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
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
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
As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the...As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the effect of water content on the small-strain dynamic properties of basalt samples using free-free laboratory testing.Free-free testing,which requires minimal equipment and preparation,provides an efficient and low-cost method for determining key dynamic properties,including three wave velocities(V_(s),V_(p),and V_(c)),material damping ratios,and Poisson's ratios.These properties are crucial for numerical modeling in earthquake analyses and geotechnical site characterization.The test consists of three components:(1)direct travel-time measurement,(2)torsional resonance testing,and(3)compressional resonance testing.A total of 20 rock samples were tested under systematically controlled water contents,ranging from fully dried to saturated,to quantify the effects on Poisson's ratio and material damping ratios.The results showed significant increases in both parameters with rising water content.The Poisson's ratio increased by up to 320%for aphanitic basalt and 150%for vesicular basalt,while the damping ratio rose up to 30-fold(D_(c,min))and 16-fold(D_(s,min)).These findings highlight the critical need to incorporate consideration of water content when characterizing dynamic rock properties for seismic and geotechnical analyses.The practical applicability of this research lies in improving in situ property interpretation and enhancing seismic design reliability by providing engineers with precise relationships between water content and dynamic rock behavior.展开更多
Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we eval...Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data.Methods:In this prospective cohort study,1,175 patients with thyroid nodules were evaluated for inclusion,of which 218 patients with Bethesda I nodules met our inclusion criteria.The primary outcome was diagnostic accuracy of molecular testing,and the secondary outcome was the performance of a predictive model integrating molecular and clinical data.Results:Final histopathology identified 165 benign and 53 malignant nodules.Molecular testing detected 10distinct point mutations and seven gene fusions.Among benign nodules,147 tested negative and 18 tested positive,whereas 44 malignant nodules tested positive and nine tested negative.In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology(FNAC)results categorized as non-diagnostic,molecular testing achieved sensitivity of 83.00%,specificity of 89.00%,positive predictive value(PPV)of 71.00%,negative predictive value(NPV)of94.20%,and overall accuracy of 87.60%.The predictive model incorporated 18 clinical and 19 molecular features.Eleven non-zero predictors were selected via least absolute shrinkage and selection operator(LASSO),and the model achieved area under curve(AUC)of 0.95 in the training set and 0.96 in the testing set.Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches.Conclusions:Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules.Integrating molecular and clinical data enabled the development of a robust predictive model,facilitating precise,individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.展开更多
Objective:To analyze factors affecting the utilization of human immunodeficiency virus counseling and testing(HCT)service among human immunodeficiency virus risk groups at Hessa Air Genting Health Center,Asahan Regenc...Objective:To analyze factors affecting the utilization of human immunodeficiency virus counseling and testing(HCT)service among human immunodeficiency virus risk groups at Hessa Air Genting Health Center,Asahan Regency,North Sumatera,Indonesia.Methods:This quantitative unmatched case-control study was conducted from April 2024 to April 2025 at Hessa Air Genting Health Center,Asahan Regency,North Sumatra Province,Indonesia.Female sex workers and men who have sex with men were selected using purposive sampling based on predefined inclusion and exclusion criteria.Data were collected via questionnaires and analyzed using SPSS version 18.0,with univariate analysis,bivariate analysis(Chi-square test),and multivariate analysis(logistic regression analysis).Results:Comprehensive analysis of 75 cases and 75 controls was conducted to identify factors affecting the utilization of HCT services.Specifically,this study identified significant effects of knowledge(OR 3.2,95%CI 1.5-7.0,P=0.003),perception(OR 5.6,95%CI 2.5-12.5,P<0.001),information media(OR 3.1,95%CI 1.4-6.8,P=0.005),and health workers encouragement(OR 4.0,95%CI 1.5-10.4,P=0.005).In contrast,access to health services did not have a significant effect.Conclusions:Knowledge,perception,information media,and health worker encouragement had significant effects on HCT service utilization,with perception identified as the dominant factor.To improve utilization,strengthening positive perceptions,targeted training for healthcare workers,strengthened partnerships with local non-governmental organizations,and the use of social media for health promotion are recommended.展开更多
The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail ...The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail inspection methods,specifically involving real-time,precise detection,and assessment of rail defects.Current applications fail to address the evolving requirements,prompting the need for advancements.This paper provides a summary of various types of rail defects and outlines both traditional and innovative non-destructive inspection techniques,examining their fundamental features,benefits,drawbacks,and practical suitability for railway track inspection.It also explores potential enhancements to equipment and software.The comprehensive review draws upon pertinent international research and review papers.Furthermore,the paper introduces a fusion of inspection methods aimed at enhancing the overall reliability of defect detection.展开更多
In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh enviro...In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.展开更多
In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for peo...In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for people’s production and life,require high attention from industry insiders in terms of their energy efficiency testing.Relying on energy efficiency testing can achieve the goal of energy conservation and emission reduction,and related quality control technologies will also inject new momentum into the green development of the industry.This article will discuss the practical strategies of quality control technology for energy efficiency testing of electronic and electrical products based on the significance of such testing,hoping to provide some help.展开更多
Traditional spectrophotometers have a large volume and slow scanning speed,which limits their applicability for rapid on-site detection.Herein,a micro-spectrophotometer(named ATOM)is fabricated,and its performance is ...Traditional spectrophotometers have a large volume and slow scanning speed,which limits their applicability for rapid on-site detection.Herein,a micro-spectrophotometer(named ATOM)is fabricated,and its performance is verified in water quality testing.An M-type Czerny-Turner light path structure,a broadband light emitting diode(LED)light source,and a linear charge-coupled device(CCD)photodetector were adopted in ATOM.The performance of ATOM was validated through iron content determination by using o-phenanthroline spectrophotometry.The experiment results showed that the linear correlation coefficient of determination R^(2) was 0.9997 for mass concentrations ranging from 0 to 2.0μg/mL.The relative standard deviation was 0.37%,and the relative error compared to a commercial large-scale spectrophotometer was below 1.4%.The dimensions of ATOM are 75 mm×60 mm×25 mm,with hardware costs of approximately 1000 CNY.ATOM features compact size,low cost,rapid measurement,high integration and high precision,making it suitable for portable on-site rapid detection.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
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
T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection met...T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection methods face challenges owing to surface oxide scales,and conventional high-precision magnetic sensors are ineffective at high temperatures.To overcome these limitations,a small coil sensor was employed to measure the residual magnetism strength in oscillation traces,using metal magnetic memory and electromagnetic induction methods,which can carry out detection without an external excitation source.Using this technology,the proposed scheme successfully detects defects at high tempe-ratures(up to 670℃)without a cooling device.The key findings include the ability to detect both surface and near-surface defects,such as cracks and oscillation marks,with an enhanced signal-to-noise ratio(SNR)of 7.2 dB after signal processing.The method’s practicality was validated in a steel mill environment,where testing on casting slabs effectively detected defects,providing a foundation for improving industrial quality control.The proposed detection scheme offers a significant advancement in nondestructive testing(NDT)for high-temperature applications,contributing to more efficient and accurate monitoring of ferromagnetic material integrity.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
At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systema...At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated,which limits the effective implementation of environmental monitoring.In response to this key technical gap,this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation–Atomic Fluorescence Spectrometry(HG-AFS).Ten laboratories participated in inter-laboratory collaborative tests,and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2—2004 and GB/T 6379.4—2006.The consistency and outliers of the data were tested by Mandel's h and k statistics,the Grubbs test and the Cochran test,and the outliers were removed to optimize the data,thereby significantly improving the reliability and accuracy.Based on the optimized data,parameters such as the repeatability limit(r),reproducibility limit(R),and method bias value(δ)were determined,and the trueness of the method was statistically evaluated.At the same time,precision-function relationships were established,and all results met the requirements.The results show that the lower the antimony content,the lower the repeatability limit(r)and reproducibility limit(R),indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity.Therefore,improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision.This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.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
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 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.
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.
文摘As environmental concerns drive shifts in construction materials,rock is increasingly considered as an alternative to sand,reinforcing the importance of understanding its dynamic properties.This study investigates the effect of water content on the small-strain dynamic properties of basalt samples using free-free laboratory testing.Free-free testing,which requires minimal equipment and preparation,provides an efficient and low-cost method for determining key dynamic properties,including three wave velocities(V_(s),V_(p),and V_(c)),material damping ratios,and Poisson's ratios.These properties are crucial for numerical modeling in earthquake analyses and geotechnical site characterization.The test consists of three components:(1)direct travel-time measurement,(2)torsional resonance testing,and(3)compressional resonance testing.A total of 20 rock samples were tested under systematically controlled water contents,ranging from fully dried to saturated,to quantify the effects on Poisson's ratio and material damping ratios.The results showed significant increases in both parameters with rising water content.The Poisson's ratio increased by up to 320%for aphanitic basalt and 150%for vesicular basalt,while the damping ratio rose up to 30-fold(D_(c,min))and 16-fold(D_(s,min)).These findings highlight the critical need to incorporate consideration of water content when characterizing dynamic rock properties for seismic and geotechnical analyses.The practical applicability of this research lies in improving in situ property interpretation and enhancing seismic design reliability by providing engineers with precise relationships between water content and dynamic rock behavior.
基金supported by Military Key Clinical Speciality(No.51561Z23612)Chongqing Talents Project(No.cstc2022ycjh-bgzxm0091)。
文摘Objective:Non-diagnostic thyroid nodules(Bethesda I)account for 5%-20%of all thyroid nodules.Accurate differentiation of benign and malignant nodules can reduce unnecessary surgeries and repeat biopsies.Herein we evaluated the diagnostic efficacy of multigene testing in non-diagnostic thyroid nodules and developed a predictive model integrating molecular and clinical data.Methods:In this prospective cohort study,1,175 patients with thyroid nodules were evaluated for inclusion,of which 218 patients with Bethesda I nodules met our inclusion criteria.The primary outcome was diagnostic accuracy of molecular testing,and the secondary outcome was the performance of a predictive model integrating molecular and clinical data.Results:Final histopathology identified 165 benign and 53 malignant nodules.Molecular testing detected 10distinct point mutations and seven gene fusions.Among benign nodules,147 tested negative and 18 tested positive,whereas 44 malignant nodules tested positive and nine tested negative.In nodules with ultrasound grades 4-5 and fine-needle aspiration cytology(FNAC)results categorized as non-diagnostic,molecular testing achieved sensitivity of 83.00%,specificity of 89.00%,positive predictive value(PPV)of 71.00%,negative predictive value(NPV)of94.20%,and overall accuracy of 87.60%.The predictive model incorporated 18 clinical and 19 molecular features.Eleven non-zero predictors were selected via least absolute shrinkage and selection operator(LASSO),and the model achieved area under curve(AUC)of 0.95 in the training set and 0.96 in the testing set.Decision curve analysis indicated greater net benefit compared with conventional diagnostic approaches.Conclusions:Molecular testing significantly improved diagnostic accuracy for Bethesda I thyroid nodules.Integrating molecular and clinical data enabled the development of a robust predictive model,facilitating precise,individualized patient management and reducing the need for repeat FNAC and unnecessary surgeries.
文摘Objective:To analyze factors affecting the utilization of human immunodeficiency virus counseling and testing(HCT)service among human immunodeficiency virus risk groups at Hessa Air Genting Health Center,Asahan Regency,North Sumatera,Indonesia.Methods:This quantitative unmatched case-control study was conducted from April 2024 to April 2025 at Hessa Air Genting Health Center,Asahan Regency,North Sumatra Province,Indonesia.Female sex workers and men who have sex with men were selected using purposive sampling based on predefined inclusion and exclusion criteria.Data were collected via questionnaires and analyzed using SPSS version 18.0,with univariate analysis,bivariate analysis(Chi-square test),and multivariate analysis(logistic regression analysis).Results:Comprehensive analysis of 75 cases and 75 controls was conducted to identify factors affecting the utilization of HCT services.Specifically,this study identified significant effects of knowledge(OR 3.2,95%CI 1.5-7.0,P=0.003),perception(OR 5.6,95%CI 2.5-12.5,P<0.001),information media(OR 3.1,95%CI 1.4-6.8,P=0.005),and health workers encouragement(OR 4.0,95%CI 1.5-10.4,P=0.005).In contrast,access to health services did not have a significant effect.Conclusions:Knowledge,perception,information media,and health worker encouragement had significant effects on HCT service utilization,with perception identified as the dominant factor.To improve utilization,strengthening positive perceptions,targeted training for healthcare workers,strengthened partnerships with local non-governmental organizations,and the use of social media for health promotion are recommended.
文摘The rapid progress in the construction of heavy-haul and high-speed railways has led to a surge in rail defects and unforeseen failures.Addressing this issue necessitates the implementation of more sophisticated rail inspection methods,specifically involving real-time,precise detection,and assessment of rail defects.Current applications fail to address the evolving requirements,prompting the need for advancements.This paper provides a summary of various types of rail defects and outlines both traditional and innovative non-destructive inspection techniques,examining their fundamental features,benefits,drawbacks,and practical suitability for railway track inspection.It also explores potential enhancements to equipment and software.The comprehensive review draws upon pertinent international research and review papers.Furthermore,the paper introduces a fusion of inspection methods aimed at enhancing the overall reliability of defect detection.
基金supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2024ZD0608100)the National Natural Science Foundation of China(62332017,U22A2022)
文摘In high-risk industrial environments like nuclear power plants,precise defect identification and localization are essential for maintaining production stability and safety.However,the complexity of such a harsh environment leads to significant variations in the shape and size of the defects.To address this challenge,we propose the multivariate time series segmentation network(MSSN),which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates.To tackle the classification difficulty caused by structural signal variance,MSSN employs logarithmic normalization to adjust instance distributions.Furthermore,it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences.Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95%localization and demonstrates the capture capability on the synthetic dataset.In a nuclear plant's heat transfer tube dataset,it captures 90%of defect instances with75%middle localization F1 score.
文摘In the current social environment,the importance of energy conservation and emission reduction is increasing day by day for both the country and its people.Electronic and electrical products,as important items for people’s production and life,require high attention from industry insiders in terms of their energy efficiency testing.Relying on energy efficiency testing can achieve the goal of energy conservation and emission reduction,and related quality control technologies will also inject new momentum into the green development of the industry.This article will discuss the practical strategies of quality control technology for energy efficiency testing of electronic and electrical products based on the significance of such testing,hoping to provide some help.
基金National Natural Science Foundation of China(No.62375048)Natural Science Foundation of Shanghai,China(No.22ZR1402600。
文摘Traditional spectrophotometers have a large volume and slow scanning speed,which limits their applicability for rapid on-site detection.Herein,a micro-spectrophotometer(named ATOM)is fabricated,and its performance is verified in water quality testing.An M-type Czerny-Turner light path structure,a broadband light emitting diode(LED)light source,and a linear charge-coupled device(CCD)photodetector were adopted in ATOM.The performance of ATOM was validated through iron content determination by using o-phenanthroline spectrophotometry.The experiment results showed that the linear correlation coefficient of determination R^(2) was 0.9997 for mass concentrations ranging from 0 to 2.0μg/mL.The relative standard deviation was 0.37%,and the relative error compared to a commercial large-scale spectrophotometer was below 1.4%.The dimensions of ATOM are 75 mm×60 mm×25 mm,with hardware costs of approximately 1000 CNY.ATOM features compact size,low cost,rapid measurement,high integration and high precision,making it suitable for portable on-site rapid detection.
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.
文摘T he residual stray magnetic fields present in ferromagnetic casting slabs were investigated in this work,which result from the magnetic fields generated during the steel casting process.Existing optical detection methods face challenges owing to surface oxide scales,and conventional high-precision magnetic sensors are ineffective at high temperatures.To overcome these limitations,a small coil sensor was employed to measure the residual magnetism strength in oscillation traces,using metal magnetic memory and electromagnetic induction methods,which can carry out detection without an external excitation source.Using this technology,the proposed scheme successfully detects defects at high tempe-ratures(up to 670℃)without a cooling device.The key findings include the ability to detect both surface and near-surface defects,such as cracks and oscillation marks,with an enhanced signal-to-noise ratio(SNR)of 7.2 dB after signal processing.The method’s practicality was validated in a steel mill environment,where testing on casting slabs effectively detected defects,providing a foundation for improving industrial quality control.The proposed detection scheme offers a significant advancement in nondestructive testing(NDT)for high-temperature applications,contributing to more efficient and accurate monitoring of ferromagnetic material integrity.
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supported by the National Natural Science Foundation of China(Project No.42307555).
文摘At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated,which limits the effective implementation of environmental monitoring.In response to this key technical gap,this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation–Atomic Fluorescence Spectrometry(HG-AFS).Ten laboratories participated in inter-laboratory collaborative tests,and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2—2004 and GB/T 6379.4—2006.The consistency and outliers of the data were tested by Mandel's h and k statistics,the Grubbs test and the Cochran test,and the outliers were removed to optimize the data,thereby significantly improving the reliability and accuracy.Based on the optimized data,parameters such as the repeatability limit(r),reproducibility limit(R),and method bias value(δ)were determined,and the trueness of the method was statistically evaluated.At the same time,precision-function relationships were established,and all results met the requirements.The results show that the lower the antimony content,the lower the repeatability limit(r)and reproducibility limit(R),indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity.Therefore,improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision.This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.
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.
