Reliability Evaluation Method Research Articles

Development and Testing of the Spinal Cord Injury Bladder and Bowel Control Questionnaire (SCI-BBC-Q).

Recovery of lower urinary tract (LUT) and lower gastrointestinal tract (LGIT) is a high priority for people with lived experience following spinal cord injury (SCI). A universally accepted validated patient-reported outcome (PRO) measure of the individual sensory and motor components of LGIT and LUT function, which allows tracking of recovery is lacking. To address this literature gap, the SCI Bladder and Bowel Control Questionnaire (SCI-BBC-Q) was developed. The SCI-BBC-Q was developed as a direct assessment of the micturition and defecation experiences of an individual with SCI with possible neurogenic LUT and LGIT dysfunction. The SCI-BBC-Q development process consisted of two phases, measure development and evaluation. Measure development was guided by a conceptual framework, review of existing instruments and literature, and an iterative process of item incorporation, review, feedback, and consensus revision. Evaluation included cognitive interviewing, and assessments of feasibility, reliability, and content validity. The final 6-item SCI-BBC-Q is a PRO, which assesses motor and sensory function related to micturition and defecation, requiring ~5 min to complete. Assessments of clarity of the instrument components with regard to understanding of what is being asked in the questionnaire, feasibility of administration, reliability, internal consistency, and agreement with proxy measures have demonstrated that the SCI-BBC-Q provides consistent, stable, and reproducible data. Significant correlations were found between SCI-BBC-Q scores and the anorectal motor and sensory components of the International Standards for the Neurological Classification of SCI. The SCI-BBC-Q is a practical and reliable method for baseline and ongoing evaluation of patients with neurogenic LUT and LGIT dysfunction, especially in the acute and subacute period when function is changing due to neurological plasticity. It is also appropriate for use in monitoring response to treatments related to neurological recovery.

Reliability modelling and evaluation method for Carnot Battery systems considering component performance degradation

Reliability modelling and evaluation method for Carnot Battery systems considering component performance degradation

Test methods for reliability evaluation based on environmental factors

Test methods for reliability evaluation based on environmental factors

Reliability evaluation of rolling bearings based on generative adversarial network sample enhancement and maximum entropy method

Aiming at the difficulty of extracting vibration data under actual working conditions of rolling bearings, this paper proposes a bearing reliability evaluation method based on generative adversarial network sample enhancement and maximum entropy method under the condition of few samples. Based on generative adversarial network, data sample enhancement under few samples is carried out, and the reliability analysis model is established by using the maximum entropy principle and Poisson process. The reliability is evaluated according to the reliability variation frequency, variation speed and variation acceleration. The analysis results show that with the gradual increase of running time, the reliability variation frequency shows a nonlinear growth trend, which can be roughly divided into the initial running-in stage, the stable running-in stage and the intense running-in stage. The reliability variation speed is then used to distinguish the specific starting time of the three stages, and finally the preliminary relationship between the reliability variation acceleration and the remaining life is obtained. The experimental results of the XJTU-SY dataset show that compared with the existing reliability evaluation model, the proposed model has the advantages of less samples, no need for preprocessing and higher accuracy. The proposed model has made a beneficial supplement to the existing reliability analysis methods.

The impact of high‐voltage circuit breaker condition on power system reliability indices

AbstractCondition monitoring data can be used to assess the health of a power system component but is rarely used to assess the reliability of the power system they are part of. For high‐voltage circuit breakers (HVCBs), the susceptibility to various failures can be assessed by examining trip coil current (TCC) measurements. HVCBs have two failure modes, failure to trip on command and tripping without a command, which are triggered by various failure mechanisms that in turn may depend on the technical condition of the HVCBs. The aim of this article is to demonstrate a methodology for quantifying the impact that the technical condition of HVCBs has on the power system reliability indices. An equivalent transmission line failure rate considering protection system failures, including failures related to HVCBs, can be calculated by computing contributions from various fault types (FTs). This article proposes a framework that can quantify the frequency of the FTs that are affected by HVCB condition. The system‐level effects are then evaluated using approximate methods for power system reliability evaluation. To demonstrate the principles and benefits of this methodology, a case study is presented that incorporates HVCB condition data from the Icelandic transmission grid into an illustrative 4‐bus test system; a dataset of 83 TCC measurements from 38 HVCBs from the Icelandic Transmission System Operator is used, together with an outage database of life histories of 464 HVCBs from the Icelandic transmission grid. Results indicate that the condition deterioration associated with the probability of an HVCB failing to trip on command can significantly degrade the reliability indices.

Data-Quality-Navigated Machine Learning Strategy with Chemical Intuition to Improve Generalization.

Generalizing real-world data has been one of the most difficult challenges for application of machine learning (ML) in practice. Most ML works focused on improvements in algorithms and feature representations. However, the data quality, as the foundation of ML, has been largely overlooked, also leading to the absence of data evaluation and processing methods in ML fields. Motivated by the challenge and need, we selected an important but difficult reorganization energy (RE) prediction task as a test platform, which is an important parameter for the charge mobility of organic semiconductors (OSCs), to propose a data-quality-navigated strategy with chemical intuition. We developed a data diversity evaluation based on structure characteristics of OSC molecules, a reliability evaluation method based on prediction accuracy, a data filtering method based on the uncertainty of K-fold division, and a data split technique by clustering and stratified sampling based on four molecular descriptor-associated REs. Consequently, a representative RE data set (15,989 molecules) with high reliability and diversity can be obtained. For the feature representation, a complementary strategy is proposed by considering the chemical nature of REs and the structure characteristics of OCS molecules as well as the model algorithm. In addition, an ensemble framework consisting of two deep learning models is constructed to avoid the risk of local optimization of the single model. The robustness and generalization of our model are strongly validated against different OSC-like molecules with diverse structures and a wide range of REs and real OSC molecules, greatly outperforming eight adversarial controls. Collectively, our work not only provides a quick and reliable tool to screen efficient OSCs but also offers methodological guidelines for improving the generalization of ML.

High-efficient non-iterative reliability-based design optimization based on the design space virtually conditionalized reliability evaluation method

Dynamic-reliability-based design optimization (DRBDO) is a promising methodology to address the significant challenge posed by the new generation of structural design theories centered around reliability considerations. Solving DRBDO problems typically requires iterations ranging from a dozen to several hundreds, with each iteration dedicated to updating the values of design variables. Furthermore, DRBDO necessitates hundreds of or even more representative structural analyses at each iteration to compute the reliability measure, which serves as a foundation for determining the search direction in the subsequent iteration. This results in the double-loop problem confronted by DRBDO, leading to substantial computational costs for structural re-computing, particularly in the cases involving complex nonlinear stochastic dynamical systems. In the present paper, a non-iterative DRBDO paradigms is proposed by combining a novel virtually conditional reliability evaluation and the newly proposed decoupled multi-probability density evolution method (M-PDEM). By leveraging the decoupled M-PDEM, a series of one-dimensional partial differential equations (PDEs) named Li-Chen equations are solved to calculate the joint PDF of multiple responses. This enables efficient computation of the joint probability density function (PDF) of design variables and extreme response as well as the conditional PDF of the extreme response given the values of design variables based on finite representative structural analyses. Then, the reliability of different designs can be regarded as the integral of the conditional PDF, which yields the reliability feasible domain. For problems that the objective function is monotonic to each design variables, by combining with a direct search technique, this method transforms the optimization process into true iteration-free calculations, and thereby eliminates the significant computational burden associated with structural re-computing at different intermediate designs in optimization iterations. Finally, the accuracy and effectiveness of this novel method are validated through numerical examples.

Abstract 4124293: Exercise Capacity in Patients with Fontan Physiology

Introduction/Background: In the absence of a sub-pulmonary ventricle in the Fontan circulation, augmentation of pulmonary blood flow and filling of the systemic ventricle during exercise are limited. Cardiopulmonary exercise testing (CPET) is a reliable method for evaluation of aerobic capacity, gas exchange, and other physiologic parameters used for risk stratification in this population. Despite the ubiquitous use of CPET, reference ranges for patients with Fontan physiology are limited. The purpose of this study is to describe exercise characteristics and their relationship with different characteristics of Fontan physiology, imaging, and hemodynamics. Methods/Approach: In this retrospective cohort study, CPET results were reviewed for adult Fontan patients at the University of Washington between May 2021 and February 2024. Demographics, comorbidities, medications, type of Fontan conduit, imaging characteristics, hemodynamics, and exercise characteristics were included. Results were compared to a large previously published cohort of Fontan patients. Univariate linear regression was used to identify associations between demographics, cardiac anatomy, and exercise parameters. A p-value of less than 0.05 was considered statistically significant. Results/Data: Baseline demographics were notable for an older patient population (31±7 years) with lower indexed peak VO 2 (20.5±5.6 mL/kg/min) and greater V E /VCO 2 slope (37±7) compared to the historic cohort. Indexed peak VO2 was associated with age, BMI, type of Fontan conduit, FEV1, FVC, resting oxygen saturation, peak systolic blood pressure, and peak heart rate. Indexed peak VO 2 was notably not significantly associated with sex, systemic ventricular morphology, ventricular systolic function, Fontan pressure, or the presence of conventional heart failure medical therapy. Conclusions: In the growing population of older patients with Fontan physiology, development of age-specific population normative data may increase the clinical utility of CPET. Further investigation of the relationship between imaging characteristics, hemodynamics, and exercise parameters is needed in the Fontan population.

Multi-Modal Vision Transformer with Explainable Shapley Additive Explanations Value Embedding for Cymbidium goeringii Quality Grading

Cymbidium goeringii (Rchb. f.) is a traditional Chinese flower with highly valued biological, cultural, and artistic properties. However, the valuation of Rchb. f. mainly relies on subjective judgment, lacking a standardized digital evaluation and grading methods. Traditional grading methods solely rely on unimodal data and are based on fuzzy grading standards; the key features for values are especially inexplicable. Accurately evaluating Rchb. f. quality through multi-modal algorithms and clarifying the impact mechanism of key features on Rchb. f. value is essential for providing scientific references for online orchid trading. A multi-modal Transformer for Rchb. f. quality grading combined with the Shapley Additive Explanations (SHAP) algorithm was proposed, which mainly includes one embedding layer, one UNet, one Vision Transformer (ViT) and one Encoder layer. A multi-modal orchid dataset including images and text was obtained from Orchid Trading Website, and seven key features were extracted. Based on petals’ RGB segmented from UNet and global fine-grained features extracted from ViT, text features and image features were organically fused into Transformer Encoders throughout concatenation operation, a 93.13% accuracy was achieved. Furthermore, SHAP algorithm was utilized to quantify and rank the importance of seven features, clarifying the impact mechanism of key features on Rchb. f. quality and value. This multi-modal Transformer with SHAP algorithm for Rchb. f. grading provided a novel idea to represent the explainable features accurately, exhibiting good potential for establishing a reliable digital evaluation method for agricultural products with high value.

Real‐time reliability evaluation method for IGBT module in MMC based on junction temperature and bond wire failure online monitoring

AbstractThe lack of an MMC reliability evaluation approach that incorporates data on essential components' health monitoring in the already related research prevents reliability from being further improved. Therefore, this article proposes a real‐time reliability evaluation method of the multi‐state IGBT module in MMC through iterative calculation of the Markov chain model. The degradation process of the IGBT module is divided into multiple states based on the monitored bond wire failure, and the failure rate of the IGBT module is updated in real time using the accumulated monitoring data of junction temperature. The proposed method can more accurately reflect the real state of MMC compared with the binary state evaluation method. Besides, a multi‐channel high‐speed data acquisition board is designed to measuring the gate voltage and collector voltage of the IGBT for the health evaluation of IGBT modules in MMC; the typically measured results validate the effectiveness of the data acquisition board.

Research on network time reliability evaluation method based on uncertainty theory

In view of the problem that existing network time reliability assessment methods often only consider the impact of inherent uncertainty, while ignoring the impact of epistemic uncertainty caused by insufficient fault information on reliability assessment results, a new method based on uncertainty theory is proposed. According to the node range of network reliability, two types of measurement parameters of single node pair and multi-node pair time reliability are designed. The extended uncertain network model is proposed to directly model the epistemic uncertainty characteristics on nodes and links, and further single node and multi-node pair time reliability algorithms based on the most reliable path and the most reliable extended uncertain subnetwork are designed. Finally, taking a six-node network and the backbone network of China Education and Research Network (CERNET) as examples, the method is applied to evaluate two time reliability indicators, verifying the correctness and effectiveness of the method.

Reliability evaluation of direct current gathering system in onshore wind farm based on reliability block diagram-sequential monte carlo

The full DC wind power generation system has effectively overcome the harmonic resonance, reactive power transmission, and other problems of the traditional AC wind power system, which has broad prospects for development. As a key component of the mentioned system, the reliability of the collection system is critical to the safe and stable operation of the entire onshore wind farm. Firstly, this paper investigates the key equipment and topology of the onshore wind farm DC collection system. Secondly, considering both the internal components and external environment of the wind farm, a component outage probability model based on weather factors is constructed to provide accurate data for the reliability evaluation of the DC collection system of the wind farm. The Reliability Block Diagram is used to analyze the internal logical connection of different topologies of onshore wind farm DC collection systems in detail. Then, a reliability evaluation method of an onshore full DC wind farm collection system based on Reliability Block Diagram-Sequential Monte Carlo is proposed. Finally, a 50 MW onshore wind farm is studied as a sample to compare and analyze the assessment results of the reliability of different collection system topologies. The results show that the reliability of the DC collection system of onshore wind farms has significant advantages.

Advances in Spectroscopic Methods for Predicting Cheddar Cheese Maturity: A Review of FT-IR, NIR, and NMR Techniques

The quest for reliable techniques to predict Cheddar cheese maturity has gained momentum to ensure quality and consistency in large-scale production. Given the complexity of cheese ripening and the industry’s need for fast and reliable evaluation methods, this review addresses the challenge by scrutinising the application of spectroscopic techniques such as Fourier transform infrared (FT-IR), near-infrared (NIR), and nuclear magnetic resonance (NMR). These methods are evaluated for their noninvasive and rapid on-site analysis capabilities, which are essential for ensuring quality in cheese production. This review synthesises current research findings, discusses the potential and limitations of each technique, and highlights future research directions. Overall, NIR spectroscopy emerges as the most promising, offering quick, nondestructive assessments and reasonably accurate compositional predictions, crucial for real-time maturation monitoring. It provides rapid results within minutes, making it significantly faster than FT-IR and NMR. While FT-IR also offers high accuracy, it typically requires longer analysis times due to extensive calibration and can be sensitive to sample conditions, while NMR, although highly accurate, involves complex and time-consuming procedures. Nonetheless, further studies are necessary to refine these spectroscopic techniques, enhance their predictive accuracy, and deepen the understanding of the correlations between chemical attributes and sensory qualities in Cheddar cheese.

Reliability Evaluation of Distribution Network with Electric Vehicle Virtual Energy Storage

To address the problem that the occurrence of large-scale blackouts in the future may cause the lack of power supply in the area and lead to the loss of power to the important places in it and a significant decrease in the living standard of the residents, this paper researches the reliability of the distribution network with electric vehicle virtual energy storage (EVVES). This paper proposes an aggregation model of EVVES applied to distribution networks, establishes the relevant models of EVVES in a virtual energy storage system (VESS) based on it, and proposes a distribution network reliability evaluation method with EVVES. Firstly, an aggregation model of EVVES is proposed to ensure that islands and important loads in the distribution network do not lose power. Secondly, the relevant models in EVVES based on the aggregation model of EVVES are studied. Finally, a distribution network reliability evaluation method with EVVES is proposed based on the traditional reliability evaluation. The improved IEEE-RBTS Bus6 is adopted as an example for reliability analysis, and the simulation results prove the validity of the model established in the paper and demonstrate that the reliability of the distribution network can be significantly improved with EVVES.

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

Выбор метода количественной оценки микробиоты кишечника: сравнительный анализ 16S NGS и ПЦР-РВ

Recently, considerable commercialization of services for quantification of gut microbiota aimed to diagnose dysbiosis, the microbial imbalance, is observed. In the context of growing interest to the personalized approaches in medicine and preventive therapy, the diagnosis of dysbiosis is becoming increasingly important. The results of such screening are used to adjust guidelines on correction of the diet, lifestyle modification, or, where necessary, drug therapy prescription. Such assessment requires a reliable and accurate method for evaluation of microbiota, since validity of further recommendations and therapeutic interventions depends on the quality of the data obtained. The paper reports the main aspects of the two approaches used for microbiota quantification: 16S rRNA next-generation sequencing (16S NGS) and real-time PCR (qPCR). The strengths (from our perspective) and weaknesses of the approaches are also provided.

A Fully Analytical Approach for the Real-Time Dynamic Reliability Evaluation of Composite Power Systems with Renewable Energy Sources

Renewable energy sources (RES) have strong uncertainties, which significantly increase the risks of power imbalance and load shedding in composite power systems. It is thus necessary to evaluate the operational reliability for guiding economic dispatch and reducing the risks. Current methods cannot meet the requirement for the operational timeliness of reliability evaluations due to the high computational complexity of the optimal power flow (OPF) calculations of massive contingencies. This paper proposes a fully analytical approach to construct fast-to-run analytical functions of reliability indices and avoid reassessments when the load and RES change. The approach consists of uniform design (UD)-based contingency screening and a modified stochastic response surface method (mSRSM). The contingency screening method is used to select critical contingencies while considering the uncertainties. The mSRSM is used to construct the analytical functions of the load shedding to the load and RES generation for the selected contingencies. An analytical function of a smooth virtual variable that maps to the load shedding is established in such a way that, when the load and RES vary, the reliability can be assessed within a very short time rather than using laborious OPF calculations. Case studies illustrate the excellent performance of the proposed method for real-time reliability evaluation.

Assessment of the Relationship Between Anterior Teeth Colour and Skin Colour by assessing different measurement tools in Adult Patients: A Systematic Review

Background: Previous research conducted since 1968 have extensively examined the correlation between tooth color and skin color. The purpose of these studies was to determine whether skin color could effectively influence dentists in choosing tooth color. The objective of this systematic review is to examine the methodologies employed in assessing the relationship between tooth and skin color, and to identify the most precise and dependable approaches for modeling and evaluating this relationship. The systematic review was registered in the PROSPERO (CRD42022356022). This systematic review was done on four databases using PRISMA 2009 search strategy, and selected studies were chosen according to PICO model. JBI Critical Appraisal Checklist was used for quality assessment. Out of 1611 articles that were found in the databases, 10 articles matched the criteria and were selected for this systematic review study. Three main methods were found in the 10 studies: conventional methods, digital methods, conventional-digital (mixed) methods. Digital methods were found to be the more reliable methods for color evaluation. Specific settings should be applied during digital color measuring methods. Conventional and digital assessment methods showed either a negative or a positive significant association between the color of teeth and skin. However, the combined method (digital for tooth and visual for skin) did not show any such relation. It is imperative to conduct and apply research in Artificial Intelligence to forecast tooth color based on knowledge of skin color.

Stability of Resistance of Maize to Ear Rots (Fusarium graminearum, F. verticillioides and Aspergillus flavus) and Their Resistance to Toxin Contamination and Conclusions for Variety Registration.

All major ear rots (F. graminearum, F. verticillioides, and Aspergillus flavus) and their toxins are present in maize of preharvest origin in Hungary. Resistance can be an important tool in reducing the infection and toxin contamination from these rots in maize. Previous results identified resistance differences in maize hybrids that were suitable for use in evaluating their risk from toxigenic fungi and their toxins. During the tests, two methodical improvements were achieved: the use of three isolates of the fungus secured and a more precise estimation of resistance to ear rots and their resistance to toxin accumulation or overproduction. The improvement in sampling and the tests of subsamples made the evaluation for the statistics much more exact. This way, we were able to reduce the Within value, providing a statistically more reliable method of evaluation. Earlier data had confirmed that toxin contamination could not be predicted well from visual ear rot severity data. Contradictory results for hybrid ranking were often identified between isolates. The resistance to disease and toxin contamination is not generally valid. The new suggested methodology compares the performance of hybrids in a large number of epidemic situations to identify adaptable hybrids that can respond to diverse conditions; therefore, the stability of resistance and toxin response is decisive information to evaluate risk analyses. The increased number of disease toxin data allowed for lower LSD 5% values for toxins, a much finer analysis of toxin overproduction and underproduction, and a wider database for stability analyses. This way, we obtained important additional separated information about resistance to accumulation of toxins and about maize resistance to these pathogens that is suitable to provide much more reliable testing than was possible until now. Globally, about 50-100 million metric tons can be saved by excluding susceptible hybrids from commercial production.

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.