Living systems exhibit massive cross-scale communication and energetic feedback, and increasingly so do engineered systems, such as adaptive robotics and adaptive organisations. Hierarchy theory only applies to those multiscale systems where the time scales of different levels can be separated, and cross-scale interactions are insignificant. The Achievement Emotions Questionnaire for Physical Education (AEQ-PE) was developed by Fierro-Suero et al. [22]. It comprises 24 items divided into 6 categories of achievement emotions, including enjoyment, pride, anger, anxiety, hopelessness, and boredom, each encompassing 4 items.
Similarly, DL applied to high-dimensional reliability problems was demonstrated by Li et al. [32], and is a promising tool for addressing dimensionality. Xu et al. [33] provided a synthesis and roadmap of machine learning for reliability engineering and safety applications, and highlighted future possibilities. DL was introduced into identification of structural vibration signals as a new approach to structural health monitoring [34]. DL was also applied to microscopic material image problems with powerful image processing. Xiang et al. [35] predicted the effective mechanical properties of heterogeneous materials using image-based modelling and deep learning, which was essentially a regression problem. Finite element analysis was used to obtain the training set containing image samples and corresponding property targets.
Multi-scale approach for reliability-based design optimization with metamodel upscaling
The Chinese version of the Achievement Emotions Questionnaire for Physical Education (AEQ-PE-C) consists of 6 dimensions and 24 items. The questionnaire has demonstrated good reliability and validity and exhibits measurement invariance across both gender and grade groups, making it an effective tool for measuring achievement emotions related to physical education among Chinese university students. Firstly, the survey population is limited to university students in Shanghai, China, and may not fully represent the situation of Chinese university students as a whole. Future studies should aim to expand the sample to include university students from other regions in China. Secondly, the reliability analysis of the AEQ-PE-C questionnaire was based on a small sample size of only 45 subjects who participated in the test–retest.
Quite likely, people will guess differently, the different measures will be inconsistent, and therefore, the “guessing” technique of measurement is unreliable. A more reliable measurement may be to use a weight scale, where you are likely to get the same value every time you step on the scale, unless your weight has actually changed between measurements. One is to build a link between the microscale parameters and the macroscale structural response, and another is to calculate the reliability with hybrid variables, which results in a two-loop nested optimization problem. The AEQ-PE questionnaire was initially translated into Chinese by two graduate students proficient in both Chinese and English, and with a background in sports science. During the translation process, these two students actively sought guidance from professional translators.
Appl Math Modell
Due to the multi-scale characteristics of composites and the lack of statistical data, the reliability assessment of FRP truss bridge faces more complex problems such as multi-source uncertainties and multi-scale uncertainties compared with traditional bridges. The effectiveness and efficiency of the method are compared with the Monte Carlo simulation method through a theoretical example. The comparison with the other three interval variable processing methods demonstrates that the proposed method can effectively evaluate the reliability of FRP truss bridge in a multi-source and multi-scale approach.
- The data collected were analyzed using statistical analysis software, specifically SPSS 26.0 and AMOS 23.0.
- The recent revision of the Malaysian version of the AEQ-PE questionnaire mentioned, among the limitations, that future revisions of this questionnaire should consider invariance testing to provide further evidence for the questionnaire’s validity [23].
- Each of the selected items is reexamined by judges for face validity and content validity.
- Hierarchy theory does assume that higher and lower levels are dynamically screened off from each other due to order of magnitude differences between levels, and that information exchange must therefore be interpreted at the boundaries between levels [Salthe, 2002].
- Xiang et al. [35] predicted the effective mechanical properties of heterogeneous materials using image-based modelling and deep learning, which was essentially a regression problem.
- Similar concept was adopted by [30], [31] to develop a surrogate model to address the computational cost problem when involving coupled FEA.
- According to a study, pride, enjoyment, and hopelessness are the primary emotions that explain physical activity intentions, while enjoyment and boredom significantly impact academic performance [19].
Probability laws such as normal, lognormal, gamma, and Weibull distributions are generally used in random variable models based on experimental evidence and engineering judgment. The mesoscale uncertainty model was established and first applied to aerospace structures in a reliability-based aircraft design [10]. A wide range of strategies for uncertainty modelling and reliability estimation were developed over the next two decades [11]. The mechanical properties of the lamina are usually modelled as uncertain variables, and a reliability method such as the first-order reliability method (FORM) or Monte Carlo simulation (MCS) is used to estimate the reliability index. With multiscale design requirements, multiscale reliability analysis has become increasingly popular.
Measurement invariance testing
Future research possibilities in the direction of “agency cost mitigation” and “synergy between econophysics and behavioral finance in stock market forecasting” are also suggested in the paper. As shown in Table 6, the factor loadings of the items corresponding to the six factors of pride, enjoyment, anger, anxiety, hopelessness, and boredom exceeded 0.6, indicating high representativeness of the items. Furthermore, all six dimensions had AVE values (ranging from 0.606 to 0.834) greater than 0.5 and CR values (ranging from 0.859 to 0.953) greater than 0.7, indicating ideal convergent validity of the AEQ-PE-C. However, as research deepens, it has been found that achievement emotions manifest differently in various subjects. Research conducted by Goetz et al. indicates a weak and inconsistent relationship among the academic emotions experienced by students in mathematics, physics, German, and English subjects [14]. Therefore, AEQ was developed by later scholars as a tool to measure students’ emotions in different subjects, such as the Academic Emotions Questionnaire-Mathematics (AEQ-M) [15] and the Achievement Emotions Questionnaire-Foreign Language Class (AEQ-FLC) [16].
It’s important to consider reliability when planning your research design, collecting and analyzing your data, and writing up your research. The type of reliability you should calculate depends on the type of research and your methodology. If you want to use multiple different versions of a test (for example, to avoid respondents repeating the same answers from memory), you first need to make sure that all the sets of questions or measurements give reliable results. Where K is the number of items, is the average inter-item correlation, i.e., the mean of K ( K -1)/2 coefficients in the upper triangular (or lower triangular) correlation matrix. The data collected were analyzed using statistical analysis software, specifically SPSS 26.0 and AMOS 23.0. Thus, a shape granulometry is an ordered set of operators that are anti-extensive, scale-invariant, and idempotent.
Composites B
The powerful graphical analysis capabilities of computer vision allow identification of microscopic image features of composite materials, establishing a bridge between the microscopic world of the material and structure reliability. Defects exist in fibre including waviness, misalignments, uneven distribution and broken fibres; voids are commonly seen in matrix; https://wizardsdev.com/en/news/multiscale-analysis/ and interface defects can be unbonded regions on fibre surfaces and delamination between layers. Manufacturing defects are main sources that induce variations in material properties and geometry [12], [13], and these uncertainties consequently lead to scatters in strength in structural components, and structural responses in the structural system [13], [14].
As regards the filter ASFCOm,pf, it denotes the adaptive ASF of order p using the adaptive SEs with the luminance criterion mapping f and the homogeneity tolerance m. Multi-scale analysis of non-equilibrium hypersonic rarefied diatomic gas flow was presented by using a parallel DSMC method with the DMC model for a diatomic gas molecular collision and with the MS model for a gas-surface interaction model. The parallel implementation of the DSMC code shows to have linear scalability using the dynamic load balancing technique. The DSMC simulations revealed that the leading edge angle, gas-surface interaction effects affected on the flow over the plate, however, the three-dimensional effects would be small near the symmetric line of the plate in this flow conditions. From the three-dimensional simulations, the three-dimensional flow structure exists due to the viscous effects near the span edge. The figure below shows the reliability indices and the mean absolute percentage error (MAPE) calculated using SORM with low fidelity and high fidelity models (LFM, HFM) and different multi-fidelity models (MF).
These results show that the connectedness of adaptive ASFs and usual ASFs by reconstruction is an overwhelming advantage. Indeed, the edges are quickly damaged by the usual ASFs (Figure 19b–fig20d), while they are preserved with the connected ASFs. Moreover, the filters by reconstruction remove fine details (as revealed in the scene on the camera), for the eye of the human face and for the buildings (Figure 19e–fig20g), although they are connected. On the contrary, the decomposition of the original image with ANMM-based filters does not decimate relevant structures from fine-to-coarse scales (Figure 19h–j). One approach to analysing multiscale systems with emergent properties is the complexity profile, which analyses the amount of information required to describe a system at every scale [Bar-Yam, 2004]. The complexity profile of an organisation can reveal how well it matches the complexity of its environment, and identify whether either increasing fine scale variety or enhancing large scale coordination is likely to improve the organisation’s fitness.
A measure can be reliable but not valid, if it is measuring something very consistently but is consistently measuring the wrong construct. Likewise, a measure can be valid but not reliable if it is measuring the right construct, but not doing so in a consistent manner. Using the analogy of a shooting target, as shown in Figure 7.1, a multiple-item measure of a construct that is both reliable and valid consists of shots that clustered within a narrow range near the center of the target.
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