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.

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.

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.

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.

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)'.

Identification of potential invasive alien species in Spain through horizon scanning.

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.

Polarity status of trigger signal during ECG-gating affects parameters of LV function in gated myocardial perfusion SPECT.

INTRODUCTION AND BACKGROUND: The polarity status is one of the important items of specifications of trigger signal from cardiac trigger monitors with two options, either positive or negative. Some systems allow the user to set the polarity of trigger signal before imaging. Efforts should be made to set the polarity status according to the recommendations provided by the manufacturers. In case of inappropriate selection, changes in computation of end-systolic and end-diastolic volumes as well as ejection fraction may occur. OBJECTIVE: To investigate the effect of the polarity status of trigger signals in synchronization process during 8- and 16-frame gated SPECT imaging on the systolic and diastolic parameters of LV function. METHODS: Thirty-four patients referred for a myocardial perfusion SPECT were consecutively included in the study. The rest scan for each patient was performed with 8- and 16-frame gating simultaneously with positive trigger signal set by the operator in a cardiac trigger monitor and then repeated after manual selection of negative polarity. In total, the 4 imaging modes acquired were 8-frame/positive-trigger, 16-frame/positive-trigger, 8-frame/negative-trigger, and 16-frame/negative-trigger. All SPECT images were reconstructed and processed with the same values of parameters. Systolic and diastolic indices of LV function were derived in QGS of the Cedars-Sinai software and then were compared using various statistical tests, and a reliability analysis was also performed. RESULTS: The age of patients recruited in the study was 58.41 ± 8.94, and 16 (47.1%) males and 18 (52.9%) females. All the correlation coefficients between corresponding parameters in positive and negative trigger signals were statistically significant. The difference between the parameters of systolic function including EF, EDV, and ESV in positive and negative trigger signals was statistically significant in paired sample t test. Likewise, a statistically significant difference was also found between mean phase angle in scans with positive and negative trigger signals by a phase difference of 147.91 (41.0% of an average cardiac cycle) and 149 (41.3% of an average cardiac cycle) degrees in 8- and 16-frame gating modes, respectively. Strong agreement (according to high values of intra-class correlation coefficient) was found for all four pairs. According to Bland-Altman results, an offset of about 3 percentage units was found, both between imaging in 8-frame gating compared to 16-frame gating, higher value in favor of 16-frame gating, and also between imaging with positive polarity trigger compared to negative-polarity trigger, again higher value in favor of positive-polarity triggering. CONCLUSION: The status of the polarity of trigger signals or similar CTM-camera delays in synchronization process during 8- and 16-frame gated SPECT imaging can be considered as one of the factors that may influence systolic and diastolic indices of LV function.

ANN and LEFM-Based Fatigue Reliability Analysis and Truck Weight Limits of Steel Bridges after Crack Detection.

Fatigue of steel bridges is a major concern for bridge engineers. Previous fatigue-based weight-limiting method of steel bridges is founded on the Palmgren-Miner's rule and S-N curves, which overlook the effect of existing cracks on the fatigue life of in-service steel bridges. In the present study, based on the theory of linear elastic fracture mechanics, a framework combining the artificial neural networks and Monte Carlo simulations is proposed to analyze the fatigue reliability of steel bridges with the effects of cracks and truck weight limits considered. Using the framework, a new method of setting the gross vehicle weight limit for in-service steel bridges with cracks is proposed. The influences of four key parameters, including the average daily truck traffic, the gross vehicle weight limit, the violation rate, and the detected crack size, on the fatigue reliability of a steel bridge are analyzed quantitatively with the new framework. Results show that the suggested framework can enhance the fatigue reliability assessment process in terms of accuracy and efficiency. The method of setting gross vehicle weight limits can effectively control the fatigue failure probability to be within 2.3% according to the desired remaining service time and the detected crack size.

Structural Reliability Analysis by Using Non-Probabilistic Multi-Cluster Ellipsoidal Model.

Uncertainties are normally unavoidable in engineering practice, which should be taken into account in the structural design and optimization so as to reduce the relevant risks. Yet, the probabilistic models of the uncertainties are often unavailable in the problems due to the lack of samples, and the precision of the conventional non-probabilistic models are not satisfactory when the samples are of multi-cluster distribution. In view of this, an improved method by using a non-probabilistic multi-cluster ellipsoidal model (multi-CEM) for the critical structural reliability analysis is proposed in this paper, which describes the samples in a more accurate and compact way and helps to acquire more satisfactory reliability analysis results. Firstly, a Gaussian mixture model (GMM) is built for the multi-cluster samples with performing expectation maximization (EM) algorithm, based on which the multi-CEM can be constructed. In the structural reliability analysis, two cases, respectively, considering whether the components of the multi-CEM are intersected or not are researched in detail. The non-probabilistic reliability (NPR) indexes for each component of the multi-CEM are computed using the Hasofer-Lind-Rackwitz-Fiessler (HL-RF) algorithm, and then the multidimensional volume ratios of the safe domain to the whole uncertainty domain are computed based on these indexes, indicating the structural NPR. In the end, two numerical examples and a practical application are conducted and analyzed to testify the effectiveness of the method.