Background:In this study,we aimed to validate and test the reliability of the Yonsei lifestyle profile(YLP)in assessing multifaceted health lifestyle levels in a study population from the United States.Methods:The YLP...Background:In this study,we aimed to validate and test the reliability of the Yonsei lifestyle profile(YLP)in assessing multifaceted health lifestyle levels in a study population from the United States.Methods:The YLP-English version and health-promoting lifestyle profile II were administered to 100 individuals living in the United States.Concurrent validity was analyzed using Pearson's correlation coefficient,and discriminant validity was examined by comparing sex and age differences through t-tests and multiple variance analysis.Internal consistency was assessed using Cronbach'sαfor each sub-factor.Results:The YLP-English Version demonstrated concurrent validity with the Health-Promoting Lifestyle Profile II,showing consistent correlations for the total score(0.3,p<0.01)and frequency sub-factors(0.25-0.69,p<0.01).Among the satisfaction sub-factors,only nutrition showed a weak negative correlation(-0.19,p<0.01);all others were nonsignificant.Discriminant validity revealed no significant sex differences,but physical activity frequency varied across age groups.Internal consistency was high(Cronbach'sα=0.80-0.86).Conclusion:In this study,we validated the YLP-English version as a reliable instrument for assessing health-related lifestyle behaviors.The YLP uniquely captures both lifestyle frequency and satisfaction,offering a comprehensive perspective on health behaviors.Although this tool is currently most applicable in population-level studies,future research should establish clinical thresholds to enhance its utility in individualized health assessments and interventions.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in...While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.展开更多
This study examines the reliability and validity of AI-generated scoring for continuation writing tasks.By comparing GPT-4 with eight experienced human raters across 21 student responses,it evaluates AI’s consistency...This study examines the reliability and validity of AI-generated scoring for continuation writing tasks.By comparing GPT-4 with eight experienced human raters across 21 student responses,it evaluates AI’s consistency,severity,and alignment with human scoring criteria.Results show that AI exhibits high self-consistency and adapts effectively to different scoring roles(e.g.,teacher vs.highstakes rater).However,AI scores were more lenient than human raters and demonstrated divergent evaluation focuses—prioritizing narrative coherence and emotional depth,while teachers emphasized linguistic accuracy and richness of detail.The findings suggest AI’s potential as a supplementary assessment tool,offering rapid,holistic feedback,but highlight the need for further calibration to align with educational standards.Implications include exploring hybrid evaluation models that leverage the strengths of both AI and human raters to achieve more equitable,efficient,and pedagogically meaningful writing assessments.展开更多
A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and rel...A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.展开更多
In this work,we demonstrated the InSnO(ITO)TFTs passivated with SiO_(2)via the PECVD process compatible with large-area production for the first time.The passivated ITO TFTs with various channel thicknesses(t_(ch)=4,5...In this work,we demonstrated the InSnO(ITO)TFTs passivated with SiO_(2)via the PECVD process compatible with large-area production for the first time.The passivated ITO TFTs with various channel thicknesses(t_(ch)=4,5,6 nm)exhibit excellent electrical performance and superior uniformity.The reliability properties of ITO TFTs were evaluated in detail under positive bias stress(PBS)conditions before and after passivation.Compared to the devices without passivation,the passivated devices have only 50%threshold voltage degradation(ΔV_(th))and 50%newly generated traps due to excellent isolation of the ambient atmosphere.The negligible performance degradation of ITO TFTs with passivation during negative bias stress(NBS)and negative bias temperature stress(NBTS)verifies the outstanding immunity to the water vapor of the SiO_(2)passivation layer.Overall,the ITO TFT with the t_(ch)of 6 nm and with SiO_(2)passivation exhibits the best performance in terms of electrical properties,uniformity,and reliability,which is promising in large-area production.展开更多
Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our ...Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our members have been very active over the past 12 months and this has resulted in new technologies for the production of technical fibres and fabrics,the introduction of AI and machine learning into process control systems and significant advances in materials testing,”says BTMA CEO Jason Kent.“There’s real excitement about what can be achieved in 2026 as we look ahead to upcoming exhibitions such as JEC Composites in Paris in March and Techtextil in Frankfurt in April.”展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
To study the durability of concrete in harsh environments in Northwest China,concrete was prepared with various durability-improving materials such as concrete anti-erosion inhibitor(SBT-TIA),acrylate polymer(AP),supe...To study the durability of concrete in harsh environments in Northwest China,concrete was prepared with various durability-improving materials such as concrete anti-erosion inhibitor(SBT-TIA),acrylate polymer(AP),super absorbent resin(SAP).The erosion mode and internal deterioration mechanism under salt freeze-thaw cycle and dry-wet cycle were explored.The results show that the addition of enhancing materials can effectively improve the resistance of concrete to salt freezing and sulfate erosion:the relevant indexes of concrete added with X-AP and T-AP are improved after salt freeze-thaw cycles;concrete added with SBTTIA shows optimal sulfate corrosion resistance;and concrete added with AP displays the best resistance to salt freezing.Microanalysis shows that the increase in the number of cycles decreases the generation of internal hydration products and defects in concrete mixed with enhancing materials and improves the related indexes.Based on the Wiener model analysis,the reliability of concrete with different lithologies and enhancing materials is improved,which may provide a reference for the application of manufactured sand concrete and enhancing materials in Northwest China,especially for the study of the improvement effects and mechanism of enhancing materials on the performance of concrete.展开更多
Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the ten...Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the tensile stress in the area has exceeded the tensile strength of the soil,leading to local instability.To explore the impact of tensile failure behavior on the stability and failure modes of reservoir landslides,the Huangtupo Riverside Slump#1 is taken as a case study.By considering local tensile failure,potential tensile cracks are incorporated into the analysis via the limit equilibrium method and reliability theory.The reliability of landslides under different tensile failure scenarios is quantified.Strain-softening characteristics of the soil are combined to further analyze the failure transmission path of the landslide.Finally,these potential failure modes were validated through physical model tests.The results show that cracks developing at rear positions reduce the stability of the slope and increase the probability of instability.During the destruction process,retrogressive failures with multiple sliding surfaces are likely to occur.However,tensile failure at the forefront reduces the likelihood of an individual slide mass descending.Progressive failure results in both regular and skip transmission patterns.Additionally,cracks and water level changes can also lead to shifts in the positions of the most dangerous blocks.Therefore,in practical landslide analysis and prevention,it is necessary to consider local tensile damage and identify potential tensile crack locations in advance to optimize prevention measures and accurately evaluate landslide risk.展开更多
In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order relia...In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.展开更多
With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raisin...With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.展开更多
Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen t...Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.展开更多
The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one...The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.展开更多
To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a mult...To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.展开更多
Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be per...Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.展开更多
As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effe...As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effective for traditional networks and applications but lack the capabilities to address DL-specific threats,such asmodel inversion,membership inference,and adversarial attacks.This review provides a comprehensive analysis of penetration testing for the privacy of DL models,examining the shortfalls of existing frameworks,tools,and testing methodologies.Through systematic evaluation of existing literature and empirical analysis,we identify three major contributions:(i)a critical assessment of traditional penetration testing frameworks’inadequacies when applied to DL-specific privacy vulnerabilities,(ii)a comprehensive evaluation of state-of-the-art privacy-preserving methods and their integration with penetration testing workflows,and(iii)the development of a structured framework that combines reconnaissance,threat modeling,exploitation,and post-exploitation phases specifically tailored for DL privacy assessment.Moreover,this review evaluates popular solutions such as IBMAdversarial Robustness Toolbox and TensorFlowPrivacy,alongside privacy-preserving techniques(e.g.,Differential Privacy,Homomorphic Encryption,and Federated Learning),which we systematically analyze through comparative studies of their effectiveness,computational overhead,and practical deployment constraints.While these techniques offer promising safeguards,their adoption is hindered by accuracy loss,performance overheads,and the rapid evolution of attack strategies.Our findings reveal that no single existing solution provides comprehensive protection,which leads us to propose a hybrid approach that strategically combines multiple privacy-preserving mechanisms.The findings of this survey underscore an urgent need for automated,regulationcompliant penetration testing frameworks specifically tailored to DL systems.We argue for hybrid privacy solutions that combinemultiple protectivemechanisms to ensure bothmodel accuracy and privacy.Building on our analysis,we present actionable recommendations for developing adaptive penetration testing strategies that incorporate automated vulnerability assessment,continuous monitoring,and regulatory compliance verification.展开更多
The performance degradation of micro light-emitting diodes(micro-LEDs)is closely associated with the deterioration of sidewall passivation layers under prolonged electrical bias.We investigate reliability improvements...The performance degradation of micro light-emitting diodes(micro-LEDs)is closely associated with the deterioration of sidewall passivation layers under prolonged electrical bias.We investigate reliability improvements in 20μm×20μm InGaN/GaN blue micro-LEDs by suppressing the formation of an unstable interfacial layer during sidewall passivation.SiO_(2)is deposited on the etched mesa sidewalls using either Sputtering or plasma-enhanced chemical vapor deposition(PECVD).Comparative analysis reveals that PECVD-passivated devices experience more severe performance degradation,primarily due to the increased leakage current.After 100 h of accelerated aging,external quantum efficiency decreases by 44%in PECVD-passivated samples,whereas Sputter-passivated devices exhibit only an11%reduction.This discrepancy is attributed to the formation of a thicker and chemically unstable gallium oxynitride(Ga-O_(X)-N_(1-X))interfacial layer at the SiO_(2)∕GaN-based interface,which facilitates the generation of sidewall defects.Suppressing the formation of this interlayer enhances the crystallinity and structural stability of the passivation layer,thereby mitigating the activation of point defects.Notably,Sputter deposition is more effective in minimizing the formation of Ga-O-N interlayer.These findings emphasize the critical role of achieving low-defect-density sidewall passivation to improve the reliability of micro-LEDs for next-generation high-resolution display applications.展开更多
Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhib...Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.展开更多
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Republic of Korea(NRF-2021S1A3A2A02096338).
文摘Background:In this study,we aimed to validate and test the reliability of the Yonsei lifestyle profile(YLP)in assessing multifaceted health lifestyle levels in a study population from the United States.Methods:The YLP-English version and health-promoting lifestyle profile II were administered to 100 individuals living in the United States.Concurrent validity was analyzed using Pearson's correlation coefficient,and discriminant validity was examined by comparing sex and age differences through t-tests and multiple variance analysis.Internal consistency was assessed using Cronbach'sαfor each sub-factor.Results:The YLP-English Version demonstrated concurrent validity with the Health-Promoting Lifestyle Profile II,showing consistent correlations for the total score(0.3,p<0.01)and frequency sub-factors(0.25-0.69,p<0.01).Among the satisfaction sub-factors,only nutrition showed a weak negative correlation(-0.19,p<0.01);all others were nonsignificant.Discriminant validity revealed no significant sex differences,but physical activity frequency varied across age groups.Internal consistency was high(Cronbach'sα=0.80-0.86).Conclusion:In this study,we validated the YLP-English version as a reliable instrument for assessing health-related lifestyle behaviors.The YLP uniquely captures both lifestyle frequency and satisfaction,offering a comprehensive perspective on health behaviors.Although this tool is currently most applicable in population-level studies,future research should establish clinical thresholds to enhance its utility in individualized health assessments and interventions.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
文摘While parametric Software Reliability Growth Models(SRGMs)serve as a cornerstone in software reliability assessment,their reliance on known fault-detection time distributions often presents a significant limitation in practical software testing.In this study,the authors develop a novel shaperestricted spline estimator for quantifying software reliability.Compared with parametric SRGMs,the proposed estimator not only shares a key characteristic with parametric SRGMs,but also obviates the need for specifying fault-detection time distributions.More importantly,it effectively utilizes the critical shape information of the mean value function(MVF)of fault-detection process,a detail seldom considered in prior work.Moreover,the authors investigate the predictive performance of the proposed methods by employing the so-called one-step look-ahead prediction method.Furthermore,the authors show that under certain conditions,the shape-restricted spline estimator will attain the point-wise convergence rate O_P(n~(-3/7)).In numerical experiment,the authors show that spline estimators under restriction demonstrate competitive performance compared to parametric and certain non-parametric models.
文摘This study examines the reliability and validity of AI-generated scoring for continuation writing tasks.By comparing GPT-4 with eight experienced human raters across 21 student responses,it evaluates AI’s consistency,severity,and alignment with human scoring criteria.Results show that AI exhibits high self-consistency and adapts effectively to different scoring roles(e.g.,teacher vs.highstakes rater).However,AI scores were more lenient than human raters and demonstrated divergent evaluation focuses—prioritizing narrative coherence and emotional depth,while teachers emphasized linguistic accuracy and richness of detail.The findings suggest AI’s potential as a supplementary assessment tool,offering rapid,holistic feedback,but highlight the need for further calibration to align with educational standards.Implications include exploring hybrid evaluation models that leverage the strengths of both AI and human raters to achieve more equitable,efficient,and pedagogically meaningful writing assessments.
基金Project supported by the National Natural Science Foundation of China(Grant No.12472033)。
文摘A stochastic predator-prey system with Markov switching is explored.We have developed a new chasing technique to efficiently solve the Fokker-Planck-Kolmogorov and backward Kolmogorov equations.Dynamic balance and reliability of the switching system are evaluated via stationary probability density function and first-passage failure theory,taking into account factors such as switching frequencies,noise intensities,and initial conditions.Results reveal that Markov switching leads to stochastic P-bifurcation,enhancing dynamic balance and reducing white-noise-induced oscillations.But frequent switching can heighten initial value dependence,harming reliability.Further,the influence of the subsystem on the switching system is not proportional to its action probabilities.Monte Carlo simulations validate the findings,offering an in-depth exploration of these dynamics.
基金supported in part by the National Natural Science Foundation of China(62404110,62274033)Natural Science Foundation of Jiangsu Province(BK20221453)+1 种基金Fundamental Research Funds for the Central UniversitiesNatural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(NY223159)。
文摘In this work,we demonstrated the InSnO(ITO)TFTs passivated with SiO_(2)via the PECVD process compatible with large-area production for the first time.The passivated ITO TFTs with various channel thicknesses(t_(ch)=4,5,6 nm)exhibit excellent electrical performance and superior uniformity.The reliability properties of ITO TFTs were evaluated in detail under positive bias stress(PBS)conditions before and after passivation.Compared to the devices without passivation,the passivated devices have only 50%threshold voltage degradation(ΔV_(th))and 50%newly generated traps due to excellent isolation of the ambient atmosphere.The negligible performance degradation of ITO TFTs with passivation during negative bias stress(NBS)and negative bias temperature stress(NBTS)verifies the outstanding immunity to the water vapor of the SiO_(2)passivation layer.Overall,the ITO TFT with the t_(ch)of 6 nm and with SiO_(2)passivation exhibits the best performance in terms of electrical properties,uniformity,and reliability,which is promising in large-area production.
文摘Members of the British Textile Machinery Association(BTMA)can look back on 2025 as a year marked by notable technological advances and continued progress in global trade,despite an uncertain and volatile market.“Our members have been very active over the past 12 months and this has resulted in new technologies for the production of technical fibres and fabrics,the introduction of AI and machine learning into process control systems and significant advances in materials testing,”says BTMA CEO Jason Kent.“There’s real excitement about what can be achieved in 2026 as we look ahead to upcoming exhibitions such as JEC Composites in Paris in March and Techtextil in Frankfurt in April.”
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金Funded by the National Natural Science Foundation of China(No.52178216)the Research on the Durability and Application of High-performance Concrete for Highway Engineering in the Cold and Arid Salt Areas of Northwest China(No.2022-24)the Construction Project of the Scientific Research Platform of Provincial Enterprises Supported by the Capital Operating Budget of Gansu Province(No.2023GZ018)。
文摘To study the durability of concrete in harsh environments in Northwest China,concrete was prepared with various durability-improving materials such as concrete anti-erosion inhibitor(SBT-TIA),acrylate polymer(AP),super absorbent resin(SAP).The erosion mode and internal deterioration mechanism under salt freeze-thaw cycle and dry-wet cycle were explored.The results show that the addition of enhancing materials can effectively improve the resistance of concrete to salt freezing and sulfate erosion:the relevant indexes of concrete added with X-AP and T-AP are improved after salt freeze-thaw cycles;concrete added with SBTTIA shows optimal sulfate corrosion resistance;and concrete added with AP displays the best resistance to salt freezing.Microanalysis shows that the increase in the number of cycles decreases the generation of internal hydration products and defects in concrete mixed with enhancing materials and improves the related indexes.Based on the Wiener model analysis,the reliability of concrete with different lithologies and enhancing materials is improved,which may provide a reference for the application of manufactured sand concrete and enhancing materials in Northwest China,especially for the study of the improvement effects and mechanism of enhancing materials on the performance of concrete.
基金supported by the Major Program of National Natural Science Foundation of China(Grant No.42090055)the National Key ScientificInstruments and Equipment Development Projects of China(Grant No.41827808)the National Nature Science Foundation of China(Grant No.42207216).
文摘Reservoir-induced landslides in China's Three Gorges Reservoir area are prone to tensile cracks due to the influenceof their own weight and fluctuationsin water levels.The presence of cracks indicates that the tensile stress in the area has exceeded the tensile strength of the soil,leading to local instability.To explore the impact of tensile failure behavior on the stability and failure modes of reservoir landslides,the Huangtupo Riverside Slump#1 is taken as a case study.By considering local tensile failure,potential tensile cracks are incorporated into the analysis via the limit equilibrium method and reliability theory.The reliability of landslides under different tensile failure scenarios is quantified.Strain-softening characteristics of the soil are combined to further analyze the failure transmission path of the landslide.Finally,these potential failure modes were validated through physical model tests.The results show that cracks developing at rear positions reduce the stability of the slope and increase the probability of instability.During the destruction process,retrogressive failures with multiple sliding surfaces are likely to occur.However,tensile failure at the forefront reduces the likelihood of an individual slide mass descending.Progressive failure results in both regular and skip transmission patterns.Additionally,cracks and water level changes can also lead to shifts in the positions of the most dangerous blocks.Therefore,in practical landslide analysis and prevention,it is necessary to consider local tensile damage and identify potential tensile crack locations in advance to optimize prevention measures and accurately evaluate landslide risk.
基金National Natural Science Foundation of China(No.52375236)Fundamental Research Funds for the Central Universities of China(No.23D110316)。
文摘In reliability analyses,the absence of a priori information on the most probable point of failure(MPP)may result in overlooking critical points,thereby leading to biased assessment outcomes.Moreover,second-order reliability methods exhibit limited accuracy in highly nonlinear scenarios.To overcome these challenges,a novel reliability analysis strategy based on a multimodal differential evolution algorithm and a hypersphere integration method is proposed.Initially,the penalty function method is employed to reformulate the MPP search problem as a conditionally constrained optimization task.Subsequently,a differential evolution algorithm incorporating a population delineation strategy is utilized to identify all MPPs.Finally,a paraboloid equation is constructed based on the curvature of the limit-state function at the MPPs,and the failure probability of the structure is calculated by using the hypersphere integration method.The localization effectiveness of the MPPs is compared through multiple numerical cases and two engineering examples,with accuracy comparisons of failure probabilities against the first-order reliability method(FORM)and the secondorder reliability method(SORM).The results indicate that the method effectively identifies existing MPPs and achieves higher solution precision.
基金supported by the Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(KLCS20240211)。
文摘With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.
基金financially supported by ARC Linkage project(LP210200642)ARC Center of Excellence for Quantum Biotechnology(grant no.CE230100021)+1 种基金National Health and Medical Research Council Investigator Fellowship—(grant no.APP2017499)Chan Zuckerberg Initiative Deep Tissue Imaging Phase 2(grant no.DT12-0000000182).
文摘Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.
基金supported by the National Natural Science Foundation of China under Grant 52022016China Postdoctoral Science Foundation under grant 2021M693711Fundamental Research Funds for the Central Universities under grant 2021CDJQY-037.
文摘The rapid development of wind energy in the power sectors raises the question about the reliability of wind turbines for power system planning and operation.The electrical subsystem of wind turbines(ESWT),which is one of the most vulnerable parts of the wind turbine,is investigated in this paper.The hygrothermal aging of power electronic devices(PEDs)is modeled for the first time in the comprehensive reliability evaluation of ESWT,by using a novel stationary“circuit-like”approach.First,the failure mechanism of the hygrothermal aging,which includes the solder layer fatigue damage and packaging material performance degradation,is explained.Then,a moisture diffusion resistance concept and a hygrothermal equivalent circuit are proposed to quantitate the hygrothermal aging behavior.A conditional probability function is developed to calculate the time-varying failure rate of PEDs.At last,the stochastic renewal process is simulated to evaluate the reliability for ESWT through the sequential Monte Carlo simulation,in which failure,repair,and replacement states of devices are all included.The effectiveness of our proposed reliability evaluation method is verified on an ESWT in a 2 MW wind turbine use time series data collected from a wind farm in China.
基金supported by Natural Science Foundation of China(NSFC),Grant number 5247052693.
文摘To address the insufficient prediction accuracy of multi-state parameters in electro-hydraulic servo material fatigue testing machines under complex loading and nonlinear coupling conditions,this paper proposes a multivariate sequence-to-sequence prediction model integrating a Long Short-Term Memory(LSTM)encoder,a Gated Recurrent Unit(GRU)decoder,and a multi-head attention mechanism.This approach enhances prediction accuracy and robustness across different control modes and load spectra by leveraging multi-channel inputs and cross-variable feature interactions,thereby capturing both short-term high-frequency dynamics and long-term slow drift characteristics.Experiments using long-term data from real test benches demonstrate that the model achieves a stable MSE below 0.01 on the validation set,with MAE and RMSE of approximately 0.018 and 0.052,respectively,and a coefficient of determination reaching 0.98.This significantly outperforms traditional identification methods and single RNN models.Sensitivity analysis indicates that a prediction stride of 10 achieves an optimal balance between accuracy and computational overhead.Ablation experiments validated the contribution of multi-head attention and decoder architecture to enhancing cross-variable coupling modeling capabilities.This model can be applied to residualdriven early warning in health monitoring,and risk assessment with scheme optimization in test design.It enables near-real-time deployment feasibility,providing a practical data-driven technical pathway for reliability assurance in advanced equipment.
基金supported by the National Key R&D Program of China(2024YFA1802300)the Major Science and Technology Program of Hainan Province(ZDKJ2021037)+4 种基金the Regional Innovation and Development Joint Fund of the National Natural Science Foundation of China(U24A20677)Hainan Province Science and Technology Special Fund(ZDYF2020117,ZDY2024SHFZ143)Hainan Province Science and TechnologyProject(LCXY202102,LCYX202203,LCYX202301,LCYx202502)Innovative research project for postgraduate students in Hainan Medical University(HYYB2021A05)the Hainan Province Clinical Medical Center,and the specific research fund of The Innovation Platform for Academicians of Hainan Province(YSPTZX202310).
文摘Patients affected by monogenic diseases impose a substantial burden on both themselves and their families.The primary preventive measure,i.e.,invasive prenatal diagnosis,carries a risk of miscarriage and cannot be performed early in pregnancy.Hence,there is a need for non-invasive prenatal testing(NIPT)for monogenic diseases.By utilizing enriched cell-free fetal DNA(cffDNA)from maternal plasma,we refine the NIPT method,which combines targeted region capture technology,haplotyping,and analysis of informative site frequency.We apply this method to 93 clinical families at genetic risk for thalassemia,encompassing various genetic variant types,to establish a workflow and evaluate its efficiency.Our approach requires only 3 ng of DNA input to generate 0.1 Gb informative target genomic data and leverages a minimum of 3%cffDNA.This method has a 98.16%success rate and 100%concordance with conventional invasive methods.Furthermore,we demonstrate the ability to analyze fetal genotypes as early as eight weeks of gestation.This study establishes an optimized NIPT method for the early detection of various thalassemia disorders during pregnancy.This technique demonstrates high accuracy and potential for clinical application in prenatal diagnosis.
基金supported in part by the Tianjin Natural Science Foundation Project(24JCZDJC01000)the Fundamental Research Funds for the Central Universities of China(No.3122025091).
文摘As deep learning(DL)models are increasingly deployed in sensitive domains(e.g.,healthcare),concerns over privacy and security have intensified.Conventional penetration testing frameworks,such asOWASP and NIST,are effective for traditional networks and applications but lack the capabilities to address DL-specific threats,such asmodel inversion,membership inference,and adversarial attacks.This review provides a comprehensive analysis of penetration testing for the privacy of DL models,examining the shortfalls of existing frameworks,tools,and testing methodologies.Through systematic evaluation of existing literature and empirical analysis,we identify three major contributions:(i)a critical assessment of traditional penetration testing frameworks’inadequacies when applied to DL-specific privacy vulnerabilities,(ii)a comprehensive evaluation of state-of-the-art privacy-preserving methods and their integration with penetration testing workflows,and(iii)the development of a structured framework that combines reconnaissance,threat modeling,exploitation,and post-exploitation phases specifically tailored for DL privacy assessment.Moreover,this review evaluates popular solutions such as IBMAdversarial Robustness Toolbox and TensorFlowPrivacy,alongside privacy-preserving techniques(e.g.,Differential Privacy,Homomorphic Encryption,and Federated Learning),which we systematically analyze through comparative studies of their effectiveness,computational overhead,and practical deployment constraints.While these techniques offer promising safeguards,their adoption is hindered by accuracy loss,performance overheads,and the rapid evolution of attack strategies.Our findings reveal that no single existing solution provides comprehensive protection,which leads us to propose a hybrid approach that strategically combines multiple privacy-preserving mechanisms.The findings of this survey underscore an urgent need for automated,regulationcompliant penetration testing frameworks specifically tailored to DL systems.We argue for hybrid privacy solutions that combinemultiple protectivemechanisms to ensure bothmodel accuracy and privacy.Building on our analysis,we present actionable recommendations for developing adaptive penetration testing strategies that incorporate automated vulnerability assessment,continuous monitoring,and regulatory compliance verification.
基金supported by the Samsung Research Funding&Incubation Center of Samsung Electronics under Project No.SRFC-MA2402-05supported by the KENTECH Center for Shared Research Facilities。
文摘The performance degradation of micro light-emitting diodes(micro-LEDs)is closely associated with the deterioration of sidewall passivation layers under prolonged electrical bias.We investigate reliability improvements in 20μm×20μm InGaN/GaN blue micro-LEDs by suppressing the formation of an unstable interfacial layer during sidewall passivation.SiO_(2)is deposited on the etched mesa sidewalls using either Sputtering or plasma-enhanced chemical vapor deposition(PECVD).Comparative analysis reveals that PECVD-passivated devices experience more severe performance degradation,primarily due to the increased leakage current.After 100 h of accelerated aging,external quantum efficiency decreases by 44%in PECVD-passivated samples,whereas Sputter-passivated devices exhibit only an11%reduction.This discrepancy is attributed to the formation of a thicker and chemically unstable gallium oxynitride(Ga-O_(X)-N_(1-X))interfacial layer at the SiO_(2)∕GaN-based interface,which facilitates the generation of sidewall defects.Suppressing the formation of this interlayer enhances the crystallinity and structural stability of the passivation layer,thereby mitigating the activation of point defects.Notably,Sputter deposition is more effective in minimizing the formation of Ga-O-N interlayer.These findings emphasize the critical role of achieving low-defect-density sidewall passivation to improve the reliability of micro-LEDs for next-generation high-resolution display applications.
基金supported by the Fundamental Research Funds for the Central Universities in UIBE under Grant No.CXTD14-05。
文摘Smartphone-based electrocardiograms(ECGs)are increasingly utilized for monitoring atrial fibrillation(AF)recurrence after catheter ablation(CA),referred to as smartphone AF burden(SMURDEN).The SMURDEN data often exhibit complex patterns of zero AF episodes,which may arise from either true AF-free status(structural zeros)or missed AF episodes due to intermittent monitoring(random zeros).Such a mixture of AF-free and at-risk patients can lead to zero-inflation in the data.The authors propose a novel zero-inflation test for binomial regression models to identify recurrence-free AF populations.Unlike traditional approaches requiring fully specified zero-inflated models,the proposed test utilizes a weighted average of the discrepancies between observed and expected zero proportions,with weights determined by binomial sizes.A closed-form test statistic is developed,and its asymptotic distribution is derived using estimating equations.Simulations demonstrate superior performance over existing methods,and real-world AF monitoring data validate the practical utility of our proposed test.
