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Establishing a positive peer climate in elementary school classrooms is an important goal for educators because peer dynamics are thought to affect academic learning. Thus, it is important to: (1) understand the relationship between children's peer dynamics and academic functioning; and (2) identify teacher practices that influence both peer processes and academic outcomes. In this pilot study, we explored whether specific teacher strategies that promote positive behaviors in children and positive peer dynamics influence children's better academic enablers, as well as whether they do so indirectly via improving peer sociometric ratings. Such teacher strategies may be particularly relevant for supporting children who demonstrate impairment in both social and academic domains, such as children at risk for attention-deficit/hyperactivity disorder (ADHD). Thus, we also examined whether these relationships differ for children with elevated ADHD symptoms and peer problems (i.e., target students), relative to classmates (i.e., non-target students). Participants were 194 children in the classrooms of 12 teachers (grades K-4) who participated in an open-trial pilot study of the school-based version of the Making Socially Accepting Inclusive Classrooms (MOSAIC) program. In the fall and spring of a school year, we assessed children's sociometric ratings received from peers, and academic enabler skills as rated by teachers. Throughout one academic year, we obtained assessments of teachers' use of MOSAIC strategies (observed and self-reported). Results showed that, after accounting for fall academic enablers, the teacher strategy of CARE time (involving one-on-one interaction with the student to build the teacher-student relationship) was positively associated with spring academic enablers. However, findings did not support the hypothesized indirect effect of peer sociometric ratings on the relationship between teacher strategy use and academic enablers, or the moderated indirect effect by target student status. Implications for future research and classroom interventions are discussed.

This study examined bidirectional associations between cross-racial friendships and children's social and academic adjustment. Participants were 583 elementary school-age children in western Canada, or the midwestern United States (4-10 years; 279 girls; 143 Asian, 88 Black, 65 Hispanic or Latinx, 171 White, 116 mixed). Children's adjustment (social preference, academic enablers, academic performance) and friendship nominations (reciprocated, received, given) were measured in fall and spring over one school year from 2017 to 2018, or from 2018 to 2019. Regarding "reciprocated" nominations, fall adjustment positively predicted spring reciprocated cross-racial friendships, but not vice-versa. For "received" nominations, academic enablers and received cross-racial friendship nominations were positively and bidirectionally related to one another. Fall same-racial friendship nominations positively predicted spring academic performance and social preference. Effect sizes were small.

Recent research has linked mindfulness to adults' false memory formation. This study investigated the effects of mindfulness on adolescents' event memory and suggestibility by using an "extensive" 8-week mindfulness program, an active control group, and a participatory to-be-remembered event. Students aged 13 to 14 were randomly assigned to a mindfulness or active control condition (socioemotional learning). After the seventh week, students participated in a target event and were immediately interviewed during which misleading information was presented. A week later, those in the mindfulness condition incorrectly reported more false information compared with those in the control group. Mindful practice did not affect memory encoding but had a negative effect during retention and retrieval. These findings suggest that adolescents who are exposed to mindfulness exercises may be susceptible to memory intrusions. Professionals should therefore be especially careful to avoid using suggestive and leading questions during investigative interviews with mindful adolescents.

Despite the numerous studies on bearing fault diagnosis based on frequency domain or time-frequency domain analyses, there is a lack of a fair assessment on which method or methods are practically effective in identifying the fault frequencies of damaged bearings in noisy environments. Most methods were developed based on experiments with simple lab test rigs equipped with bearings having manufactured artificial defects, and the signal-to-noise ratio under lab conditions is too ideal to be useful for verifying the effectiveness of a signal processing method. The purpose of this study is to evaluate the effectiveness of advanced signal processing methods applied in a high-speed train operating environment with multi-source interference. In this work, the most advanced signal processing methods (including spectral kurtosis, deconvolution, and mode decomposition) are studied, and the shortcomings of each method are analyzed. Based on the characteristics of high-speed train wheel set bearings (HSTWSBs), the concept of fault characteristic signal-to-noise ratio (FCSNR) is put forward to quantitatively evaluate the fault periodicity intensity, and corresponding improved methods are proposed by combining the FCSNR with existing signal processing methods; all these methods consider the periodic characteristics and impact characteristics of the bearing fault. The simulation signal and actual signals of HSTWSB with natural defects help verify the effectiveness of the proposed methods. Finally, the advantages and disadvantages of the different signal processing methods are objectively evaluated, and the application scope of each method is analyzed and prospected. This study provides a reference and new ideas for the fault diagnosis of HSTWSB and other industrial bearings. [ABSTRACT FROM AUTHOR]

A wheel set bearing is an important supporting component of a high-speed train. Its quality and performance directly determine the overall safety of the train. Therefore, monitoring a wheel set bearing's conditions for an early fault diagnosis is vital to ensure the safe operation of high-speed trains. However, the collected signals are often contaminated by environmental noise, transmission path, and signal attenuation because of the complexity of high-speed train systems and poor operation conditions, making it difficult to extract the early fault features of the wheel set bearing accurately. Vibration monitoring is most widely used for bearing fault diagnosis, with the acoustic emission (AE) technology emerging as a powerful tool. This article reports a comparison between vibration and AE technology in terms of their applicability for diagnosing naturally degraded wheel set bearings. In addition, a novel fault diagnosis method based on the optimized maximum second-order cyclostationarity blind deconvolution (CYCBD) and chirp Z-transform (CZT) is proposed to diagnose early composite fault defects in a wheel set bearing. The optimization CYCBD is adopted to enhance the fault-induced impact response and eliminate the interference of environmental noise, transmission path, and signal attenuation. CZT is used to improve the frequency resolution and match the fault features accurately under a limited data length condition. Moreover, the efficiency of the proposed method is verified by the simulated bearing signal and the real datasets. The results show that the proposed method is effective in the detection of wheel set bearing faults compared with the minimum entropy deconvolution (MED) and maximum correlated kurtosis deconvolution (MCKD) methods. This research is also the first to compare the effectiveness of applying AE and vibration technologies to diagnose a naturally degraded high-speed train bearing, particularly close to actual line operation conditions. [ABSTRACT FROM AUTHOR]