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
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
This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation...This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.展开更多
Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust...Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.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
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
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
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
This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This eva...This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.展开更多
This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by spec...This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by specimen geometry.A rapid,non-destructive testing method based on the impulse excitation technique(IET)and a shape factor coefficient was proposed.Three types of shaped ceramic tiles were selected.The elastic modulus of standard rectangular specimens obtained by destructive sampling was used as the reference value,and the shape factor coefficient for each tile type was calibrated by combining the mass and fundamental frequency of the whole tile.Using this coefficient,the elastic modulus of whole tiles was calculated solely from non-destructively measured mass and frequency.The results show that the deviation between the elastic modulus derived from the proposed method and that from destructive testing is less than 5%,confirming the accuracy and reliability of the approach.The method overcomes the shape restrictions inherent in traditional testing,offering a fast,non-destructive solution suitable for onsite quality assessment and process control during the production of shaped ceramic components.展开更多
In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean func...In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.展开更多
The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we condu...The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we conducted a series of triaxial tests on calcareous sand with varying Dri and stress paths,examining particle breakage and critical state behavior.Key findingsinclude:(1)At a constant stress ratio(η),B follows a hyperbolic relationship with mean effective stress(p'),and for a given p',B increases proportionally withη;(2)The critical state line(CSL)moves downward with increasing Dri,whereas the critical state friction angle(φcs)decreases with increasing B.Based on these findings,we propose a unifiedbreakage evolution model to quantify particle breakage in calcareous sand under various loading conditions.Integrating this model with the Normal Consolidation Line(NCL)and CSL equations,we successfully simulate the steepening of NCL and CSL slopes as B increases with the onset of particle breakage.Furthermore,we quantitatively evaluate the effect of B onφcs.Finally,within the framework of Critical State Soil Mechanics and Hypoplasticity theory,we develop a hypoplastic model incorporating B and Dri.The model is validated through strong agreement with experimental results across various initial relative densities,stress paths and drainage conditions.展开更多
X-rays are widely used in the non-destructive testing(NDT)of electrical equipment.Radio frequency(RF)electron linear accelerators can generate MeV high-energy X-rays with strong penetrating ability;however,the system ...X-rays are widely used in the non-destructive testing(NDT)of electrical equipment.Radio frequency(RF)electron linear accelerators can generate MeV high-energy X-rays with strong penetrating ability;however,the system generally has a large scale,which is not suitable for on-site testing.Compared with the S-band(S-linac)at the same stage of beam energy,the accelerator working in the X-band(X-linac)can compress the facility scale by over 2/3 in the longitudinal direction,which is convenient for the on-site NDT of electrical equipment.To address the beam quality and design complexity simultaneously,the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ),which is a multi-objective genetic algorithm(MOGA),was developed to optimize the cavity chain design of the X-linac.Additionally,the designs of the focusing coils,electron gun,and RF couplers,which are other key components of the X-linac,were introduced in this context.In particular,the focusing coil distributions were optimized using a genetic algorithm.Furthermore,after designing such key components,PARMELA software was adopted to perform beam dynamics calculations with the optimized accelerating fields and magnetic fields.The results show that the beam performance was obtained with a capture ratio of more than 90%,an energy spread of less than 10%,and an average energy of approximately 3 MeV.The design and simulation results indicate that the proposed NSGAⅡ-based approach is feasible for X-linac accelerator design.Furthermore,it can be generalized as a universal technique for industrial electron linear accelerators provided that specific optimization objectives and constraints are set according to different application scenarios and requirements.展开更多
Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance fo...Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance for mass screening of cancer,so the cost-effective and simple detection methods of sEVpp are urgently demanded.Herein,we constructed a paper-based multichannel sEVpp POCT device(sEVpp-PAD)enabled by functional DNA probes and metal-organic framework(MOF).The core components are aptamer/MOF-modified paper chips.The modified aptamers can immunocapture the sEV expressing corresponding proteins,while the modified MOF can provide abundant sites for aptamer-modification,reduce the nonspecific protein absorption,and act as reference for ratiometric detection.Simply powered by two syringes,the sEVpp-PAD can efficiently capture sEVs expressing corresponding protein from cell culture media and sera.Furthermore,a detection probe(DP)consisted of CD63 aptamer and G-quadruplex was developed for the colorimetric detection of captured sEVs.Utilizing this device,the sEVpp in various hepatocellular carcinoma cell culture medium and,more importantly,in human sera can be accurately determined,only with$2 device,$0.2 detection reagents and 1.8 h procedure.This simple strategy for sEVpp detection can innovatively promote the POCT and subtyping of cancer based on sEV-related liquid biopsy.展开更多
AIM:To compare the tear film quantity and stability parameters in keratoconus(KCN)and normal eyes using test breakup time(TBUT),noninvasive TBUT(NITBUT),and Schirmer test.METHODS:All participants(n=166),including pati...AIM:To compare the tear film quantity and stability parameters in keratoconus(KCN)and normal eyes using test breakup time(TBUT),noninvasive TBUT(NITBUT),and Schirmer test.METHODS:All participants(n=166),including patients with KCN and age-matched healthy individuals with normal corneas,were recruited from those referred to Farabi Eye Hospital,Iran,in 2023.To better account for genetic and environmental factors,the control group comprised healthy individuals who were relatives of KCN patients and had normal corneal topography.Tear quantity parameters were evaluated in the following order:NITBUT,TBUT,and Schirmer tests.RESULTS:The mean age of cases in KCN(61.7%males)and normal(63.5%males)participants was 27.54±5.44y(range 19 to 38)and 27.52±5.63y(range 20 to 38),respectively(P=0.976).NIBUT,TBUT,and Schirmer’s tests were significantly lower in KCN group compared to normal controls(all P<0.001).The mean difference for NIBUT was-7.81s(P<0.001),and for TBUT was-7.61s(P<0.001).Schirmer test values were also significantly lower in the KCN group,with a mean difference of-5.61 mm compared to normal people(P<0.001).CONCLUSION:Our findings demonstrate significant tear film impairment in KCN.The reductions in NIBUT,TBUT and Schirmer scores highlight an underlying tear film dysfunction in KCN that extends beyond the morphological changes of the cornea.展开更多
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.
文摘This study aims to construct a virtual twin testing framework for the safety of the intended functionality of intelligent connected vehicles to address the safety requirements of intelligent driving and transportation systems.The research methods include the construction of a theoretical model of safety for intelligent connected vehicles based on the concept of virtual twins,the correlation study between key concepts and functional safety,and the application research of virtual twin technology in the safety testing of intelligent connected vehicles.The results reveal that the virtual twin testing framework can effectively enhance the functional safety of intelligent connected vehicles,reduce development costs,and shorten the product launch cycle.The conclusion suggests that this framework provides strong support for the healthy development of the intelligent connected vehicle industry and has a positive impact on the safety and efficiency of intelligent transportation systems.
文摘Generative Artificial Intelligence(GenAI)systems have achieved remarkable capabilities across text,code,and image generation;however,their outputs remain prone to errors,hallucinations,and biases.Users often overtrust these outputs due to limited transparency,which can lead to misuse and decision errors.This study addresses the challenge of calibrating trust in GenAI through a human centered testing framework enhanced with adaptive explainability.We introduce a methodology that adjusts explanations dynamically according to user expertise,model output confidence,and contextual risk factors,providing guidance that is informative but not overwhelming.The framework was evaluated using outputs from OpenAI’s Generative Pretrained Transformer 4(GPT-4)for text and code generation and Stable Diffusion,a deep generative image model,for image synthesis.The evaluation covered text,code,and visual modalities.A dataset of 5000 GenAI outputs was created and reviewed by a diverse participant group of 360 individuals categorized by expertise level.Results show that adaptive explanations improve error detection rates,reduce the mean squared trust calibration error,and maintain efficient decision making compared with both static and no explanation conditions.Theframework increased error detection by up to 16% across expertise levels,a gain that can provide practical benefits in high stakes fields.For example,in healthcare it may help identify diagnostic errors earlier,and in law it may prevent reliance on flawed evidence in judicial work.These improvements highlight the framework’s potential to make Artificial Intelligence(AI)deployment safer and more accountable.Visual analyses,including trust accuracy plots,reliability diagrams,and misconception maps,show that the adaptive approach reduces overtrust and reveals patterns of misunderstanding across modalities.Statistical results confirmthe robustness of thesefindings across novice,intermediate,and expert users.The study offers insights for designing explanations that balance completeness and simplicity to improve trust calibration and cognitive load.The approach has implications for safe and transparent GenAI deployment and can inform both AI interface design and policy development for responsible AI use.
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 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 Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science(East China Normal University),Ministry of Educationsupported by the National Natural Scientific Foundation of China under Grant No.NSFC12131006the Scientific and Technological Innovation Project of China Academy of Chinese Medical Science under Grant No.CI2023C063YLL。
文摘This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain.This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions.Traditional regression model checks and twosample regression tests are insufficient to address this issue.To overcome these limitations,the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free.Under the null hypothesis,the test follows a chi-square weak limit,preserving the significance level and enabling critical value determination without resampling techniques.Additionally,the authors systematically analyze the test's power performance,highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis.Numerical studies,including simulations,assess finite-sample performance,and a real-world data example is provided for illustration.
基金National Key Research and Development Program of China(2023YFB3711200)Key Research and Development Project of Henan Province(231111230700).
文摘This study addresses the challenge of directly determining the elastic modulus of complex shaped ceramic products—such as gas turbine combustor tiles—using conventional standardized methods,which are limited by specimen geometry.A rapid,non-destructive testing method based on the impulse excitation technique(IET)and a shape factor coefficient was proposed.Three types of shaped ceramic tiles were selected.The elastic modulus of standard rectangular specimens obtained by destructive sampling was used as the reference value,and the shape factor coefficient for each tile type was calibrated by combining the mass and fundamental frequency of the whole tile.Using this coefficient,the elastic modulus of whole tiles was calculated solely from non-destructively measured mass and frequency.The results show that the deviation between the elastic modulus derived from the proposed method and that from destructive testing is less than 5%,confirming the accuracy and reliability of the approach.The method overcomes the shape restrictions inherent in traditional testing,offering a fast,non-destructive solution suitable for onsite quality assessment and process control during the production of shaped ceramic components.
基金supported by the National Natural Science Foundation of China under Grant No.12071267。
文摘In this paper,the authors propose a class of test procedures to check the fitness of parametric forms of the variance function in regression models when the mean function is unknown.By evaluating the unknown mean function with the classical kernel estimator,the proposed test statistics are built upon a modified minimum distance between a nonparametric fit and a parametric estimator under the null hypothesis for the variance function.Asymptotic properties of the estimator of the parameters in the variance function are discussed,and the large sample distribution of the test statistics under the null hypothesis is established,as well as the consistency and the power under some local alternative hypotheses.Extensive numerical studies demonstrate that the proposed test procedures have satisfactory finite sample performance.Finally,two real data examples further showcase the effectiveness of the proposed test in real applications.
基金support to this study from the National Natural Science Foundation of China,NSFC(Grant No.52278367)The Belt and Road Special Foundation of the National Key Laboratory ofWater Disaster Prevention(Grant No.2024nkms08).
文摘The stress-strain behavior of calcareous sand is significantly influencedby particle breakage(B)and initial relative density(Dri),but few constitutive models consider their combined effects.To bridge this gap,we conducted a series of triaxial tests on calcareous sand with varying Dri and stress paths,examining particle breakage and critical state behavior.Key findingsinclude:(1)At a constant stress ratio(η),B follows a hyperbolic relationship with mean effective stress(p'),and for a given p',B increases proportionally withη;(2)The critical state line(CSL)moves downward with increasing Dri,whereas the critical state friction angle(φcs)decreases with increasing B.Based on these findings,we propose a unifiedbreakage evolution model to quantify particle breakage in calcareous sand under various loading conditions.Integrating this model with the Normal Consolidation Line(NCL)and CSL equations,we successfully simulate the steepening of NCL and CSL slopes as B increases with the onset of particle breakage.Furthermore,we quantitatively evaluate the effect of B onφcs.Finally,within the framework of Critical State Soil Mechanics and Hypoplasticity theory,we develop a hypoplastic model incorporating B and Dri.The model is validated through strong agreement with experimental results across various initial relative densities,stress paths and drainage conditions.
基金supported by the National Natural Science Foundation of China(Nos.12341501 and 12575164)。
文摘X-rays are widely used in the non-destructive testing(NDT)of electrical equipment.Radio frequency(RF)electron linear accelerators can generate MeV high-energy X-rays with strong penetrating ability;however,the system generally has a large scale,which is not suitable for on-site testing.Compared with the S-band(S-linac)at the same stage of beam energy,the accelerator working in the X-band(X-linac)can compress the facility scale by over 2/3 in the longitudinal direction,which is convenient for the on-site NDT of electrical equipment.To address the beam quality and design complexity simultaneously,the non-dominated sorting genetic algorithmⅡ(NSGA-Ⅱ),which is a multi-objective genetic algorithm(MOGA),was developed to optimize the cavity chain design of the X-linac.Additionally,the designs of the focusing coils,electron gun,and RF couplers,which are other key components of the X-linac,were introduced in this context.In particular,the focusing coil distributions were optimized using a genetic algorithm.Furthermore,after designing such key components,PARMELA software was adopted to perform beam dynamics calculations with the optimized accelerating fields and magnetic fields.The results show that the beam performance was obtained with a capture ratio of more than 90%,an energy spread of less than 10%,and an average energy of approximately 3 MeV.The design and simulation results indicate that the proposed NSGAⅡ-based approach is feasible for X-linac accelerator design.Furthermore,it can be generalized as a universal technique for industrial electron linear accelerators provided that specific optimization objectives and constraints are set according to different application scenarios and requirements.
基金supported by the National Key Research and Development Program of China(No.2022YFE0201800)the National Natural Science Foundation of China(Nos.22274169 and 22474161)+5 种基金Guangdong Basic and Applied Basic Research Foundation(No.2024A1515030160)the Science and Technology Program of Guangzhou City(No.2023B03J1380)Shenzhen Science and Technology Innovation Commission(No.GJHZ20210705142200001)the Scientific Technology Project of Guangzhou City(No.202103000003)the Guangdong Science and Technology Plan Project(No.2020B1212060077)the Open Research Fund of State Key Laboratory of Analytical Chemistry for Life Science,School of Chemistry and Chemical Engineering,Nanjing University.
文摘Small extracellular vesicles(sEVs)membrane protein profile(sEVpp)is a novel biomarker for cancer,and it can reveal the in-depth phenotype information.The point-of-care testing(POCT)of sEVpp holds great significance for mass screening of cancer,so the cost-effective and simple detection methods of sEVpp are urgently demanded.Herein,we constructed a paper-based multichannel sEVpp POCT device(sEVpp-PAD)enabled by functional DNA probes and metal-organic framework(MOF).The core components are aptamer/MOF-modified paper chips.The modified aptamers can immunocapture the sEV expressing corresponding proteins,while the modified MOF can provide abundant sites for aptamer-modification,reduce the nonspecific protein absorption,and act as reference for ratiometric detection.Simply powered by two syringes,the sEVpp-PAD can efficiently capture sEVs expressing corresponding protein from cell culture media and sera.Furthermore,a detection probe(DP)consisted of CD63 aptamer and G-quadruplex was developed for the colorimetric detection of captured sEVs.Utilizing this device,the sEVpp in various hepatocellular carcinoma cell culture medium and,more importantly,in human sera can be accurately determined,only with$2 device,$0.2 detection reagents and 1.8 h procedure.This simple strategy for sEVpp detection can innovatively promote the POCT and subtyping of cancer based on sEV-related liquid biopsy.
文摘AIM:To compare the tear film quantity and stability parameters in keratoconus(KCN)and normal eyes using test breakup time(TBUT),noninvasive TBUT(NITBUT),and Schirmer test.METHODS:All participants(n=166),including patients with KCN and age-matched healthy individuals with normal corneas,were recruited from those referred to Farabi Eye Hospital,Iran,in 2023.To better account for genetic and environmental factors,the control group comprised healthy individuals who were relatives of KCN patients and had normal corneal topography.Tear quantity parameters were evaluated in the following order:NITBUT,TBUT,and Schirmer tests.RESULTS:The mean age of cases in KCN(61.7%males)and normal(63.5%males)participants was 27.54±5.44y(range 19 to 38)and 27.52±5.63y(range 20 to 38),respectively(P=0.976).NIBUT,TBUT,and Schirmer’s tests were significantly lower in KCN group compared to normal controls(all P<0.001).The mean difference for NIBUT was-7.81s(P<0.001),and for TBUT was-7.61s(P<0.001).Schirmer test values were also significantly lower in the KCN group,with a mean difference of-5.61 mm compared to normal people(P<0.001).CONCLUSION:Our findings demonstrate significant tear film impairment in KCN.The reductions in NIBUT,TBUT and Schirmer scores highlight an underlying tear film dysfunction in KCN that extends beyond the morphological changes of the cornea.
