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Using three-wave longitudinal data, this study tested the potential mediating roles of teacher-student relationship quality and teachers' career support efficacy in the association between Chinese adolescents' family socioeconomic status (SES) and career development (N = 1410). Results showed that adolescents' family SES at Wave 1 was negatively associated with their career ambivalence at Wave 3 via positive associations with both teacher-student relationship quality and teachers' career support efficacy at Wave 2. Moreover, adolescents' family SES at Wave 1 was positively related to career adaptability at Wave 3 via its positive association with teachers' career support efficacy at Wave 2. This study highlighted the important role of teacher-student interaction in adolescents' career development.

High-accuracy interferometric measurement is crucial for integrated photonic interferometric imaging. In this paper, we propose an online calibration approach for large-scale integrated photonic beam combination that can realize high-accuracy measurement. By leveraging a simple four-step switching condition and the optical path differences between interferometric beams, the instrumental characteristics of the beam combiner, including the transmittances, visibilities, and phase relations, could be calibrated accurately. Compared to the conventional approach, the calibration procedure and estimation method are greatly simplified in our proposed scheme. The effectiveness of the proposed approach is validated using a photonic integrated circuit with two interferometric channels. The calibration results show that the differences between the phase relations are less than 1.41% between the proposed and conventional approaches. The proposed scheme shows significant advantages in optical synthetic aperture imaging systems using photonic integrated circuits especially for applications in harsh environments.

As one variant of the conventional flipped model, online flipped learning is increasingly implemented and it becomes important to explore how learners may be ready for it and how learner readiness may relate to motivation, attitude, and support. Informed by a multidimensional conceptualization of learner readiness and motivation and engagement, this study investigates the current situation of learner readiness, focusing on the moderating roles of learner attitude and environmental support in moderating the interrelationships between learner readiness and motivation and engagement in online flipped learning. Based on survey responses from 6,364 English learners across 11 Chinese universities, the results reveal that the students exhibited a generally high level of readiness for online flipped learning, though with significant demographic differences. The results also indicate that learner attitudes and environmental support moderated the impact of learner readiness upon learner motivation and engagement in online flipped learning. The findings call for attention to examine closely what dimensions of learner readiness could shape what aspects of motivation and engagement rather than simply assuming a linear causal relationship between readiness and motivation and engagement. A potential polarizing effect in online flipped learning is also highlighted, with implications discussed. [ABSTRACT FROM AUTHOR]

The array‐based frequency‐Bessel transform method has been demonstrated to effectively extract dispersion curves of higher‐mode surface waves from the empirical Green's functions (EGFs) of displacement fields reconstructed by ambient noise interferometry. Distributed acoustic sensing (DAS), a novel dense array observation technique, has been widely implemented in surface wave imaging to estimate subsurface velocity structure in practice. However, there is still no clear understanding in theory about how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To address this, we extend the frequency‐Bessel transform method by deriving Green's functions (GFs) for horizontal strain fields, making it applicable to DAS data. First, we test its performance using synthetic GFs and verify the correctness of extracted dispersion spectrograms with theoretical results. Then, we apply it to three field DAS ambient‐noise data sets, two recorded on land and one in the seabed. The reliability and advantages of the method are confirmed by comparing results with the widely used phase shift method. The results demonstrate that our extended frequency‐Bessel transform method is reliable and can provide more abundant and higher‐quality dispersion information of surface waves. Moreover, our method is also adaptable for active‐source DAS data with simple modifications to the derived transform formulas. We also find that the gauge length in the DAS system significantly impacts the polarity and value of extracted dispersion energy. Overall, our study provides a theoretical framework and practical tool for multimodal surface wave dispersion measurement using DAS data. Plain Language Summary: Ambient noise surface wave imaging is one of the most widely used methods for estimating the Earth's internal shear wave velocity structure, exploiting the dispersion characteristics of surface waves. The array‐based frequency‐Bessel transform method has been proven effective in extracting dispersion curves of higher‐mode surface waves from empirical Green's functions (EGFs) retrieved via ambient noise interferometry. Distributed acoustic sensing (DAS), which is a novel dense array observation technique, has become widely adopted in practical surface wave imaging. Nevertheless, there remains a theoretical gap in our understanding of how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To bridge this gap, starting from the perspective of strain field theory, we propose an extension of the frequency‐Bessel transform method, which can account for Green's functions of horizontal strain fields and the reconstructed EGFs from DAS ambient noise data. Both synthetic tests and applications to field DAS data demonstrate that our proposed frequency‐Bessel transform method can be confidently and effectively utilized for multimodal dispersion measurement of surface waves derived from DAS observation data. This work can offer a theoretical basis and practical tool for DAS‐based surface wave imaging. Key Points: We derive Green's functions for horizontal strain fields, extending the frequency‐Bessel transform method for applications in distributed acoustic sensing (DAS) dataThe extended frequency‐Bessel transform method reliably measures multimodal surface‐wave dispersion in both synthetic and field DAS recordsThe method excels in extracting high‐quality multimodal dispersion information from both active and passive DAS data for surface waves [ABSTRACT FROM AUTHOR]

Based on 4 annual waves of data from a large sample of Chinese college students (N = 2,329, M[subscript age] = 18.40 years old, SD = 0.85; 63.10% females), this study examines the within-person and between-person effects in the association between problematic Internet use (PIU) and mental health issues. Results of analyses using the developmental equilibrium model (i.e., an autoregressive, cross-lagged panel model) demonstrate a "reciprocal" positive association between PIU and mental health issues consistently across waves. In contrast, results of analyses utilizing the random intercept, cross-lagged panel model (i.e., a model that can disaggregate within-person and between-person effects) indicate a "unidirectional" positive within-person effect from PIU to mental health issues (rather than the reverse) consistently over time, while controlling for the between-person effects that exist when comparing different individuals. Such findings highlight the importance of disaggregating within-person and between-person effects in understanding the nature of the temporal dynamics of the association between PIU and mental health.

Ambient Noise Imaging (ANI) of subsurface structures relies on seismic interferometry of diffuse seismic wavefields. However, the lack of effective methods to quantify and identify highly diffuse waves hampers applications of ANI, particularly in evaluating seismic attenuation and monitoring structural changes with high temporal resolution. Conventional ANI approaches require data normalization, which effectively suppresses the non‐diffuse component with large amplitude but also results in significant loss of amplitude and phase information in the continuous seismic records. In this study, we propose a frequency domain method to quantitatively evaluate the degree of diffuseness of seismic wavefields by analyzing their statistical characteristics of modal amplitudes for stationarity and randomness. Tests on synthetic waveform and field nodal records show that the proposed method can effectively distinguish between diffuse and non‐diffuse waveforms for either single‐ or three‐component data. As an application, we identify a 60‐s‐long diffuse coda of a local M 2.2 earthquake recorded by a dense nodal array on the San Jacinto Fault Zone, and successfully extract high‐quality dispersion curve and Q‐value without performing data normalization. These results are consistent with those obtained by conventional methods that assess the correlation between coherency and the Green's function, and by modeling ballistic waves generated by road traffic. Our proposed method can advance the imaging of subsurface velocity and attenuation structures as well as monitoring temporal changes for scientific studies and engineering applications. Plain Language Summary: Earthquakes, explosions, and traffic events can generate seismic waves that travel through the Earth. As these direct waves encounter heterogeneous earth interior, they scatter and change direction, leading to more diffusive propagation. Fully diffuse waves can be used to image the subsurface structures. In this study, we develop a reliable and efficient method to measure how diffuse a seismic wavefield is at different frequencies. Tests on synthetic and field data show that the developed method can reliably differentiate between diffuse and non‐diffuse waves. This method can improve our ability to use spread out wavefields for imaging and monitoring the Earth's interior. Key Points: We present an efficient methodology to quantify seismic wavefield diffuseness based on stationarity and randomness in the frequency domainThe method avoids waveform normalization and extracts reliable empirical Green's functions from interferometry using diffuse waveformsWe validate the method by extracting dispersion curves and Q‐values from the 60‐s‐long diffuse coda of a local M 2.2 earthquake [ABSTRACT FROM AUTHOR]