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
Advances in the identification of molecular biomarkers and the development of targeted therapies have enhanced the prognosis of patients with advanced gastric cancer.Several established biomarkers have been widely int...Advances in the identification of molecular biomarkers and the development of targeted therapies have enhanced the prognosis of patients with advanced gastric cancer.Several established biomarkers have been widely integrated into routine clinical diagnostics of gastric cancer to guide personalized treatment.Human epidermal growth factor receptor 2(HER2)was the first molecular biomarker to be used in gastric cancer with trastuzumab being the first approved targeted therapy for HER2-positive gastric cancer.Programmed death-ligand 1 positivity and microsatellite instability can guide the use of immunotherapies,such as pembrolizumab and nivolumab.More recently,zolbetuximab has been approved for patients with claudin 18.2-positive diseases in some countries.More targeted therapies,including savolitinib for MET-positive patients,are currently under clinical investigation.However,the clinical application of these diagnostic approaches could be hampered by many existing challenges,including invasive and costly sampling methods,variability in immunohistochemistry interpretation,high costs and long turnaround times for next-generation sequencing,the absence of standardized and clinically validated diagnostic cut-off values for some biomarkers,and tumor heterogeneity.Novel testing and analysis techniques,such as artificial intelligence-assisted image analysis and multiplex immunohistochemistry,and emerging therapeutic strategies,including combination therapies that integrate immune checkpoint inhibitors with targeted therapies,offer potential solutions to some of these challenges.This article reviews recent progress in gastric cancer testing,outlines current challenges,and explores future directions for biomarker testing and targeted therapy for gastric cancer.展开更多
Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are ...Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.展开更多
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
With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raisin...With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.展开更多
Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen t...Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.展开更多
Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly diffic...Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.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
Reinforced concrete(RC)beams face potential near-field blast threats as key structural components in building structures.To investigate the failure modes and dynamic responses of RC beams subjected to near-field blast...Reinforced concrete(RC)beams face potential near-field blast threats as key structural components in building structures.To investigate the failure modes and dynamic responses of RC beams subjected to near-field blast loading,this paper presents both blast tests and numerical simulation studies on RC beams.First,near-field blast tests were conducted on five RC beam specimens under strong and weak-axis bending loading.Then,a refined finite element model of RC beams was established to verify the applicability of the adopted finite element analysis method.Finally,based on the calibrated finite element model,the failure mechanisms of RC beams were explored,and the influence of blast incidence angle on the failure modes and dynamic responses of RC beams was investigated.The results indicate:(i)Near-field blast loading demonstrates pronounced non-uniform distribution patterns.Under strong-axis incidence,clearing effects beyond the mid-span region are more significant than weak-axis incidence,leading to accelerated impulse attenuation.(ii)Three consecutive developmental stages primarily control the damage mechanism of RC beams:stress wave-induced local damage,local deformation causing plastic hinge propagation,and free vibration of the beam;(iii)As the scaled distance decreases,the failure mode of RC beams under weak-axis blast loading evolves from flexural failure to local failure.The resistance mechanism of RC beams under weak-axis blast loading is more prone to transition from compressive membrane action to tensile membrane action,reducing their blast resistance capacity;(iv)As the explosion incident angleθincreases from 0°to 90°,the blast wave-structure interaction transitions from regular reflection to Mach reflection and back to normal reflection,causing the dynamic response of RC beams to first decrease then increase,with corner concrete spalling damage being the primary failure mode.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 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
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
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
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support by grants from Capital’s Funds for Health Improvement and Research(Grant No.2024-2-1024)Beijing Natural Science Foundation(Grant No.7232018).
文摘Advances in the identification of molecular biomarkers and the development of targeted therapies have enhanced the prognosis of patients with advanced gastric cancer.Several established biomarkers have been widely integrated into routine clinical diagnostics of gastric cancer to guide personalized treatment.Human epidermal growth factor receptor 2(HER2)was the first molecular biomarker to be used in gastric cancer with trastuzumab being the first approved targeted therapy for HER2-positive gastric cancer.Programmed death-ligand 1 positivity and microsatellite instability can guide the use of immunotherapies,such as pembrolizumab and nivolumab.More recently,zolbetuximab has been approved for patients with claudin 18.2-positive diseases in some countries.More targeted therapies,including savolitinib for MET-positive patients,are currently under clinical investigation.However,the clinical application of these diagnostic approaches could be hampered by many existing challenges,including invasive and costly sampling methods,variability in immunohistochemistry interpretation,high costs and long turnaround times for next-generation sequencing,the absence of standardized and clinically validated diagnostic cut-off values for some biomarkers,and tumor heterogeneity.Novel testing and analysis techniques,such as artificial intelligence-assisted image analysis and multiplex immunohistochemistry,and emerging therapeutic strategies,including combination therapies that integrate immune checkpoint inhibitors with targeted therapies,offer potential solutions to some of these challenges.This article reviews recent progress in gastric cancer testing,outlines current challenges,and explores future directions for biomarker testing and targeted therapy for gastric cancer.
基金support of this project through the Southwest Regional Partnership on Carbon Sequestration(Grant No.DE-FC26-05NT42591)Improving Production in the Emerging Paradox Oil Play(Grant No.DE-FE0031775).
文摘Geomechanical properties of rocks vary across different measurement scales,primarily due to heterogeneity.Micro-scale geomechanical tests,including micro-scale“scratch tests”and nano-scale nanoindentation tests,are attractive at different scales.Each method requires minimal sample volume,is low cost,and includes a relatively rapid measurement turnaround time.However,recent micro-scale test results–including scratch test results and nanoindentation results–exhibit tangible variance and uncertainty,suggesting a need to correlate mineral composition mapping to elastic modulus mapping to isolate the relative impact of specific minerals.Different research labs often utilize different interpretation methods,and it is clear that future micro-mechanical tests may benefit from standardized testing and interpretation procedures.The objectives of this study are to seek options for standardized testing and interpretation procedures,through two specific objectives:(1)Quantify chemical and physical controls on micro-mechanical properties and(2)Quantify the source of uncertainties associated with nanoindentation measurements.To reach these goals,we conducted mechanical tests on three different scales:triaxial compression tests,scratch tests,and nanoindentation tests.We found that mineral phase weight percentage is highly correlated with nanoindentation elastic modulus distribution.Finally,we conclude that nanoindentation testing is a mineralogy and microstructure-based method and generally yields significant uncertainty and overestimation.The uncertainty of the testing method is largely associated with not mapping pore space a priori.Lastly,the uncertainty can be reduced by combining phase mapping and modulus mapping with substantial and random data sampling.
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 Open Foundation of Key Laboratory of Cyberspace Security,Ministry of Education of China(KLCS20240211)。
文摘With the rapid development of Internet technology,REST APIs(Representational State Transfer Application Programming Interfaces)have become the primary communication standard in modern microservice architectures,raising increasing concerns about their security.Existing fuzz testing methods include random or dictionary-based input generation,which often fail to ensure both syntactic and semantic correctness,and OpenAPIbased approaches,which offer better accuracy but typically lack detailed descriptions of endpoints,parameters,or data formats.To address these issues,this paper proposes the APIDocX fuzz testing framework.It introduces a crawler tailored for dynamic web pages that automatically simulates user interactions to trigger APIs,capturing and extracting parameter information from communication packets.A multi-endpoint parameter adaptation method based on improved Jaccard similarity is then used to generalize these parameters to other potential API endpoints,filling in gaps in OpenAPI specifications.Experimental results demonstrate that the extracted parameters can be generalized with 79.61%accuracy.Fuzz testing using the enriched OpenAPI documents leads to improvements in test coverage,the number of valid test cases generated,and fault detection capabilities.This approach offers an effective enhancement to automated REST API security testing.
基金financially supported by ARC Linkage project(LP210200642)ARC Center of Excellence for Quantum Biotechnology(grant no.CE230100021)+1 种基金National Health and Medical Research Council Investigator Fellowship—(grant no.APP2017499)Chan Zuckerberg Initiative Deep Tissue Imaging Phase 2(grant no.DT12-0000000182).
文摘Lateral flow immunoassays(LFIAs)are low-cost,rapid,and easy to use for pointof-care testing(POCT),but the majority of the available LFIA tests are indicative,rather than quantitative,and their sensitivity in antigen tests are usually limited at the nanogram range,which is primarily due to the passive capillary fluidics through nitrocellulose membranes,often associated with non-specific bindings and high background noise.To overcome this challenge,we report a Beads-on-a-Tip design by replacing nitrocellulose membranes with a pipette tip loaded with magnetic beads.The beads are pre-conjugated with capture antibodies that support a typical sandwich immunoassay.This design enriches the low-abundant antigen proteins and allows an active washing process to significantly reduce non-specific bindings.To further improve the detection sensitivity,we employed upconversion nanoparticles(UCNPs)as luminescent reporters and SARS-CoV-2 spike(S)antigen as a model analyte to benchmark the performance of this design against our previously reported methods.We found that the key to enhance the immunocomplex formation and signal-to-noise ratio lay in optimizing incubation time and the UCNP-to-bead ratio.We therefore successfully demonstrated that the new method can achieve a very large dynamic range from 500 fg/mL to 10μg/mL,across over 7 digits,and a limit of detection of 706 fg/mL,nearly another order of magnitude lower than the best reported LFIA using UCNPs in COVID-19 spike antigen detection.Our system offers a promising solution for ultra-sensitive and quantitative POCT diagnostics.
基金funded by the National Natural Science Foundation of China(Nos.52474165 and 52522404)。
文摘Cemented paste backfill(CPB)is a technology that achieves safe mining by filling the goaf with waste rocks,tailings,and other materials.It is an inevitable choice to deal with the development of deep and highly difficult mines and meet the requirements of environmental protection and safety regulations.It promotes the development of a circular economy in mines through the development of lowgrade resources and the resource utilization of waste,and extends the service life of mines.The mass concentration of solid content(abbreviated as“concentration”)is a critical parameter for CPB.However,discrepancies often arise between the on-site measurements and the pre-designed values due to factors such as groundwater inflow and segregation within the goaf,which cannot be evaluated after the solidification of CPB.This paper innovatively provides an in-situ non-destructive approach to identify the real concentration of CPB after curing for certain days using hyperspectral imaging(HSI)technology.Initially,the spectral variation patterns under different concentration conditions were investigated through hyperspectral scanning experiments on CPB samples.The results demonstrate that as the CPB concentration increases from 61wt%to 73wt%,the overall spectral reflectance gradually increases,with two distinct absorption peaks observed at 1407 and 1917 nm.Notably,the reflectance at 1407 nm exhibited a strong linear relationship with the concentration.Subsequently,the K-nearest neighbors(KNN)and support vector machine(SVM)algorithms were employed to classify and identify different concentrations.The study revealed that,with the KNN algorithm,the highest accuracy was achieved when K(number of nearest neighbors)was 1,although this resulted in overfitting.When K=3,the model displayed the optimal balance between accuracy and stability,with an accuracy of 95.03%.In the SVM algorithm,the highest accuracy of 98.24%was attained with parameters C(regularization parameter)=200 and Gamma(kernel coefficient)=10.A comparative analysis of precision,accuracy,and recall further highlighted that the SVM provided superior stability and precision for identifying CPB concentration.Thus,HSI technology offers an effective solution for the in-situ,non-destructive monitoring of CPB concentration,presenting a promising approach for optimizing and controlling CPB characteristic parameters.
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 National Natural Science Foundation of China(Grant Nos.52178445,52578544)Open Research Fund of State Key Laboratory of Target Vulnerability Assessment,Defense Engineering Institute,AMS(Grant No.YSX2024KFYS002).
文摘Reinforced concrete(RC)beams face potential near-field blast threats as key structural components in building structures.To investigate the failure modes and dynamic responses of RC beams subjected to near-field blast loading,this paper presents both blast tests and numerical simulation studies on RC beams.First,near-field blast tests were conducted on five RC beam specimens under strong and weak-axis bending loading.Then,a refined finite element model of RC beams was established to verify the applicability of the adopted finite element analysis method.Finally,based on the calibrated finite element model,the failure mechanisms of RC beams were explored,and the influence of blast incidence angle on the failure modes and dynamic responses of RC beams was investigated.The results indicate:(i)Near-field blast loading demonstrates pronounced non-uniform distribution patterns.Under strong-axis incidence,clearing effects beyond the mid-span region are more significant than weak-axis incidence,leading to accelerated impulse attenuation.(ii)Three consecutive developmental stages primarily control the damage mechanism of RC beams:stress wave-induced local damage,local deformation causing plastic hinge propagation,and free vibration of the beam;(iii)As the scaled distance decreases,the failure mode of RC beams under weak-axis blast loading evolves from flexural failure to local failure.The resistance mechanism of RC beams under weak-axis blast loading is more prone to transition from compressive membrane action to tensile membrane action,reducing their blast resistance capacity;(iv)As the explosion incident angleθincreases from 0°to 90°,the blast wave-structure interaction transitions from regular reflection to Mach reflection and back to normal reflection,causing the dynamic response of RC beams to first decrease then increase,with corner concrete spalling damage being the primary failure mode.
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(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.
