forecasting model based on ConvLSTM for the northern SCS and,in conjunction with the ocean forecasting system LICOM Forecast System(LFS),constructed a hybrid Fusion model using Wasserstein-Distance optimization.The ability of these three models to forecast key MHW metrics with a 10-day lead was assessed during the summer of 2022 in the SCS.Overall,the Fusion model takes advantage of LFS and ConvLSTM,providing superior forecasts for both the duration and intensity of MHWs in the southern SCS.LFS(ConvLSTM)overestimates(underestimates)the duration of MHWs and all models exhibit limitations in forecasting the intensity of MHWs in part of the SCS.The Fusion model's superior forecast skill for MHWs may be attributable to its more realistic representation of the upper-ocean thermal structure with shallower mixed-layer depths during MHWs.This study highlights that combining the deep learning technique with a dynamical model can improve MHW forecasting and has certain physical interpretability.
基金funded by Humanities and Social Sciences of Ministry of Education Planning Fund of China,grant number 21YJA790009National Natural Science Foundation of China,grant number 72140001.
文摘With the rapid proliferation of electric vehicles,their charging loads pose new challenges to power grid stability and operational efficiency.To address this,this study employs a Monte Carlo simulation model to analyze the charging load characteristics of six battery electric vehicle categories in Hebei Province,leveraging multi-source probabilistic distribution data under typical operational scenarios.The findings reveal that electric vehicle charging loads are primarily concentrated during midday and nighttime periods,with significant load fluctuations exerting substantial pressure on the grid.In response,this paper proposes strategic interventions including optimized charging infrastructure planning,time-of-use electricity pricing mechanisms,and smart charging technologies to balance grid loads.The results provide a theoretical foundation for electric vehicle load forecasting,smart grid dispatching,and vehicle-grid integration,thereby enhancing grid operational efficiency and sustainability.
文摘Objective:To investigate the long-term prognosis and postoperative cosmetic outcomes of breast-conserving surgery combined with sentinel lymph node biopsy in patients with early-stage breast cancer,providing a reference for the selection of clinical treatment plans.Methods:A retrospective analysis was conducted on the clinical data of 68 patients with early-stage breast cancer admitted from January 2022 to December 2025.Based on the surgical approach,patients were divided into an observation group(breast-conserving surgery+sentinel lymph node biopsy)and a control group(other surgical methods such as modified radical mastectomy/total mastectomy).Clinical and pathological characteristics,incidence of postoperative complications,follow-up prognosis,and satisfaction with cosmetic outcomes were compared between the two groups.Results:Among the 68 patients,41 were in the observation group and 27 in the control group.The average age of patients in the observation group was(54.32±8.15)years,while that in the control group was(62.45±9.76)years.The average tumor size in the observation group was(1.86±0.72)cm,compared to(3.21±1.45)cm in the control group.The incidence of postoperative complications in the observation group was 9.76%,significantly lower than that in the control group at 33.33%(P<0.05).The 6-month disease-free survival rate was 95.12%in the observation group and 88.89%in the control group,with no statistically significant difference between the two groups(P>0.05).The excellent and good rate of cosmetic outcomes in the observation group was 87.80%,significantly higher than that in the control group at 29.63%(P<0.05).Conclusion:Breast-conserving surgery combined with sentinel lymph node biopsy for early-stage breast cancer can achieve long-term prognostic outcomes comparable to those of traditional radical surgery,with the advantages of fewer postoperative complications and superior cosmetic results.This approach is worthy of clinical promotion and application,particularly for early-stage breast cancer patients who have a demand for preserving breast morphology.
基金supported by the National Key R&D Program for Developing Basic Sciences [grant number 2022YFC3104805]the National Natural Science Foundation of China [grant numbers 92358302 and 42306219]+1 种基金supported by the Tai Shan Scholar Program [grant number tstp20231237]Laoshan Laboratory project [grant number LSKJ202300301]。
文摘This research evaluates the performance of an eddy-resolving forecast system(LFS)in simulating mesoscale eddies over the South China Sea(SCs)through a comparative analysis with satellite observations and the reanalysis dataset from the Global Ocean Physics Reanalysis product(CMEMS).The findings indicate that the spatial characteristics of eddy kinetic energy,number,and amplitude of coherent mesoscale eddies simulated by LFS exhibit a reasonable agreement with satellite observations.The reproduced seasonal variations are also comparable to outputs from the CMEMS reanalysis dataset.Nevertheless,certain systematic biases have also been identified.In the SCS,LFS generates approximately 17%fewer eddies than observed.Such biases are also evident in the CMEMS reanalysis dataset.Similar to the statistics shown in the CMEMS reanalysis dataset,both cyclonic and anticyclonic eddies are significantly weaker in LFS compared to the observations.Additionally,the composite three-dimensional structures of mesoscale eddies simulated by LFS exhibit a remarkable similarity to those identified in the CMEMS reanalysis datasets.This work lays the foundation for further studies using LFS to investigate the predictability of mesoscale eddies and enhance the accuracy of simulations.
文摘Taking the rural low-income population of Zhejiang Province as its subject, this paper examines how to build a sustainable income-growth mechanism and identify feasible implementation paths within the context of the common prosperity strategy. The research identifies key obstacles to income expansion, including an undiversified industrial structure, insufficient human capital, and a lack of robust social protection. These call for systemic solutions featuring institutional innovation, resource consolidation, and capability enhancement. Building on Zhejiang's experience as a common prosperity demonstration zone, the article constructs an integrated framework centered on four pillars: industrial empowerment, education upgrading, social security reinforcement, and digital coordination. It further offers concrete policy proposals involving the cultivation of localized industries, vocational skill training, enhanced safety nets, and the adoption of digital tools. The study thus offers both theoretical insights and practical paradigms for tackling the challenge of raising incomes in low-income rural areas.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
文摘AIM:To investigate the long-term outcomes in acute primary angle closure(APAC)patients treated with lens extraction(LE)surgery and to identify risk factors for glaucomatous optic neuropathy(GON).METHODS:In this longitudinal observational study,detailed medical histories of APAC patients and comprehensive ophthalmic examinations at final followup were collected.Logistic regression analysis was performed to identify predictors of blindness.Univariate and multivariate linear regression analyses were conducted to determine risk factors associated with visual outcomes.RESULTS:This study included 39 affected eyes of 31 subjects(26 females)with an average age of 74.1±8.0y.At 6.7±4.2y after APAC attack,2(5.7%)eyes had bestcorrected visual acuity(VA)worse than 3/60.Advanced glaucomatous visual field loss was observed in 15(39.5%)affected eyes and 5(25.0%)fellow eyes.Nine affected eyes(23.7%)had GON,and 11(28.9%)were blind.Six(15.4%)affected eyes and 2(9.1%)fellow eyes had suspicious progression.A significantly higher blindness rate in factory workers compared to office workers.Logistic regression identified that worse VA at attack(OR 10.568,95%CI 1.288-86.695;P=0.028)and worse early postoperative VA(OR 13.214,95%CI 1.157-150.881;P=0.038)were risk factors for blindness.Multivariate regression showed that longer duration of elevated intraocular pressure(P=0.004)and worse early postoperative VA(P=0.009)were associated with worse visual outcomes.CONCLUSION:Despite LE surgery,some APAC patients experience continued visual function deterioration.Lifelong monitoring is necessary.Target pressure and progression rates should be re-evaluated during follow-up.
基金National Natural Science Foundation of China,No.81901336(to JM).
文摘Subarachnoid hemorrhage is a subtype of stroke that causes severe neurological damage and is associated with poor long-term prognosis.Cognitive impairment is a major manifestation of long-term neurological dysfunction in patients with subarachnoid hemorrhage.However,there is notable absence of biological markers to predict long-term prognosis in this patient population.Given the aging-like neurocognitive phenomena associated with subarachnoid hemorrhage,this study postulates that telomere length,a recognized biomarker for aging,could be used as a prognostic indicator for subarachnoid hemorrhage.A left internal carotid artery intravascular puncture mouse model was used to simulate subarachnoid hemorrhage.Comprehensive neurological test scores were obtained through neurobehavioral assessments conducted at one-month intervals.Concurrently,the relative telomere length was analyzed by quantitative polymerase chain reaction,which was performed using DNA extracted from ear notch and brain tissue after each assessment.Furthermore,proteomic analysis was employed to investigate differential protein expression in hippocampal tissue.Subarachnoid hemorrhage mice exhibited persistent neurocognitive impairment over a prolonged period of time.There was a significant positive correlation between telomere length and neurological test scores,confirming the usefulness of telomere length as a prognostic indicator in subarachnoid hemorrhage.Hippocampal tissue from subarachnoid hemorrhage mice showed reduced expression of acetyl-coenzyme A synthetase-2 and abnormalities in the expression of proteins related to ribosomes,energy metabolism,and cellular signal transduction.This study confirmed telomere shortening in the brain and metabolic disturbances in the hippocampi of subarachnoid hemorrhage mice.Thus,telomere length is a predictive marker for long-term impairment of cognitive function in mice following experimental subarachnoid hemorrhage.
