Thermal quenching limits further large-scale applications of Pr^(3+)-doped phosphors.Research shows that intervalence charge transfer(IVCT)can be used as both the quenching luminescence channel and the red-emitting co...Thermal quenching limits further large-scale applications of Pr^(3+)-doped phosphors.Research shows that intervalence charge transfer(IVCT)can be used as both the quenching luminescence channel and the red-emitting compensation channel for Pr^(3+)-doped phosphors due to its unique dual-function mechanism.The problem of how to reduce quenching and improve compensation effects through IVCT has become an important issue.In this contribution,a dual compensation mechanism is proposed by designing an excitation-driven strategy of charge transfer(CT)and IVCT,thus realizing anti-thermal quenching of red light.Under different excitation-driven strategies,YNbO_(4):x%Pr^(3+)phosphors exhibited both single compensation and dual compensation effects in red emission.In the case of excitation of the IVCT band alone,the 3P0 energy level electrons of Pr^(3+)were transferred to the^(1)D_(2)level through the IVCT state formed between Pr^(3+)and Nb^(5+),resulting in enhanced red emission from the^(1)D_(2)→^(3)H_(4)transition of Pr^(3+).In the case of simultaneously exciting the charge transfer band(CTB)and the IVCT band,there is a charge transfer from both the host matrix and the Pr^(3+)3P_(0)level to the^(1)D_(2)level,leading to higher red emission intensities in all samples within the temperature range of 303 K to 523 K compared to single compensation.Notably,the YNbO_(4):0.1%Pr^(3+)sample exhibited a maximum comprehensive intensity of red emission under dual compensation,reaching 2.18 times its intensity at 303 K,significantly higher than the 1.38 times its intensity under single compensation.Furthermore,this phosphor exhibits high optical temperature sensing sensitivity and excellent thermochromic properties,allowing for both quantitative and qualitative temperature measurements.This work provided a new avenue for the development of Pr^(3+)-doped niobate anti-thermal quenching luminescent materials.展开更多
A Pr^(3+)/Tb^(3+)co-doped LuNbO_(4) phosphor was synthesized as a self-calibrated optical thermometer using a high-temperature solid-state method,and its structure and luminescent property were elaborately investigate...A Pr^(3+)/Tb^(3+)co-doped LuNbO_(4) phosphor was synthesized as a self-calibrated optical thermometer using a high-temperature solid-state method,and its structure and luminescent property were elaborately investigated.Under intervalence charge transfer(IVCT)excitation at 305 nm,the sample LuNbO_(4):Pr^(3+)/Tb^(3+)exhibited several bands in the green and red regions,originating from characteristics transitions of Pr^(3+)and Tb^(3+)ions.Through monitoring thermo-responsive emission intensity ratios from the ^(1)D_(2)→^(3)H_(4)(Pr^(3+))and ^(5)D_(4)→^(7)F_(5)(Tb^(3+))transitions,sensitive thermometry with good signal discriminability was achieved in phosphor LuNbO_(4):Pr^(3+)/Tb^(3+)owing to a significant difference in thermal activation energy between Pr^(3+)and Tb^(3+)under IVCT excitation.Moreover,the maximum absolute sensitivity and relative sensitivity reached about 0.024 K^(-1) at 493 K and 1.26%K^(-1) at 463 K,respectively,for the LuNbO_(4):Pr^(3+)/Tb^(3+)sample,which implies that the present phosphor could be considered as a promising candidate for self-calibrated optical thermometers.展开更多
This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theo...This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theorem,fixed point index theory and the analytic technique,we give the bifurcation point of the parameter which divides the range of parameter for the existence of at least two,one and no positive solutions for the problem.And,by using a fixed point theorem of generalized concave operator and cone theory,we establish the maximum parameter interval for the existence of the unique positive solution for the problem and show that such a positive solution continuously depends on the parameter.In the end,some examples are given to illustrate our main results.展开更多
Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation fo...Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation for the elastically supported fluid-conveying pipe is transformed into ordinary differential equations using the Galerkin truncation method.The Chebyshev interval approach,integrated with the assumed mode method is then used to investigate the effects of uncertainties of support stiffness,fluid speed,and pipe length on the natural frequencies and mode shapes of the pipe.Additionally,both symmetrical and asymmetrical support stiffnesses are discussed.The accuracy and effectiveness of the Chebyshev interval approach are verified through comparison with the Monte Carlo method.The results reveal that,for the same deviation coefficient,uncertainties in symmetrical support stiffness have a greater impact on the first four natural frequencies than those of the asymmetrical one.There may be significant differences in the sensitivity of natural frequencies and mode shapes of the same order to uncertain parameters.Notably,mode shapes susceptible to uncertain parameters exhibit wider fluctuation intervals near the elastic supports,requiring more attention.展开更多
In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Curre...In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.展开更多
Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular...Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.展开更多
Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competiti...Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.展开更多
Unresectable hepatocellular carcinoma(HCC)remains a global challenge,with limited effective treatment options for advanced-stage disease.The HIMALAYA trial(phase III randomized study that evaluated the STRIDE regimen)...Unresectable hepatocellular carcinoma(HCC)remains a global challenge,with limited effective treatment options for advanced-stage disease.The HIMALAYA trial(phase III randomized study that evaluated the STRIDE regimen)introduced the Single Tremelimumab Regular Interval Durvalumab(STRIDE)regimen,an immunotherapy-based approach that achieved a median overall survival(OS)of 16.43 months compared to 13.77 months with sorafenib.While statistically significant,this~2.7 months OS gain warrants scrutiny in light of STRIDE’s increased immune-related toxicity and cost.This commentary evaluates STRIDE’s impact within the broader landscape of first-line systemic therapy for unresectable HCC,alongside other regimens such as atezolizumab plus bevacizumab and nivolumab plus ipilimumab.We explore STRIDE’s mechanism of action,safety profile,modest progression-free survival(PFS)improvement,and implementation challenges,incorporating insights from 2023-2025 research.In addition,we discussed its limitations in non-viral HCC and Child-Pugh B patients,the role of emerging biomarkers,and the potential of radiation to enhance immunotherapy efficacy.As a dual immune checkpoint inhibitor(ICI)strategy,STRIDE offers an important advance that may not only extend survival but also open the door to future curative approaches.However,optimizing its use will require refined patient selection and further investigation of synergistic combination therapies.展开更多
With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation ...With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.展开更多
Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives ...Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.展开更多
BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time ...BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.展开更多
Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when dat...Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when data are scarce.We introduce a fuzzy–set framework usingα-cut interval analysis to represent imprecise wave heights,periods,added mass,damping,and stiffness as fuzzy numbers.These are incorporated into the multi-body equations of motion and solved via a fuzzy Runge–Kutta scheme across nestedα-levels.A simulation architecture iterates overα-cuts and time-steps to produce interval bounds on heavy responses.A case study off the Karnataka coast,with realistic sea-state data for moderate and severe scenarios,yields heave-amplitude envelopes whose widths quantify response uncertainty.At mid-confidence(α=0.5),moderate seas produce amplitudes of 8.30–9.65 m(±15%),while severe seas yield 7.15–8.90 m(±22%).Envelope narrowing asα→1 confirms that increased parameter confidence reduces prediction spread,and bias analysis against crisp baselines highlights the impact of imprecision on mean responses.This non-probabilistic approach provides interpretable,worst-and best-case motion bounds without requiring large datasets,offering marine engineers robust safety margins and guidance for targeted data collection and real-time uncertainty updating.展开更多
To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-dec...To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.展开更多
This study investigates the application of large language models in analyzing sentiment features within the exchange rate markets.Traditional natural language processing methods,such as LDA and BERT,are effective in e...This study investigates the application of large language models in analyzing sentiment features within the exchange rate markets.Traditional natural language processing methods,such as LDA and BERT,are effective in extracting topics from text;however,they fail to assess the relative importance of these topics in relation to target exchange rates.To bridge this gap,this paper employs ChatGPT to extract topics from texts and evaluate their importance scores,further enhancing exchange rate forecasting by integrating topic importance into the sentiment analysis framework.Through empirical analysis,the superiority of ChatGPT over LDA and BERT in both topic extraction and importance assessment is demonstrated.Furthermore,this study utilizes the topic importance scores generated by ChatGPT to develop a novel interval-valued sentiment index(TIS index).This index not only accounts for the relative importance of various events influencing exchange rate fluctuations but also captures the dynamic evolution of market sentiment within an interval.Empirical results highlight that the TIS Index significantly enhances the forecasting accuracy of interval models such as TARI and IMLP for exchange rates.These findings further demonstrate the advantages of ChatGPT in sentiment analysis within the foreign exchange market.These findings offer new insights into the application of ChatGPT in financial text research.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.21401130)the Opening Research Fund of the State Key Laboratory of Rare Earth Resource Utilization,Changchun Institute of Applied Chemistry,Chinese Academy of Sciences(RERU2014005).
文摘Thermal quenching limits further large-scale applications of Pr^(3+)-doped phosphors.Research shows that intervalence charge transfer(IVCT)can be used as both the quenching luminescence channel and the red-emitting compensation channel for Pr^(3+)-doped phosphors due to its unique dual-function mechanism.The problem of how to reduce quenching and improve compensation effects through IVCT has become an important issue.In this contribution,a dual compensation mechanism is proposed by designing an excitation-driven strategy of charge transfer(CT)and IVCT,thus realizing anti-thermal quenching of red light.Under different excitation-driven strategies,YNbO_(4):x%Pr^(3+)phosphors exhibited both single compensation and dual compensation effects in red emission.In the case of excitation of the IVCT band alone,the 3P0 energy level electrons of Pr^(3+)were transferred to the^(1)D_(2)level through the IVCT state formed between Pr^(3+)and Nb^(5+),resulting in enhanced red emission from the^(1)D_(2)→^(3)H_(4)transition of Pr^(3+).In the case of simultaneously exciting the charge transfer band(CTB)and the IVCT band,there is a charge transfer from both the host matrix and the Pr^(3+)3P_(0)level to the^(1)D_(2)level,leading to higher red emission intensities in all samples within the temperature range of 303 K to 523 K compared to single compensation.Notably,the YNbO_(4):0.1%Pr^(3+)sample exhibited a maximum comprehensive intensity of red emission under dual compensation,reaching 2.18 times its intensity at 303 K,significantly higher than the 1.38 times its intensity under single compensation.Furthermore,this phosphor exhibits high optical temperature sensing sensitivity and excellent thermochromic properties,allowing for both quantitative and qualitative temperature measurements.This work provided a new avenue for the development of Pr^(3+)-doped niobate anti-thermal quenching luminescent materials.
基金supported by National Natural Science Foundation of China(No.51672215,11274251)Research Fund for the Doctoral Program of Higher Education of China(RFDP)(No.20136101110017).
文摘A Pr^(3+)/Tb^(3+)co-doped LuNbO_(4) phosphor was synthesized as a self-calibrated optical thermometer using a high-temperature solid-state method,and its structure and luminescent property were elaborately investigated.Under intervalence charge transfer(IVCT)excitation at 305 nm,the sample LuNbO_(4):Pr^(3+)/Tb^(3+)exhibited several bands in the green and red regions,originating from characteristics transitions of Pr^(3+)and Tb^(3+)ions.Through monitoring thermo-responsive emission intensity ratios from the ^(1)D_(2)→^(3)H_(4)(Pr^(3+))and ^(5)D_(4)→^(7)F_(5)(Tb^(3+))transitions,sensitive thermometry with good signal discriminability was achieved in phosphor LuNbO_(4):Pr^(3+)/Tb^(3+)owing to a significant difference in thermal activation energy between Pr^(3+)and Tb^(3+)under IVCT excitation.Moreover,the maximum absolute sensitivity and relative sensitivity reached about 0.024 K^(-1) at 493 K and 1.26%K^(-1) at 463 K,respectively,for the LuNbO_(4):Pr^(3+)/Tb^(3+)sample,which implies that the present phosphor could be considered as a promising candidate for self-calibrated optical thermometers.
基金Supported by the National Natural Science Foundation of China(11361047)Fundamental Research Program of Shanxi Province(20210302124529)。
文摘This paper is concerned with a class of nonlinear fractional differential equations with a disturbance parameter in the integral boundary conditions on the infinite interval.By using Guo-Krasnoselskii fixed point theorem,fixed point index theory and the analytic technique,we give the bifurcation point of the parameter which divides the range of parameter for the existence of at least two,one and no positive solutions for the problem.And,by using a fixed point theorem of generalized concave operator and cone theory,we establish the maximum parameter interval for the existence of the unique positive solution for the problem and show that such a positive solution continuously depends on the parameter.In the end,some examples are given to illustrate our main results.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272211,12072181,and 12121002).
文摘Uncertain parameters are widespread in engineering systems.This study investigates the modal analysis of a fluid-conveying pipe subjected to elastic supports with unknown-but-bound parameters.The governing equation for the elastically supported fluid-conveying pipe is transformed into ordinary differential equations using the Galerkin truncation method.The Chebyshev interval approach,integrated with the assumed mode method is then used to investigate the effects of uncertainties of support stiffness,fluid speed,and pipe length on the natural frequencies and mode shapes of the pipe.Additionally,both symmetrical and asymmetrical support stiffnesses are discussed.The accuracy and effectiveness of the Chebyshev interval approach are verified through comparison with the Monte Carlo method.The results reveal that,for the same deviation coefficient,uncertainties in symmetrical support stiffness have a greater impact on the first four natural frequencies than those of the asymmetrical one.There may be significant differences in the sensitivity of natural frequencies and mode shapes of the same order to uncertain parameters.Notably,mode shapes susceptible to uncertain parameters exhibit wider fluctuation intervals near the elastic supports,requiring more attention.
基金supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption(2018YFB0904200).
文摘In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.
基金supported in part by the Central Guidance for Local Science and Technology Development Funds under Grant No.YDZJSX2025D049Shanxi Provincial Graduate Innovation Research Program under Grant No.2024KY652.
文摘Federated Learning(FL)provides an effective framework for efficient processing in vehicular edge computing.However,the dynamic and uncertain communication environment,along with the performance variations of vehicular devices,affect the distribution and uploading processes of model parameters.In FL-assisted Internet of Vehicles(IoV)scenarios,challenges such as data heterogeneity,limited device resources,and unstable communication environments become increasingly prominent.These issues necessitate intelligent vehicle selection schemes to enhance training efficiency.Given this context,we propose a new scenario involving FL-assisted IoV systems under dynamic and uncertain communication conditions,and develop a dynamic interval multi-objective optimization algorithm to jointly optimize various factors including training experiments,system energy consumption,and bandwidth utilization to meet multi-criteria resource optimization requirements.For the problem at hand,we design a dynamic interval multi-objective optimization algorithm based on interval overlap detection.Simulation results demonstrate that our method outperforms other solutions in terms of accuracy,training cost,and server utilization.It effectively enhances training efficiency under wireless channel environments while rationally utilizing bandwidth resources,thus possessing significant scientific value and application potential in the field of IoV.
基金National Social Science Fund of China,No.24BTJ037Significant Project of the National Social Science Foundation of China,No.23&ZD102+1 种基金The Key Research Base for Philosophy and Social Sciences in Hangzhou:ESG and Sustainable Development Research Center,No.25JD053Zhejiang Provincial Statistical Scientific Research Project,No.25TJZZ12。
文摘Establishing a Regional Marine Innovation Ecosystem(RMIE)is crucial for advancing China’s maritime power strategy.Concurrently,developing a competitive RMIE serves as a strategic lever to enhance the global competitiveness of China’s marine science sector.However,research on the competitiveness of RMIE is limited.To this end,this study constructs an evaluation index system based on ecological niche theory to assess the competitiveness of RMIE in China from 2008 to 2020.The findings indicate generally fluctuating upward trends in RMIE’s competitiveness,with Shandong,Jiangsu,and Guangdong showing relatively strong positions.Notably,there are significant intra-regional imbalances and inter-regional asynchrony in RMIE’s competitiveness across China’s three major marine economic circles.Recognizing that forecasting RMIE competitiveness can inform policy formulation,this paper proposes a systematic multivariate grey interval prediction model that incorporates spatial proximity effects.This model effectively captures the interval and uncertainty characteristics of RMIE’s competitiveness while considering spatial relationships among regions.Results from comparative analysis,robustness tests,and sensitivity analysis demonstrate its superior applicability and forecasting accuracy.Additionally,interval forecasts and scenario analyses suggest that RMIE competitiveness will maintain stable growth,although unbalanced and unsynchronized development is likely to persist.Overall,the approach developed for evaluating and forecasting RMIE competitiveness offers valuable insights for effective policy formulation.
文摘Unresectable hepatocellular carcinoma(HCC)remains a global challenge,with limited effective treatment options for advanced-stage disease.The HIMALAYA trial(phase III randomized study that evaluated the STRIDE regimen)introduced the Single Tremelimumab Regular Interval Durvalumab(STRIDE)regimen,an immunotherapy-based approach that achieved a median overall survival(OS)of 16.43 months compared to 13.77 months with sorafenib.While statistically significant,this~2.7 months OS gain warrants scrutiny in light of STRIDE’s increased immune-related toxicity and cost.This commentary evaluates STRIDE’s impact within the broader landscape of first-line systemic therapy for unresectable HCC,alongside other regimens such as atezolizumab plus bevacizumab and nivolumab plus ipilimumab.We explore STRIDE’s mechanism of action,safety profile,modest progression-free survival(PFS)improvement,and implementation challenges,incorporating insights from 2023-2025 research.In addition,we discussed its limitations in non-viral HCC and Child-Pugh B patients,the role of emerging biomarkers,and the potential of radiation to enhance immunotherapy efficacy.As a dual immune checkpoint inhibitor(ICI)strategy,STRIDE offers an important advance that may not only extend survival but also open the door to future curative approaches.However,optimizing its use will require refined patient selection and further investigation of synergistic combination therapies.
基金funded by Jilin Province Science and Technology Development Plan Project,grant number 20220203163SF.
文摘With the increasing integration of large-scale distributed energy resources into the grid,traditional distribution network optimization and dispatch methods struggle to address the challenges posed by both generation and load.Accounting for these issues,this paper proposes a multi-timescale coordinated optimization dispatch method for distribution networks.First,the probability box theory was employed to determine the uncertainty intervals of generation and load forecasts,based on which,the requirements for flexibility dispatch and capacity constraints of the grid were calculated and analyzed.Subsequently,a multi-timescale optimization framework was constructed,incorporating the generation and load forecast uncertainties.This framework included optimization models for dayahead scheduling,intra-day optimization,and real-time adjustments,aiming to meet flexibility needs across different timescales and improve the economic efficiency of the grid.Furthermore,an improved soft actor-critic algorithm was introduced to enhance the uncertainty exploration capability.Utilizing a centralized training and decentralized execution framework,a multi-agent SAC network model was developed to improve the decision-making efficiency of the agents.Finally,the effectiveness and superiority of the proposed method were validated using a modified IEEE-33 bus test system.
基金supported by the National Natural Science Foundation of China,Nos.82071426,81873784Clinical Cohort Construction Program of Peking University Third Hospital,No.BYSYDL2019002(all to DF)。
文摘Amyotrophic lateral sclerosis is a rare neurodegenerative disease characterized by the involvement of both upper and lower motor neurons.Early bilateral limb involvement significantly affects patients'daily lives and may lead them to be confined to bed.However,the effect of upper and lower motor neuron impairment and other risk factors on bilateral limb involvement is unclear.To address this issue,we retrospectively collected data from 586 amyotrophic lateral sclerosis patients with limb onset diagnosed at Peking University Third Hospital between January 2020 and May 2022.A univariate analysis revealed no significant differences in the time intervals of spread in different directions between individuals with upper motor neuron-dominant amyotrophic lateral sclerosis and those with classic amyotrophic lateral sclerosis.We used causal directed acyclic graphs for risk factor determination and Cox proportional hazards models to investigate the association between the duration of bilateral limb involvement and clinical baseline characteristics in amyotrophic lateral sclerosis patients.Multiple factor analyses revealed that higher upper motor neuron scores(hazard ratio[HR]=1.05,95%confidence interval[CI]=1.01–1.09,P=0.018),onset in the left limb(HR=0.72,95%CI=0.58–0.89,P=0.002),and a horizontal pattern of progression(HR=0.46,95%CI=0.37–0.58,P<0.001)were risk factors for a shorter interval until bilateral limb involvement.The results demonstrated that a greater degree of upper motor neuron involvement might cause contralateral limb involvement to progress more quickly in limb-onset amyotrophic lateral sclerosis patients.These findings may improve the management of amyotrophic lateral sclerosis patients with limb onset and the prediction of patient prognosis.
文摘BACKGROUND Meniscal tears are one of the most common knee injuries.After the diagnosis of a meniscal tear has been made,there are several factors physicians use to guide clinical decision-making.The influence of time between injury and isolated meniscus repair on patient outcomes is not well described.Assessing this relationship is important as it may influence clinical decision-making and can add to the preoperative patient education process.We hypothesized that increasing the time from injury to meniscus surgery would worsen postoperative outcomes.AIM To investigate the current literature for data on the relationship between time between meniscus injury and repair on patient outcomes.METHODS PubMed,Academic Search Complete,MEDLINE,CINAHL,and SPORTDiscus were searched for studies published between January 1,1995 and July 13,2023 on isolated meniscus repair.Exclusion criteria included concomitant ligament surgery,incomplete outcomes or time to surgery data,and meniscectomies.Patient demographics,time to injury,and postoperative outcomes from each study were abstracted and analyzed.RESULTS Five studies met all inclusion and exclusion criteria.There were 204(121 male,83 female)patients included.Three of five(60%)studies determined that time between injury and surgery was not statistically significant for postoperative Lysholm scores(P=0.62),Tegner scores(P=0.46),failure rate(P=0.45,P=0.86),and International Knee Documentation Committee scores(P=0.65).Two of five(40%)studies found a statistically significant increase in Lysholm scores with shorter time to surgery(P=0.03)and a statistically significant association between progression of medial meniscus extrusion ratio(P=0.01)and increasing time to surgery.CONCLUSION Our results do not support the hypothesis that increased time from injury to isolated meniscus surgery worsens postoperative outcomes.Decision-making primarily based on injury interval is thus not recommended.
文摘Vessel motions in offshore operations are heavily influenced by uncertain wave loads and hydrodynamic parameters.Yet,traditional deterministic or probabilistic models often fail to capture epistemic ambiguity when data are scarce.We introduce a fuzzy–set framework usingα-cut interval analysis to represent imprecise wave heights,periods,added mass,damping,and stiffness as fuzzy numbers.These are incorporated into the multi-body equations of motion and solved via a fuzzy Runge–Kutta scheme across nestedα-levels.A simulation architecture iterates overα-cuts and time-steps to produce interval bounds on heavy responses.A case study off the Karnataka coast,with realistic sea-state data for moderate and severe scenarios,yields heave-amplitude envelopes whose widths quantify response uncertainty.At mid-confidence(α=0.5),moderate seas produce amplitudes of 8.30–9.65 m(±15%),while severe seas yield 7.15–8.90 m(±22%).Envelope narrowing asα→1 confirms that increased parameter confidence reduces prediction spread,and bias analysis against crisp baselines highlights the impact of imprecision on mean responses.This non-probabilistic approach provides interpretable,worst-and best-case motion bounds without requiring large datasets,offering marine engineers robust safety margins and guidance for targeted data collection and real-time uncertainty updating.
基金financially supported by the National Natural Science Foundation of China(Nos.42277149,41502299,41372306)the Research Planning of Sichuan Education Department,China(No.16ZB0105)+3 种基金the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Independent Research Project(Nos.SKLGP2016Z007,SKLGP2018Z017,SKLGP2020Z009)Chengdu University of Technology Young and Middle Aged Backbone Program(No.KYGG201720)Sichuan Provincial Science and Technology Department Program(No.19YYJC2087)China Scholarship Council。
文摘To tackle the difficulties of the point prediction in quantifying the reliability of landslide displacement prediction,a data-driven combination-interval prediction method(CIPM)based on copula and variational-mode-decomposition associated with kernel-based-extreme-learningmachine optimized by the whale optimization algorithm(VMD-WOA-KELM)is proposed in this paper.Firstly,the displacement is decomposed by VMD to three IMF components and a residual component of different fluctuation characteristics.The key impact factors of each IMF component are selected according to Copula model,and the corresponding WOA-KELM is established to conduct point prediction.Subsequently,the parametric method(PM)and non-parametric method(NPM)are used to estimate the prediction error probability density distribution(PDF)of each component,whose prediction interval(PI)under the 95%confidence level is also obtained.By means of the differential evolution algorithm(DE),a weighted combination model based on the PIs is built to construct the combination-interval(CI).Finally,the CIs of each component are added to generate the total PI.A comparative case study shows that the CIPM performs better in constructing landslide displacement PI with high performance.
基金supported by the National Natural Science Foundation of China under Grants No.72171223,No.71988101the Youth Innovation Promotion Association of the Chinese Academy of Sciences.
文摘This study investigates the application of large language models in analyzing sentiment features within the exchange rate markets.Traditional natural language processing methods,such as LDA and BERT,are effective in extracting topics from text;however,they fail to assess the relative importance of these topics in relation to target exchange rates.To bridge this gap,this paper employs ChatGPT to extract topics from texts and evaluate their importance scores,further enhancing exchange rate forecasting by integrating topic importance into the sentiment analysis framework.Through empirical analysis,the superiority of ChatGPT over LDA and BERT in both topic extraction and importance assessment is demonstrated.Furthermore,this study utilizes the topic importance scores generated by ChatGPT to develop a novel interval-valued sentiment index(TIS index).This index not only accounts for the relative importance of various events influencing exchange rate fluctuations but also captures the dynamic evolution of market sentiment within an interval.Empirical results highlight that the TIS Index significantly enhances the forecasting accuracy of interval models such as TARI and IMLP for exchange rates.These findings further demonstrate the advantages of ChatGPT in sentiment analysis within the foreign exchange market.These findings offer new insights into the application of ChatGPT in financial text research.
