Circular rubber aggregate CFST stub columns under axial compression: prediction and reliability analysis.

Extensive studies support using steel tubes to enhance the structural integrity of rubber aggregate concrete (RBAC), namely RBAC-filled steel tubes (RCFST). However, current design codes for assessing the axial compressive behaviour of circular stub RCFST (CS-RCFST) columns are limited. Furthermore, there is a scarcity of studies focused on ensuring the structural safety of these columns. Based on an extensive experimental database comprising 145 columns, this study explores machine learning (ML) capabilities for predicting the axial strength of CS-RCFST columns, using six typical machine-learning models, i.e., symbolic regression (SR), XGBoost, CatBoost, random forest, LightGBM, and Gaussian process regression models. The hyperparameter tuning of the introduced ML models is performed using the Bayesian Optimization technique. The comparison results show that the CatBoost model is the most reliable and accurate ML model (R2 = 0.999 and 0.993 for the training and testing sets, respectively). In addition, a simple and practical design expression for CS-RCFST columns has been developed with acceptable accuracy based on the SR model (an average test-to-prediction ratio of 0.99 and CoV of 0.132). Meanwhile, the axial strength predicted by ML models was compared with two prominent practice codes (i.e., AISC360 and EC4). The comparison results indicated that the ML models could introduce a highly reliable and accurate approach over current design standards for strength prediction. Furthermore, a reliability analysis is conducted on two different ML models to evaluate the reliability of utilising ML models in practical design applications. This assessment involves identifying the statistical properties associated with the compressive strength of RBAC, as well as introducing the required resistance design factors aligned with the target reliability recommended by code standards.

Research on risk management of ship maintenance projects based on multi agent swarm model simulation method.

In recent years, the role of naval ship power has become increasingly prominent in regional conflicts and military operations worldwide, and the powerful capabilities demonstrated by naval forces have also been valued by countries around the world. While naval forces are frequently deployed to carry out various tasks, the intensity of equipment wear and tear on ships is also significantly increasing, posing a huge challenge to the maintenance and support capabilities of ship equipment. In the actual process of ship equipment maintenance, a series of risk factors such as cost, schedule, and quality will have a significant impact on the entire ship maintenance project, and even affect the success or failure of the entire project. This article first decomposes the project process of ship maintenance projects, further analyzes the various risk factors of ship maintenance projects based on the project decomposition situation, and then distinguishes the overall project, project process, and project risk factors to establish an intelligent agent group model for simulation analysis. Through the above simulation analysis, we will conduct in-depth research on the specific impact of project risks on ship maintenance projects, and analyze and verify the positive role and practical significance of project risk management in project risk control and project efficiency improvement. In the process of the above research content, methods and measures for risk management of ship maintenance projects are proposed, providing scientific and efficient risk management suggestions for such projects, improving the scientific management efficiency of ship maintenance projects, and contributing to the improvement of the quality and efficiency of project management.

Preparation of Imidazole Compounds as Latent Curing Agents and Their Application in RGB LED Packaging.

LED packaging miniaturization has raised more requirements for LED materials. As a material contacting the LED chip directly, the reliability of LED non-conductive adhesive has also garnered increasing attention. This study optimized the formula for non-conductive adhesives for an imidazole curing system. The optimized composition of the adhesive is 25%wt for the curing agent and 30%wt for the silica. The prepared non-conductive adhesive has a 7-day pot life and 9-month storage stability. The shear strength reached 87 g and 72 g at 25 °C and 160 °C, respectively. The reliability of the LED modules packaged with the non-conductive adhesive was researched. The green and blue light intensity change was 4.7%, 43.7%, respectively, indicating good anti-aging properties. The blue light decay was mainly due to adhesive aging. The non-conductive adhesive effectively prevented "caterpillar" growth. This provides useful and practical guidelines for industry for applications of adhesive in different packages.

Cross-cultural reliability of Van Rie TB perceived Stigma Scale among TB survivors in North India.

BACKGROUND: As India progresses towards TB elimination five years ahead of Sustainable Development Goals (SDG), Community-based perceptions of tuberculosis stigma become particularly relevant in the context of TB elimination efforts. This study aims to assess the cross-cultural reliability of 'Community Perspectives Van Rie TB Stigma Scale' which can been used for screening and rapid evaluation of prevalent stigma themes in community. METHODS: A community based cross-sectional study was conducted in North India among 400 TB survivors to assess the cross-cultural reliability of 'Community Perspective Van Rie TB stigma Scale'. Cronbach Alpha and McDonald's Omega were used for reliability analysis. RESULTS: Cronbach ɑ value for the Van Rie TB Stigma Scale was 0.826 and McDonald's ω is 0.832 indicative of good reliability of the TB Stigma Scale. CONCLUSION: The study is one the first studies to assess the cross-cultural reliability of Community Perspective of Van Rie TB Stigma Score in India. The reliability analysis indicates that the 'Community Perspectives Van Rie TB Stigma Scale' can be used in the Indian settings for screening of TB associated stigma.

Evaluating the reliability and validity of the Questionnaire on Well-Being: a validation study for a clinically informed measurement of subjective well-being.

Researchers and clinicians are becoming increasingly aware of the importance of assessing positive functioning to inform clinical outcomes. This paper evaluates the Questionnaire on Well-Being (QWB, available for free https://doi.org/10.17605/OSF.IO/GSC3R), a clinically informed instrument that assesses subjective well-being, across two studies. Study One, consisting of treatment-seeking individuals in an assertiveness training sample (n = 495), explored the factorial structure of the QWB, assessed the four-week test-retest reliability, criterion-related validity, and identified a preliminary cutoff point for the QWB with clinical significance. Study Two, including participants from the general public (n = 1561), confirmed the factorial structure of the QWB and further evaluated criterion-related validity. The results provided support for a unidimensional structure for the QWB. Furthermore, the QWB exhibited excellent internal reliability (Cronbach's alpha = 0.93 and 0.94 in Study One and Two, respectively), high test-retest reliability (ICC3 = .50 at a four-week follow-up in Study One), and appropriate criterion-related validity demonstrating positive correlations with positive affect and negative correlations with psychopathology. Finally, a cutoff point on the QWB below 50 was associated with marked psychopathology. These findings provide preliminary support for the usage of the QWB in clinical and non-clinical settings, establishing the QWB as a reliable indicator of subjective well-being.

Power flow control and reliability improvement through adaptive PSO based network reconfiguration.

Ensuring stable power flow and reliable supply could maintain system security, improve system efficiency, minimize power loss, and reduce the risk of supply outage. Power flow management can be employed to enhance bus voltage and decrease power losses. The reliability of the system is critical for both the customers and the utility to ensure supply continuity and improved revenue. With the growing demand for reliable power supplies, it is crucial that utilities devote efforts to ensure a consistent power supply to meet customer needs. However, the frequent occurrence of power interruptions and the prolonged duration of interruption pose significant challenges to power distribution systems in the town of Wolaita Sodo. This study aims to explore power flow and reliability control through the utilization of optimal distribution network reconfiguration (DNR). The optimal placement of tie-switches (TS) to address the power flow and reliability issues is done through the adaptive particle swarm optimization (APSO) algorithm. With the help of APSO, five TS units achieved the reliability indices within the national standard boundary. The backward/forward sweep (BFS) and Markov chain-based Monte Carlo simulation (MCMCS) methods are used for load flow and reliability analysis. Through simulation, with integration of five TS, SAIFI decreases from a value of 557 to about 34, SAIDI decreases from 573.59h to about 43.87h and EENS decreases from 1835.5 MWh to about 140.38 MWh annually, active power loss decreases from 1631.15 kW to about 559.35 kW, the minimum bus voltage increases from 0.7537pu to 0.9502pu. Finally, the evaluation of the suggested algorithm variants is conducted by taking into account the duration it takes to respond, the level of convergence achieved, and the extent to which power loss is minimized.

Reliability of pre-resection ligament tension assessment in imageless robotic assisted total knee replacement.

BACKGROUND: Ligament tension balance is a major determinant for the success of total knee replacement (TKR). The present study aimed at determining the inter-rater and intra-rater reliability in performing ligament tension assessment using an imageless robotic-assisted TKR. METHODS: Twenty-four knees in 21 patients who received robotic-assisted TKR for end-stage varus osteoarthritis were examined. Three orthopedic specialists and six orthopedic trainees participated in the operations. Data from the ligament tension assessment were collected during the operations. RESULTS: For the inter-rater reliability, "extension medial" and "flexion medial" had excellent reliability whilst "extension lateral" and "flexion lateral" had good-to-excellent reliability. For the intra-rater reliability, "extension medial" showed excellent reliability, "extension lateral" and "flexion medial" showed good-to-excellent reliability, and "flexion lateral" showed moderate-to-excellent reliability. CONCLUSIONS: Robotic-assisted technology provides a reliable solution to improve ligament tension assessment. All ligament tension assessments with the use of the technology could demonstrate at least good-to-excellent reliability except for the intra-rater reliability of "flexion lateral".

Psychometric assessment of the Chinese adaptation of the patient participation scale targeting inpatients: a validation research.

Objective: The objective of this research was to introduce, translate, and verify the Patient Participation Scale (PPS) within a Chinese context. Methods: We applied a combination of internal consistency testing, item analysis, exploratory factor analysis, and confirmatory factor analysis. The research involved 453 individuals, comprising both outpatients and inpatients, across three Jinzhou Medical University-affiliated hospitals in China. Additionally, a subgroup of 50 patients underwent a retest after a 2-week interval to assess reliability. Results: The adapted Chinese edition of the PPS included 21 items. Exploratory factor analysis identified four distinct factors, accounting for 66.199% of the total variance. Confirmatory factor analysis supported a suitable four-factor structure ( χ / d f : 2.045, RMSEA: 0.048, GFI: 0.935, AGFI: 0.914, TLI: 0.958, CFI: 0.965, and PGFI: 0.712). The factor loadings corresponded to each item exceeded 0.6, the average variance extracted (AVE) exceeded 0.5, and the composite reliability (CR) exceeded 0.7. The correlation coefficients stayed below the square root of the AVE, demonstrated relatively favourable convergent and discriminant validity.The Chinese PPS edition demonstrated high internal consistency (Cronbach's alpha: 0.919), with dimensional Cronbach's alpha ranged from 0.732 to 0.918. Split-half as well as retest reliabilities were recorded at 0.737 and 0.864, respectively. The content validity index for the Chinese PPS edition stood at 0.974. Conclusion: The Chinese edition of the PPS emerges as a valid and reliable tool for assessing patient engagement in their own treatment as well as care, applicable in both inpatient as well as outpatient settings.

Evaluating the effectiveness of artificial intelligence-based tools in detecting and understanding sleep health misinformation: Comparative analysis using Google Bard and OpenAI ChatGPT-4.

This study evaluates the performance of two major artificial intelligence-based tools (ChatGPT-4 and Google Bard) in debunking sleep-related myths. More in detail, the present research assessed 20 sleep misconceptions using a 5-point Likert scale for falseness and public health significance, comparing responses of artificial intelligence tools with expert opinions. The results indicated that Google Bard correctly identified 19 out of 20 statements as false (95.0% accuracy), not differing from ChatGPT-4 (85.0% accuracy, Fisher's exact test p = 0.615). Google Bard's ratings of the falseness of the sleep misconceptions averaged 4.25 ± 0.70, showing a moderately negative skewness (-0.42) and kurtosis (-0.83), and suggesting a distribution with fewer extreme values compared with ChatGPT-4. In assessing public health significance, Google Bard's mean score was 2.4 ± 0.80, with skewness and kurtosis of 0.36 and -0.07, respectively, indicating a more normal distribution compared with ChatGPT-4. The inter-rater agreement between Google Bard and sleep experts had an intra-class correlation coefficient of 0.58 for falseness and 0.69 for public health significance, showing moderate alignment (p = 0.065 and p = 0.014, respectively). Text-mining analysis revealed Google Bard's focus on practical advice, while ChatGPT-4 concentrated on theoretical aspects of sleep. The readability analysis suggested Google Bard's responses were more accessible, aligning with 8th-grade level material, versus ChatGPT-4's 12th-grade level complexity. The study demonstrates the potential of artificial intelligence in public health education, especially in sleep health, and underscores the importance of accurate, reliable artificial intelligence-generated information, calling for further collaboration between artificial intelligence developers, sleep health professionals and educators to enhance the effectiveness of sleep health promotion.

Effect of Temperature and Humidity Coupling on the Ageing Failure of Carbon Fiber Composite/Titanium Bonded Joints.

Temperature and humidity coupling has a more significant effect on the failure properties of bonded joints than a single factor, and there is not enough research on this. In this paper, joints bonded with strong toughness structural adhesives are selected for the experimental analysis of joints aged for 240 h, 480 h, and 720 h at temperatures of 40 °C and 60 °C and a humidity of 95% and 100%. The sequential double Fick's model was used to fit the water absorption of the joints, and the comparison yielded that the water absorption of the adhesive was in accordance with Fick's law. The quasi-static tensile tests revealed that the reduction in mechanical properties of the joints was positively correlated with the moisture content in the environment, while the competing mechanisms of post-temperature curing and hydroplasticization resulted in a slight increase in the failure strength and energy uptake of the aged joints, which is in agreement with the experimental results of the Fourier infrared spectroscopy. A combination of macroscopic failure sections and scanning electron microscope (SEM) images yielded that the failure mode of the joints changed from cohesive failure to interfacial failure with increasing ageing time. In addition, reliability analyses for the fatigue testing of joints are expected to provide guidance for the life design of bonding technology in the vehicle service temperature range.

Causal chain event graphs for remedial maintenance.

The analysis of system reliability has often benefited from graphical tools such as fault trees and Bayesian networks. In this article, instead of conventional graphical tools, we apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system. The CEG is derived from an event tree and can flexibly represent the unfolding of asymmetric processes. For this application, we need to define a new class of formal intervention we call remedial to model the causal effects of remedial maintenance. This fixes the root causes of a failure and returns the status of the system to as good as new. We demonstrate that the semantics of the CEG are rich enough to express this novel type of intervention. Furthermore, through the bespoke causal algebras, the CEG provides a transparent framework with which to guide and express the rationale behind predictive inferences about the effects of various types of remedial intervention. A backdoor theorem is adapted to apply to these interventions to help discover when a system is only partially observed.

Analysis of microstate features for Parkinson's disease based on reliability validation.

BACKGROUND: Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution. NEW METHOD: Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression. RESULTS: Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS. COMPARISON WITH EXISTING METHOD(S): High reliability of PD microstate features obtained by our approach. CONCLUSION: EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.

Metamodeling-based reliability analysis framework for activated sludge processes.

The reliability of activated sludge processes will be adversely affected by alterations in wastewater production and pollutant loading foreseen due to population growth, urbanization, and climate change, as well as the tendency to amend environmental regulations to mandate stricter effluent quality standards to alleviate water pollution. Until now, there was no framework capable of effectively managing these multifaceted challenges in reliability analysis. Previous attempts conducted a low number of simulations leading to insufficient statistical significance to properly validate reliability quantification. A metamodeling-based reliability analysis framework for the activated sludge process is introduced to cope with alterations in wastewater production and pollutant loading, assesses the reliability under different effluent regulations, and leverages metamodels to conduct extensive simulation work, to estimate the reliability. All metamodels produced high-resolution results, enabling reliability estimation after 100 000 simulations. The framework effectively assessed the annual failure rates of various activated sludge facility designs under four regulations, demonstrating the impact of stricter effluent quality standards. Integrating metamodels for reliability analysis greatly lowers computational costs, making the framework a time and resource-efficient choice for quick decision-making in facility design.

Reliability-based analysis of horizontal curve design by evaluating the impact of vehicle automation on roadway departure crashes and safety performance.

Roadway departure (RwD) crashes are significant safety concerns, especially at horizontal curves. The design of these curves plays a crucial role in mitigating RwD crashes. Thus, a thorough understanding of the interaction between driver behavior, vehicle automation, and geometric design is vital. Substantive safety, which emphasizes the inherent safety in a road's design and function, serves as the foundation of our approach. Building on this, the study employs a safe system approach to investigate the performance of horizontal curves under both non-automated and partially automated conditions, using a reliability-based analysis focusing on Stopping Sight Distance as the primary driver demand. Factors including Perception-Brake Time and Take-Over Time for automated vehicles are examined. The analysis covers horizontal curves, characterized by their geometric design and crash data. Our findings highlight a shift in the performance of horizontal curves under automation, emphasizing the need to consider automation in roadway design within the safe system approach. This study demonstrates how a reliability-based analysis can guide designers in making informed decisions regarding the geometric design of horizontal curves to reduce RwD crashes. To enhance transportation safety in the era of increasing automation, ongoing exploration of the relationships between driver behavior, automation, and road design is indispensable.

Evaluation of Non-Compressive Transvaginal Cervical Elastography as a Quantitative Imaging Biomarker in Pregnancy: A Repeatability and Reliability Analysis.

OBJECTIVES: Non-compressive strain elastography has been proposed as a novel quantitative imaging biomarker for assessing the structure and function of the cervix. The current study aims to assess the repeatability, and intra- and inter-observer reliability of transvaginal non-compressive cervical strain elastography in a clinical setting. METHODS: We conducted a dual-phase single-center prospective feasibility study of singleton gestations >16-weeks gestation that required a clinically-indicated transvaginal ultrasound. Each study participant, n = 43 in phase 1 and n = 13 in phase 2, had elastography performed by two trained observers that each performed multiple image acquisitions. We performed a multivariable regression to adjust for changes in clinical characteristics between study phases and calculated the repeatability coefficients, limits of agreement, and intraclass correlations for each quantitative elastography parameter. We compared quantitative elastography parameters to cervical length measurements, acquired from the same images. RESULTS: The repeatability coefficients and percent limits of agreement were wide for all of the quantitative elastography parameters, demonstrating poor repeatability. Intraclass correlation coefficients were poor-moderate for both intra-observer (0.31-0.77) and inter-observer reliability (0.35-0.77) in both study phases, while cervical length showed excellent reliability with intraclass correlations consistently >0.90. CONCLUSIONS: Non-compressive transvaginal strain cervical elastography did not demonstrate adequate repeatability or reliability. Our results highlight the importance of rigorously assessing novel quantitative imaging biomarkers before clinical application.

Eigenvibrations of Kirchhoff Rectangular Random Plates on Time-Fractional Viscoelastic Supports via the Stochastic Finite Element Method.

The present work's main objective is to investigate the natural vibrations of the thin (Kirchhoff-Love) plate resting on time-fractional viscoelastic supports in terms of the Stochastic Finite Element Method (SFEM). The behavior of the supports is described by the fractional order derivatives of the Riemann-Liouville type. The subspace iteration method, in conjunction with the continuation method, is used as a tool to solve the non-linear eigenproblem. A deterministic core for solving structural eigenvibrations is the Finite Element Method. The probabilistic analysis includes the Monte-Carlo simulation and the semi-analytical approach, as well as the iterative generalized stochastic perturbation method. Probabilistic structural response in the form of up to the second-order characteristics is investigated numerically in addition to the input uncertainty level. Finally, the probabilistic relative entropy and the safety measure are estimated. The presented investigations can be applied to the dynamics of foundation plates resting on viscoelastic soil.

Reliability Analysis of HHV Prediction Models for Organic Materials Using Bond Dissociation Energies.

The purpose of this study is to analyze the reliability of predictive models for higher heating values related to organic materials. A theoretical model was developed, which utilizes bond dissociation energies (BDEs) to establish correlations between elemental composition and calorific values. Our analysis indicates that the energy contribution of one mole of hydrogen atoms is approximately equal to -144.4 kJ mol-1. Further investigation reveals significant variations in the bond dissociation energies of carbon atoms within organic compounds, resulting in a range of energy outputs from -414.30 to -275.34 kJ mol-1 per mole of carbon atoms. The presence of oxygen atoms in organic compounds has a negative impact on the magnitude of combustion heat, with values ranging from 131.1 to 207.17 kJ mol-1. The combustion mechanism imposes certain constraints, leading to the equation HHVg = -31.34·[C] - 144.44·[H] + 10.57·[O] for organic compounds. Based on the parameter sensitivity analysis, the coefficient associated with carbon mass fraction exhibits a significantly greater impact on result prediction accuracy, demonstrating a sensitivity value of 92.65%. The results of further analysis indicate that empirical correlations involving the mass fractions of the elements N and S in lignocellulosic materials may be prone to over-fitting, with sensitivity indices of 1.59% and 0.016%, respectively.

Fatigue reliability analysis of aeroengine blade-disc systems using physics-informed ensemble learning.

For the fatigue reliability analysis of aeroengine blade-disc systems, the traditional direct integral modelling methods or separate independent modelling methods will lead to low computational efficiency or accuracy. In this work, a physics-informed ensemble learning (PIEL) method is proposed, i.e. firstly, based on the physical characteristics of blade-disc systems, the complex multi-component reliability analysis is split into a series of single-component reliability analyses; moreover, the PIEL model is established by introducing the mapping of multiple constitutive responses and the multi-material physical characteristics into the ensemble learning; finally, the PIEL-based system reliability framework is established by quantifying the failure correlation with the Copula function. The reliability analysis of a typical aeroengine high-pressure turbine blade-disc system is regarded as an example to verify the effectiveness of the proposed method. Compared with the direct Monte Carlo, support vector regression, neural network, ensemble learning and physics-informed neural network, the proposed method exhibits the highest computing accuracy and efficiency, and is validated to be an efficient method for the reliability analysis of blade-disc systems. The current work can provide a novel insight for physics-informed modelling and fatigue reliability analyses. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Fault logic and data-driven model for operation reliability analysis of the flap deflection angle.

To effectively perform the reliability analysis of the flap deflection angle, the reliability analysis framework is developed by introducing fault logic and a data-driven model. Herein, the fault logic analysis is used to study the fault mechanism and filter out the characteristic fault parameters that can be used to collect input data for data-driven modelling; the data-driven modelling is employed to establish a reliability analysis model with a small amount of input data. Under this proposed framework, the improved dung beetle optimization algorithm for back propagation (IDBO-BP) method is developed to perform the reliability modelling of the flap deflection angle. To validate the effectiveness of the proposed framework, we study the fault logic of flap symmetry and establish a surrogate model of flap deflection based on the fault parameters and the IDBO-BP algorithm. According to the predicted results of the flap deflection angle, the reliability model based on the fault mechanism can reflect the actual flap motion. At the same time, the proposed IDBO-BP algorithm has excellent modelling and simulation property by comparing with other optimization algorithms. Thus, the efforts of this study provide a new solution to the problem of reliable analysis with uncertain fault parameters. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Brief Report: Does the Number of Response Options Matter for the BFI-2? Conceptual Replication and Extension.

We evaluated how the number of response options affects the psychometric properties of the Big Five Inventory-2 (BFI-2). Using two large samples collected from a market research company (Ns = 893 and 1,213), we tested how different response options of the BFI-2 influenced scale score distributions, internal consistency estimates, convergent validity correlations, and criterion validity correlations. Results suggest that score distributions were impacted by the number of response options such that ceiling and floor effects were more common when using two or three response options than when using more options. Estimates of Cronbach's alpha were generally lower with fewer scale points as compared with more scale points, but these effects disappeared when ordinal alpha was used. There were no systematic effects of response options on convergent validity and criterion validity correlations. Given these results, there seems to be few psychometric reasons for deciding whether to administer personality items with five, six, or seven scale points.