Reliability Analysis Tool Research Articles

Bimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.

Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in disease regulation. However, the practical application of EV-circRNAs in the early diagnosis of GC is yet to be thoroughly explored due to the low accuracy of EV-circRNAs analysis. In this study, a hybridization chain reaction system based on rectangular DNA framework guidance and constructing a bimodal EV-circRNA in situ analyzer (BEISA) is developed. The analyzer can provide dual signal outputs in the fluorescence and electrochemical modes, enabling a self-correcting detection mechanism that significantly improves the accuracy of the assay. It has a broad detection range and an extremely low limit of detection. In a clinical cohort study, the BEISA used four circRNAs as biomarkers, combining them with machine learning for multiparametric analysis, which effectively differentiated between healthy donors and patients with early-stage GC. It is believed that the BEISA, in conjunction with machine learning technology, provides an efficient, sensitive, and reliable tool for EV-circRNA analysis, aiding in the early diagnosis of GC.

Comparative Effectiveness of Intraoral Scanners and Articulating Paper in Occlusal Contact Analysis

Background/Objectives: This study evaluates the accuracy of maximum intercuspation analysis using both conventional articulating paper and three digital intraoral scanners. The goal was to determine the reliability and equivalency of traditional and digital methods in recording occlusal contact points. Methods: Ten subjects underwent occlusal contact point analysis using articulating paper, 3Shape® Trios 3, Omnicam Cerec®, and Medit i700® intra-oral scanners. Results were compared visually and statistically to evaluate method equivalence. Results: Analysis revealed no statistically significant differences in the accuracy of occlusal contact points among the four methods tested, confirming the reliability of both traditional and digital approaches. Conclusion: Articulating paper remains a reliable tool for occlusal analysis, even with the advent of advanced intraoral scanners. The findings support the feasibility of using any of the tested scanners for accurate occlusal contact analysis. This study underscores the potential for integrating both conventional and digital methods, depending on clinical needs and available technology, without compromising diagnostic accuracy.

Extending Generalized Explicit Terms and Applying Euler–Bernoulli Beam Theory to Enhance Dynamic Response Prediction in Receptance Coupling Method

This paper presents a theoretical framework to enhance the prediction of dynamic responses in complex mechanical systems, such as vehicle structures, by incorporating both translational and rotational degrees of freedom. Traditional receptance coupling methods often neglect rotational effects, leading to significant inaccuracies at higher frequencies. Additionally, approaches that implicitly include full dynamics frequently result in redundancy of generalized coordinates, especially at connection points. To address these limitations, the generalized receptance coupling method using Frequency-Based Substructuring is extended to explicitly account for rotational dynamics resulting in a refined GRCFBS approach. This extension enhances both the understanding and prediction of system responses, which are represented through the receptance matrix or Frequency Response Function. Building on Jetmundsen’s foundational work, the proposed framework introduces a practical, generalized formulation that explicitly incorporates full translational and rotational dynamics at each substructure node. This explicit definition provides deeper insights into system behavior, particularly for complex interactions between substructures under weak and strong coupling scenarios at interface points. The Euler–Bernoulli beam theory is employed to model rotational behavior at critical points, yielding reduced-order and explicit receptance matrices for substructures in the coupling process. The methodology’s accuracy and applicability in capturing resonance and anti-resonance modes are validated through two case studies: the coupling of two flexible subsystems and the integration of flexible and rigid components. Results are benchmarked against numerical finite element analysis, and all limitations and potential improvements are discussed. By directly incorporating rotational dynamics directly, this approach enables more reliable dynamic response predictions under multi-directional loading conditions, particularly for vehicle and machinery system design. The GRCFBS method offers a versatile and reliable tool for dynamic system analysis, with significant potential for vibration analysis over a broad frequency range.

Image Quality Assessment and Reliability Analysis of Artificial Intelligence-Based Tumor Classification of Stimulated Raman Histology of Tumor Biobank Samples.

Stimulated Raman histology (SRH) is a label-free optical imaging method for rapid intraoperative analysis of fresh tissue samples. Analysis of SRH images using Convolutional Neural Networks (CNN) has shown promising results for predicting the main histopathological classes of neurooncological tumors. Due to the relatively low number of rare tumor representations in CNN training datasets, a valid prediction of rarer entities remains limited. To develop new reliable analysis tools, larger datasets and greater tumor variety are crucial. One way to accomplish this is through research biobanks storing frozen tumor tissue samples. However, there is currently no data available regarding the pertinency of previously frozen tissue samples for SRH analysis. The aim of this study was to assess image quality and perform a comparative reliability analysis of artificial intelligence-based tumor classification using SRH in fresh and frozen tissue samples. In a monocentric prospective study, tissue samples from 25 patients undergoing brain tumor resection were obtained. SRH was acquired in fresh and defrosted samples of the same specimen after varying storage durations at -80 °C. Image quality was rated by an experienced neuropathologist, and prediction of histopathological diagnosis was performed using two established CNNs. The image quality of SRH in fresh and defrosted tissue samples was high, with a mean image quality score of 1.96 (range 1-5) for both groups. CNN analysis showed high internal consistency for histo-(Cα 0.95) and molecular (Cα 0.83) pathological tumor classification. The results were confirmed using a dataset with samples from the local tumor biobank (Cα 0.91 and 0.53). Our results showed that SRH appears comparably reliable in fresh and frozen tissue samples, enabling the integration of tumor biobank specimens to potentially improve the diagnostic range and reliability of CNN prediction tools.

Triple Collocation-Based Uncertainty Analysis and Data Fusion of Multi-Source Evapotranspiration Data Across China

Accurate estimation of evapotranspiration (ET) is critical for understanding land-atmospheric interactions. Despite the advancement in ET measurement, a single ET estimate still suffers from inherent uncertainties. Data fusion provides a viable option for improving ET estimation by leveraging the strengths of individual ET products, especially the triple collocation (TC) method, which has a prominent advantage in not relying on the availability of “ground truth” data. In this work, we proposed a framework for uncertainty analysis and data fusion based on the extended TC (ETC) and multiple TC (MTC) variants. Three different sources of ET products, i.e., the Global Land Evaporation and Amsterdam Model (GLEAM), the fifth generation of European Reanalysis-Land (ERA5-Land), and the complementary relationship model (CR), were selected as the TC triplet. The analyses were conducted based on different climate zones and land cover types across China. Results show that ETC presents outstanding performance as most areas conform to the zero-error correlations assumption, while nearly half of the areas violate this assumption when using MTC. In addition, the ETC method derives a lower root mean square error (RMSE) and higher correlation coefficient (Corr) than the MTC one over most climate zones and land cover types. Among the ET products, GLEAM performs the best, while CR performs the worst. The merged ET estimates from both ETC and MTC methods are generally superior to the original triplets at the site scale. The findings indicate that the TC-based method could be a reliable tool for uncertainty analysis and data fusion.

Dual Power Transformation and Yeo–Johnson Techniques for Static and Dynamic Reliability Assessments

This paper addresses key challenges in the static and dynamic reliability analysis of engineering structures, particularly the difficulty in accurately estimating large reliability indices and small failure probabilities. For static reliability problems, a dual power transformation is employed to transform the performance function into a form approaching a normal distribution. The high-order unscented transformation is then applied to compute the first four moments of the transformed performance function. Subsequently, the fourth-moment method is used to calculate large reliability indices, offering a novel improvement over traditional methods such as FORM and SORM. For dynamic reliability problems, the low-discrepancy sampling technique is integrated to efficiently compute structural responses under random seismic excitation, improving computational efficiency for complex dynamic systems. The Yeo–Johnson transformation is introduced to normalize the extreme values of dynamic responses, and the first four moments of the transformed extreme values are statistically evaluated. Additionally, a third-order polynomial transformation (TPT) is applied to approximate the probability density function, leading to the derivation of the probability of exceedance (POE) curve. The optimal transformation parameters for both the dual power and Yeo–Johnson transformations are determined using the Jarque–Bera (JB) test. Four numerical examples, coupled with Monte Carlo simulation, validate the proposed framework’s accuracy and efficiency, providing a robust tool for static and dynamic reliability analysis. This unified approach represents a significant advancement by integrating novel transformations and fourth-moment method, providing a powerful and efficient tool for static and dynamic reliability analysis of engineering structures.

Validation of a 3D Markerless Motion Capture Tool Using Multiple Pose and Depth Estimations for Quantitative Gait Analysis.

Gait analysis is essential for evaluating walking patterns and identifying functional limitations. Traditional marker-based motion capture tools are costly, time-consuming, and require skilled operators. This study evaluated a 3D Marker-less Motion Capture (3D MMC) system using pose and depth estimations with the gold-standard Motion Capture (MOCAP) system for measuring hip and knee joint angles during gait at three speeds (0.7, 1.0, 1.3 m/s). Fifteen healthy participants performed gait tasks which were captured by both systems. The 3D MMC system demonstrated good accuracy (LCC > 0.96) and excellent inter-session reliability (RMSE < 3°). However, moderate-to-high accuracy with constant biases was observed during specific gait events, due to differences in sample rates and kinematic methods. Limitations include the use of only healthy participants and limited key points in the pose estimation model. The 3D MMC system shows potential as a reliable tool for gait analysis, offering enhanced usability for clinical and research applications.

Structural Reliability Analysis Using Stochastic Finite Element Method Based on Krylov Subspace

The stochastic finite element method is an important tool for structural reliability analysis. In order to improve the calculation efficiency, a stochastic finite element method based on the Krylov subspace is proposed for the static reliability analysis of structures. The first step of the proposed method is to preprocess the static response equation considering randomness to reduce the condition number of the coefficient matrix. The second step of the proposed method is to construct a Krylov subspace based on the preprocessed static response equation. Then, the static displacement of random sampling is expressed as a linear combination of subspace basis vectors to achieve the purpose of a fast solution. Finally, statistics and failure probability are calculated according to the static response obtained from thousands of random samples. Three numerical examples are given to compare the proposed method with the stochastic finite element method based on the Neumann series. The results show that the stochastic finite element method based on the Krylov subspace is more accurate and efficient than the stochastic finite element method based on the Neumann series.

The Efficiency of Block Hybrid Method for Solving Malthusian Growth Model and Prothero-Robinson Oscillatory Differential Equations

The efficiency of block hybrid method for solving Malthusian Growth Model, Prothero-Robinson equation and highly stiff oscillatory differential equations was proposed using a power series polynomial through interpolation and collocation. The new method's basic properties, including order, error constant, consistency, zero-stability, and stability regions, were comprehensively analyzed and satisfied all necessary conditions for analysis. Tested on various real-life problems, the new method demonstrated superior performance compared to existing techniques. The study highlights the innovative approach's enhanced convergence and stability properties, providing a more reliable numerical analysis tool for researchers and practitioners. Practical applications validate the method's effectiveness, showcasing its superior performance across different examples and establishing it as a highly effective solution for Malthusian growth model and oscillatory differential equations.

Instance segmentation of cells and nuclei from multi-organ cross-protocol microscopic images.

Light microscopy is a widely used technique in cell biology due to its satisfactory resolution for cellular structure analysis, prevalent availability of fluorescent probes for staining, and compatibility for the dynamic analysis of live cells. However, the segmentation of cells and nuclei from microscopic images is not a straightforward process because it has several challenges such as high variation in morphology and shape, the presence of noise and diverse contrast in backgrounds, clustering or overlapping nature of cells. Dealing with these challenges and facilitating more reliable analysis necessitates the implementation of computer-aided methods that leverage image processing techniques and deep learning algorithms. The major goal of this study is to propose a model, for instance segmentation of cells and nuclei, applying the most cutting-edge deep learning techniques. A fine-tuned You Only Look at Once version 9 extended (YOLOv9-E) model is initially applied as a prompt generator to generate bounding box prompts. Using the generated prompts, a pre-trained segment anything model (SAM) is subsequently applied through zero-short inferencing to produce raw segmentation masks. These segmentation masks are then refined using non-max suppression and simple image processing methods such as image addition and morphological processing. The proposed method is developed and evaluated using an open-sourced dataset called Expert Visual Cell Annotation (EVICAN), which is relatively large and contains 4,738 microscopy images extracted from cross organs using different protocols. Based on the evaluation results on three different levels of EVICAN test sets, the proposed method demonstrates noticeable performances showing average mAP50 [mean average precision at intersection over union (IoU) =0.50] scores of 96.25, 95.05, and 94.18 for cell segmentation, and 68.04, 54.66, and 38.29 for nucleus segmentation on easy, medium, and difficult test sets, respectively. Our proposed method for instance segmentation of cells and nuclei provided favorable performance compared to the existing methods in literature, indicating its potential utility as an assistive tool for cell culture experts, facilitating prompt and reliable analysis.

Dumbbell probe initiated multi-rolling circle amplification assisted CRISPR/Cas12a for highly sensitive detection of clinical microRNA

A novel miRNA detection technique named Dumbbell probe initiated multi-Rolling Circle Amplification assisted CRISPR/Cas12a (DBmRCA) was developed relying on the ligation-free dumbbell probe and the high-sensitivity CRISPR/Cas12a signal out strategy. This DBmRCA assay streamlines miRNA quantification within a mere 30-min timeframe and with exceptional analytical precision. The efficacy of this method was validated by assessing miRNA levels in clinical samples, revealing distinct expression panel of miR-200a and miR-126 in lung cancer/adjacent/normal tissue specimens. Moreover, a predictive model was established to classify benign and malignant tumor. Due to its time efficiency, enhanced sensitivity, and streamlined workflow, this assay would be a reliable tool for miRNA analysis in clinical settings, offering potential guidance for early diagnosis and treatment of lung cancer.

Unveiling Impurities: A Comprehensive RP-HPLC Method for Accurate Atorvastatin Impurity Analysis in Pharmaceuticals Using Accuracy Profile Approach.

Accurate determination of active pharmaceutical ingredients and impurities is essential for ensuring the safety and effectiveness of medications. This study focuses on the validation of a high-performance liquid chromatography (HPLC) method for quantifying Atorvastatin and its impurities, addressing a critical aspect of pharmaceutical analysis. The primary objective is to conduct a comprehensive validation study for the HPLC method, covering specificity assessment, response function establishment, and a detailed analysis of precision, trueness, and tolerance intervals. The emphasis is on demonstrating the method's precision, accuracy, and stability-indicating capabilities across various concentrations and compounds. The HPLC method is validated through rigorous assessments, including specificity, response function establishment, and analyses of precision, trueness, and tolerance intervals. Induced degradation experiments are conducted to explore Atorvastatin's behavior under extreme conditions. Insights into the compound's synthesis and degradation pathways are provided through a proposed mechanism for intramolecular esterification. The results affirm the precision, accuracy, and stability-indicating capabilities of the validated HPLC method. The method effectively differentiates between Atorvastatin and its impurities, showcasing its suitability for pharmaceutical quality control. The validated HPLC method emerges as a robust and reliable tool for Atorvastatin analysis, contributing significantly to pharmaceutical research and quality control. Its application ensures the safety and efficacy of medications, reinforcing its role in pharmaceutical analysis. This study not only validates a crucial HPLC method for Atorvastatin analysis but also provides insights into the compound's behavior under extreme conditions and its synthesis and degradation pathways. The validated method serves as a cornerstone in pharmaceutical research and quality control, ensuring medication safety and efficacy.

A new device for high-resolution Li K X-ray spectroscopy using an electron microprobe

Lithium is one of the key elements in today's materials and battery industry, and the interest in non-destructive characterization techniques at a micron scale for materials containing lithium is steadily increasing. The electron microprobe is a reliable and accessible analysis tool widely used for this purpose, but performing X-ray emission spectroscopy in the low-energy range is still challenging.In this work, we demonstrate that spectroscopy in the Li K energy range is feasible by integrating a new detection system composed of a multilayer and ultra-thin separation windows into a commercial wavelength dispersive spectrometer of a microprobe. The detection system is described in detail, and the results, in form of spectra showing the Li K emission in LiF and in a ternary quasicrystalline sample, are presented and analyzed. In LiF the emission band is centered at 54.5 eV and has varying intensities for different beam exposure times. The spectra obtained in the ternary quasicrystal clearly demonstrate that the detection system has sufficient energy resolution to separate the Li emission band and the Al L2,3 and Cu M2,3 emission bands, enabling chemical state analysis by comparing the shapes of the emissions. To our knowledge, this is the first time such a detection system, implemented in a WDS spectrometer and enabling the acquisition of different Li K spectra in various compounds has been described in detail. This kind of system can be used for Li quantification using a common procedure in electron probe microanalysis.

Additional confirmation of successful cell suspension fractionation of metastatic axillary node tissue

We present herein an extension to our recently developed and published method termed "Fractionation of Nodal Cell Suspension" (FNCS). The method enables efficient subcellular fractionation into nuclear (N) and cytosolic (C) compartments of extremely fibrous and problematic metastatic axillary lymph node (mALN) tissue, using the entire nodule. For the purpose of the present study, a case of invasive lobular breast cancer (BC) patient with pT2N3aMx status and defined primary tumor markers (ERα 8, PR-B 8, and HER2 score 0) was available. Initially, the mALN tissue of this patient was analyzed by immunohistochemistry (IHC), and a positive correlation of nodal ERα, PR-B and HER2 biomarkers to those of the primary tumor was obtained. Subsequently, the mALN was FNCS fractionated into N and C, and Western blot (WB) analysis demonstrated a single band for ERα, PR-B and nuclear loading control (HDAC1) in nuclear, but not in the cytosolic compartments, confirming the efficiency of our fractionation protocol. At the same time, HER2 bands were not observed in either compartment, in accordance with HER2 negativity determined by IHC in both primary tumor and mALN tissue. In conclusion, by confirming the nuclear expression of ERα and PR-B biomarkers in metastatic loci, we demonstrate the purity of the FNCS-generated compartments - the protocol that offers a reliable tool for further analysis of nuclear versus cytosolic content in downstream analysis of novel biomarkers in the whole mALN of BC patients.

Improving the Information and Legal Support of the Judicial System of Ukraine: Experience of the European Court of Human Rights

The research proposes a comprehensive approach to modernizing the information and legal support (ILS) of the judicial system of Ukraine, given European integration processes and the war of Russia against Ukraine. The need for substantial modernization of existing court automation systems to integrate with European systems and improve analytical capabilities for processing large data sets was identified. It was specified that the legal framework for the functioning of court information systems (IS) must be adapted to European legislation, and the organization of data warehouses and information flows must comply with EU standards. The principles of the functioning of court information systems should ensure the possibility of easy integration with the IS of the EU. To investigate war crimes committed by Russia in Ukraine, it is proposed to establish reliable ILS, given the case law of the European Court of Human Rights (ECHR) in cases of human rights violations during conflicts. An innovative approach based on the use of artificial intelligence (AI) tools is proposed to increase the efficiency of judicial decision analysis and improve the ILS of the judicial system of Ukraine. It has been proven that the integration of the judicial systems of Ukraine and the EU is possible, provided there are reliable data analysis tools, process standardization, digitalization, data security, and integrated registers. The effectiveness of applying IT tools in the ILS of courts has been confirmed. It is proposed to establish specialized hybrid courts in Ukraine with international experts to address war crimes, harmonize legislation with international law, and implement the case law of the ECHR.

Generalized variables approach to generalized inverted exponential distribution reliability analyses with progressively type II censored data

Reliability analysis plays a crucial role in various fields, including engineering, manufacturing, and qualitycontrol. It provides valuable insights into the failure behavior of systems and products. One commonly used distribution in reliability modeling is the generalized inverted exponential distribution (GIED). The GIED distribution is known for its flexibility and adaptability to a wide range of failure data. This paper presents a new method for estimating confidence intervals and testing hypotheses for GIED distribution reliability functions based on a generalized value approach. By transforming the reliability function into the generalized value domain and calculating the generalized lower confidence limit, the proposed method offers enhanced accuracy and precision. Furthermore, the generalized p-value approach for hypothesis testing provides a robust and computationally efficient method for analyzing reliability data. The results from a real data set and Monte Carlo simulations confirm the superiority of the proposed approach over classical methods. The proposed method offers improved accuracy and computational efficiency, making it a valuable tool for reliability analysis using GIED distributions.

A reliability analysis method for electromagnet performance degradation based on FMEA and fuzzy inference system

AbstractElectromagnets are often used in indirect control for industrial applications. The ability of an electromagnet to control objects should decrease with performance degradation. And electromagnets product poses a danger to people and objects in the working environment. So, it is very difficult to analyze the reliability of electromagnetic performance degradation because of the complicated working condition. Failure Mode and Effect Analysis (FMEA) is the most commonly used tool for product reliability analysis. The new version of FMEA uses integer as evaluation value, which cannot represent the hesitation psychology of the evaluator. The Action Priority (AP) table of the FMEA describes the relationship between the evaluation of influencing factors and the risk level of the failure mode, which provides rules for determining the risk level of the failure mode. However, the AP table may result in multiple failure modes having the same ranking, which does not align with the intention of FMEA to prevent failures. Therefore, this paper proposes a reliability analysis method for electromagnetic performance degradation based on FMEA and FIS. Firstly, the Double Hierarchy Hesitant Fuzzy Linguistic Term Set (DHHFLTS) is used as the evaluation language to describe the hesitation psychology of evaluators. Secondly, the AP table of FMEA is used as FIS fuzzy inference rule. In this way, the idea of FMEA to determine the risk level of failure mode is retained and the problem of FIS fuzzy rule making is overcome. Then, FIS defuzzification AP table inference results to determine the risk ranking of failure modes. This avoids situations where the order of failure modes is equal. Finally, a performance degradation model of the electromagnet is constructed based on the Wiener process, and the calculation results of the new method are verified.

Ellipsoid Zone Reflectivity: Exploring its Potential as a Novel Non-Invasive Biomarker for Assessing Mitochondrial Function

ABSTRACT The ellipsoid zone (EZ) on macular optical coherence tomography (OCT) scans exhibits high intensity due to a high density of light-scattering mitochondria, making its reflectivity a potential marker for mitochondrial function. Here, we developed a reliable analysis tool for extracting relative EZ reflectivity and explore its potential as a biomarker in various diseases. We analysed OCT scans of patients with optic neuritis (ON), primary progressive optic neuropathy (PPON), chronic progressive external ophthalmoplegia (CPEO), dominant optic atrophy (DOA), and healthy controls. EZ reflectivity (normalised to the retina pigment epithelium (RPE) and outer nuclear layer (ONL)) was evaluated. Reliability was assessed using intraclass correlation coefficients (ICC), and group differences were analysed through multivariable linear regression, adjusting for relevant confounders. In total, 12 controls, 23 ON patients, 7 CPEO patients, 13 DOA patients, and 13 PPON patients were included. EZ/RPE20% and EZ/ONL ratios demonstrated good test–retest reliability with ICCs of 0.76 (p < .001) and 0.63 (p = .013), respectively. Multivariable regression analysis revealed that median EZ/RPE20% and EZ/ONL ratios were lower in CPEO (r = -0.12, p = .036, and r = -0.59, p = .011), DOA (r = -0.16, p = .049, and r = -0.55, p = .082), PPON (r = -0.17, p = .014, and r = -0.57, p = .037), and ON (r = -0.11, p = .013, and r = -0.42, p = .006) compared to controls, respectively. These data show that EZ reflectivity can be reliably determined from OCT scans and appears to be reduced in neuroinflammatory and mitochondrial disorders. Further validation in larger prospective cohorts is warranted, but our findings suggest that EZ reflectivity might serve as a non-invasive in-vivo biomarker for mitochondrial health.

An integrated hybrid approach for assembly tolerance transfer and allocation

PurposeThis study aims to develop an optimal tolerance allocation strategy involves integrating the unique transfer (UT) approach and the difficulty coefficient evaluation (DCE) routine in an interactive hybrid method. This method combines the strengths of both UT and DCE, ensuring simultaneous utilization for enhanced performance. The proposed tolerancing model manifests an integrated computer-aided design (CAD) tool.Design/methodology/approachBy combining UT and DCE based on failure mode, effects and criticality analysis (FMECA) tool and the Ishikawa diagram, the proposed collaborative hybrid tool ensures an efficient and optimal tolerance allocation approach. The integration of these methodologies not only addresses specific transfer challenges through UT but also conducts a thorough evaluation of difficulty coefficients via DCE routine using reliability analysis tools as FMECA tool and the Ishikawa diagram. This comprehensive framework contributes to a robust and informed decision-making process in tolerance allocation, ultimately optimizing the design and manufacturing processes.FindingsThe presented methodology is implemented with the aim of generating allocated tolerances that align with specific difficulty requirements, facilitating the creation of a mechanical assembly characterized by high quality and low cost. To substantiate and validate the conceptual framework and methods, an integrated tool has been developed, featuring a graphical user interface (GUI) designed in MATLAB. This interface serves as a platform to showcase various interactive and integrated tolerance allocation approaches that adhere to both functional and manufacturing prerequisites. The proposed integrated tool, designed with a GUI in MATLAB, offers the capability to execute various examples that effectively demonstrate the benefits of the developed tolerance transfer and allocation methodology.Originality/valueThe originality of the proposed approach is the twining between the UT and DCE simultaneous in an integrated and concurrent tolerance transfer and allocation model. Therefore, the proposed approach is named an integrated CAD/tolerance model based on the manufacturing difficulty tool. The obtained results underscore the tangible advantages stemming from the integration of this innovative tolerance transfer and allocation approach. These benefits include a notable reduction in total cost and a concurrent enhancement in product quality.

Augmented line sampling and combination algorithm for imprecise time-variant reliability analysis

Assessment of imprecise time-variant reliability in engineering is a critical task when accounting for both the variability of structural properties and loads over time and the presence of uncertainties involved in the ambiguity of parameters simultaneously. To estimate the Imprecise Time-variant Failure Probability Function (ITFPF) and derive the imprecise reliability results as a byproduct, Adaptive Combination Augmented Line Sampling (ACALS) is proposed. It consists of three integrated features: Augmented Line Sampling (ALS), adaptive strategy, and the optimal combination. ALS is adopted as an efficient analysis tool to obtain the failure probability function w.r.t. imprecise parameters. Then, the adaptive strategy iteratively applies ALS while considering both imprecise parameters and time simultaneously. Finally, the optimal combination algorithm collects all result components in an optimal manner to minimize the Coefficient of Variance (C.o.V.) of the ITFPF estimate. Overall, the proposed ACALS method outperforms the original ALS method by efficiently estimating the ITFPF while guaranteeing a minimal C.o.V. Thus, the proposed approach can serve as an effective tool for imprecise time-variant reliability analysis in real engineering applications. Several examples are presented to demonstrate the superiority of the proposed approach in addressing the challenges of estimating the ITFPF.