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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".

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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)'.

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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)'.

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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.

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Invasive alien species have widespread impacts on native biodiversity and ecosystem services. Since the number of introductions worldwide is continuously rising, it is essential to prevent the entry, establishment and spread of new alien species through a systematic examination of future potential threats. Applying a three-step horizon scanning consensus method, we evaluated non-established alien species that could potentially arrive, establish and cause major ecological impact in Spain within the next 10 years. Overall, we identified 47 species with a very high risk (e.g. Oreochromis niloticus, Popillia japonica, Hemidactylus frenatus, Crassula helmsii or Halophila stipulacea), 61 with high risk, 93 with moderate risk, and 732 species with low risk. Many of the species categorized as very high or high risk to Spanish biodiversity are either already present in Europe and neighbouring countries or have a long invasive history elsewhere. This study provides an updated list of potential invasive alien species useful for prioritizing efforts and resources against their introduction. Compared to previous horizon scanning exercises in Spain, the current study screens potential invaders from a wider range of terrestrial, freshwater, and marine organisms, and can serve as a basis for more comprehensive risk analyses to improve management and increase the efficiency of the early warning and rapid response framework for invasive alien species. We also stress the usefulness of measuring agreement and consistency as two different properties of the reliability of expert scores, in order to more easily elaborate consensus ranked lists of potential invasive alien species.

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The continuous high-intensity and disorderly expansion of construction land in mountainous areas threatens city development; consequently, the scientific guidance of its sustainable development has become a research hotspot. This work aimed to develop a new theoretical framework for predicting land expansion. Based on DMSP/OLS-Landsat 7 data correction from 2000 to 2019, to ensure data reliability, this study quantitatively analysed the expansion law of land-use and land-cover (LULC) in Huayuan, a typical mountainous area in China. Based on the land expansion law, the patch-generating land use simulation (PLUS) model was used to predict various types of LULCs in different scenarios. The results showed that (1) the reliability of LULC under multi-source spatio-temporal data correction reached more than 0.97. (2) The expansion law of industrial and mining land, urban living land, and traffic land is sprawl, while rural living land is enclaved and the expansion direction and intensity are obviously different. (3) The scale of land expansion in the natural-oriented scenario was significantly higher than that in the humanism-oriented scenario, with a higher value of 199.33 hm2. This study expands the case study of land use analysis and prediction, and provides scientific guidance for different land expansion planning, which can avoid the mismatch and waste of land resources. Furthermore, it also deepens the exploration of LULC identification reliability method and enriches the theory of different land use prediction in mountainous areas.

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INTRODUCTION: Many surgical procedures are prone to human error, particularly in the learning phase of skills acquisition. Task standardisation has been suggested as an approach to reducing errors, but it fails to account for the human factors associated with learning. Human reliability analysis (HRA) is a structured approach to assess human error during surgery. This study used HRA methodologies to examine skills acquisition associated with carpal tunnel decompression. METHODS: The individual steps or subtasks required to complete a carpal tunnel decompression were identified using hierarchical task analysis (HTA). The systematic human error reduction and prediction approach (SHERPA) was carried out by consensus of subject matter experts. This identified the potential human errors at each subgoal, the level of risk associated with each task and how these potential errors could be prevented. RESULTS: Carpal tunnel decompression was broken down into 46 subtasks, of which 21 (45%) were medium risk and 25 (55%) were low risk. Of the 46 subtasks, 4 (9%) were assigned high probability and 18 (39%) were assigned medium probability. High probability errors (>1/50 cases) included selecting incorrect tourniquet size, failure to infiltrate local anaesthetic in a proximal-to-distal direction and completion of the World Health Organization (WHO) surgical sign-out. Three (6%) of the subtasks were assigned high criticality, which included failure to aspirate before anaesthetic injection, whereas 21 (45%) were assigned medium criticality. Remedial strategies for each potential error were devised. CONCLUSIONS: The use of HRA techniques provides surgeons with a platform to identify critical steps that are prone to error. This approach may improve surgical training and enhance patient safety.

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This paper considers the reliability analysis of a multicomponent stress-strength system which has $k$ statistically independent and identically distributed strength components, and each component is constructed by a pair of statistically dependent elements. These elements are exposed to a common random stress, and the dependence among lifetimes of elements is generated by Clayton copula with unknown copula parameter. The system is regarded to be operating only if at least $s$($1 \leq s \leq k$) strength variables in the system exceed the random stress. The maximum likelihood estimates (MLE) of unknown parameters and system reliability is established and associated asymptotic confidence interval is constructed using the asymptotic normality property and delta method, and the bootstrap confidence intervals are obtained using the sampling theory. Finally, Monte Carlo simulation is conducted to support the proposed model and methods, and one real data set is analyzed to demonstrate the applicability of our study.