Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are ...Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.展开更多
BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this ga...BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.展开更多
The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability ...The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.展开更多
Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satel...Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satellite observations in military logistics.Due to uncertainties in the processes of production,transport,and observation,the satellite-based observation and state estimation of military logistics exhibit characteristics of uncertainty.This paper proposes an attribute-based staged method to quantify uncertainty,addressing mixed uncertainties during satellite observations of logistics.First,Bayesian estimation is used to quantify the aleatory uncertainty in the process of single-stage logistics observation.Second,evidence theory is adopted to quantify the epistemic uncertainty caused by conflicts in multi-stage logistics observation results and the lack of understanding of production principles.Through the design of the identification framework and the dynamic optimization of basic reliability,key logistics elements are identified,enabling an accurate estimation of the state of military logistics.Finally,the application case is used to validate the effectiveness and accuracy of the proposed method.Compared to conventional evidence theory,the proposed method can make fuller use of multi-source information and reduce the relative error between the estimated value and the true value to below 0.015%.展开更多
BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anx...BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.展开更多
Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight tr...Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.展开更多
Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing...Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.展开更多
When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding bia...When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.展开更多
Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input data...Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr^(-1)in 2010, ranging from 2.9 to 15.8 Tg N yr^(-1)across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.展开更多
This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal...This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.展开更多
Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method...Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.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
Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying...Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.展开更多
This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need f...This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need for structured tools to guide investments amid climate volatility,technological change,and economic risk.Grounded in decision theory—especially the work of Von Neumann,Morgenstern,and Savage—the framework models renewable energy investments using subjective probabilities and quasi-option value under evolving climate conditions.The empirical component focuses on ARAMCO’s renewable energy strategy,a corporate case illustrating how fossil-fuel-dependent entities can pivot toward sustainability.The analysis uses Net Present Value(NPV)modeling and real options analysis under different discount rates and carbon pricing scenarios to assess financial feasibility.Results show that lower discount rates and moderate carbon prices improve investment attractiveness,while high carbon pricing significantly reduces project viability.The study also highlights the policy relevance of this framework.Government subsidies,adaptive regulation,and public-private partnerships emerge as critical enablers of resilient investments.It further suggests that aligning ESG reporting standards with carbon pricing policies can strengthen market signals and encourage private capital flow into renewables.By integrating theoretical modeling with corporate investment realities,this chapter offers a replicable tool for policymakers and investors.Future research should expand its application across sectors and geographies to validate generalizability and improve planning in the transition toward low-carbon economies.展开更多
This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addres...This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.展开更多
BACKGROUND Uncertainty in illness(UI)and fear of progression(FoP)are significant psycho-logical challenges for lung cancer patients.Coping styles and social support are critical mediators,influencing patients'abil...BACKGROUND Uncertainty in illness(UI)and fear of progression(FoP)are significant psycho-logical challenges for lung cancer patients.Coping styles and social support are critical mediators,influencing patients'ability to manage the emotional and psy-chological burden of UI and FoP.However,limited research has explored the chain mediation effect of these factors on the relationship between UI and FoP,particularly among Chinese lung cancer patients.Convenience sampling was used to recruit inpatients diagnosed with lung cancer at a tertiary hospital in Changde City between November and December 2023.A total of 320 participants completed the Mishel Uncertainty in Illness Scale,Simp-lified Coping Style Questionnaire,Mandarin Chinese Version of the Medical Outcomes Study Social Support Survey,and Fear of Progression Questionnaire-Short Form.The chain mediation analysis was performed using the PROCESS macro to examine the relationships between the variables.RESULTS The results revealed that UI had a significant direct effect on FoP(effect=0.224,95%CI:0.136-0.408).Additionally,three indirect pathways were identified:(1)Social support(effect=0.128,95%CI:0.045-0.153);(2)Coping style(effect=0.115,95%CI:0.048-0.157);and(3)Chain mediators involving social support and coping style(effect=0.072,95%CI:0.045-0.120).The total indirect effect of the three mediation paths is 31.5%.These results confirm that social support and coping style significantly mediate the relationship between UI and FoP.CONCLUSION Based on cross-sectional data and a chain mediation model,this study explored the mechanisms between UI,social support,coping style,and FOP.Patients with lung cancer have higher levels of FOP,and the results of this study revealed a correlation between these four factors.Social support and coping style partially mediated the effects of UI on FOP,and there was a chain-mediating effect between UI and FOP.Programs designed to strengthen social support networks should also incorporate training to develop adaptive coping strategies,ultimately reducing FOP and improving overall quality of life.展开更多
The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncerta...The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncertainty,such as model uncertainty,internal variability,and scenario uncertainty.Single-model initial-condition large ensembles(SMILEs)and a polynomial fitting method were suggested to separate these uncertainty sources.However,the applicability of a widely used polynomial fitting method in the uncertainty separation of P_(IAV)projection remains unknown.This study compares three sources of uncertainty estimated from five SMILEs and 28 models with one ensemble member in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Results show that the internal uncertainty based on models with one ensemble member calculated using the polynomial fitting method is significantly underestimated compared to SMILEs.However,internal variability in CMIP6 as represented in the pre-industrial control run,aligns closely with SMILEs.At 1.5°C warming above the preindustrial level,internal variability dominates globally,masking the externally forced P_(IAV)signal.At 2.0°C warming,both internal and model uncertainties are significant over regions like Central Africa,the equatorial Indian Ocean,the Maritime Continent,and the Arctic,while internal variability still dominates elsewhere.In some regions,the forced signal becomes distinguishable from internal variability.This study reveals the limitations of the polynomial fitting method in separating P_(IAV)projection uncertainties and emphasizes the importance of SMILEs for accurately quantifying uncertainty sources.It also suggests that improving the intermodel agreement at warming levels of 1.5°C and 2.0°C will not substantially reduce uncertainty in most regions.展开更多
Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise ...Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise development path to accelerate deployment time.Uncertainty and sensitivity analyses of accidents guide nuclear reactor design and safety analyses.Uncertainty analysis can ascertain the safety margin,and sensitivity analysis can reveal the correlation between accident consequences and input parameters.Loss of forced cooling(LOFC)represents an accident scenario of the SM-MSR,and the study of LOFC could offer useful information to improve physical thermohydraulic and structural designs.Therefore,this study investigates the uncertainty of LOFC consequences and the sensitivity of related parameters.The uncertainty of the LOFC consequences was analyzed using the Monte Carlo method,and multiple linear regression was employed to analyze the sensitivity of the input parameters.The uncertainty and sensitivity analyses showed that the maximum reactor outlet fuel salt temperature was 725.5℃,which is lower than the acceptable criterion,and five important parameters influencing LOFC consequences were identified.展开更多
Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee th...Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.展开更多
In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capabilit...In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.展开更多
基金support of this project through the Southwest Regional Partnership on Carbon Sequestration(Grant No.DE-FC26-05NT42591)Improving Production in the Emerging Paradox Oil Play(Grant No.DE-FE0031775).
文摘Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.
基金Supported by National Natural Science Foundation of China,No.81602792The Natural Science Foundation of the Jiangsu Higher Education Institutions of China,No.23KJB310023+5 种基金Jiangsu Provincial Medical Key Discipline,No.ZDXK202235The Maternal and Child Health Research Project of Jiangsu Province,No.F202210The Project of State Key Laboratory of Radiation Medicine and Protection,Soochow University,No.GZK1202101Suzhou Science and Technology Development Plan Project,No.KJXW2020008BOXI Natural Science Cultivation Foundation of China of The First Affiliated Hospital of Soochow University,No.BXQN202107Clinical Diagnosis and Treatment Technology Innovation Project Youth Characteristic Technology Project of The First Affiliated Hospital of Soochow University,No.2100201.
文摘BACKGROUND Currently,there is limited research examining the relationship between anxiety,depression,coping styles,and illness uncertainty in patients with cervical cancer(CC)undergoing radiotherapy.Addressing this gap could provide valuable insights and more reliable evidence for clinical practice targeting this patient population.AIM To analyze the anxiety,depression,and coping styles of patients with CC undergoing radiotherapy and explore their correlations with illness uncertainty.METHODS A total of 200 patients with CC undergoing radiotherapy at The First Affiliated Hospital of Soochow University between June 2018 and June 2022 were enrolled.Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale(HADS),comprising subscales for anxiety(HADS-A)and depression(HADS-D).Coping styles were evaluated using the Jalowiec Coping Scale(JCS-60),comprising dimensions such as confrontive,evasive,optimistic,fatalistic,emotive,palliative,supportive,and self-reliant.Illness uncertainty was measured using the Mishel Uncertainty in Illness Scale(MUIS),encompassing ambiguity,complexity,information deficit,and unpredictability.Correlations among anxiety,depression,coping styles,and illness uncertainty were analyzed.RESULTS During radiotherapy,the mean scores were 7.12±3.39 for HADS-A,6.68±3.49 for HADS-D,1.52±0.23 for JCS-60,and 93.40±7.44 for MUIS.Anxiety(HADS-A≥8)was present in 39.5%of patients,depression(HADS-D≥8)in 41.0%,and both in 14.0%.Anxiety was significantly positively correlated with ambiguity,unpredictability,and total MUIS score(P<0.05).Depression was significantly positively correlated with ambiguity,information deficit,unpredictability,and total MUIS score(P<0.05).Most patients adopted an optimistic coping style,whereas the emotive style was least utilized.Evasive,fatalistic,and emotive coping styles were significantly positively correlated with illness uncertainty,whereas the self-reliant style was significantly negatively correlated with unpredictability(P<0.05).CONCLUSION Anxiety,depression,and coping styles in patients with CC undergoing radiotherapy correlate significantly with their level of illness uncertainty.Medical staff should address patients’psychological status and coping strategies by providing targeted information to reduce negative emotions,foster adaptive coping styles,and decrease illness uncertainty.
基金supported by the National Natural Science Foundation of China(Grant Nos.42005005 and 42030607)the Science and Technology Department of Shaanxi Province(Grant No.2024JC-YBQN-0248)+2 种基金the Education Department of Shaanxi Province(Grant No.23JK0686)a Xi'an Science and Technology Project(Grant No.22GXFW0131)the Young Talent fund of the University Association for Science and Technology in Shaanxi(Grant No.20210706)。
文摘The practical predictability of hail precipitation rates is significantly influenced by initial meteorological perturbations,stemming from various uncertainty sources.This study thoroughly assessed the predictability of hail precipitation rates in both climatologically and flow-dependent perturbed ensembles(CEns and FEns).These ensembles incorporated initial meteorological uncertainties derived separately from two operational ensembles.Leveraging the Weather Research and Forecasting model,we conducted cloud-resolving simulations of an idealized hailstorm.The practical predictability of hail responded comparably to both climatological and flow-dependent uncertainties,which was revealed across the entire ensemble of 50 members.However,a notable difference emerged when comparing the peak hail precipitation rates among the top 10 and bottom 10 members.From a thermodynamic perspective,the primary source of uncertainty in hail precipitation lay in the significant variations in temperature stratification,particularly at-20℃and-40℃.On the microphysical front,perturbations within CEns generated greater uncertainty in the process of rainwater collection by hail,contributing significantly to the microphysical growth mechanisms of hail.Furthermore,the findings reveal a stronger dependency of hail precipitation uncertainty on thermodynamic perturbations compared to kinematic perturbations.These insights enhance the comprehension of the practical predictability of hail and contribute significantly to the understanding of ensemble forecasting for hail events.
文摘Modern warfare is increasingly dependent on logistical support.The improvement in satellite imaging technology and the increase in the number of satellites in orbit have provided a technical foundation for using satellite observations in military logistics.Due to uncertainties in the processes of production,transport,and observation,the satellite-based observation and state estimation of military logistics exhibit characteristics of uncertainty.This paper proposes an attribute-based staged method to quantify uncertainty,addressing mixed uncertainties during satellite observations of logistics.First,Bayesian estimation is used to quantify the aleatory uncertainty in the process of single-stage logistics observation.Second,evidence theory is adopted to quantify the epistemic uncertainty caused by conflicts in multi-stage logistics observation results and the lack of understanding of production principles.Through the design of the identification framework and the dynamic optimization of basic reliability,key logistics elements are identified,enabling an accurate estimation of the state of military logistics.Finally,the application case is used to validate the effectiveness and accuracy of the proposed method.Compared to conventional evidence theory,the proposed method can make fuller use of multi-source information and reduce the relative error between the estimated value and the true value to below 0.015%.
文摘BACKGROUND Glaucoma,a condition frequently linked to severe depression,anxiety,and sleep disturbances,affects treatment adherence while potentially compromising effectiveness.AIM To explore illness uncertainty(IU),anxiety,and depressive symptoms in primary glaucoma and to discuss underlying triggers.METHODS We recruited 120 primary glaucoma cases between January 2022 and November 2023.The Mishel Uncertainty in Illness Scale(MUIS)and the Hospital Anxiety and Depression Scale(HADS)[include HADS-anxiety subscale(HADS-A)and HADS-depression subscale(HADS-D)]subscales,were used to assess IU and emotional distress(anxiety/depression),respectively.The MUIS-HADS subscale interrelationships were determined by Pearson correlation.IU-associated determinants were identified using univariate and binary logistic regression analyses.RESULTS The cohort showed a mean MUIS score of 79.73±8.97,corresponding to a moderately high IU level.The HADS-A and HADS-D scores averaged 6.57±3.89 and 7.08±5.05 points,respectively,with 15.00%of participants showing anxiety symptoms and 24.17%exhibiting depressive signs.Significant positive connections were observed between MUIS and both HADS-A(r=0.359,P<0.001)and HADSD(r=0.426,P<0.001).Univariate analysis revealed that disease duration,insomnia,monthly household income per capita,and the presence of comorbid chronic conditions were significantly associated with anxiety or depression.Multivariate analysis identified insomnia as a risk factor and higher monthly household income as a protective factor.CONCLUSION Patients with primary glaucoma experience moderate IU levels,generally low anxiety,and mild depression.Specifically,the anxiety and depression risks were 15.00%and 24.17%,respectively.A significant positive correlation existed between IU and anxiety/depression in these patients.Additionally,insomnia or lower monthly household income elevated anxiety/depression risks,enabling reliable anxiety/depression risk categorization among patients.
基金this paper was supported by Humanities and Social Science Youth Foundation of Ministry of Education,China[23YJC740018]。
文摘Interpreting is a fast-paced activity where interpreters must make quick choices when faced with uncertainty. This study looks at how professional interpreters handle linguistic uncertainty in English-Chinese sight translation, with a focus on the strategies they use. By analyzing transcription data alongside instructor evaluations, we found that interpreters relied most on creative interpretation and omission, while strategies like paraphrasing, simplification, transformation, addition, and generalization appeared less often. The results show a clear preference for strategies that keep communication flowing without adding unnecessary cognitive load. These findings support the Processing Economy Hypothesis, which suggests interpreters naturally seek efficient ways to process language while maintaining meaning. The study also highlights practical implications for interpreter training, emphasizing the value of flexible, economy-oriented strategies to help interpreters stay fluent under pressure.
文摘Response analysis of structures involving non-probabilistic uncertain parameters can be closely related to optimization.This paper provides a review on optimization-based methods for uncertainty analysis,with focusing attention on specific properties of adopted numerical optimization approaches.We collect and discuss the methods based on nonlinear programming,semidefinite programming,mixed-integer programming,mathematical programming with complementarity constraints,difference-of-convex programming,optimization methods using surrogate models and machine learning techniques,and metaheuristics.As a closely related topic,we also overview the methods for assessing structural robustness using non-probabilistic uncertainty modeling.We conclude the paper by drawing several remarks through this review.
文摘When assessing seismic liquefaction potential with data-driven models,addressing the uncertainties of establishing models,interpreting cone penetration tests(CPT)data and decision threshold is crucial for avoiding biased data selection,ameliorating overconfident models,and being flexible to varying practical objectives,especially when the training and testing data are not identically distributed.A workflow characterized by leveraging Bayesian methodology was proposed to address these issues.Employing a Multi-Layer Perceptron(MLP)as the foundational model,this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity,accuracy,and resistance to overfitting.The analysis revealed that,while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios,Bayesian neural networks showed great potential for preventing overfitting.Additionally,integrating decision thresholds through various evaluative principles offers insights for challenging decisions.Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data,employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics.Overall,the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation,showing improved robustness against overfitting and greater versatility in addressing practical challenges.This research contributes to the seismic liquefaction assessment field by providing a structured,adaptable methodology for accurate and reliable analysis.
基金supported by the National Key Research and Development Program of China (2023YFD1902703)the National Natural Science Foundation of China (Key Program) (U23A20158)。
文摘Cropland nitrate leaching is the major nitrogen(N) loss pathway, and it contributes significantly to water pollution. However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods. Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods. The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr^(-1)in 2010, ranging from 2.9 to 15.8 Tg N yr^(-1)across 27 different estimates. The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%. The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty. This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.
基金supported by the National Key Research&Development Program of China(2021YFB3301100)the National Natural Science Foundation of China(52004014)the Fundamental Research Funds for the Central Universities(ZY2406).
文摘This paper proposed a new systematic approach-functional evidential reasoning model(FERM) for exploring hazardous chemical operational accidents under uncertainty. First, FERM was introduced to identify various causal factors and their performance changes in hazardous chemical operational accidents, along with determining the functional failure link relationships. Subsequently, FERM was employed to elucidate both qualitative and quantitative operational accident information within a unified framework, which could be regarded as the input of information fusion to obtain the fuzzy belief distribution of each cause factor. Finally, the derived risk values of the causal factors were ranked while constructing multi-level accident causation chains to unveil the weak links in system functionality and the primary roots of operational accidents. Using the specific case of the “1·15” major explosion and fire accident at Liaoning Panjin Haoye Chemical Co., Ltd., seven causal factors and their corresponding performance changes were identified. Additionally, five accident causation chains were uncovered based on the fuzzy joint distribution of the functional assessment level(FAL) and reliability distribution(RD),revealing an overall increase in risk along the accident evolution path. The research findings demonstrated that FERM enabled the effective characterization, rational quantification and accurate analysis of the inherent uncertainties in hazardous chemical operational accident risks from a systemic perspective.
基金supported by the National Natural Science Foundation of China(Grant No.U23B20105).
文摘Engineering tests can yield inaccurate data due to instrument errors,human factors,and environmental interference,introducing uncertainty in numerical model updating.This study employs the probability-box(p-box)method for representing observational uncertainty and develops a two-step approximate Bayesian computation(ABC)framework using time-series data.Within the ABC framework,Euclidean and Bhattacharyya distances are employed as uncertainty quantification metrics to delineate approximate likelihood functions in the initial and subsequent steps,respectively.A novel variational Bayesian Monte Carlo method is introduced to efficiently apply the ABC framework amidst observational uncertainty,resulting in rapid convergence and accurate parameter estimation with minimal iterations.The efficacy of the proposed updating strategy is validated by its application to a shear frame model excited by seismic wave and an aviation pump force sensor for thermal output analysis.The results affirm the efficiency,robustness,and practical applicability of the proposed method.
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.42530801,42425208)the Natural Science Foundation of Hubei Province(China)(No.2023AFA001)+1 种基金the MOST Special Fund from State Key Laboratory of Geological Processes and Mineral Resources,China University of Geosciences(No.MSFGPMR2025-401)the China Scholarship Council(No.202306410181)。
文摘Geochemical survey data are essential across Earth Science disciplines but are often affected by noise,which can obscure important geological signals and compromise subsequent prediction and interpretation.Quantifying prediction uncertainty is hence crucial for robust geoscientific decision-making.This study proposes a novel deep learning framework,the Spatially Constrained Variational Autoencoder(SC-VAE),for denoising geochemical survey data with integrated uncertainty quantification.The SC-VAE incorporates spatial regularization,which enforces spatial coherence by modeling inter-sample relationships directly within the latent space.The performance of the SC-VAE was systematically evaluated against a standard Variational Autoencoder(VAE)using geochemical data from the gold polymetallic district in the northwestern part of Sichuan Province,China.Both models were optimized using Bayesian optimization,with objective functions specifically designed to maintain essential geostatistical characteristics.Evaluation metrics include variogram analysis,quantitative measures of spatial interpolation accuracy,visual assessment of denoised maps,and statistical analysis of data distributions,as well as decomposition of uncertainties.Results show that the SC-VAE achieves superior noise suppression and better preservation of spatial structure compared to the standard VAE,as demonstrated by a significant reduction in the variogram nugget effect and an increased partial sill.The SC-VAE produces denoised maps with clearer anomaly delineation and more regularized data distributions,effectively mitigating outliers and reducing kurtosis.Additionally,it delivers improved interpolation accuracy and spatially explicit uncertainty estimates,facilitating more reliable and interpretable assessments of prediction confidence.The SC-VAE framework thus provides a robust,geostatistically informed solution for enhancing the quality and interpretability of geochemical data,with broad applicability in mineral exploration,environmental geochemistry,and other Earth Science domains.
文摘This study contributes to renewable energy policy modeling by developing a dynamic decision-making framework that incorporates uncertainty,irreversibility,and the value of information.It responds to the growing need for structured tools to guide investments amid climate volatility,technological change,and economic risk.Grounded in decision theory—especially the work of Von Neumann,Morgenstern,and Savage—the framework models renewable energy investments using subjective probabilities and quasi-option value under evolving climate conditions.The empirical component focuses on ARAMCO’s renewable energy strategy,a corporate case illustrating how fossil-fuel-dependent entities can pivot toward sustainability.The analysis uses Net Present Value(NPV)modeling and real options analysis under different discount rates and carbon pricing scenarios to assess financial feasibility.Results show that lower discount rates and moderate carbon prices improve investment attractiveness,while high carbon pricing significantly reduces project viability.The study also highlights the policy relevance of this framework.Government subsidies,adaptive regulation,and public-private partnerships emerge as critical enablers of resilient investments.It further suggests that aligning ESG reporting standards with carbon pricing policies can strengthen market signals and encourage private capital flow into renewables.By integrating theoretical modeling with corporate investment realities,this chapter offers a replicable tool for policymakers and investors.Future research should expand its application across sectors and geographies to validate generalizability and improve planning in the transition toward low-carbon economies.
基金supported by the Postgraduate Education Reform and Quality Improvement Project of Henan Province(Grant Nos.YJS2023JD52 and YJS2025GZZ48)the Zhumadian 2023 Major Science and Technology Special Project(Grant No.ZMD SZDZX2023002)+1 种基金2025 Henan Province International Science and Technology Cooperation Project(Cultivation Project,No.252102521011)Research Merit-Based Funding Program for Overseas Educated Personnel in Henan Province(Letter of Henan Human Resources and Social Security Office[2025]No.37).
文摘This study introduces a hybrid Cuckoo Search-Deep Neural Network(CS-DNN)model for uncertainty quantification and composition optimization of Na_(1/2)Bi_(1/2)TiO_(3)(NBT)-based dielectric energy storage ceramics.Addressing the limitations of traditional ferroelectric materials—such as hysteresis loss and low breakdown strength under high electric fields—we fabricate(1−x)NBBT8-xBMT solid solutions via chemical modification and systematically investigate their temperature stability and composition-dependent energy storage performance through XRD,SEM,and electrical characterization.The key innovation lies in integrating the CS metaheuristic algorithm with a DNN,overcoming localminima in training and establishing a robust composition-property prediction framework.Our model accurately predicts room-temperature dielectric constant(ε_(r)),maximum dielectric constant(ε_(max)),dielectric loss(tanδ),discharge energy density(W_(rec)),and charge-discharge efficiency(η)from compositional inputs.A Monte Carlo-based uncertainty quantification framework,combined with the 3σ statistical criterion,demonstrates that CSDNN outperforms conventional DNN models in three critical aspects:Higher prediction accuracy(R^(2)=0.9717 vs.0.9382 for ε_(max));Tighter error distribution,satisfying the 99.7% confidence interval under the 3σprinciple;Enhanced robustness,maintaining stable predictions across a 25% composition span in generalization tests.While the model’s generalization is constrained by both the limited experimental dataset(n=45)and the underlying assumptions of MC-based data augmentation,the CS-DNN framework establishes a machine learning-guided paradigm for accelerated discovery of high-temperature dielectric capacitors through its unique capability in quantifying composition-level energy storage uncertainties.
基金Supported by Hunan Provincial Natural Science Foundation of China,No.2024JJ9579 and No.2025JJ80410The Science and Technology Innovation Program of Changde City,No.2023YD23.
文摘BACKGROUND Uncertainty in illness(UI)and fear of progression(FoP)are significant psycho-logical challenges for lung cancer patients.Coping styles and social support are critical mediators,influencing patients'ability to manage the emotional and psy-chological burden of UI and FoP.However,limited research has explored the chain mediation effect of these factors on the relationship between UI and FoP,particularly among Chinese lung cancer patients.Convenience sampling was used to recruit inpatients diagnosed with lung cancer at a tertiary hospital in Changde City between November and December 2023.A total of 320 participants completed the Mishel Uncertainty in Illness Scale,Simp-lified Coping Style Questionnaire,Mandarin Chinese Version of the Medical Outcomes Study Social Support Survey,and Fear of Progression Questionnaire-Short Form.The chain mediation analysis was performed using the PROCESS macro to examine the relationships between the variables.RESULTS The results revealed that UI had a significant direct effect on FoP(effect=0.224,95%CI:0.136-0.408).Additionally,three indirect pathways were identified:(1)Social support(effect=0.128,95%CI:0.045-0.153);(2)Coping style(effect=0.115,95%CI:0.048-0.157);and(3)Chain mediators involving social support and coping style(effect=0.072,95%CI:0.045-0.120).The total indirect effect of the three mediation paths is 31.5%.These results confirm that social support and coping style significantly mediate the relationship between UI and FoP.CONCLUSION Based on cross-sectional data and a chain mediation model,this study explored the mechanisms between UI,social support,coping style,and FOP.Patients with lung cancer have higher levels of FOP,and the results of this study revealed a correlation between these four factors.Social support and coping style partially mediated the effects of UI on FOP,and there was a chain-mediating effect between UI and FOP.Programs designed to strengthen social support networks should also incorporate training to develop adaptive coping strategies,ultimately reducing FOP and improving overall quality of life.
基金funded by the National Natural Science Foundation of China(Grant No.42425504).
文摘The change in interannual precipitation variability(P_(IAV)),especially the part driven by El Niño–Southern Oscillation over the Pacific,has sparked worldwide concern.However,it is plagued by substantial uncertainty,such as model uncertainty,internal variability,and scenario uncertainty.Single-model initial-condition large ensembles(SMILEs)and a polynomial fitting method were suggested to separate these uncertainty sources.However,the applicability of a widely used polynomial fitting method in the uncertainty separation of P_(IAV)projection remains unknown.This study compares three sources of uncertainty estimated from five SMILEs and 28 models with one ensemble member in phase 6 of the Coupled Model Intercomparison Project(CMIP6).Results show that the internal uncertainty based on models with one ensemble member calculated using the polynomial fitting method is significantly underestimated compared to SMILEs.However,internal variability in CMIP6 as represented in the pre-industrial control run,aligns closely with SMILEs.At 1.5°C warming above the preindustrial level,internal variability dominates globally,masking the externally forced P_(IAV)signal.At 2.0°C warming,both internal and model uncertainties are significant over regions like Central Africa,the equatorial Indian Ocean,the Maritime Continent,and the Arctic,while internal variability still dominates elsewhere.In some regions,the forced signal becomes distinguishable from internal variability.This study reveals the limitations of the polynomial fitting method in separating P_(IAV)projection uncertainties and emphasizes the importance of SMILEs for accurately quantifying uncertainty sources.It also suggests that improving the intermodel agreement at warming levels of 1.5°C and 2.0°C will not substantially reduce uncertainty in most regions.
基金supported by the Youth Innovation Promotion Association(YIPA)(No.E329290101)of the Chinese Academy of Sciences。
文摘Molten salt reactors(MSRs)are a promising candidate for Generation IV reactor technologies,and the small modular molten salt reactor(SM-MSR),which utilizes low-enriched uranium and thorium fuels,is regarded as a wise development path to accelerate deployment time.Uncertainty and sensitivity analyses of accidents guide nuclear reactor design and safety analyses.Uncertainty analysis can ascertain the safety margin,and sensitivity analysis can reveal the correlation between accident consequences and input parameters.Loss of forced cooling(LOFC)represents an accident scenario of the SM-MSR,and the study of LOFC could offer useful information to improve physical thermohydraulic and structural designs.Therefore,this study investigates the uncertainty of LOFC consequences and the sensitivity of related parameters.The uncertainty of the LOFC consequences was analyzed using the Monte Carlo method,and multiple linear regression was employed to analyze the sensitivity of the input parameters.The uncertainty and sensitivity analyses showed that the maximum reactor outlet fuel salt temperature was 725.5℃,which is lower than the acceptable criterion,and five important parameters influencing LOFC consequences were identified.
基金National Key R&D Program of China under Grant Nos.2018YFC1504504 and 2018YFC0809404。
文摘Damage to electrical equipment in an earthquake can lead to power outage of power systems.Seismic fragility analysis is a common method to assess the seismic reliability of electrical equipment.To further guarantee the efficiency of analysis,multi-source uncertainties including the structure itself and seismic excitation need to be considered.A method for seismic fragility analysis that reflects structural and seismic parameter uncertainty was developed in this study.The proposed method used a random sampling method based on Latin hypercube sampling(LHS)to account for the structure parameter uncertainty and the group structure characteristics of electrical equipment.Then,logistic Lasso regression(LLR)was used to find the seismic fragility surface based on double ground motion intensity measures(IM).The seismic fragility based on the finite element model of an±1000 kV main transformer(UHVMT)was analyzed using the proposed method.The results show that the seismic fragility function obtained by this method can be used to construct the relationship between the uncertainty parameters and the failure probability.The seismic fragility surface did not only provide the probabilities of seismic damage states under different IMs,but also had better stability than the fragility curve.Furthermore,the sensitivity analysis of the structural parameters revealed that the elastic module of the bushing and the height of the high-voltage bushing may have a greater influence.
基金supported in part by the National Natural Science Foundation of China under Grants 62301117,62001094,and U19B2014in part by the National Key Laboratory of Wireless Communications Foundation under Grant 2023KP01602in part by the Natural Science Foundation of Xinjiang Uygur Autonomous Region under Grant 2022D01B184 and 2022D01A297.
文摘In this paper,we investigate covert communications under multi-antenna detection,and explore the impacts of the warden’s channel state information(CSI)availability and the noise uncertainty on system covert capability.The detection performance at warden is analyzed in two cases under the perfect and statistical CSI at warden,respectively.In particular,for the former one,the warden utilizes the likelihood ratio(LR)detector,while for the latter one,the generalized likelihood ratio(GLR)detector is adopted.We first consider the scenario where the blocklength is finite,and demonstrate that the covert rate under both cases asymptotically goes to zero as the blocklength goes to infinity.Subsequently,we take the noise uncertainty at the warden into account which leads to positive covert rate,and characterize the covert rate for infinite blocklength.Specially,we derive the optimal transmit power for the legitimate transmitter that maximizes the covert rate.Besides,the rate gap under two cases,with different CSI availability at the warden,can be presented in closed form.Finally,numerical results validate the effectiveness of our theoretical analysis and also demonstrate the impacts of the factors studied on the system covertness.
